Application of artificial neural network to fMRI regression analysis.

PubMed

Misaki, Masaya; Miyauchi, Satoru

2006-01-15

We used an artificial neural network (ANN) to detect correlations between event sequences and fMRI (functional magnetic resonance imaging) signals. The layered feed-forward neural network, given a series of events as inputs and the fMRI signal as a supervised signal, performed a non-linear regression analysis. This type of ANN is capable of approximating any continuous function, and thus this analysis method can detect any fMRI signals that correlated with corresponding events. Because of the flexible nature of ANNs, fitting to autocorrelation noise is a problem in fMRI analyses. We avoided this problem by using cross-validation and an early stopping procedure. The results showed that the ANN could detect various responses with different time courses. The simulation analysis also indicated an additional advantage of ANN over non-parametric methods in detecting parametrically modulated responses, i.e., it can detect various types of parametric modulations without a priori assumptions. The ANN regression analysis is therefore beneficial for exploratory fMRI analyses in detecting continuous changes in responses modulated by changes in input values.

Robustness of meta-analyses in finding gene × environment interactions

PubMed Central

Shi, Gang; Nehorai, Arye

2017-01-01

Meta-analyses that synthesize statistical evidence across studies have become important analytical tools for genetic studies. Inspired by the success of genome-wide association studies of the genetic main effect, researchers are searching for gene × environment interactions. Confounders are routinely included in the genome-wide gene × environment interaction analysis as covariates; however, this does not control for any confounding effects on the results if covariate × environment interactions are present. We carried out simulation studies to evaluate the robustness to the covariate × environment confounder for meta-regression and joint meta-analysis, which are two commonly used meta-analysis methods for testing the gene × environment interaction or the genetic main effect and interaction jointly. Here we show that meta-regression is robust to the covariate × environment confounder while joint meta-analysis is subject to the confounding effect with inflated type I error rates. Given vast sample sizes employed in genome-wide gene × environment interaction studies, non-significant covariate × environment interactions at the study level could substantially elevate the type I error rate at the consortium level. When covariate × environment confounders are present, type I errors can be controlled in joint meta-analysis by including the covariate × environment terms in the analysis at the study level. Alternatively, meta-regression can be applied, which is robust to potential covariate × environment confounders. PMID:28362796

Regression analysis of mixed panel count data with dependent terminal events.

PubMed

Yu, Guanglei; Zhu, Liang; Li, Yang; Sun, Jianguo; Robison, Leslie L

2017-05-10

Event history studies are commonly conducted in many fields, and a great deal of literature has been established for the analysis of the two types of data commonly arising from these studies: recurrent event data and panel count data. The former arises if all study subjects are followed continuously, while the latter means that each study subject is observed only at discrete time points. In reality, a third type of data, a mixture of the two types of the data earlier, may occur and furthermore, as with the first two types of the data, there may exist a dependent terminal event, which may preclude the occurrences of recurrent events of interest. This paper discusses regression analysis of mixed recurrent event and panel count data in the presence of a terminal event and an estimating equation-based approach is proposed for estimation of regression parameters of interest. In addition, the asymptotic properties of the proposed estimator are established, and a simulation study conducted to assess the finite-sample performance of the proposed method suggests that it works well in practical situations. Finally, the methodology is applied to a childhood cancer study that motivated this study. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

The relationship between hemoglobin level and the type 1 diabetic nephropathy in Anhui Han's patients.

PubMed

Jiang, Jun; Lei, Lan; Zhou, Xiaowan; Li, Peng; Wei, Ren

2018-02-20

Recent studies have shown that low hemoglobin (Hb) level promote the progression of chronic kidney disease. This study assessed the relationship between Hb level and type 1 diabetic nephropathy (DN) in Anhui Han's patients. There were a total of 236 patients diagnosed with type 1 diabetes mellitus and (T1DM) seen between January 2014 and December 2016 in our centre. Hemoglobin levels in patients with DN were compared with those without DN. The relationship between Hb level and the urinary albumin-creatinine ratio (ACR) was examined by Spearman's correlational analysis and multiple stepwise regression analysis. The binary logistic multivariate regression analysis was performed to analyze the correlated factors for type 1 DN, calculate the Odds Ratio (OR) and 95%confidence interval (CI). The predicting value of Hb level for DN was evaluated by area under receiver operation characteristic curve (AUROC) for discrimination and Hosmer-Lemeshow goodness-of-fit test for calibration. The average Hb levels in the DN group (116.1 ± 20.8 g/L) were significantly lower than the non-DN group (131.9 ± 14.4 g/L) , P < 0.001. Hb levels were independently correlated with the urinary ACR in multiple stepwise regression analysis. The logistic multivariate regression analysis showed that the Hb level (OR: 0.936, 95% CI: 0.910 to 0.963, P < 0.001) was inversely correlated with DN in patients with T1DM. In sub-analysis, low Hb level (Hb < 120g/L in female, Hb < 130g/L in male) was still negatively associated with DN in patients with T1DM. The AUROC was 0.721 (95% CI: 0.655 to 0.787) in assessing the discrimination of the Hb level for DN. The value of P was 0.593 in Hosmer-Lemeshow goodness-of-fit test. In Anhui Han's patients with T1DM, the Hb level is inversely correlated with urinary ACR and DN. This article is protected by copyright. All rights reserved.

Effective psychological and psychosocial approaches to reduce repetition of self-harm: a systematic review, meta-analysis and meta-regression

PubMed Central

Robinson, Jo; Spittal, Matthew J; Carter, Greg

2016-01-01

Objective To examine the efficacy of psychological and psychosocial interventions for reductions in repeated self-harm. Design We conducted a systematic review, meta-analysis and meta-regression to examine the efficacy of psychological and psychosocial interventions to reduce repeat self-harm in adults. We included a sensitivity analysis of studies with a low risk of bias for the meta-analysis. For the meta-regression, we examined whether the type, intensity (primary analyses) and other components of intervention or methodology (secondary analyses) modified the overall intervention effect. Data sources A comprehensive search of MEDLINE, PsycInfo and EMBASE (from 1999 to June 2016) was performed. Eligibility criteria for selecting studies Randomised controlled trials of psychological and psychosocial interventions for adult self-harm patients. Results Forty-five trials were included with data available from 36 (7354 participants) for the primary analysis. Meta-analysis showed a significant benefit of all psychological and psychosocial interventions combined (risk ratio 0.84; 95% CI 0.74 to 0.96; number needed to treat=33); however, sensitivity analyses showed that this benefit was non-significant when restricted to a limited number of high-quality studies. Meta-regression showed that the type of intervention did not modify the treatment effects. Conclusions Consideration of a psychological or psychosocial intervention over and above treatment as usual is worthwhile; with the public health benefits of ensuring that this practice is widely adopted potentially worth the investment. However, the specific type and nature of the intervention that should be delivered is not yet clear. Cognitive–behavioural therapy or interventions with an interpersonal focus and targeted on the precipitants to self-harm may be the best candidates on the current evidence. Further research is required. PMID:27660314

The Effect of Sitagliptin on the Regression of Carotid Intima-Media Thickening in Patients with Type 2 Diabetes Mellitus: A Post Hoc Analysis of the Sitagliptin Preventive Study of Intima-Media Thickness Evaluation.

PubMed

Mita, Tomoya; Katakami, Naoto; Shiraiwa, Toshihiko; Yoshii, Hidenori; Gosho, Masahiko; Shimomura, Iichiro; Watada, Hirotaka

2017-01-01

Background. The effect of dipeptidyl peptidase-4 (DPP-4) inhibitors on the regression of carotid IMT remains largely unknown. The present study aimed to clarify whether sitagliptin, DPP-4 inhibitor, could regress carotid intima-media thickness (IMT) in insulin-treated patients with type 2 diabetes mellitus (T2DM). Methods . This is an exploratory analysis of a randomized trial in which we investigated the effect of sitagliptin on the progression of carotid IMT in insulin-treated patients with T2DM. Here, we compared the efficacy of sitagliptin treatment on the number of patients who showed regression of carotid IMT of ≥0.10 mm in a post hoc analysis. Results . The percentages of the number of the patients who showed regression of mean-IMT-CCA (28.9% in the sitagliptin group versus 16.4% in the conventional group, P  = 0.022) and left max-IMT-CCA (43.0% in the sitagliptin group versus 26.2% in the conventional group, P  = 0.007), but not right max-IMT-CCA, were higher in the sitagliptin treatment group compared with those in the non-DPP-4 inhibitor treatment group. In multiple logistic regression analysis, sitagliptin treatment significantly achieved higher target attainment of mean-IMT-CCA ≥0.10 mm and right and left max-IMT-CCA ≥0.10 mm compared to conventional treatment. Conclusions . Our data suggested that DPP-4 inhibitors were associated with the regression of carotid atherosclerosis in insulin-treated T2DM patients. This study has been registered with the University Hospital Medical Information Network Clinical Trials Registry (UMIN000007396).

Which Measurement of Blood Pressure Is More Associated With Albuminuria in Patients With Type 2 Diabetes: Central Blood Pressure or Peripheral Blood Pressure?

PubMed

Kitagawa, Noriyuki; Okada, Hiroshi; Tanaka, Muhei; Hashimoto, Yoshitaka; Kimura, Toshihiro; Nakano, Koji; Yamazaki, Masahiro; Hasegawa, Goji; Nakamura, Naoto; Fukui, Michiaki

2016-08-01

The aim of this study was to investigate whether central systolic blood pressure (SBP) was associated with albuminuria, defined as urinary albumin excretion (UAE) ≥30 mg/g creatinine, and, if so, whether the relationship of central SBP with albuminuria was stronger than that of peripheral SBP in patients with type 2 diabetes. The authors performed a cross-sectional study in 294 outpatients with type 2 diabetes. The relationship between peripheral SBP or central SBP and UAE using regression analysis was evaluated, and the odds ratios of peripheral SBP or central SBP were calculated to identify albuminuria using logistic regression model. Moreover, the area under the receiver operating characteristic curve (AUC) of central SBP was compared with that of peripheral SBP to identify albuminuria. Multiple regression analysis demonstrated that peripheral SBP (β=0.255, P<.0001) or central SBP (r=0.227, P<.0001) was associated with UAE. Multiple logistic regression analysis demonstrated that peripheral SBP (odds ratio, 1.029; 95% confidence interval, 1.016-1.043) or central SBP (odds ratio, 1.022; 95% confidence interval, 1.011-1.034) was associated with an increased odds of albuminuria. In addition, AUC of peripheral SBP was significantly greater than that of central SBP to identify albuminuria (P=0.035). Peripheral SBP is superior to central SBP in identifying albuminuria, although both peripheral and central SBP are associated with UAE in patients with type 2 diabetes. © 2016 Wiley Periodicals, Inc.

Binary Logistic Regression Analysis for Detecting Differential Item Functioning: Effectiveness of R[superscript 2] and Delta Log Odds Ratio Effect Size Measures

ERIC Educational Resources Information Center

Hidalgo, Mª Dolores; Gómez-Benito, Juana; Zumbo, Bruno D.

2014-01-01

The authors analyze the effectiveness of the R[superscript 2] and delta log odds ratio effect size measures when using logistic regression analysis to detect differential item functioning (DIF) in dichotomous items. A simulation study was carried out, and the Type I error rate and power estimates under conditions in which only statistical testing…

AN IMPROVED STRATEGY FOR REGRESSION OF BIOPHYSICAL VARIABLES AND LANDSAT ETM+ DATA. (R828309)

EPA Science Inventory

Empirical models are important tools for relating field-measured biophysical variables to remote sensing data. Regression analysis has been a popular empirical method of linking these two types of data to provide continuous estimates for variables such as biomass, percent wood...

Analysis and Interpretation of Findings Using Multiple Regression Techniques

ERIC Educational Resources Information Center

Hoyt, William T.; Leierer, Stephen; Millington, Michael J.

2006-01-01

Multiple regression and correlation (MRC) methods form a flexible family of statistical techniques that can address a wide variety of different types of research questions of interest to rehabilitation professionals. In this article, we review basic concepts and terms, with an emphasis on interpretation of findings relevant to research questions…

Multilevel Models for Binary Data

ERIC Educational Resources Information Center

Powers, Daniel A.

2012-01-01

The methods and models for categorical data analysis cover considerable ground, ranging from regression-type models for binary and binomial data, count data, to ordered and unordered polytomous variables, as well as regression models that mix qualitative and continuous data. This article focuses on methods for binary or binomial data, which are…

Retinal microaneurysm count predicts progression and regression of diabetic retinopathy. Post-hoc results from the DIRECT Programme.

PubMed

Sjølie, A K; Klein, R; Porta, M; Orchard, T; Fuller, J; Parving, H H; Bilous, R; Aldington, S; Chaturvedi, N

2011-03-01

To study the association between baseline retinal microaneurysm score and progression and regression of diabetic retinopathy, and response to treatment with candesartan in people with diabetes. This was a multicenter randomized clinical trial. The progression analysis included 893 patients with Type 1 diabetes and 526 patients with Type 2 diabetes with retinal microaneurysms only at baseline. For regression, 438 with Type 1 and 216 with Type 2 diabetes qualified. Microaneurysms were scored from yearly retinal photographs according to the Early Treatment Diabetic Retinopathy Study (ETDRS) protocol. Retinopathy progression and regression was defined as two or more step change on the ETDRS scale from baseline. Patients were normoalbuminuric, and normotensive with Type 1 and Type 2 diabetes or treated hypertensive with Type 2 diabetes. They were randomized to treatment with candesartan 32 mg daily or placebo and followed for 4.6 years. A higher microaneurysm score at baseline predicted an increased risk of retinopathy progression (HR per microaneurysm score 1.08, P < 0.0001 in Type 1 diabetes; HR 1.07, P = 0.0174 in Type 2 diabetes) and reduced the likelihood of regression (HR 0.79, P < 0.0001 in Type 1 diabetes; HR 0.85, P = 0.0009 in Type 2 diabetes), all adjusted for baseline variables and treatment. Candesartan reduced the risk of microaneurysm score progression. Microaneurysm counts are important prognostic indicators for worsening of retinopathy, thus microaneurysms are not benign. Treatment with renin-angiotensin system inhibitors is effective in the early stages and may improve mild diabetic retinopathy. Microaneurysm scores may be useful surrogate endpoints in clinical trials. © 2011 The Authors. Diabetic Medicine © 2011 Diabetes UK.

[How medical students perform academically by admission types?].

PubMed

Kim, Se-Hoon; Lee, Keumho; Hur, Yera; Kim, Ji-Ha

2013-09-01

Despite the importance of selecting students whom are capable for medical education and to become a good doctor, not enough studies have been done in the category. This study focused on analysing the medical students' academic performance (grade point average, GPA) differences, flunk and dropout rates by admission types. From 2004 to 2010, we gathered 369 Konyang University College of Medicine's students admission data and analyzed the differences between admission method and academic achievement, differences in failure and dropout rates. Analysis of variance (ANOVA), ordinary least square, and logistic regression were used. The rolling students showed higher academic achievement from year 1 to 3 than regular students (p < 0.01). Using admission type variable as control variable in multiple regression model similar results were shown. But unlike the results of ANOVA, GPA differences by admission types were shown not only in lower academic years but also in year 6 (p < 0.01). From the regression analysis of flunk and dropout rate by admission types, regular admission type students showed higher drop out rate than the rolling ones which demonstrates admission types gives significant effect on flunk or dropout rates in medical students (p < 0.01). The rolling admissions type students tend to show lower flunk rate and dropout rates and perform better academically. This implies selecting students primarily by Korean College Scholastic Ability Test does not guarantee their academic success in medical education. Thus we suggest a more in-depth comprehensive method of selecting students that are appropriate to individual medical school's educational goal.

Comparison of different functional EIT approaches to quantify tidal ventilation distribution.

PubMed

Zhao, Zhanqi; Yun, Po-Jen; Kuo, Yen-Liang; Fu, Feng; Dai, Meng; Frerichs, Inez; Möller, Knut

2018-01-30

The aim of the study was to examine the pros and cons of different types of functional EIT (fEIT) to quantify tidal ventilation distribution in a clinical setting. fEIT images were calculated with (1) standard deviation of pixel time curve, (2) regression coefficients of global and local impedance time curves, or (3) mean tidal variations. To characterize temporal heterogeneity of tidal ventilation distribution, another fEIT image of pixel inspiration times is also proposed. fEIT-regression is very robust to signals with different phase information. When the respiratory signal should be distinguished from the heart-beat related signal, or during high-frequency oscillatory ventilation, fEIT-regression is superior to other types. fEIT-tidal variation is the most stable image type regarding the baseline shift. We recommend using this type of fEIT image for preliminary evaluation of the acquired EIT data. However, all these fEITs would be misleading in their assessment of ventilation distribution in the presence of temporal heterogeneity. The analysis software provided by the currently available commercial EIT equipment only offers either fEIT of standard deviation or tidal variation. Considering the pros and cons of each fEIT type, we recommend embedding more types into the analysis software to allow the physicians dealing with more complex clinical applications with on-line EIT measurements.

Interactions between cadmium and decabrominated diphenyl ether on blood cells count in rats-Multiple factorial regression analysis.

PubMed

Curcic, Marijana; Buha, Aleksandra; Stankovic, Sanja; Milovanovic, Vesna; Bulat, Zorica; Đukić-Ćosić, Danijela; Antonijević, Evica; Vučinić, Slavica; Matović, Vesna; Antonijevic, Biljana

2017-02-01

The objective of this study was to assess toxicity of Cd and BDE-209 mixture on haematological parameters in subacutely exposed rats and to determine the presence and type of interactions between these two chemicals using multiple factorial regression analysis. Furthermore, for the assessment of interaction type, an isobologram based methodology was applied and compared with multiple factorial regression analysis. Chemicals were given by oral gavage to the male Wistar rats weighing 200-240g for 28days. Animals were divided in 16 groups (8/group): control vehiculum group, three groups of rats were treated with 2.5, 7.5 or 15mg Cd/kg/day. These doses were chosen on the bases of literature data and reflect relatively high Cd environmental exposure, three groups of rats were treated with 1000, 2000 or 4000mg BDE-209/kg/bw/day, doses proved to induce toxic effects in rats. Furthermore, nine groups of animals were treated with different mixtures of Cd and BDE-209 containing doses of Cd and BDE-209 stated above. Blood samples were taken at the end of experiment and red blood cells, white blood cells and platelets counts were determined. For interaction assessment multiple factorial regression analysis and fitted isobologram approach were used. In this study, we focused on multiple factorial regression analysis as a method for interaction assessment. We also investigated the interactions between Cd and BDE-209 by the derived model for the description of the obtained fitted isobologram curves. Current study indicated that co-exposure to Cd and BDE-209 can result in significant decrease in RBC count, increase in WBC count and decrease in PLT count, when compared with controls. Multiple factorial regression analysis used for the assessment of interactions type between Cd and BDE-209 indicated synergism for the effect on RBC count and no interactions i.e. additivity for the effects on WBC and PLT counts. On the other hand, isobologram based approach showed slight antagonism for the effects on RBC and WBC while no interactions were proved for the joint effect on PLT count. These results confirm that the assessment of interactions between chemicals in the mixture greatly depends on the concept or method used for this evaluation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Managing Complexity in Evidence Analysis: A Worked Example in Pediatric Weight Management.

PubMed

Parrott, James Scott; Henry, Beverly; Thompson, Kyle L; Ziegler, Jane; Handu, Deepa

2018-05-02

Nutrition interventions are often complex and multicomponent. Typical approaches to meta-analyses that focus on individual causal relationships to provide guideline recommendations are not sufficient to capture this complexity. The objective of this study is to describe the method of meta-analysis used for the Pediatric Weight Management (PWM) Guidelines update and provide a worked example that can be applied in other areas of dietetics practice. The effects of PWM interventions were examined for body mass index (BMI), body mass index z-score (BMIZ), and waist circumference at four different time periods. For intervention-level effects, intervention types were identified empirically using multiple correspondence analysis paired with cluster analysis. Pooled effects of identified types were examined using random effects meta-analysis models. Differences in effects among types were examined using meta-regression. Context-level effects are examined using qualitative comparative analysis. Three distinct types (or families) of PWM interventions were identified: medical nutrition, behavioral, and missing components. Medical nutrition and behavioral types showed statistically significant improvements in BMIZ across all time points. Results were less consistent for BMI and waist circumference, although four distinct patterns of weight status change were identified. These varied by intervention type as well as outcome measure. Meta-regression indicated statistically significant differences between the medical nutrition and behavioral types vs the missing component type for both BMIZ and BMI, although the pattern varied by time period and intervention type. Qualitative comparative analysis identified distinct configurations of context characteristics at each time point that were consistent with positive outcomes among the intervention types. Although analysis of individual causal relationships is invaluable, this approach is inadequate to capture the complexity of dietetics practice. An alternative approach that integrates intervention-level with context-level meta-analyses may provide deeper understanding in the development of practice guidelines. Copyright © 2018 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

Something old, something new, something borrowed, something blue: a framework for the marriage of health econometrics and cost-effectiveness analysis.

PubMed

Hoch, Jeffrey S; Briggs, Andrew H; Willan, Andrew R

2002-07-01

Economic evaluation is often seen as a branch of health economics divorced from mainstream econometric techniques. Instead, it is perceived as relying on statistical methods for clinical trials. Furthermore, the statistic of interest in cost-effectiveness analysis, the incremental cost-effectiveness ratio is not amenable to regression-based methods, hence the traditional reliance on comparing aggregate measures across the arms of a clinical trial. In this paper, we explore the potential for health economists undertaking cost-effectiveness analysis to exploit the plethora of established econometric techniques through the use of the net-benefit framework - a recently suggested reformulation of the cost-effectiveness problem that avoids the reliance on cost-effectiveness ratios and their associated statistical problems. This allows the formulation of the cost-effectiveness problem within a standard regression type framework. We provide an example with empirical data to illustrate how a regression type framework can enhance the net-benefit method. We go on to suggest that practical advantages of the net-benefit regression approach include being able to use established econometric techniques, adjust for imperfect randomisation, and identify important subgroups in order to estimate the marginal cost-effectiveness of an intervention. Copyright 2002 John Wiley & Sons, Ltd.

Evaluation of SLAR and thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

NASA Technical Reports Server (NTRS)

Hoffer, R. M. (Principal Investigator)

1979-01-01

The spatial characteristics of the data were evaluated. A program was developed to reduce the spatial distortions resulting from variable viewing distance, and geometrically adjusted data sets were generated. The potential need for some level of radiometric adjustment was evidenced by an along track band of high reflectance across different cover types in the Varian imagery. A multiple regression analysis was employed to explore the viewing angle effect on measured reflectance. Areas in the data set which appeared to have no across track stratification of cover type were identified. A program was developed which computed the average reflectance by column for each channel, over all of the scan lines in the designated areas. A regression analysis was then run using the first, second, and third degree polynomials, for each channel. An atmospheric effect as a component of the viewing angle source of variance is discussed. Cover type maps were completed and training and test field selection was initiated.

Introduction to methodology of dose-response meta-analysis for binary outcome: With application on software.

PubMed

Zhang, Chao; Jia, Pengli; Yu, Liu; Xu, Chang

2018-05-01

Dose-response meta-analysis (DRMA) is widely applied to investigate the dose-specific relationship between independent and dependent variables. Such methods have been in use for over 30 years and are increasingly employed in healthcare and clinical decision-making. In this article, we give an overview of the methodology used in DRMA. We summarize the commonly used regression model and the pooled method in DRMA. We also use an example to illustrate how to employ a DRMA by these methods. Five regression models, linear regression, piecewise regression, natural polynomial regression, fractional polynomial regression, and restricted cubic spline regression, were illustrated in this article to fit the dose-response relationship. And two types of pooling approaches, that is, one-stage approach and two-stage approach are illustrated to pool the dose-response relationship across studies. The example showed similar results among these models. Several dose-response meta-analysis methods can be used for investigating the relationship between exposure level and the risk of an outcome. However the methodology of DRMA still needs to be improved. © 2018 Chinese Cochrane Center, West China Hospital of Sichuan University and John Wiley & Sons Australia, Ltd.

Predictors of postoperative outcomes of cubital tunnel syndrome treatments using multiple logistic regression analysis.

PubMed

Suzuki, Taku; Iwamoto, Takuji; Shizu, Kanae; Suzuki, Katsuji; Yamada, Harumoto; Sato, Kazuki

2017-05-01

This retrospective study was designed to investigate prognostic factors for postoperative outcomes for cubital tunnel syndrome (CubTS) using multiple logistic regression analysis with a large number of patients. Eighty-three patients with CubTS who underwent surgeries were enrolled. The following potential prognostic factors for disease severity were selected according to previous reports: sex, age, type of surgery, disease duration, body mass index, cervical lesion, presence of diabetes mellitus, Workers' Compensation status, preoperative severity, and preoperative electrodiagnostic testing. Postoperative severity of disease was assessed 2 years after surgery by Messina's criteria which is an outcome measure specifically for CubTS. Bivariate analysis was performed to select candidate prognostic factors for multiple linear regression analyses. Multiple logistic regression analysis was conducted to identify the association between postoperative severity and selected prognostic factors. Both bivariate and multiple linear regression analysis revealed only preoperative severity as an independent risk factor for poor prognosis, while other factors did not show any significant association. Although conflicting results exist regarding prognosis of CubTS, this study supports evidence from previous studies and concludes early surgical intervention portends the most favorable prognosis. Copyright © 2017 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

Relationship between serum bilirubin concentrations and diabetic nephropathy in Shanghai Han's patients with type 1 diabetes mellitus.

PubMed

Li, Xu; Zhang, Lei; Chen, Haibing; Guo, Kaifeng; Yu, Haoyong; Zhou, Jian; Li, Ming; Li, Qing; Li, Lianxi; Yin, Jun; Liu, Fang; Bao, Yuqian; Han, Junfeng; Jia, Weiping

2017-03-31

Recent studies highlight a negative association between total bilirubin concentrations and albuminuria in patients with type 2 diabetes mellitus. Our study evaluated the relationship between bilirubin concentrations and the prevalence of diabetic nephropathy (DN) in Chinese patients with type 1 diabetes mellitus (T1DM). A total of 258 patients with T1DM were recruited and bilirubin concentrations were compared between patients with or without diabetic nephropathy. Multiple stepwise regression analysis was used to examine the relationship between bilirubin concentrations and 24 h urinary microalbumin. Binary logistic regression analysis was performed to assess independent risk factors for diabetic nephropathy. Participants were divided into four groups according to the quartile of total bilirubin concentrations (Q1, 0.20-0.60; Q2, 0.60-0.80; Q3, 0.80-1.00; Q4, 1.00-1.90 mg/dL) and the chi-square test was used to compare the prevalence of DN in patients with T1DM. The median bilirubin level was 0.56 (interquartile: 0.43-0.68 mg/dL) in the DN group, significantly lower than in the non-DN group (0.70 [interquartile: 0.58-0.89 mg/dL], P < 0.001). Spearman's correlational analysis showed bilirubin concentrations were inversely correlated with 24 h urinary microalbumin (r = -0.13, P < 0.05) and multiple stepwise regression analysis showed bilirubin concentrations were independently associated with 24 h urinary microalbumin. In logistic regression analysis, bilirubin concentrations were significantly inversely associated with nephropathy. In addition, in stratified analysis, from the first to the fourth quartile group, increased bilirubin concentrations were associated with decreased prevalence of DN from 21.90% to 2.00%. High bilirubin concentrations are independently and negatively associated with albuminuria and the prevalence of DN in patients with T1DM.

A general framework for the use of logistic regression models in meta-analysis.

PubMed

Simmonds, Mark C; Higgins, Julian Pt

2016-12-01

Where individual participant data are available for every randomised trial in a meta-analysis of dichotomous event outcomes, "one-stage" random-effects logistic regression models have been proposed as a way to analyse these data. Such models can also be used even when individual participant data are not available and we have only summary contingency table data. One benefit of this one-stage regression model over conventional meta-analysis methods is that it maximises the correct binomial likelihood for the data and so does not require the common assumption that effect estimates are normally distributed. A second benefit of using this model is that it may be applied, with only minor modification, in a range of meta-analytic scenarios, including meta-regression, network meta-analyses and meta-analyses of diagnostic test accuracy. This single model can potentially replace the variety of often complex methods used in these areas. This paper considers, with a range of meta-analysis examples, how random-effects logistic regression models may be used in a number of different types of meta-analyses. This one-stage approach is compared with widely used meta-analysis methods including Bayesian network meta-analysis and the bivariate and hierarchical summary receiver operating characteristic (ROC) models for meta-analyses of diagnostic test accuracy. © The Author(s) 2014.

Effects of categorization method, regression type, and variable distribution on the inflation of Type-I error rate when categorizing a confounding variable.

PubMed

Barnwell-Ménard, Jean-Louis; Li, Qing; Cohen, Alan A

2015-03-15

The loss of signal associated with categorizing a continuous variable is well known, and previous studies have demonstrated that this can lead to an inflation of Type-I error when the categorized variable is a confounder in a regression analysis estimating the effect of an exposure on an outcome. However, it is not known how the Type-I error may vary under different circumstances, including logistic versus linear regression, different distributions of the confounder, and different categorization methods. Here, we analytically quantified the effect of categorization and then performed a series of 9600 Monte Carlo simulations to estimate the Type-I error inflation associated with categorization of a confounder under different regression scenarios. We show that Type-I error is unacceptably high (>10% in most scenarios and often 100%). The only exception was when the variable categorized was a continuous mixture proxy for a genuinely dichotomous latent variable, where both the continuous proxy and the categorized variable are error-ridden proxies for the dichotomous latent variable. As expected, error inflation was also higher with larger sample size, fewer categories, and stronger associations between the confounder and the exposure or outcome. We provide online tools that can help researchers estimate the potential error inflation and understand how serious a problem this is. Copyright © 2014 John Wiley & Sons, Ltd.

Changes of visual-field global indices after cataract surgery in primary open-angle glaucoma patients.

PubMed

Seol, Bo Ram; Jeoung, Jin Wook; Park, Ki Ho

2016-11-01

To determine changes of visual-field (VF) global indices after cataract surgery and the factors associated with the effect of cataracts on those indices in primary open-angle glaucoma (POAG) patients. A retrospective chart review of 60 POAG patients who had undergone phacoemulsification and intraocular lens insertion was conducted. All of the patients were evaluated with standard automated perimetry (SAP; 30-2 Swedish interactive threshold algorithm; Carl Zeiss Meditec Inc.) before and after surgery. VF global indices before surgery were compared with those after surgery. The best-corrected visual acuity, intraocular pressure (IOP), number of glaucoma medications before surgery, mean total deviation (TD) values, mean pattern deviation (PD) value, and mean TD-PD value were also compared with the corresponding postoperative values. Additionally, postoperative peak IOP and mean IOP were evaluated. Univariate and multivariate logistic regression analyses were performed to identify the factors associated with the effect of cataract on global indices. Mean deviation (MD) after cataract surgery was significantly improved compared with the preoperative MD. Pattern standard deviation (PSD) and visual-field index (VFI) after surgery were similar to those before surgery. Also, mean TD and mean TD-PD were significantly improved after surgery. The posterior subcapsular cataract (PSC) type showed greater MD changes than did the non-PSC type in both the univariate and multivariate logistic regression analyses. In the univariate logistic regression analysis, the preoperative TD-PD value and type of cataract were associated with MD change. However, in the multivariate logistic regression analysis, type of cataract was the only associated factor. None of the other factors was associated with MD change. MD was significantly affected by cataracts, whereas PSD and VFI were not. Most notably, the PSC type showed better MD improvement compared with the non-PSC type after cataract surgery. Clinicians therefore should carefully analyze VF examination results for POAG patients with the PSC type.

Cement Leakage in Percutaneous Vertebral Augmentation for Osteoporotic Vertebral Compression Fractures: Analysis of Risk Factors.

PubMed

Xie, Weixing; Jin, Daxiang; Ma, Hui; Ding, Jinyong; Xu, Jixi; Zhang, Shuncong; Liang, De

2016-05-01

The risk factors for cement leakage were retrospectively reviewed in 192 patients who underwent percutaneous vertebral augmentation (PVA). To discuss the factors related to the cement leakage in PVA procedure for the treatment of osteoporotic vertebral compression fractures. PVA is widely applied for the treatment of osteoporotic vertebral fractures. Cement leakage is a major complication of this procedure. The risk factors for cement leakage were controversial. A retrospective review of 192 patients who underwent PVA was conducted. The following data were recorded: age, sex, bone density, number of fractured vertebrae before surgery, number of treated vertebrae, severity of the treated vertebrae, operative approach, volume of injected bone cement, preoperative vertebral compression ratio, preoperative local kyphosis angle, intraosseous clefts, preoperative vertebral cortical bone defect, and ratio and type of cement leakage. To study the correlation between each factor and cement leakage ratio, bivariate regression analysis was employed to perform univariate analysis, whereas multivariate linear regression analysis was employed to perform multivariate analysis. The study included 192 patients (282 treated vertebrae), and cement leakage occurred in 100 vertebrae (35.46%). The vertebrae with preoperative cortical bone defects generally exhibited higher cement leakage ratio, and the leakage is typically type C. Vertebrae with intact cortical bones before the procedure tend to experience type S leakage. Univariate analysis showed that patient age, bone density, number of fractured vertebrae before surgery, and vertebral cortical bone were associated with cement leakage ratio (P<0.05). Multivariate analysis showed that the main factors influencing bone cement leakage are bone density and vertebral cortical bone defect, with standardized partial regression coefficients of -0.085 and 0.144, respectively. High bone density and vertebral cortical bone defect are independent risk factors associated with bone cement leakage.

Applications of cluster analysis to satellite soundings

NASA Technical Reports Server (NTRS)

Munteanu, M. J.; Jakubowicz, O.; Kalnay, E.; Piraino, P.

1984-01-01

The advantages of the use of cluster analysis in the improvement of satellite temperature retrievals were evaluated since the use of natural clusters, which are associated with atmospheric temperature soundings characteristic of different types of air masses, has the potential for improving stratified regression schemes in comparison with currently used methods which stratify soundings based on latitude, season, and land/ocean. The method of discriminatory analysis was used. The correct cluster of temperature profiles from satellite measurements was located in 85% of the cases. Considerable improvement was observed at all mandatory levels using regression retrievals derived in the clusters of temperature (weighted and nonweighted) in comparison with the control experiment and with the regression retrievals derived in the clusters of brightness temperatures of 3 MSU and 5 IR channels.

Morningness-eveningness questionnaire score and metabolic parameters in patients with type 2 diabetes mellitus.

PubMed

Osonoi, Yusuke; Mita, Tomoya; Osonoi, Takeshi; Saito, Miyoko; Tamasawa, Atsuko; Nakayama, Shiho; Someya, Yuki; Ishida, Hidenori; Kanazawa, Akio; Gosho, Masahiko; Fujitani, Yoshio; Watada, Hirotaka

2014-11-01

"Morningness" and "Eveningness" represent lifestyle patterns including sleep-wake patterns. Although previous studies described a relationship between the morningness-eveningness trait and glycemic control in patients with type 2 diabetes mellitus (T2DM), the mechanism underlying this association remains unknown. The study participants comprised 725 Japanese T2DM outpatients free of history of cardiovascular diseases. Various lifestyles were analyzed using self-reported questionnaires, including morningness-eveningness questionnaire (MEQ). The relationships between morningness-eveningness trait and various biochemical parameters were investigated by linear regression analysis and logistic regression analysis. We classified the study patients into three groups, morning type (n=117), neither type (n=424) and evening type (n=184). Subjects of the evening type had high levels of alanine aminotransferase, triglyceride, fasting blood glucose and HbA1c and low high-density lipoprotein-cholesterol level in a model adjusted for age and gender. Furthermore, multivariate analysis showed that the evening type was associated with high HbA1c and estimated glomerular filtration rate even after adjustment for other lifestyle factors known to affect metabolic control. The results suggest that T2DM patients with eveningness trait are under inadequate metabolic control independent of other lifestyle factors. Thus, the evening trait of T2DM patients represents an important target for intervention to ensure appropriate metabolic function.

Utility-Based Instruments for People with Dementia: A Systematic Review and Meta-Regression Analysis.

PubMed

Li, Li; Nguyen, Kim-Huong; Comans, Tracy; Scuffham, Paul

2018-04-01

Several utility-based instruments have been applied in cost-utility analysis to assess health state values for people with dementia. Nevertheless, concerns and uncertainty regarding their performance for people with dementia have been raised. To assess the performance of available utility-based instruments for people with dementia by comparing their psychometric properties and to explore factors that cause variations in the reported health state values generated from those instruments by conducting meta-regression analyses. A literature search was conducted and psychometric properties were synthesized to demonstrate the overall performance of each instrument. When available, health state values and variables such as the type of instrument and cognitive impairment levels were extracted from each article. A meta-regression analysis was undertaken and available covariates were included in the models. A total of 64 studies providing preference-based values were identified and included. The EuroQol five-dimension questionnaire demonstrated the best combination of feasibility, reliability, and validity. Meta-regression analyses suggested that significant differences exist between instruments, type of respondents, and mode of administration and the variations in estimated utility values had influences on incremental quality-adjusted life-year calculation. This review finds that the EuroQol five-dimension questionnaire is the most valid utility-based instrument for people with dementia, but should be replaced by others under certain circumstances. Although no utility estimates were reported in the article, the meta-regression analyses that examined variations in utility estimates produced by different instruments impact on cost-utility analysis, potentially altering the decision-making process in some circumstances. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

How Bias Reduction Is Affected by Covariate Choice, Unreliability, and Mode of Data Analysis: Results from Two Types of within-Study Comparisons

ERIC Educational Resources Information Center

Cook, Thomas D.; Steiner, Peter M.; Pohl, Steffi

2009-01-01

This study uses within-study comparisons to assess the relative importance of covariate choice, unreliability in the measurement of these covariates, and whether regression or various forms of propensity score analysis are used to analyze the outcome data. Two of the within-study comparisons are of the four-arm type, and many more are of the…

Ecologic regression analysis and the study of the influence of air quality on mortality.

PubMed Central

Selvin, S; Merrill, D; Wong, L; Sacks, S T

1984-01-01

This presentation focuses entirely on the use and evaluation of regression analysis applied to ecologic data as a method to study the effects of ambient air pollution on mortality rates. Using extensive national data on mortality, air quality and socio-economic status regression analyses are used to study the influence of air quality on mortality. The analytic methods and data are selected in such a way that direct comparisons can be made with other ecologic regression studies of mortality and air quality. Analyses are performed by use of two types of geographic areas, age-specific mortality of both males and females and three pollutants (total suspended particulates, sulfur dioxide and nitrogen dioxide). The overall results indicate no persuasive evidence exists of a link between air quality and general mortality levels. Additionally, a lack of consistency between the present results and previous published work is noted. Overall, it is concluded that linear regression analysis applied to nationally collected ecologic data cannot be used to usefully infer a causal relationship between air quality and mortality which is in direct contradiction to other major published studies. PMID:6734568

The photosynthesis - leaf nitrogen relationship at ambient and elevated atmospheric carbon dioxide: a meta-analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Andrew G. Peterson; J. Timothy Ball; Yiqi Luo

1998-09-25

Estimation of leaf photosynthetic rate (A) from leaf nitrogen content (N) is both conceptually and numerically important in models of plant, ecosystem and biosphere responses to global change. The relationship between A and N has been studied extensively at ambient CO{sub 2} but much less at elevated CO{sub 2}. This study was designed to (1) assess whether the A-N relationship was more similar for species within than between community and vegetation types, and (2) examine how growth at elevated CO{sub 2} affects the A-N relationship. Data were obtained for 39 C{sub 3} species grown at ambient CO{sub 2} and 10more » C{sub 3} species grown at ambient and elevated CO{sub 2}. A regression model was applied to each species as well as to species pooled within different community and vegetation types. Cluster analysis of the regression coefficients indicated that species measured at ambient CO{sub 2} did not separate into distinct groups matching community or vegetation type. Instead, most community and vegetation types shared the same general parameter space for regression coefficients. Growth at elevated CO{sub 2} increased photosynthetic nitrogen use efficiency for pines and deciduous trees. When species were pooled by vegetation type, the A-N relationship for deciduous trees expressed on a leaf-mass bask was not altered by elevated CO{sub 2}, while the intercept increased for pines. When regression coefficients were averaged to give mean responses for different vegetation types, elevated CO{sub 2} increased the intercept and the slope for deciduous trees but increased only the intercept for pines. There were no statistical differences between the pines and deciduous trees for the effect of CO{sub 2}. Generalizations about the effect of elevated CO{sub 2} on the A-N relationship, and differences between pines and deciduous trees will be enhanced as more data become available.« less

A method for nonlinear exponential regression analysis

NASA Technical Reports Server (NTRS)

Junkin, B. G.

1971-01-01

A computer-oriented technique is presented for performing a nonlinear exponential regression analysis on decay-type experimental data. The technique involves the least squares procedure wherein the nonlinear problem is linearized by expansion in a Taylor series. A linear curve fitting procedure for determining the initial nominal estimates for the unknown exponential model parameters is included as an integral part of the technique. A correction matrix was derived and then applied to the nominal estimate to produce an improved set of model parameters. The solution cycle is repeated until some predetermined criterion is satisfied.

Prediction of coagulation and flocculation processes using ANN models and fuzzy regression.

PubMed

Zangooei, Hossein; Delnavaz, Mohammad; Asadollahfardi, Gholamreza

2016-09-01

Coagulation and flocculation are two main processes used to integrate colloidal particles into larger particles and are two main stages of primary water treatment. Coagulation and flocculation processes are only needed when colloidal particles are a significant part of the total suspended solid fraction. Our objective was to predict turbidity of water after the coagulation and flocculation process while other parameters such as types and concentrations of coagulants, pH, and influent turbidity of raw water were known. We used a multilayer perceptron (MLP), a radial basis function (RBF) of artificial neural networks (ANNs) and various kinds of fuzzy regression analysis to predict turbidity after the coagulation and flocculation processes. The coagulant used in the pilot plant, which was located in water treatment plant, was poly aluminum chloride. We used existing data, including the type and concentrations of coagulant, pH and influent turbidity, of the raw water because these types of data were available from the pilot plant for simulation and data was collected by the Tehran water authority. The results indicated that ANNs had more ability in simulating the coagulation and flocculation process and predicting turbidity removal with different experimental data than did the fuzzy regression analysis, and may have the ability to reduce the number of jar tests, which are time-consuming and expensive. The MLP neural network proved to be the best network compared to the RBF neural network and fuzzy regression analysis in this study. The MLP neural network can predict the effluent turbidity of the coagulation and the flocculation process with a coefficient of determination (R 2 ) of 0.96 and root mean square error of 0.0106.

Obscure phenomena in statistical analysis of quantitative structure-activity relationships. Part 1: Multicollinearity of physicochemical descriptors.

PubMed

Mager, P P; Rothe, H

1990-10-01

Multicollinearity of physicochemical descriptors leads to serious consequences in quantitative structure-activity relationship (QSAR) analysis, such as incorrect estimators and test statistics of regression coefficients of the ordinary least-squares (OLS) model applied usually to QSARs. Beside the diagnosis of the known simple collinearity, principal component regression analysis (PCRA) also allows the diagnosis of various types of multicollinearity. Only if the absolute values of PCRA estimators are order statistics that decrease monotonically, the effects of multicollinearity can be circumvented. Otherwise, obscure phenomena may be observed, such as good data recognition but low predictive model power of a QSAR model.

Resection of complex pancreatic injuries: Benchmarking postoperative complications using the Accordion classification

PubMed Central

Krige, Jake E; Jonas, Eduard; Thomson, Sandie R; Kotze, Urda K; Setshedi, Mashiko; Navsaria, Pradeep H; Nicol, Andrew J

2017-01-01

AIM To benchmark severity of complications using the Accordion Severity Grading System (ASGS) in patients undergoing operation for severe pancreatic injuries. METHODS A prospective institutional database of 461 patients with pancreatic injuries treated from 1990 to 2015 was reviewed. One hundred and thirty patients with AAST grade 3, 4 or 5 pancreatic injuries underwent resection (pancreatoduodenectomy, n = 20, distal pancreatectomy, n = 110), including 30 who had an initial damage control laparotomy (DCL) and later definitive surgery. AAST injury grades, type of pancreatic resection, need for DCL and incidence and ASGS severity of complications were assessed. Uni- and multivariate logistic regression analysis was applied. RESULTS Overall 238 complications occurred in 95 (73%) patients of which 73% were ASGS grades 3-6. Nineteen patients (14.6%) died. Patients more likely to have complications after pancreatic resection were older, had a revised trauma score (RTS) < 7.8, were shocked on admission, had grade 5 injuries of the head and neck of the pancreas with associated vascular and duodenal injuries, required a DCL, received a larger blood transfusion, had a pancreatoduodenectomy (PD) and repeat laparotomies. Applying univariate logistic regression analysis, mechanism of injury, RTS < 7.8, shock on admission, DCL, increasing AAST grade and type of pancreatic resection were significant variables for complications. Multivariate logistic regression analysis however showed that only age and type of pancreatic resection (PD) were significant. CONCLUSION This ASGS-based study benchmarked postoperative morbidity after pancreatic resection for trauma. The detailed outcome analysis provided may serve as a reference for future institutional comparisons. PMID:28396721

A comparison of methods for the analysis of binomial clustered outcomes in behavioral research.

PubMed

Ferrari, Alberto; Comelli, Mario

2016-12-01

In behavioral research, data consisting of a per-subject proportion of "successes" and "failures" over a finite number of trials often arise. This clustered binary data are usually non-normally distributed, which can distort inference if the usual general linear model is applied and sample size is small. A number of more advanced methods is available, but they are often technically challenging and a comparative assessment of their performances in behavioral setups has not been performed. We studied the performances of some methods applicable to the analysis of proportions; namely linear regression, Poisson regression, beta-binomial regression and Generalized Linear Mixed Models (GLMMs). We report on a simulation study evaluating power and Type I error rate of these models in hypothetical scenarios met by behavioral researchers; plus, we describe results from the application of these methods on data from real experiments. Our results show that, while GLMMs are powerful instruments for the analysis of clustered binary outcomes, beta-binomial regression can outperform them in a range of scenarios. Linear regression gave results consistent with the nominal level of significance, but was overall less powerful. Poisson regression, instead, mostly led to anticonservative inference. GLMMs and beta-binomial regression are generally more powerful than linear regression; yet linear regression is robust to model misspecification in some conditions, whereas Poisson regression suffers heavily from violations of the assumptions when used to model proportion data. We conclude providing directions to behavioral scientists dealing with clustered binary data and small sample sizes. Copyright © 2016 Elsevier B.V. All rights reserved.

Variation of Annual ET Determined from Water Budgets Across Rural Southeastern Basins Differing in Forest Types

NASA Astrophysics Data System (ADS)

Younger, S. E.; Jackson, C. R.

2017-12-01

In the Southeastern United States, evapotranspiration (ET) typically accounts for 60-70% of precipitation. Watershed and plot scale experiments show that evergreen forests have higher ET rates than hardwood forests and pastures. However, some plot experiments indicate that certain hardwood species have higher ET than paired evergreens. The complexity of factors influencing ET in mixed land cover watersheds makes identifying the relative influences difficult. Previous watershed scale studies have relied on regression to understand the influences or low flow analysis to indicate growing season differences among watersheds. Existing studies in the southeast investigating ET rates for watersheds with multiple forest cover types have failed to identify a significant forest type effect, but these studies acknowledge small sample sizes. Trends of decreasing streamflow have been recognized in the region and are generally attributed to five key factors, 1.) influences from multiple droughts, 2.) changes in distribution of precipitation, 3.) reforestation of agricultural land, 4.) increasing consumptive uses, or 5.) a combination of these and other factors. This study attempts to address the influence of forest type on long term average annual streamflow and on stream low flows. Long term annual ET rates were calculated as ET = P-Q for 46 USGS gaged basins with daily data for the 1982 - 2014 water years, >40% forest cover, and no large reservoirs. Land cover data was regressed against ET to describe the relationship between each of the forest types in the National Land Cover Database. Regression analysis indicates evergreen land cover has a positive relationship with ET while deciduous and total forest have a negative relationship with ET. Low flow analysis indicates low flows tend to be lower in watersheds with more evergreen cover, and that low flows increase with increasing deciduous cover, although these relationships are noisy. This work suggests considering forest cover type improves understanding of watershed scale ET at annual and seasonal levels which is consistent with historic paired watershed experiments and some plot scale data.

Weighted analysis methods for mapped plot forest inventory data: Tables, regressions, maps and graphs

Treesearch

Paul C. Van Deusen; Linda S. Heath

2010-01-01

Weighted estimation methods for analysis of mapped plot forest inventory data are discussed. The appropriate weighting scheme can vary depending on the type of analysis and graphical display. Both statistical issues and user expectations need to be considered in these methods. A weighting scheme is proposed that balances statistical considerations and the logical...

A framework for longitudinal data analysis via shape regression

NASA Astrophysics Data System (ADS)

Fishbaugh, James; Durrleman, Stanley; Piven, Joseph; Gerig, Guido

2012-02-01

Traditional longitudinal analysis begins by extracting desired clinical measurements, such as volume or head circumference, from discrete imaging data. Typically, the continuous evolution of a scalar measurement is estimated by choosing a 1D regression model, such as kernel regression or fitting a polynomial of fixed degree. This type of analysis not only leads to separate models for each measurement, but there is no clear anatomical or biological interpretation to aid in the selection of the appropriate paradigm. In this paper, we propose a consistent framework for the analysis of longitudinal data by estimating the continuous evolution of shape over time as twice differentiable flows of deformations. In contrast to 1D regression models, one model is chosen to realistically capture the growth of anatomical structures. From the continuous evolution of shape, we can simply extract any clinical measurements of interest. We demonstrate on real anatomical surfaces that volume extracted from a continuous shape evolution is consistent with a 1D regression performed on the discrete measurements. We further show how the visualization of shape progression can aid in the search for significant measurements. Finally, we present an example on a shape complex of the brain (left hemisphere, right hemisphere, cerebellum) that demonstrates a potential clinical application for our framework.

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet

2010-05-01

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping.

Effect of Home Blood Pressure on Inducing Remission/Regression of Microalbuminuria in Patients With Type 2 Diabetes Mellitus.

PubMed

Nishimura, Motonobu; Kato, Yasuhisa; Tanaka, Tsuyoshi; Taki, Hideki; Tone, Atsuhito; Yamada, Kazunori; Suzuki, Seiji; Saito, Miho; Ando, Yutaka; Hoshiyama, Yoshiharu

2017-08-01

The Home Blood Pressure for Diabetic Nephropathy study is a prospective observational study conducted to determine the effect of home blood pressure (HBP) on remission/regression of microalbuminuria in patients with type 2 diabetes mellitus (DM). Patients with type 2 DM having microalbuminuria were followed-up for 3 years. Remission of microalbuminuria was defined as shift from microalbuminuria to normoalbuminuria. Regression of microalbuminuria was defined as a 50% reduction in urinary albumin-creatinine ratio from baseline. All measurements of morning and evening HBP were averaged every year and defined as all HBP. In total, 235 patients were followed up. The 3-year cumulative incidences of remission and regression were 32.3% and 44.7%, respectively. Following analysis of all cases, the degree of decline in all home systolic blood pressure (AHSBP), rather than mean AHSBP, influenced the incidence of remission/regression. There was a strong relationship between the decline in AHSBP during the follow-up period and AHSBP at baseline. Therefore, separate analyses of the patients with AHSBP below 140 mm Hg at baseline were performed, which revealed that mean AHSBP during the follow-up period independently affected the incidence of remission/regression. The hazard ratio for inducing remission/regression was significantly lower in patients with AHSBP during the follow-up period above 130 mm Hg than in those with AHSBP below 120 mm Hg. Optimal AHSBP for the induction of remission/regression of microalbuminuria might be below 130 mm Hg. It is required to confirm whether keeping AHSBP below 130 mm Hg leads to subsequent renoprotection or not. Trial Number UMIN000000804. © American Journal of Hypertension, Ltd 2017. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Associations of financial stressors and physical intimate partner violence perpetration.

PubMed

Schwab-Reese, Laura M; Peek-Asa, Corinne; Parker, Edith

2016-12-01

Contextual factors, such as exposure to stressors, may be antecedents to IPV perpetration. These contextual factors may be amenable to modification through intervention and prevention. However, few studies have examined specific contextual factors. To begin to address this gap, we examined the associations between financial stressors and three types of physical IPV perpetration. This analysis used data from Wave IV of The National Longitudinal Study of Adolescent to Adult Health. We used logistic regression to examine the associations of financial stressors and each type of IPV (minor, severe, causing injury), and multinomial logit regression to examine the associations of financial stressors and patterns of co-occurring types of IPV perpetration (only minor; only severe; minor and severe; minor, severe, and causing injury; compared with no perpetration). Fewer men perpetrated threats/minor physical IPV (6.7 %) or severe physical IPV (3.4 %) compared with women (11.4 % and 8.8 %, respectively). However, among physical IPV perpetrators, a higher percentage of men (32.0 %) than women (21.0 %) reported their partner was injured as a result of the IPV. In logistic regression models of each type of IPV perpetration, both the number of stressors experienced and several types of financial stressors were associated with perpetrating each type of IPV. Utilities nonpayment, housing nonpayment, food insecurity, and no phone service were associated with increased odds of perpetrating each form of IPV in adjusted analysis. Eviction was associated with perpetrating severe physical IPV but not threats/minor IPV or IPV causing injury. In multinomial logit regression comparing patterns of IPV perpetration to perpetrating no physical IPV, the relationships of financial stressors were less consistent. Food insecurity was associated with perpetrating only minor physical IPV. Comparatively, overall number of financial stressors and four types of financial stressors (utilities nonpayment, housing nonpayment, food insecurity, and disconnected phone service) were associated with perpetrating all three forms of physical IPV. Combined with prior research, our results suggested interventions to improve financial well-being may be a novel way to reduce physical IPV perpetration.

Associations of financial stressors and physical intimate partner violence perpetration.

PubMed

Schwab-Reese, Laura M; Peek-Asa, Corinne; Parker, Edith

Contextual factors, such as exposure to stressors, may be antecedents to IPV perpetration. These contextual factors may be amenable to modification through intervention and prevention. However, few studies have examined specific contextual factors. To begin to address this gap, we examined the associations between financial stressors and three types of physical IPV perpetration. This analysis used data from Wave IV of The National Longitudinal Study of Adolescent to Adult Health. We used logistic regression to examine the associations of financial stressors and each type of IPV (minor, severe, causing injury), and multinomial logit regression to examine the associations of financial stressors and patterns of co-occurring types of IPV perpetration ( only minor; only severe; minor and severe; minor, severe, and causing injury; compared with no perpetration). Fewer men perpetrated threats/minor physical IPV (6.7 %) or severe physical IPV (3.4 %) compared with women (11.4 % and 8.8 %, respectively). However, among physical IPV perpetrators, a higher percentage of men (32.0 %) than women (21.0 %) reported their partner was injured as a result of the IPV. In logistic regression models of each type of IPV perpetration, both the number of stressors experienced and several types of financial stressors were associated with perpetrating each type of IPV. Utilities nonpayment, housing nonpayment, food insecurity, and no phone service were associated with increased odds of perpetrating each form of IPV in adjusted analysis. Eviction was associated with perpetrating severe physical IPV but not threats/minor IPV or IPV causing injury. In multinomial logit regression comparing patterns of IPV perpetration to perpetrating no physical IPV, the relationships of financial stressors were less consistent. Food insecurity was associated with perpetrating only minor physical IPV. Comparatively, overall number of financial stressors and four types of financial stressors (utilities nonpayment, housing nonpayment, food insecurity, and disconnected phone service) were associated with perpetrating all three forms of physical IPV. Combined with prior research, our results suggested interventions to improve financial well-being may be a novel way to reduce physical IPV perpetration.

Factors Affecting Optimal Aortic Remodeling After Thoracic Endovascular Aortic Repair of Type B (IIIb) Aortic Dissection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, I-Ming; Chen, Po-Lin; Huang, Chun-Yang

PurposeThe purpose of this study was to determine factors associated with entire aortic remodeling after thoracic endovascular aortic repair (TEVAR) in patients with type B dissection.Materials and MethodsThe patients with type B (IIIb) dissections who underwent TEVAR from 2006 to 2013 with minimum of 2 years of follow-up computed tomography data were retrospectively reviewed. Based on the status of false lumen remodeling of entire aorta, patients were divided into three groups: complete regression, total thrombosis, and inadequate regression with patent abdominal false lumen.ResultsA total of 90 patients (72 males, 18 females; mean age 56.6 ± 16.4 years) were included and divided into the completemore » regression (n = 22), total thrombosis (n = 18), and inadequate regression (n = 50) groups. Multivariate logistic regression analysis indicated that dissection extension to iliac arteries, increased preoperative number of dissection tear over abdominal aorta, and decreased preoperative abdominal aorta bifurcation true lumen ratio, as compared between the inadequate and complete regression groups, were associated with a persistent false lumen (odds ratio = 33.33, 2.304, and 0.021; all, p ≤ 0.012). Comparison of 6, 12, and 24 months postoperative data revealed no significant differences at any level, suggesting that the true lumen area ratio might not change after 6 months postoperatively.ConclusionsIncreased preoperative numbers of dissection tear around the abdominal visceral branches, dissection extension to the iliac arteries, and decreased preoperative true lumen area ratio of abdominal aorta are predictive of entire aortic remodeling after TEVAR in patients with type B dissection.Level of EvidenceIII.« less

Survival Data and Regression Models

NASA Astrophysics Data System (ADS)

Grégoire, G.

2014-12-01

We start this chapter by introducing some basic elements for the analysis of censored survival data. Then we focus on right censored data and develop two types of regression models. The first one concerns the so-called accelerated failure time models (AFT), which are parametric models where a function of a parameter depends linearly on the covariables. The second one is a semiparametric model, where the covariables enter in a multiplicative form in the expression of the hazard rate function. The main statistical tool for analysing these regression models is the maximum likelihood methodology and, in spite we recall some essential results about the ML theory, we refer to the chapter "Logistic Regression" for a more detailed presentation.

Evaluation of open source data mining software packages

Treesearch

Bonnie Ruefenacht; Greg Liknes; Andrew J. Lister; Haans Fisk; Dan Wendt

2009-01-01

Since 2001, the USDA Forest Service (USFS) has used classification and regression-tree technology to map USFS Forest Inventory and Analysis (FIA) biomass, forest type, forest type groups, and National Forest vegetation. This prior work used Cubist/See5 software for the analyses. The objective of this project, sponsored by the Remote Sensing Steering Committee (RSSC),...

Regression Analysis of Mixed Recurrent-Event and Panel-Count Data with Additive Rate Models

PubMed Central

Zhu, Liang; Zhao, Hui; Sun, Jianguo; Leisenring, Wendy; Robison, Leslie L.

2015-01-01

Summary Event-history studies of recurrent events are often conducted in fields such as demography, epidemiology, medicine, and social sciences (Cook and Lawless, 2007; Zhao et al., 2011). For such analysis, two types of data have been extensively investigated: recurrent-event data and panel-count data. However, in practice, one may face a third type of data, mixed recurrent-event and panel-count data or mixed event-history data. Such data occur if some study subjects are monitored or observed continuously and thus provide recurrent-event data, while the others are observed only at discrete times and hence give only panel-count data. A more general situation is that each subject is observed continuously over certain time periods but only at discrete times over other time periods. There exists little literature on the analysis of such mixed data except that published by Zhu et al. (2013). In this paper, we consider the regression analysis of mixed data using the additive rate model and develop some estimating equation-based approaches to estimate the regression parameters of interest. Both finite sample and asymptotic properties of the resulting estimators are established, and the numerical studies suggest that the proposed methodology works well for practical situations. The approach is applied to a Childhood Cancer Survivor Study that motivated this study. PMID:25345405

System dynamic modeling: an alternative method for budgeting.

PubMed

Srijariya, Witsanuchai; Riewpaiboon, Arthorn; Chaikledkaew, Usa

2008-03-01

To construct, validate, and simulate a system dynamic financial model and compare it against the conventional method. The study was a cross-sectional analysis of secondary data retrieved from the National Health Security Office (NHSO) in the fiscal year 2004. The sample consisted of all emergency patients who received emergency services outside their registered hospital-catchments area. The dependent variable used was the amount of reimbursed money. Two types of model were constructed, namely, the system dynamic model using the STELLA software and the multiple linear regression model. The outputs of both methods were compared. The study covered 284,716 patients from various levels of providers. The system dynamic model had the capability of producing various types of outputs, for example, financial and graphical analyses. For the regression analysis, statistically significant predictors were composed of service types (outpatient or inpatient), operating procedures, length of stay, illness types (accident or not), hospital characteristics, age, and hospital location (adjusted R(2) = 0.74). The total budget arrived at from using the system dynamic model and regression model was US$12,159,614.38 and US$7,301,217.18, respectively, whereas the actual NHSO reimbursement cost was US$12,840,805.69. The study illustrated that the system dynamic model is a useful financial management tool, although it is not easy to construct. The model is not only more accurate in prediction but is also more capable of analyzing large and complex real-world situations than the conventional method.

Regression analysis of mixed recurrent-event and panel-count data

PubMed Central

Zhu, Liang; Tong, Xinwei; Sun, Jianguo; Chen, Manhua; Srivastava, Deo Kumar; Leisenring, Wendy; Robison, Leslie L.

2014-01-01

In event history studies concerning recurrent events, two types of data have been extensively discussed. One is recurrent-event data (Cook and Lawless, 2007. The Analysis of Recurrent Event Data. New York: Springer), and the other is panel-count data (Zhao and others, 2010. Nonparametric inference based on panel-count data. Test 20, 1–42). In the former case, all study subjects are monitored continuously; thus, complete information is available for the underlying recurrent-event processes of interest. In the latter case, study subjects are monitored periodically; thus, only incomplete information is available for the processes of interest. In reality, however, a third type of data could occur in which some study subjects are monitored continuously, but others are monitored periodically. When this occurs, we have mixed recurrent-event and panel-count data. This paper discusses regression analysis of such mixed data and presents two estimation procedures for the problem. One is a maximum likelihood estimation procedure, and the other is an estimating equation procedure. The asymptotic properties of both resulting estimators of regression parameters are established. Also, the methods are applied to a set of mixed recurrent-event and panel-count data that arose from a Childhood Cancer Survivor Study and motivated this investigation. PMID:24648408

Automatic Classification of Users’ Health Information Need Context: Logistic Regression Analysis of Mouse-Click and Eye-Tracker Data

PubMed Central

Pian, Wenjing; Khoo, Christopher SG

2017-01-01

Background Users searching for health information on the Internet may be searching for their own health issue, searching for someone else’s health issue, or browsing with no particular health issue in mind. Previous research has found that these three categories of users focus on different types of health information. However, most health information websites provide static content for all users. If the three types of user health information need contexts can be identified by the Web application, the search results or information offered to the user can be customized to increase its relevance or usefulness to the user. Objective The aim of this study was to investigate the possibility of identifying the three user health information contexts (searching for self, searching for others, or browsing with no particular health issue in mind) using just hyperlink clicking behavior; using eye-tracking information; and using a combination of eye-tracking, demographic, and urgency information. Predictive models are developed using multinomial logistic regression. Methods A total of 74 participants (39 females and 35 males) who were mainly staff and students of a university were asked to browse a health discussion forum, Healthboards.com. An eye tracker recorded their examining (eye fixation) and skimming (quick eye movement) behaviors on 2 types of screens: summary result screen displaying a list of post headers, and detailed post screen. The following three types of predictive models were developed using logistic regression analysis: model 1 used only the time spent in scanning the summary result screen and reading the detailed post screen, which can be determined from the user’s mouse clicks; model 2 used the examining and skimming durations on each screen, recorded by an eye tracker; and model 3 added user demographic and urgency information to model 2. Results An analysis of variance (ANOVA) analysis found that users’ browsing durations were significantly different for the three health information contexts (P<.001). The logistic regression model 3 was able to predict the user’s type of health information context with a 10-fold cross validation mean accuracy of 84% (62/74), followed by model 2 at 73% (54/74) and model 1 at 71% (52/78). In addition, correlation analysis found that particular browsing durations were highly correlated with users’ age, education level, and the urgency of their information need. Conclusions A user’s type of health information need context (ie, searching for self, for others, or with no health issue in mind) can be identified with reasonable accuracy using just user mouse clicks that can easily be detected by Web applications. Higher accuracy can be obtained using Google glass or future computing devices with eye tracking function. PMID:29269342

Regression analysis for LED color detection of visual-MIMO system

NASA Astrophysics Data System (ADS)

Banik, Partha Pratim; Saha, Rappy; Kim, Ki-Doo

2018-04-01

Color detection from a light emitting diode (LED) array using a smartphone camera is very difficult in a visual multiple-input multiple-output (visual-MIMO) system. In this paper, we propose a method to determine the LED color using a smartphone camera by applying regression analysis. We employ a multivariate regression model to identify the LED color. After taking a picture of an LED array, we select the LED array region, and detect the LED using an image processing algorithm. We then apply the k-means clustering algorithm to determine the number of potential colors for feature extraction of each LED. Finally, we apply the multivariate regression model to predict the color of the transmitted LEDs. In this paper, we show our results for three types of environmental light condition: room environmental light, low environmental light (560 lux), and strong environmental light (2450 lux). We compare the results of our proposed algorithm from the analysis of training and test R-Square (%) values, percentage of closeness of transmitted and predicted colors, and we also mention about the number of distorted test data points from the analysis of distortion bar graph in CIE1931 color space.

Identifying Autocorrelation Generated by Various Error Processes in Interrupted Time-Series Regression Designs: A Comparison of AR1 and Portmanteau Tests

ERIC Educational Resources Information Center

Huitema, Bradley E.; McKean, Joseph W.

2007-01-01

Regression models used in the analysis of interrupted time-series designs assume statistically independent errors. Four methods of evaluating this assumption are the Durbin-Watson (D-W), Huitema-McKean (H-M), Box-Pierce (B-P), and Ljung-Box (L-B) tests. These tests were compared with respect to Type I error and power under a wide variety of error…

Association between serum endogenous secretory receptor for advanced glycation end products and risk of type 2 diabetes mellitus with combined depression in the Chinese population.

PubMed

Chen, Gang; Wu, Yulian; Wang, Tao; Liang, Jixing; Lin, Wei; Li, Liantao; Wen, Junping; Lin, Lixiang; Huang, Huibin

2012-10-01

The role of the endogenous secretory receptor for advanced glycation end products (esRAGE) in depression of diabetes patients and its clinical significance are unclear. This study investigated the role of serum esRAGE in patients with type 2 diabetes mellitus with depression in the Chinese population. One hundred nineteen hospitalized patients with type 2 diabetes were recruited at Fujian Provincial Hospital (Fuzhou, China) from February 2010 to January 2011. All selected subjects were assessed with the Hamilton Rating Scale for Depression (HAMD). Among them, 71 patients with both type 2 diabetes and depression were included. All selected subjects were examined for the following: esRAGE concentration, glycosylated hemoglobin (HbA1c), blood lipids, C-reactive protein, trace of albumin in urine, and carotid artery intima-media thickness (IMT). Association between serum esRAGE levels and risk of type 2 diabetes mellitus with depression was also analyzed. There were statistically significant differences in gender, age, body mass index, waist circumference, and treatment methods between the group with depression and the group without depression (P<0.05). Multiple linear regression analysis showed that HAMD scores were negatively correlated with esRAGE levels (standard regression coefficient -0.270, P<0.01). HAMD-17 scores were positively correlated with IMT (standard regression coefficient 0.183, P<0.05) and with HbA1c (standard regression coefficient 0.314, P<0.01). Female gender, younger age, obesity, poor glycemic control, complications, and insulin therapy are all risk factors of type 2 diabetes mellitus with combined depression in the Chinese population. Inflammation and atherosclerosis play an important role in the pathogenesis of depression. esRAGE is a protective factor of depression among patients who have type 2 diabetes.

A toolbox to visually explore cerebellar shape changes in cerebellar disease and dysfunction.

PubMed

Abulnaga, S Mazdak; Yang, Zhen; Carass, Aaron; Kansal, Kalyani; Jedynak, Bruno M; Onyike, Chiadi U; Ying, Sarah H; Prince, Jerry L

2016-02-27

The cerebellum plays an important role in motor control and is also involved in cognitive processes. Cerebellar function is specialized by location, although the exact topographic functional relationship is not fully understood. The spinocerebellar ataxias are a group of neurodegenerative diseases that cause regional atrophy in the cerebellum, yielding distinct motor and cognitive problems. The ability to study the region-specific atrophy patterns can provide insight into the problem of relating cerebellar function to location. In an effort to study these structural change patterns, we developed a toolbox in MATLAB to provide researchers a unique way to visually explore the correlation between cerebellar lobule shape changes and function loss, with a rich set of visualization and analysis modules. In this paper, we outline the functions and highlight the utility of the toolbox. The toolbox takes as input landmark shape representations of subjects' cerebellar substructures. A principal component analysis is used for dimension reduction. Following this, a linear discriminant analysis and a regression analysis can be performed to find the discriminant direction associated with a specific disease type, or the regression line of a specific functional measure can be generated. The characteristic structural change pattern of a disease type or of a functional score is visualized by sampling points on the discriminant or regression line. The sampled points are used to reconstruct synthetic cerebellar lobule shapes. We showed a few case studies highlighting the utility of the toolbox and we compare the analysis results with the literature.

A toolbox to visually explore cerebellar shape changes in cerebellar disease and dysfunction

NASA Astrophysics Data System (ADS)

Abulnaga, S. Mazdak; Yang, Zhen; Carass, Aaron; Kansal, Kalyani; Jedynak, Bruno M.; Onyike, Chiadi U.; Ying, Sarah H.; Prince, Jerry L.

2016-03-01

The cerebellum plays an important role in motor control and is also involved in cognitive processes. Cerebellar function is specialized by location, although the exact topographic functional relationship is not fully understood. The spinocerebellar ataxias are a group of neurodegenerative diseases that cause regional atrophy in the cerebellum, yielding distinct motor and cognitive problems. The ability to study the region-specific atrophy patterns can provide insight into the problem of relating cerebellar function to location. In an effort to study these structural change patterns, we developed a toolbox in MATLAB to provide researchers a unique way to visually explore the correlation between cerebellar lobule shape changes and function loss, with a rich set of visualization and analysis modules. In this paper, we outline the functions and highlight the utility of the toolbox. The toolbox takes as input landmark shape representations of subjects' cerebellar substructures. A principal component analysis is used for dimension reduction. Following this, a linear discriminant analysis and a regression analysis can be performed to find the discriminant direction associated with a specific disease type, or the regression line of a specific functional measure can be generated. The characteristic structural change pattern of a disease type or of a functional score is visualized by sampling points on the discriminant or regression line. The sampled points are used to reconstruct synthetic cerebellar lobule shapes. We showed a few case studies highlighting the utility of the toolbox and we compare the analysis results with the literature.

Regression analysis of mixed recurrent-event and panel-count data with additive rate models.

PubMed

Zhu, Liang; Zhao, Hui; Sun, Jianguo; Leisenring, Wendy; Robison, Leslie L

2015-03-01

Event-history studies of recurrent events are often conducted in fields such as demography, epidemiology, medicine, and social sciences (Cook and Lawless, 2007, The Statistical Analysis of Recurrent Events. New York: Springer-Verlag; Zhao et al., 2011, Test 20, 1-42). For such analysis, two types of data have been extensively investigated: recurrent-event data and panel-count data. However, in practice, one may face a third type of data, mixed recurrent-event and panel-count data or mixed event-history data. Such data occur if some study subjects are monitored or observed continuously and thus provide recurrent-event data, while the others are observed only at discrete times and hence give only panel-count data. A more general situation is that each subject is observed continuously over certain time periods but only at discrete times over other time periods. There exists little literature on the analysis of such mixed data except that published by Zhu et al. (2013, Statistics in Medicine 32, 1954-1963). In this article, we consider the regression analysis of mixed data using the additive rate model and develop some estimating equation-based approaches to estimate the regression parameters of interest. Both finite sample and asymptotic properties of the resulting estimators are established, and the numerical studies suggest that the proposed methodology works well for practical situations. The approach is applied to a Childhood Cancer Survivor Study that motivated this study. © 2014, The International Biometric Society.

Methods for calculating confidence and credible intervals for the residual between-study variance in random effects meta-regression models

PubMed Central

2014-01-01

Background Meta-regression is becoming increasingly used to model study level covariate effects. However this type of statistical analysis presents many difficulties and challenges. Here two methods for calculating confidence intervals for the magnitude of the residual between-study variance in random effects meta-regression models are developed. A further suggestion for calculating credible intervals using informative prior distributions for the residual between-study variance is presented. Methods Two recently proposed and, under the assumptions of the random effects model, exact methods for constructing confidence intervals for the between-study variance in random effects meta-analyses are extended to the meta-regression setting. The use of Generalised Cochran heterogeneity statistics is extended to the meta-regression setting and a Newton-Raphson procedure is developed to implement the Q profile method for meta-analysis and meta-regression. WinBUGS is used to implement informative priors for the residual between-study variance in the context of Bayesian meta-regressions. Results Results are obtained for two contrasting examples, where the first example involves a binary covariate and the second involves a continuous covariate. Intervals for the residual between-study variance are wide for both examples. Conclusions Statistical methods, and R computer software, are available to compute exact confidence intervals for the residual between-study variance under the random effects model for meta-regression. These frequentist methods are almost as easily implemented as their established counterparts for meta-analysis. Bayesian meta-regressions are also easily performed by analysts who are comfortable using WinBUGS. Estimates of the residual between-study variance in random effects meta-regressions should be routinely reported and accompanied by some measure of their uncertainty. Confidence and/or credible intervals are well-suited to this purpose. PMID:25196829

Methods for estimating the magnitude and frequency of peak streamflows for unregulated streams in Oklahoma

USGS Publications Warehouse

Lewis, Jason M.

2010-01-01

Peak-streamflow regression equations were determined for estimating flows with exceedance probabilities from 50 to 0.2 percent for the state of Oklahoma. These regression equations incorporate basin characteristics to estimate peak-streamflow magnitude and frequency throughout the state by use of a generalized least squares regression analysis. The most statistically significant independent variables required to estimate peak-streamflow magnitude and frequency for unregulated streams in Oklahoma are contributing drainage area, mean-annual precipitation, and main-channel slope. The regression equations are applicable for watershed basins with drainage areas less than 2,510 square miles that are not affected by regulation. The resulting regression equations had a standard model error ranging from 31 to 46 percent. Annual-maximum peak flows observed at 231 streamflow-gaging stations through water year 2008 were used for the regression analysis. Gage peak-streamflow estimates were used from previous work unless 2008 gaging-station data were available, in which new peak-streamflow estimates were calculated. The U.S. Geological Survey StreamStats web application was used to obtain the independent variables required for the peak-streamflow regression equations. Limitations on the use of the regression equations and the reliability of regression estimates for natural unregulated streams are described. Log-Pearson Type III analysis information, basin and climate characteristics, and the peak-streamflow frequency estimates for the 231 gaging stations in and near Oklahoma are listed. Methodologies are presented to estimate peak streamflows at ungaged sites by using estimates from gaging stations on unregulated streams. For ungaged sites on urban streams and streams regulated by small floodwater retarding structures, an adjustment of the statewide regression equations for natural unregulated streams can be used to estimate peak-streamflow magnitude and frequency.

Association between the Type of Workplace and Lung Function in Copper Miners

PubMed Central

Gruszczyński, Leszek; Wojakowska, Anna; Ścieszka, Marek; Turczyn, Barbara; Schmidt, Edward

2016-01-01

The aim of the analysis was to retrospectively assess changes in lung function in copper miners depending on the type of workplace. In the groups of 225 operators, 188 welders, and 475 representatives of other jobs, spirometry was performed at the start of employment and subsequently after 10, 20, and 25 years of work. Spirometry Longitudinal Data Analysis software was used to estimate changes in group means for FEV1 and FVC. Multiple linear regression analysis was used to assess an association between workplace and lung function. Lung function assessed on the basis of calculation of longitudinal FEV1 (FVC) decline was similar in all studied groups. However, multiple linear regression model used in cross-sectional analysis revealed an association between workplace and lung function. In the group of welders, FEF75 was lower in comparison to operators and other miners as early as after 10 years of work. Simultaneously, in smoking welders, the FEV1/FVC ratio was lower than in nonsmokers (p < 0,05). The interactions between type of workplace and smoking (p < 0,05) in their effect on FVC, FEV1, PEF, and FEF50 were shown. Among underground working copper miners, the group of smoking welders is especially threatened by impairment of lung ventilatory function. PMID:27274987

Impact of Texas high school science teacher credentials on student performance in high school science

NASA Astrophysics Data System (ADS)

George, Anna Ray Bayless

A study was conducted to determine the relationship between the credentials held by science teachers who taught at a school that administered the Science Texas Assessment on Knowledge and Skills (Science TAKS), the state standardized exam in science, at grade 11 and student performance on a state standardized exam in science administered in grade 11. Years of teaching experience, teacher certification type(s), highest degree level held, teacher and school demographic information, and the percentage of students who met the passing standard on the Science TAKS were obtained through a public records request to the Texas Education Agency (TEA) and the State Board for Educator Certification (SBEC). Analysis was performed through the use of canonical correlation analysis and multiple linear regression analysis. The results of the multiple linear regression analysis indicate that a larger percentage of students met the passing standard on the Science TAKS state attended schools in which a large portion of the high school science teachers held post baccalaureate degrees, elementary and physical science certifications, and had 11-20 years of teaching experience.

Efficacy of pulsed dye laser treatment for common warts is not influenced by the causative HPV type: a prospective study.

PubMed

Fichman, Yoseph; Levi, Assi; Hodak, Emmilia; Halachmi, Shlomit; Mazor, Sigal; Wolf, Dana; Caplan, Orit; Lapidoth, Moshe

2018-05-01

Verruca vulgaris (VV) is a prevalent skin condition caused by various subtypes of human papilloma virus (HPV). The most common causes of non-genital lesions are HPV types 2 and 4, and to a lesser extent types 1, 3, 26, 29, and 57. Although numerous therapeutic modalities exist, none is universally effective or without adverse events (AE). Pulsed dye laser (PDL) is a favorable option due to its observed efficacy and relatively low AE rate. However, it is not known which verrucae are most likely to respond to PDL, or whether the causative viral subtype influences this response. The objective of this prospective blinded study was to assess whether the HPV subtype was predictive of response to PDL. For that matter, 26 verrucae from 26 immunocompetent patients were biopsied prior to treatment by PDL. HPV coding sequences were isolated and genotyped using PCR analysis. Patients were treated by PDL (595 nm wavelength, 5 mm spot size, 1.5 ms pulse duration, 12 J/cm 2 fluence) once a month for up to 6 months, and clinical response was assessed. Binary logistic regression analysis and linear logistic regression analysis were used in order to evaluate statistical significance. Different types of HPV were identified in 22 of 26 tissue samples. Response to treatment did not correlate with HPV type, age, or gender. As no association between HPV type and response to PDL therapy could be established, it is therefore equally effective for all HPV types and remains a favorable treatment option for all VV.

Quantitative laser-induced breakdown spectroscopy data using peak area step-wise regression analysis: an alternative method for interpretation of Mars science laboratory results

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clegg, Samuel M; Barefield, James E; Wiens, Roger C

2008-01-01

The ChemCam instrument on the Mars Science Laboratory (MSL) will include a laser-induced breakdown spectrometer (LIBS) to quantify major and minor elemental compositions. The traditional analytical chemistry approach to calibration curves for these data regresses a single diagnostic peak area against concentration for each element. This approach contrasts with a new multivariate method in which elemental concentrations are predicted by step-wise multiple regression analysis based on areas of a specific set of diagnostic peaks for each element. The method is tested on LIBS data from igneous and metamorphosed rocks. Between 4 and 13 partial regression coefficients are needed to describemore » each elemental abundance accurately (i.e., with a regression line of R{sup 2} > 0.9995 for the relationship between predicted and measured elemental concentration) for all major and minor elements studied. Validation plots suggest that the method is limited at present by the small data set, and will work best for prediction of concentration when a wide variety of compositions and rock types has been analyzed.« less

Potential Changes in Tree Species Richness and Forest Community Types following Climate Change

Treesearch

Louis R. Iverson; Anantha M. Prasad

2001-01-01

Potential changes in tree species richness and forest community types were evaluated for the eastern United States according to five scenarios of future climate change resulting from a doubling of atmospheric carbon dioxide (CO2). DISTRIB, an empirical model that uses a regression tree analysis approach, was used to generate suitable habitat, or potential future...

Logistic regression for risk factor modelling in stuttering research.

PubMed

Reed, Phil; Wu, Yaqionq

2013-06-01

To outline the uses of logistic regression and other statistical methods for risk factor analysis in the context of research on stuttering. The principles underlying the application of a logistic regression are illustrated, and the types of questions to which such a technique has been applied in the stuttering field are outlined. The assumptions and limitations of the technique are discussed with respect to existing stuttering research, and with respect to formulating appropriate research strategies to accommodate these considerations. Finally, some alternatives to the approach are briefly discussed. The way the statistical procedures are employed are demonstrated with some hypothetical data. Research into several practical issues concerning stuttering could benefit if risk factor modelling were used. Important examples are early diagnosis, prognosis (whether a child will recover or persist) and assessment of treatment outcome. After reading this article you will: (a) Summarize the situations in which logistic regression can be applied to a range of issues about stuttering; (b) Follow the steps in performing a logistic regression analysis; (c) Describe the assumptions of the logistic regression technique and the precautions that need to be checked when it is employed; (d) Be able to summarize its advantages over other techniques like estimation of group differences and simple regression. Copyright © 2012 Elsevier Inc. All rights reserved.

Improving power and robustness for detecting genetic association with extreme-value sampling design.

PubMed

Chen, Hua Yun; Li, Mingyao

2011-12-01

Extreme-value sampling design that samples subjects with extremely large or small quantitative trait values is commonly used in genetic association studies. Samples in such designs are often treated as "cases" and "controls" and analyzed using logistic regression. Such a case-control analysis ignores the potential dose-response relationship between the quantitative trait and the underlying trait locus and thus may lead to loss of power in detecting genetic association. An alternative approach to analyzing such data is to model the dose-response relationship by a linear regression model. However, parameter estimation from this model can be biased, which may lead to inflated type I errors. We propose a robust and efficient approach that takes into consideration of both the biased sampling design and the potential dose-response relationship. Extensive simulations demonstrate that the proposed method is more powerful than the traditional logistic regression analysis and is more robust than the linear regression analysis. We applied our method to the analysis of a candidate gene association study on high-density lipoprotein cholesterol (HDL-C) which includes study subjects with extremely high or low HDL-C levels. Using our method, we identified several SNPs showing a stronger evidence of association with HDL-C than the traditional case-control logistic regression analysis. Our results suggest that it is important to appropriately model the quantitative traits and to adjust for the biased sampling when dose-response relationship exists in extreme-value sampling designs. © 2011 Wiley Periodicals, Inc.

Use of statistical study methods for the analysis of the results of the imitation modeling of radiation transfer

NASA Astrophysics Data System (ADS)

Alekseenko, M. A.; Gendrina, I. Yu.

2017-11-01

Recently, due to the abundance of various types of observational data in the systems of vision through the atmosphere and the need for their processing, the use of various methods of statistical research in the study of such systems as correlation-regression analysis, dynamic series, variance analysis, etc. is actual. We have attempted to apply elements of correlation-regression analysis for the study and subsequent prediction of the patterns of radiation transfer in these systems same as in the construction of radiation models of the atmosphere. In this paper, we present some results of statistical processing of the results of numerical simulation of the characteristics of vision systems through the atmosphere obtained with the help of a special software package.1

Relationships between lifestyle patterns and cardio-renal-metabolic parameters in patients with type 2 diabetes mellitus: A cross-sectional study.

PubMed

Ogihara, Takeshi; Mita, Tomoya; Osonoi, Yusuke; Osonoi, Takeshi; Saito, Miyoko; Tamasawa, Atsuko; Nakayama, Shiho; Someya, Yuki; Ishida, Hidenori; Gosho, Masahiko; Kanazawa, Akio; Watada, Hirotaka

2017-01-01

While individuals tend to show accumulation of certain lifestyle patterns, the effect of such patterns in real daily life on cardio-renal-metabolic parameters remains largely unknown. This study aimed to assess clustering of lifestyle patterns and investigate the relationships between such patterns and cardio-renal-metabolic parameters. The study participants were 726 Japanese type 2 diabetes mellitus (T2DM) outpatients free of history of cardiovascular diseases. The relationship between lifestyle patterns and cardio-renal-metabolic parameters was investigated by linear and logistic regression analyses. Factor analysis identified three lifestyle patterns. Subjects characterized by evening type, poor sleep quality and depressive status (type 1 pattern) had high levels of HbA1c, alanine aminotransferase and albuminuria. Subjects characterized by high consumption of food, alcohol and cigarettes (type 2 pattern) had high levels of γ-glutamyl transpeptidase, triglycerides, HDL-cholesterol, blood pressure, and brachial-ankle pulse wave velocity. Subjects characterized by high physical activity (type 3 pattern) had low uric acid and mild elevation of alanine aminotransferase and aspartate aminotransferase. In multivariate regression analysis adjusted by age, gender and BMI, type 1 pattern was associated with higher HbA1c levels, systolic BP and brachial-ankle pulse wave velocity. Type 2 pattern was associated with higher HDL-cholesterol levels, triglycerides, aspartate aminotransferase, ɤ- glutamyl transpeptidase levels, and diastolic BP. The study identified three lifestyle patterns that were associated with distinct cardio-metabolic-renal parameters in T2DM patients. UMIN000010932.

Study of relationship between clinical factors and velopharyngeal closure in cleft palate patients

PubMed Central

Chen, Qi; Zheng, Qian; Shi, Bing; Yin, Heng; Meng, Tian; Zheng, Guang-ning

2011-01-01

BACKGROUND: This study was carried out to analyze the relationship between clinical factors and velopharyngeal closure (VPC) in cleft palate patients. METHODS: Chi-square test was used to compare the postoperative velopharyngeal closure rate. Logistic regression model was used to analyze independent variables associated with velopharyngeal closure. RESULTS: Difference of postoperative VPC rate in different cleft types, operative ages and surgical techniques was significant (P=0.000). Results of logistic regression analysis suggested that when operative age was beyond deciduous dentition stage, or cleft palate type was complete, or just had undergone a simple palatoplasty without levator veli palatini retropositioning, patients would suffer a higher velopharyngeal insufficiency rate after primary palatal repair. CONCLUSIONS: Cleft type, operative age and surgical technique were the contributing factors influencing VPC rate after primary palatal repair of cleft palate patients. PMID:22279464

Graphical Evaluation of the Ridge-Type Robust Regression Estimators in Mixture Experiments

PubMed Central

Erkoc, Ali; Emiroglu, Esra

2014-01-01

In mixture experiments, estimation of the parameters is generally based on ordinary least squares (OLS). However, in the presence of multicollinearity and outliers, OLS can result in very poor estimates. In this case, effects due to the combined outlier-multicollinearity problem can be reduced to certain extent by using alternative approaches. One of these approaches is to use biased-robust regression techniques for the estimation of parameters. In this paper, we evaluate various ridge-type robust estimators in the cases where there are multicollinearity and outliers during the analysis of mixture experiments. Also, for selection of biasing parameter, we use fraction of design space plots for evaluating the effect of the ridge-type robust estimators with respect to the scaled mean squared error of prediction. The suggested graphical approach is illustrated on Hald cement data set. PMID:25202738

Graphical evaluation of the ridge-type robust regression estimators in mixture experiments.

PubMed

Erkoc, Ali; Emiroglu, Esra; Akay, Kadri Ulas

2014-01-01

In mixture experiments, estimation of the parameters is generally based on ordinary least squares (OLS). However, in the presence of multicollinearity and outliers, OLS can result in very poor estimates. In this case, effects due to the combined outlier-multicollinearity problem can be reduced to certain extent by using alternative approaches. One of these approaches is to use biased-robust regression techniques for the estimation of parameters. In this paper, we evaluate various ridge-type robust estimators in the cases where there are multicollinearity and outliers during the analysis of mixture experiments. Also, for selection of biasing parameter, we use fraction of design space plots for evaluating the effect of the ridge-type robust estimators with respect to the scaled mean squared error of prediction. The suggested graphical approach is illustrated on Hald cement data set.

Methods for determining magnitude and frequency of floods in California, based on data through water year 2006

USGS Publications Warehouse

Gotvald, Anthony J.; Barth, Nancy A.; Veilleux, Andrea G.; Parrett, Charles

2012-01-01

Methods for estimating the magnitude and frequency of floods in California that are not substantially affected by regulation or diversions have been updated. Annual peak-flow data through water year 2006 were analyzed for 771 streamflow-gaging stations (streamgages) in California having 10 or more years of data. Flood-frequency estimates were computed for the streamgages by using the expected moments algorithm to fit a Pearson Type III distribution to logarithms of annual peak flows for each streamgage. Low-outlier and historic information were incorporated into the flood-frequency analysis, and a generalized Grubbs-Beck test was used to detect multiple potentially influential low outliers. Special methods for fitting the distribution were developed for streamgages in the desert region in southeastern California. Additionally, basin characteristics for the streamgages were computed by using a geographical information system. Regional regression analysis, using generalized least squares regression, was used to develop a set of equations for estimating flows with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities for ungaged basins in California that are outside of the southeastern desert region. Flood-frequency estimates and basin characteristics for 630 streamgages were combined to form the final database used in the regional regression analysis. Five hydrologic regions were developed for the area of California outside of the desert region. The final regional regression equations are functions of drainage area and mean annual precipitation for four of the five regions. In one region, the Sierra Nevada region, the final equations are functions of drainage area, mean basin elevation, and mean annual precipitation. Average standard errors of prediction for the regression equations in all five regions range from 42.7 to 161.9 percent. For the desert region of California, an analysis of 33 streamgages was used to develop regional estimates of all three parameters (mean, standard deviation, and skew) of the log-Pearson Type III distribution. The regional estimates were then used to develop a set of equations for estimating flows with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities for ungaged basins. The final regional regression equations are functions of drainage area. Average standard errors of prediction for these regression equations range from 214.2 to 856.2 percent. Annual peak-flow data through water year 2006 were analyzed for eight streamgages in California having 10 or more years of data considered to be affected by urbanization. Flood-frequency estimates were computed for the urban streamgages by fitting a Pearson Type III distribution to logarithms of annual peak flows for each streamgage. Regression analysis could not be used to develop flood-frequency estimation equations for urban streams because of the limited number of sites. Flood-frequency estimates for the eight urban sites were graphically compared to flood-frequency estimates for 630 non-urban sites. The regression equations developed from this study will be incorporated into the U.S. Geological Survey (USGS) StreamStats program. The StreamStats program is a Web-based application that provides streamflow statistics and basin characteristics for USGS streamgages and ungaged sites of interest. StreamStats can also compute basin characteristics and provide estimates of streamflow statistics for ungaged sites when users select the location of a site along any stream in California.

Application of software technology to automatic test data analysis

NASA Technical Reports Server (NTRS)

Stagner, J. R.

1991-01-01

The verification process for a major software subsystem was partially automated as part of a feasibility demonstration. The methods employed are generally useful and applicable to other types of subsystems. The effort resulted in substantial savings in test engineer analysis time and offers a method for inclusion of automatic verification as a part of regression testing.

On comparison of net survival curves.

PubMed

Pavlič, Klemen; Perme, Maja Pohar

2017-05-02

Relative survival analysis is a subfield of survival analysis where competing risks data are observed, but the causes of death are unknown. A first step in the analysis of such data is usually the estimation of a net survival curve, possibly followed by regression modelling. Recently, a log-rank type test for comparison of net survival curves has been introduced and the goal of this paper is to explore its properties and put this methodological advance into the context of the field. We build on the association between the log-rank test and the univariate or stratified Cox model and show the analogy in the relative survival setting. We study the properties of the methods using both the theoretical arguments as well as simulations. We provide an R function to enable practical usage of the log-rank type test. Both the log-rank type test and its model alternatives perform satisfactory under the null, even if the correlation between their p-values is rather low, implying that both approaches cannot be used simultaneously. The stratified version has a higher power in case of non-homogeneous hazards, but also carries a different interpretation. The log-rank type test and its stratified version can be interpreted in the same way as the results of an analogous semi-parametric additive regression model despite the fact that no direct theoretical link can be established between the test statistics.

Analysis of Market Opportunities for Chinese Private Express Delivery Industry

NASA Astrophysics Data System (ADS)

Jiang, Changbing; Bai, Lijun; Tong, Xiaoqing

China's express delivery market has become the arena in which each express enterprise struggles to chase due to the huge potential demand and high profitable prospects. So certain qualitative and quantitative forecast for the future changes of China's express delivery market will help enterprises understand various types of market conditions and social changes in demand and adjust business activities to enhance their competitiveness timely. The development of China's express delivery industry is first introduced in this chapter. Then the theoretical basis of the regression model is overviewed. We also predict the demand trends of China's express delivery market by using Pearson correlation analysis and regression analysis from qualitative and quantitative aspects, respectively. Finally, we draw some conclusions and recommendations for China's express delivery industry.

Predictive occurrence models for coastal wetland plant communities: Delineating hydrologic response surfaces with multinomial logistic regression

NASA Astrophysics Data System (ADS)

Snedden, Gregg A.; Steyer, Gregory D.

2013-02-01

Understanding plant community zonation along estuarine stress gradients is critical for effective conservation and restoration of coastal wetland ecosystems. We related the presence of plant community types to estuarine hydrology at 173 sites across coastal Louisiana. Percent relative cover by species was assessed at each site near the end of the growing season in 2008, and hourly water level and salinity were recorded at each site Oct 2007-Sep 2008. Nine plant community types were delineated with k-means clustering, and indicator species were identified for each of the community types with indicator species analysis. An inverse relation between salinity and species diversity was observed. Canonical correspondence analysis (CCA) effectively segregated the sites across ordination space by community type, and indicated that salinity and tidal amplitude were both important drivers of vegetation composition. Multinomial logistic regression (MLR) and Akaike's Information Criterion (AIC) were used to predict the probability of occurrence of the nine vegetation communities as a function of salinity and tidal amplitude, and probability surfaces obtained from the MLR model corroborated the CCA results. The weighted kappa statistic, calculated from the confusion matrix of predicted versus actual community types, was 0.7 and indicated good agreement between observed community types and model predictions. Our results suggest that models based on a few key hydrologic variables can be valuable tools for predicting vegetation community development when restoring and managing coastal wetlands.

Distribution of ABO Blood Groups and Coronary Artery Calcium.

PubMed

Wang, Yao; Zhou, Bing-Yang; Zhu, Cheng-Gang; Guo, Yuan-Lin; Wu, Na-Qiong; Qing, Ping; Gao, Ying; Liu, Geng; Dong, Qian; Li, Jian-Jun

2017-06-01

ABO blood groups have been confirmed to be associated with cardiovascular diseases such as coronary artery disease. However, whether ABO blood group is correlated with coronary artery calcium (CAC) is still unknown. 301 patients with coronary artery calcium score (CACS) assessed by computed tomography were consecutively enrolled and divided into two groups: with calcium group (CACS>0, n=104) and without calcium group (CACS=0, n=197). Distribution of ABO blood groups was evaluated between the two groups. The percentage of A blood type was significantly higher (p=0.008) and O blood type was significantly lower (p=0.037) in the calcium group. Univariate regression analysis showed that age, total cholesterol, low density lipoprotein cholesterol, high-sensitivity C-reactive protein, A blood type were positively correlated with CAC, and O blood type was inversely associated with CAC. Multivariate regression analysis showed that A blood type was independently associated with CAC (odds ratio: 2.217, 95% confidence interval: 1.260-3.900, p=0.006) even after further adjustment for variables that were clearly different between the two groups. Our data has suggested for the first time that A blood type was an independent risk marker for CAC. Copyright © 2016 Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) and the Cardiac Society of Australia and New Zealand (CSANZ). Published by Elsevier B.V. All rights reserved.

Unmet Needs for Social Support and Effects on Diabetes Self-care Activities in Korean Americans With Type 2 Diabetes

PubMed Central

Song, Youngshin; Song, Hee-Jung; Han, Hae-Ra; Park, So-Youn; Nam, Soohyun; Kim, Miyong T.

2013-01-01

Objective The purpose of this study was (1) to characterize the primary sources of social support and the extent of unmet needs for support (defined as the gap between social support needs and the receipt of social support) in a sample of Korean Americans (KAs) with type 2 diabetes and (2) to examine the effect of unmet needs for support on their self-care activities. Methods Baseline data obtained from a community-based intervention trial were used for this study of 83 middle-aged KAs with type 2 diabetes. Study design and data analysis were guided by social cognitive theory. The key variables were dictated the order of the variables in multivariate regression analysis. Results Our findings indicated that for diabetic KAs, the primary source of social support differed according to gender. Unmet needs for support were significantly associated with self-care activities, but the amount of support needs and of social support received were not. Multivariate analysis also confirmed that unmet needs for social support are a significant strong predictor of inadequate type 2 diabetes self-care activities, after controlling for other covariates. The hierarchical regression model explained about 30% of total variance in self-care activities. Conclusions The findings highlight the importance of considering unmet needs for social support when addressing self-care activities in type 2 diabetes patients. Future interventions should focus on filling gaps in social support and tailoring approaches according to key determinants, such as gender or education level, to improve self-care activities in the context of type 2 diabetes care. PMID:22222514

A General Bayesian Network Approach to Analyzing Online Game Item Values and Its Influence on Consumer Satisfaction and Purchase Intention

NASA Astrophysics Data System (ADS)

Lee, Kun Chang; Park, Bong-Won

Many online game users purchase game items with which to play free-to-play games. Because of a lack of research into which there is no specified framework for categorizing the values of game items, this study proposes four types of online game item values based on an analysis of literature regarding online game characteristics. It then proposes to investigate how online game users perceive satisfaction and purchase intention from the proposed four types of online game item values. Though regression analysis has been used frequently to answer this kind of research question, we propose a new approach, a General Bayesian Network (GBN), which can be performed in an understandable way without sacrificing predictive accuracy. Conventional techniques, such as regression analysis, do not provide significant explanation for this kind of problem because they are fixed to a linear structure and are limited in explaining why customers are likely to purchase game items and if they are satisfied with their purchases. In contrast, the proposed GBN provides a flexible underlying structure based on questionnaire survey data and offers robust decision support on this kind of research question by identifying its causal relationships. To illustrate the validity of GBN in solving the research question in this study, 327 valid questionnaires were analyzed using GBN with what-if and goal-seeking approaches. The experimental results were promising and meaningful in comparison with regression analysis results.

Regression analysis of mixed recurrent-event and panel-count data.

PubMed

Zhu, Liang; Tong, Xinwei; Sun, Jianguo; Chen, Manhua; Srivastava, Deo Kumar; Leisenring, Wendy; Robison, Leslie L

2014-07-01

In event history studies concerning recurrent events, two types of data have been extensively discussed. One is recurrent-event data (Cook and Lawless, 2007. The Analysis of Recurrent Event Data. New York: Springer), and the other is panel-count data (Zhao and others, 2010. Nonparametric inference based on panel-count data. Test 20: , 1-42). In the former case, all study subjects are monitored continuously; thus, complete information is available for the underlying recurrent-event processes of interest. In the latter case, study subjects are monitored periodically; thus, only incomplete information is available for the processes of interest. In reality, however, a third type of data could occur in which some study subjects are monitored continuously, but others are monitored periodically. When this occurs, we have mixed recurrent-event and panel-count data. This paper discusses regression analysis of such mixed data and presents two estimation procedures for the problem. One is a maximum likelihood estimation procedure, and the other is an estimating equation procedure. The asymptotic properties of both resulting estimators of regression parameters are established. Also, the methods are applied to a set of mixed recurrent-event and panel-count data that arose from a Childhood Cancer Survivor Study and motivated this investigation. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Identification of five genetic variants as novel determinants of type 2 diabetes mellitus in Japanese by exome-wide association studies.

PubMed

Yamada, Yoshiji; Sakuma, Jun; Takeuchi, Ichiro; Yasukochi, Yoshiki; Kato, Kimihiko; Oguri, Mitsutoshi; Fujimaki, Tetsuo; Horibe, Hideki; Muramatsu, Masaaki; Sawabe, Motoji; Fujiwara, Yoshinori; Taniguchi, Yu; Obuchi, Shuichi; Kawai, Hisashi; Shinkai, Shoji; Mori, Seijiro; Arai, Tomio; Tanaka, Masashi

2017-10-06

We performed exome-wide association studies to identify single nucleotide polymorphisms that either influence fasting plasma glucose level or blood hemoglobin A 1c content or confer susceptibility to type 2 diabetes mellitus in Japanese. Exome-wide association studies were performed with the use of Illumina Human Exome-12 DNA Analysis or Infinium Exome-24 BeadChip arrays and with 11,729 or 8635 subjects for fasting plasma glucose level or blood hemoglobin A 1c content, respectively, or with 14,023 subjects for type 2 diabetes mellitus (3573 cases, 10,450 controls). The relation of genotypes of 41,265 polymorphisms to fasting plasma glucose level or blood hemoglobin A 1c content was examined by linear regression analysis. After Bonferroni's correction, 41 and 17 polymorphisms were significantly ( P < 1.21 × 10 -6 ) associated with fasting plasma glucose level or blood hemoglobin A 1c content, respectively, with two polymorphisms (rs139421991, rs189305583) being associated with both. Examination of the relation of allele frequencies to type 2 diabetes mellitus with Fisher's exact test revealed that 87 polymorphisms were significantly ( P < 1.21 × 10 -6 ) associated with type 2 diabetes mellitus. Subsequent multivariable logistic regression analysis with adjustment for age and sex showed that four polymorphisms (rs138313632, rs76974938, rs139012426, rs147317864) were significantly ( P < 1.44 × 10 -4 ) associated with type 2 diabetes mellitus, with rs138313632 and rs139012426 also being associated with fasting plasma glucose and rs76974938 with blood hemoglobin A 1c . Five polymorphisms-rs139421991 of CAT , rs189305583 of PDCL2 , rs138313632 of RUFY1 , rs139012426 of LOC100505549 , and rs76974938 of C21orf59 -may be novel determinants of type 2 diabetes mellitus.

Automatic Classification of Users' Health Information Need Context: Logistic Regression Analysis of Mouse-Click and Eye-Tracker Data.

PubMed

Pian, Wenjing; Khoo, Christopher Sg; Chi, Jianxing

2017-12-21

Users searching for health information on the Internet may be searching for their own health issue, searching for someone else's health issue, or browsing with no particular health issue in mind. Previous research has found that these three categories of users focus on different types of health information. However, most health information websites provide static content for all users. If the three types of user health information need contexts can be identified by the Web application, the search results or information offered to the user can be customized to increase its relevance or usefulness to the user. The aim of this study was to investigate the possibility of identifying the three user health information contexts (searching for self, searching for others, or browsing with no particular health issue in mind) using just hyperlink clicking behavior; using eye-tracking information; and using a combination of eye-tracking, demographic, and urgency information. Predictive models are developed using multinomial logistic regression. A total of 74 participants (39 females and 35 males) who were mainly staff and students of a university were asked to browse a health discussion forum, Healthboards.com. An eye tracker recorded their examining (eye fixation) and skimming (quick eye movement) behaviors on 2 types of screens: summary result screen displaying a list of post headers, and detailed post screen. The following three types of predictive models were developed using logistic regression analysis: model 1 used only the time spent in scanning the summary result screen and reading the detailed post screen, which can be determined from the user's mouse clicks; model 2 used the examining and skimming durations on each screen, recorded by an eye tracker; and model 3 added user demographic and urgency information to model 2. An analysis of variance (ANOVA) analysis found that users' browsing durations were significantly different for the three health information contexts (P<.001). The logistic regression model 3 was able to predict the user's type of health information context with a 10-fold cross validation mean accuracy of 84% (62/74), followed by model 2 at 73% (54/74) and model 1 at 71% (52/78). In addition, correlation analysis found that particular browsing durations were highly correlated with users' age, education level, and the urgency of their information need. A user's type of health information need context (ie, searching for self, for others, or with no health issue in mind) can be identified with reasonable accuracy using just user mouse clicks that can easily be detected by Web applications. Higher accuracy can be obtained using Google glass or future computing devices with eye tracking function. ©Wenjing Pian, Christopher SG Khoo, Jianxing Chi. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 21.12.2017.

Nursing home cost and ownership type: evidence of interaction effects.

PubMed

Arling, G; Nordquist, R H; Capitman, J A

1987-06-01

Due to steadily increasing public expenditures for nursing home care, much research has focused on factors that influence nursing home costs, especially for Medicaid patients. Nursing home cost function studies have typically used a number of predictor variables in a multiple regression analysis to determine the effect of these variables on operating cost. Although several authors have suggested that nursing home ownership types have different goal orientations, not necessarily based on economic factors, little attention has been paid to this issue in empirical research. In this study, data from 150 Virginia nursing homes were used in multiple regression analysis to examine factors accounting for nursing home operating costs. The context of the study was the Virginia Medicaid reimbursement system, which has intermediate care and skilled nursing facility (ICF and SNF) facility-specific per diem rates, set according to facility cost histories. The analysis revealed interaction effects between ownership and other predictor variables (e.g., percentage Medicaid residents, case mix, and region), with predictor variables having different effects on cost depending on ownership type. Conclusions are drawn about the goal orientations and behavior of chain-operated, individual for-profit, and public and nonprofit facilities. The implications of these findings for long-term care reimbursement policies are discussed.

Nursing home cost and ownership type: evidence of interaction effects.

PubMed Central

Arling, G; Nordquist, R H; Capitman, J A

1987-01-01

Due to steadily increasing public expenditures for nursing home care, much research has focused on factors that influence nursing home costs, especially for Medicaid patients. Nursing home cost function studies have typically used a number of predictor variables in a multiple regression analysis to determine the effect of these variables on operating cost. Although several authors have suggested that nursing home ownership types have different goal orientations, not necessarily based on economic factors, little attention has been paid to this issue in empirical research. In this study, data from 150 Virginia nursing homes were used in multiple regression analysis to examine factors accounting for nursing home operating costs. The context of the study was the Virginia Medicaid reimbursement system, which has intermediate care and skilled nursing facility (ICF and SNF) facility-specific per diem rates, set according to facility cost histories. The analysis revealed interaction effects between ownership and other predictor variables (e.g., percentage Medicaid residents, case mix, and region), with predictor variables having different effects on cost depending on ownership type. Conclusions are drawn about the goal orientations and behavior of chain-operated, individual for-profit, and public and nonprofit facilities. The implications of these findings for long-term care reimbursement policies are discussed. PMID:3301746

Quantifying the statistical importance of utilizing regression over classic energy intensity calculations for tracking efficiency improvements in industry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nimbalkar, Sachin U.; Wenning, Thomas J.; Guo, Wei

In the United States, manufacturing facilities account for about 32% of total domestic energy consumption in 2014. Robust energy tracking methodologies are critical to understanding energy performance in manufacturing facilities. Due to its simplicity and intuitiveness, the classic energy intensity method (i.e. the ratio of total energy use over total production) is the most widely adopted. However, the classic energy intensity method does not take into account the variation of other relevant parameters (i.e. product type, feed stock type, weather, etc.). Furthermore, the energy intensity method assumes that the facilities’ base energy consumption (energy use at zero production) is zero,more » which rarely holds true. Therefore, it is commonly recommended to utilize regression models rather than the energy intensity approach for tracking improvements at the facility level. Unfortunately, many energy managers have difficulties understanding why regression models are statistically better than utilizing the classic energy intensity method. While anecdotes and qualitative information may convince some, many have major reservations about the accuracy of regression models and whether it is worth the time and effort to gather data and build quality regression models. This paper will explain why regression models are theoretically and quantitatively more accurate for tracking energy performance improvements. Based on the analysis of data from 114 manufacturing plants over 12 years, this paper will present quantitative results on the importance of utilizing regression models over the energy intensity methodology. This paper will also document scenarios where regression models do not have significant relevance over the energy intensity method.« less

Home-measured heart rate is associated with albuminuria in patients with type 2 diabetes mellitus: a post-hoc analysis of a cross-sectional multicenter study.

PubMed

Ushigome, Emi; Fukui, Michiaki; Hamaguchi, Masahide; Tanaka, Toru; Atsuta, Haruhiko; Ohnishi, Masayoshi; Tsunoda, Sei; Yamazaki, Masahiro; Hasegawa, Goji; Nakamura, Naoto

2014-06-01

Epidemiological studies have shown that elevated heart rate (HR) is associated with an increased risk of diabetic nephropathy, as well as cardiovascular events and mortality, in patients with type 2 diabetes mellitus. Recently, the advantages of the self-measurement of blood pressure (BP) at home have been recognized. The aim of this study was to investigate the relationship between home-measured HR and albuminuria in patients with type 2 diabetes mellitus. We designed a cross-sectional multicenter analysis of 1245 patients with type 2 diabetes mellitus. We investigated the relationship between the logarithm of urinary albumin excretion (log UAE) and home-measured HR or other factors that may be related to nephropathy using univariate and multivariate analyses. Multivariate linear regression analysis indicated that age, duration of diabetes mellitus, morning HR (β=0.131, P<0.001), morning systolic BP (β=0.311, P<0.001), hemoglobin A1C, triglycerides, daily consumption of alcohol, use of angiotensin II receptor blockers and use of beta-blockers were independently associated with the log UAE. Multivariate logistic regression analysis indicated that the odds ratio (95% confidence interval) associated with 1 beat per min and 1 mm Hg increases in the morning HR and morning systolic BP for albuminuria were 1.024 ((1.008-1.040), P=0.004) and 1.039 ((1.029-1.048), P<0.001), respectively. In conclusion, home-measured HR was significantly associated with albuminuria independent of the known risk factors for nephropathy, including home-measured systolic BP, in patients with type 2 diabetes mellitus.

Selenium Exposure and Cancer Risk: an Updated Meta-analysis and Meta-regression

PubMed Central

Cai, Xianlei; Wang, Chen; Yu, Wanqi; Fan, Wenjie; Wang, Shan; Shen, Ning; Wu, Pengcheng; Li, Xiuyang; Wang, Fudi

2016-01-01

The objective of this study was to investigate the associations between selenium exposure and cancer risk. We identified 69 studies and applied meta-analysis, meta-regression and dose-response analysis to obtain available evidence. The results indicated that high selenium exposure had a protective effect on cancer risk (pooled OR = 0.78; 95%CI: 0.73–0.83). The results of linear and nonlinear dose-response analysis indicated that high serum/plasma selenium and toenail selenium had the efficacy on cancer prevention. However, we did not find a protective efficacy of selenium supplement. High selenium exposure may have different effects on specific types of cancer. It decreased the risk of breast cancer, lung cancer, esophageal cancer, gastric cancer, and prostate cancer, but it was not associated with colorectal cancer, bladder cancer, and skin cancer. PMID:26786590

The status of diabetes control in Kurdistan province, west of Iran.

PubMed

Esmailnasab, Nader; Afkhamzadeh, Abdorrahim; Roshani, Daem; Moradi, Ghobad

2013-09-17

Based on some estimation more than two million peoples in Iran are affected by Type 2 diabetes. The present study was designed to evaluate the status of diabetes control among Type 2 diabetes patients in Kurdistan, west of Iran and its associated factors. In our cross sectional study conducted in 2010, 411 Type 2 diabetes patients were randomly recruited from Sanandaj, Capital of Kurdistan. Chi square test was used in univariate analysis to address the association between HgAlc and FBS status and other variables. The significant results from Univariate analysis were entered in multivariate analysis and multinomial logistic regression model. In 38% of patients, FBS was in normal range (70-130) and in 47% HgA1c was <7% which is normal range for HgA1c. In univariate analysis, FBS level was associated with educational levels (P=0.001), referral style (P=0.001), referral time (P=0.009), and insulin injection (P=0.016). In addition, HgA1c had a relationship with sex (P=0.023), age (P=0.035), education (P=0.001), referral style (P=0.001), and insulin injection (P=0.008). After using multinomial logistic regression for significant results of univariate analysis, it was found that FBS was significantly associated with referral style. In addition HgA1c was significantly associated with referral style and Insulin injection. Although some of patients were under the coverage of specialized cares, but their diabetes were not properly controlled.

Wage differentials among Appalachian sawmills

Treesearch

Charles H. Wolf

1977-01-01

Wage differences among Appalachian sawmills were investigated, using multiple-regression analysis. Wages and fringe benefits were found to vary with type of product sawed, education of the work force, distance to urban areas, general wage levels, and use of collective-bargaining agreements between management and labor.

Tradespace Exploration for the Engineering of Resilient Systems

DTIC Science & Technology

2015-05-01

world scenarios. The types of tools within the SAE set include visualization, decision analysis, and M&S, so it is difficult to categorize this toolset... overpopulated , or questionable. ERS Tradespace Workshop Create predictive models using multiple techniques (e.g., regression, Kriging, neural nets

Predicting The Type Of Pregnancy Using Flexible Discriminate Analysis And Artificial Neural Networks: A Comparison Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hooman, A.; Mohammadzadeh, M

Some medical and epidemiological surveys have been designed to predict a nominal response variable with several levels. With regard to the type of pregnancy there are four possible states: wanted, unwanted by wife, unwanted by husband and unwanted by couple. In this paper, we have predicted the type of pregnancy, as well as the factors influencing it using three different models and comparing them. Regarding the type of pregnancy with several levels, we developed a multinomial logistic regression, a neural network and a flexible discrimination based on the data and compared their results using tow statistical indices: Surface under curvemore » (ROC) and kappa coefficient. Based on these tow indices, flexible discrimination proved to be a better fit for prediction on data in comparison to other methods. When the relations among variables are complex, one can use flexible discrimination instead of multinomial logistic regression and neural network to predict the nominal response variables with several levels in order to gain more accurate predictions.« less

Associations between INSR and MTOR polymorphisms in type 2 diabetes mellitus and diabetic nephropathy in a Northeast Chinese Han population.

PubMed

Zhu, A N; Yang, X X; Sun, M Y; Zhang, Z X; Li, M

2015-03-13

We explored the associations of INSR and mTOR, 2 key genes in the insulin signaling pathway, and the susceptibility to type 2 diabetes mellitus and diabetic nephropathy. Three single-nucleotide polymorphisms (SNPs) (rs1799817, rs1051690, and rs2059806) in INSR and 3 SNPs (rs7211818, rs7212142, and rs9674559) in mTOR were genotyped using the Sequenom MassARRAY iPLEX platform in 89 type 2 diabetes patients without diabetic nephropathy, 134 type 2 diabetes patients with diabetic nephropathy, and 120 healthy control subjects. Statistical analysis based on unconditional logistic regression was carried out to determine the odds ratio (OR) and 95% confidence interval (95%CI) for each SNP. Combination analyses between rs2059806 and rs7212142 were also performed using the X(2) test and logistic regression. Among these 6 SNPs, 4 (rs1799817, rs1051690, rs7211818, and rs9674559) showed no association with type 2 diabetes mellitus or diabetic nephropathy. However, rs2059806 in INSR was associated with both type 2 diabetes mellitus (P = 0.033) and type 2 diabetic nephropathy (P = 0.018). The rs7212142 polymorphism in mTOR was associated with type 2 diabetic nephropathy (P = 0.010, OR = 0.501, 95%CI = 0.288- 0.871), but showed no relationship with type 2 diabetes mellitus. Combination analysis revealed that rs2059806 and rs7212142 had a combined effect on susceptibility to type 2 diabetes mellitus and diabetic nephropathy. Our results suggest that both INSR and mTOR play a role in the predisposition of the Han Chinese population to type 2 diabetic nephropathy, but the genetic predisposition may show some differences.

Regression equations for sex and population detection using the lip print pattern among Egyptian and Malaysian adult.

PubMed

Abdel Aziz, Manal H; Badr El Dine, Fatma M M; Saeed, Nourhan M M

2016-11-01

Identification of sex and ethnicity has always been a challenge in the fields of forensic medicine and criminal investigations. Fingerprinting and DNA comparisons are probably the most common techniques used in this context. However, since they cannot always be used, it is necessary to apply different and less known techniques such as lip prints. Is to study the pattern of lip print in Egyptian and Malaysian populations and its relation to sex and populations difference. Also, to develop equations for sex and populations detection using lip print pattern by different populations (Egyptian and Malaysian). The sample comprised of 120 adults volunteers divided into two ethnic groups; sixty adult Egyptians (30 males and 30 females) and sixty adult Malaysians (30 males and 30 females). The lip prints were collected on a white paper. Each lip print was divided into four compartments and were classified and scored according to Suzuki and Tsuchihashi classification. Data were statistically analyzed. The results showed that type III lip print pattern (intersected grooves) was the predominant type in both the Egyptian and Malaysian populations. Type II and III were the most frequent in Egyptian males (28.3% each), while in Egyptian females type III pattern was predominant (46.7%). As regards Malaysian males, type III lip print pattern was the predominant one (41.7%), while type II lip print pattern was predominant (30.8%) in Malaysian females. Statistical analysis of different quadrants showed significant differences between males and females in the Egyptian population in the third and fourth quadrants. On the other hand, significant differences were detected only in the second quadrant between Malaysian males and females. Also, a statistically significant difference was present in the second quadrant between Egyptian and Malaysian males. Using the regression analysis, four regression equations were obtained. Copyright © 2016 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Depressive Symptoms in College Women: Examining the Cumulative Effect of Childhood and Adulthood Domestic Violence.

PubMed

Al-Modallal, Hanan

2016-10-01

The purpose of this study was to examine the cumulative effect of childhood and adulthood violence on depressive symptoms in a sample of Jordanian college women. Snowball sampling technique was used to recruit the participants. The participants were heterosexual college-aged women between the ages of 18 and 25. The participants were asked about their experiences of childhood violence (including physical violence, sexual violence, psychological violence, and witnessing parental violence), partner violence (including physical partner violence and sexual partner violence), experiences of depressive symptoms, and about other demographic and familial factors as possible predictors for their complaints of depressive symptoms. Multiple linear regression analysis was implemented to identify demographic- and violence-related predictors of their complainants of depressive symptoms. Logistic regression analysis was further performed to identify possible type(s) of violence associated with the increased risk of depressive symptoms. The prevalence of depressive symptoms in this sample was 47.4%. For the violence experience, witnessing parental violence was the most common during childhood, experienced by 40 (41.2%) women, and physical partner violence was the most common in adulthood, experienced by 35 (36.1%) women. Results of logistic regression analysis indicated that experiencing two types of violence (regardless of the time of occurrence) was significant in predicting depressive symptoms (odds ratio [OR] = 3.45, p < .05). Among college women's demographic characteristics, marital status (single vs. engaged), mothers' level of education, income, and smoking were significant in predicting depressive symptoms. Assessment of physical violence and depressive symptoms including the cumulative impact of longer periods of violence on depressive symptoms is recommended to be explored in future studies. © The Author(s) 2015.

Regression Models for the Analysis of Longitudinal Gaussian Data from Multiple Sources

PubMed Central

O’Brien, Liam M.; Fitzmaurice, Garrett M.

2006-01-01

We present a regression model for the joint analysis of longitudinal multiple source Gaussian data. Longitudinal multiple source data arise when repeated measurements are taken from two or more sources, and each source provides a measure of the same underlying variable and on the same scale. This type of data generally produces a relatively large number of observations per subject; thus estimation of an unstructured covariance matrix often may not be possible. We consider two methods by which parsimonious models for the covariance can be obtained for longitudinal multiple source data. The methods are illustrated with an example of multiple informant data arising from a longitudinal interventional trial in psychiatry. PMID:15726666

Comparison of the effect of endodontic-periodontal combined lesion on the outcome of endodontic microsurgery with that of isolated endodontic lesion: survival analysis using propensity score analysis.

PubMed

Song, Minju; Kang, Minji; Kang, Dae Ryong; Jung, Hoi In; Kim, Euiseong

2018-05-01

The purpose of this retrospective clinical study was to evaluate the effect of lesion types related to endodontic microsurgery on the clinical outcome. Patients who underwent endodontic microsurgery between March 2001 and March 2014 with a postoperative follow-up period of at least 1 year were included in the study. Survival analyses were conducted to compare the clinical outcomes between isolated endodontic lesion group (endo group) and endodontic-periodontal combined lesion group (endo-perio group) and to evaluate other clinical variables. To reduce the effect of selection bias in this study, the estimated propensity scores were used to match the cases of the endo group with those of the endo-perio group. Among the 414 eligible cases, the 83 cases in the endo-perio group were matched to 166 out of the 331 cases in the endo group based on propensity score matching (PSM). The cumulated success rates of the endo and endo-perio groups were 87.3 and 72.3%, respectively. The median success period of the endo-perio group was 12 years (95% CI: 5.507, 18.498). Lesion type was found to be significant according to both Log-rank test (P = 0.002) and Cox proportional hazard regression analysis (P = 0.001). Among the other clinical variables, sex (female or male), age, and tooth type (anterior, premolar, or molar) were determined to be significant in Cox regression analysis (P < 0.05). Endodontic-periodontal combined lesions had a negative effect on the clinical outcome based on an analysis that utilized PSM, a useful statistical matching method for observational studies. Lesion type is a significant predictor of the outcome of endodontic microsurgery.

Species Composition at the Sub-Meter Level in Discontinuous Permafrost in Subarctic Sweden

NASA Astrophysics Data System (ADS)

Anderson, S. M.; Palace, M. W.; Layne, M.; Varner, R. K.; Crill, P. M.

2013-12-01

Northern latitudes are experiencing rapid warming. Wetlands underlain by permafrost are particularly vulnerable to warming which results in changes in vegetative cover. Specific species have been associated with greenhouse gas emissions therefore knowledge of species compositional shift allows for the systematic change and quantification of emissions and changes in such emissions. Species composition varies on the sub-meter scale based on topography and other microsite environmental parameters. This complexity and the need to scale vegetation to the landscape level proves vital in our estimation of carbon dioxide (CO2) and methane (CH4) emissions and dynamics. Stordalen Mire (68°21'N, 18°49'E) in Abisko and is located at the edge of discontinuous permafrost zone. This provides a unique opportunity to analyze multiple vegetation communities in a close proximity. To do this, we randomly selected 25 1x1 meter plots that were representative of five major cover types: Semi-wet, wet, hummock, tall graminoid, and tall shrub. We used a quadrat with 64 sub plots and measured areal percent cover for 24 species. We collected ground based remote sensing (RS) at each plot to determine species composition using an ADC-lite (near infrared, red, green) and GoPro (red, blue, green). We normalized each image based on a Teflon white chip placed in each image. Textural analysis was conducted on each image for entropy, angular second momentum, and lacunarity. A logistic regression was developed to examine vegetation cover types and remote sensing parameters. We used a multiple linear regression using forwards stepwise variable selection. We found statistical difference in species composition and diversity indices between vegetation cover types. In addition, we were able to build regression model to significantly estimate vegetation cover type as well as percent cover for specific key vegetative species. This ground-based remote sensing allows for quick quantification of vegetation cover and species and also provides the framework for scaling to satellite image data to estimate species composition and shift on the landscape level. To determine diversity within our plots we calculated species richness and Shannon Index. We found that there were statistically different species composition within each vegetation cover type and also determined which species were indicative for cover type. Our logistical regression was able to significantly classify vegetation cover types based on RS parameters. Our multiple regression analysis indicated Betunla nana (Dwarf Birch) (r2= .48, p=<0.0001) and Sphagnum (r2=0.59, p=<0.0001) were statistically significant with respect to RS parameters. We suggest that ground based remote sensing methods may provide a unique and efficient method to quantify vegetation across the landscape in northern latitude wetlands.

Relationships between lifestyle patterns and cardio-renal-metabolic parameters in patients with type 2 diabetes mellitus: A cross-sectional study

PubMed Central

Ogihara, Takeshi; Osonoi, Yusuke; Osonoi, Takeshi; Saito, Miyoko; Tamasawa, Atsuko; Nakayama, Shiho; Someya, Yuki; Ishida, Hidenori; Gosho, Masahiko; Kanazawa, Akio; Watada, Hirotaka

2017-01-01

Introduction While individuals tend to show accumulation of certain lifestyle patterns, the effect of such patterns in real daily life on cardio-renal—metabolic parameters remains largely unknown. This study aimed to assess clustering of lifestyle patterns and investigate the relationships between such patterns and cardio-renal-metabolic parameters. Participants and methods The study participants were 726 Japanese type 2 diabetes mellitus (T2DM) outpatients free of history of cardiovascular diseases. The relationship between lifestyle patterns and cardio-renal-metabolic parameters was investigated by linear and logistic regression analyses. Results Factor analysis identified three lifestyle patterns. Subjects characterized by evening type, poor sleep quality and depressive status (type 1 pattern) had high levels of HbA1c, alanine aminotransferase and albuminuria. Subjects characterized by high consumption of food, alcohol and cigarettes (type 2 pattern) had high levels of γ-glutamyl transpeptidase, triglycerides, HDL-cholesterol, blood pressure, and brachial-ankle pulse wave velocity. Subjects characterized by high physical activity (type 3 pattern) had low uric acid and mild elevation of alanine aminotransferase and aspartate aminotransferase. In multivariate regression analysis adjusted by age, gender and BMI, type 1 pattern was associated with higher HbA1c levels, systolic BP and brachial-ankle pulse wave velocity. Type 2 pattern was associated with higher HDL-cholesterol levels, triglycerides, aspartate aminotransferase, ɤ- glutamyl transpeptidase levels, and diastolic BP. Conclusions The study identified three lifestyle patterns that were associated with distinct cardio-metabolic-renal parameters in T2DM patients. Trial registration UMIN000010932 PMID:28273173

Techniques for estimating flood-peak discharges of rural, unregulated streams in Ohio

USGS Publications Warehouse

Koltun, G.F.; Roberts, J.W.

1990-01-01

Multiple-regression equations are presented for estimating flood-peak discharges having recurrence intervals of 2, 5, 10, 25, 50, and 100 years at ungaged sites on rural, unregulated streams in Ohio. The average standard errors of prediction for the equations range from 33.4% to 41.4%. Peak discharge estimates determined by log-Pearson Type III analysis using data collected through the 1987 water year are reported for 275 streamflow-gaging stations. Ordinary least-squares multiple-regression techniques were used to divide the State into three regions and to identify a set of basin characteristics that help explain station-to- station variation in the log-Pearson estimates. Contributing drainage area, main-channel slope, and storage area were identified as suitable explanatory variables. Generalized least-square procedures, which include historical flow data and account for differences in the variance of flows at different gaging stations, spatial correlation among gaging station records, and variable lengths of station record were used to estimate the regression parameters. Weighted peak-discharge estimates computed as a function of the log-Pearson Type III and regression estimates are reported for each station. A method is provided to adjust regression estimates for ungaged sites by use of weighted and regression estimates for a gaged site located on the same stream. Limitations and shortcomings cited in an earlier report on the magnitude and frequency of floods in Ohio are addressed in this study. Geographic bias is no longer evident for the Maumee River basin of northwestern Ohio. No bias is found to be associated with the forested-area characteristic for the range used in the regression analysis (0.0 to 99.0%), nor is this characteristic significant in explaining peak discharges. Surface-mined area likewise is not significant in explaining peak discharges, and the regression equations are not biased when applied to basins having approximately 30% or less surface-mined area. Analyses of residuals indicate that the equations tend to overestimate flood-peak discharges for basins having approximately 30% or more surface-mined area. (USGS)

Association of T-cell reactivity with beta-cell function in recent onset type 1 diabetes patients.

PubMed

Pfleger, Christian; Meierhoff, Guido; Kolb, Hubert; Schloot, Nanette C

2010-03-01

The aim of the current study was to investigate whether autoantigen directed T-cell reactivity relates to beta-cell function during the first 78 weeks after diagnosis of type 1 diabetes. 50 adults and 49 children (mean age 27.3 and 10.9 years respectively) with recent onset type 1 diabetes who participated in a placebo-controlled trial of immune intervention with DiaPep277 were analyzed. Secretion of interferon (IFN)-gamma, interleukin (IL)-5, IL-13 and IL-10 by single peripheral mononuclear cells (PBMC) upon stimulation with islet antigens GAD65, heat shock protein 60 (Hsp60) protein-tyrosine-phosphatase-like-antigen (pIA2) or tetanus toxoid (TT) was determined applying ELISPOT; beta-cell function was evaluated by glucagon stimulated C-peptide. Multivariate regression analysis was applied. In general, number of islet antigen-reactive cells decreased over 78 weeks in both adults and children, whereas reactivity to TT was not reduced. In addition, there was an association between the quality of immune cell responses and beta-cell function. Overall, increased responses by IFN-gamma secreting cells were associated with lower beta-cell function whereas IL-5, IL-13 and IL-10 cytokine responses were positively associated with beta-cell function in adults and children. Essentially, the same results were obtained with three different models of regression analysis. The number of detectable islet-reactive immune cells decreases within 1-2 years after diagnosis of type 1 diabetes. Cytokine production by antigen-specific PBMC reactivity is related to beta-cell function as measured by stimulated C-peptide. Cellular immunity appears to regress soon after disease diagnosis and begin of insulin therapy. Copyright 2009 Elsevier Ltd. All rights reserved.

Predictive occurrence models for coastal wetland plant communities: delineating hydrologic response surfaces with multinomial logistic regression

USGS Publications Warehouse

Snedden, Gregg A.; Steyer, Gregory D.

2013-01-01

Understanding plant community zonation along estuarine stress gradients is critical for effective conservation and restoration of coastal wetland ecosystems. We related the presence of plant community types to estuarine hydrology at 173 sites across coastal Louisiana. Percent relative cover by species was assessed at each site near the end of the growing season in 2008, and hourly water level and salinity were recorded at each site Oct 2007–Sep 2008. Nine plant community types were delineated with k-means clustering, and indicator species were identified for each of the community types with indicator species analysis. An inverse relation between salinity and species diversity was observed. Canonical correspondence analysis (CCA) effectively segregated the sites across ordination space by community type, and indicated that salinity and tidal amplitude were both important drivers of vegetation composition. Multinomial logistic regression (MLR) and Akaike's Information Criterion (AIC) were used to predict the probability of occurrence of the nine vegetation communities as a function of salinity and tidal amplitude, and probability surfaces obtained from the MLR model corroborated the CCA results. The weighted kappa statistic, calculated from the confusion matrix of predicted versus actual community types, was 0.7 and indicated good agreement between observed community types and model predictions. Our results suggest that models based on a few key hydrologic variables can be valuable tools for predicting vegetation community development when restoring and managing coastal wetlands.

Multivariate functional response regression, with application to fluorescence spectroscopy in a cervical pre-cancer study.

PubMed

Zhu, Hongxiao; Morris, Jeffrey S; Wei, Fengrong; Cox, Dennis D

2017-07-01

Many scientific studies measure different types of high-dimensional signals or images from the same subject, producing multivariate functional data. These functional measurements carry different types of information about the scientific process, and a joint analysis that integrates information across them may provide new insights into the underlying mechanism for the phenomenon under study. Motivated by fluorescence spectroscopy data in a cervical pre-cancer study, a multivariate functional response regression model is proposed, which treats multivariate functional observations as responses and a common set of covariates as predictors. This novel modeling framework simultaneously accounts for correlations between functional variables and potential multi-level structures in data that are induced by experimental design. The model is fitted by performing a two-stage linear transformation-a basis expansion to each functional variable followed by principal component analysis for the concatenated basis coefficients. This transformation effectively reduces the intra-and inter-function correlations and facilitates fast and convenient calculation. A fully Bayesian approach is adopted to sample the model parameters in the transformed space, and posterior inference is performed after inverse-transforming the regression coefficients back to the original data domain. The proposed approach produces functional tests that flag local regions on the functional effects, while controlling the overall experiment-wise error rate or false discovery rate. It also enables functional discriminant analysis through posterior predictive calculation. Analysis of the fluorescence spectroscopy data reveals local regions with differential expressions across the pre-cancer and normal samples. These regions may serve as biomarkers for prognosis and disease assessment.

Preoperative Fasting C-Peptide Predicts Type 2 Diabetes Mellitus Remission in Low-BMI Chinese Patients After Roux-en-Y Gastric Bypass.

PubMed

Zhao, Lei; Li, Weizheng; Su, Zhihong; Liu, Yong; Zhu, Liyong; Zhu, Shaihong

2018-05-29

This study investigated the role of preoperative fasting C-peptide (FCP) levels in predicting diabetic outcomes in low-BMI Chinese patients following Roux-en-Y gastric bypass (RYGB) by comparing the metabolic outcomes of patients with FCP > 1 ng/ml versus FCP ≤ 1 ng/ml. The study sample included 78 type 2 diabetes mellitus patients with an average BMI < 30 kg/m 2 at baseline. Patients' parameters were analyzed before and after surgery, with a 2-year follow-up. A univariate logistic regression analysis and multivariate analysis of variance between the remission and improvement group were performed to determine factors that were associated with type 2 diabetes remission after RYGB. Linear correlation analyses between FCP and metabolic parameters were performed. Patients were divided into two groups: FCP > 1 ng/ml and FCP ≤ 1 ng/ml, with measured parameters compared between the groups. Patients' fasting plasma glucose, 2-h postprandial plasma glucose, FCP, and HbA1c improved significantly after surgery (p < 0.05). Factors associated with type 2 diabetes remission were BMI, 2hINS, and FCP at the univariate logistic regression analysis (p < 0.05). Multivariate logistic regression analysis was performed then showed the results were more related to FCP (OR = 2.39). FCP showed a significant linear correlation with fasting insulin and BMI (p < 0.05). There was a significant difference in remission rate between the FCP > 1 ng/ml and FCP ≤ 1 ng/ml groups (p = 0.01). The parameters of patients with FCP > 1 ng/ml, including BMI, plasma glucose, HbA1c, and plasma insulin, decreased markedly after surgery (p < 0.05). FCP level is a significant predictor of diabetes outcomes after RYGB in low-BMI Chinese patients. An FCP level of 1 ng/ml may be a useful threshold for predicting surgical prognosis, with FCP > 1 ng/ml predicting better clinical outcomes following RYGB.

Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA

USGS Publications Warehouse

Ohlmacher, G.C.; Davis, J.C.

2003-01-01

Landslides in the hilly terrain along the Kansas and Missouri rivers in northeastern Kansas have caused millions of dollars in property damage during the last decade. To address this problem, a statistical method called multiple logistic regression has been used to create a landslide-hazard map for Atchison, Kansas, and surrounding areas. Data included digitized geology, slopes, and landslides, manipulated using ArcView GIS. Logistic regression relates predictor variables to the occurrence or nonoccurrence of landslides within geographic cells and uses the relationship to produce a map showing the probability of future landslides, given local slopes and geologic units. Results indicated that slope is the most important variable for estimating landslide hazard in the study area. Geologic units consisting mostly of shale, siltstone, and sandstone were most susceptible to landslides. Soil type and aspect ratio were considered but excluded from the final analysis because these variables did not significantly add to the predictive power of the logistic regression. Soil types were highly correlated with the geologic units, and no significant relationships existed between landslides and slope aspect. ?? 2003 Elsevier Science B.V. All rights reserved.

Robust mislabel logistic regression without modeling mislabel probabilities.

PubMed

Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

2018-03-01

Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

Classification and prediction of rice wines with different marked ages by using a voltammetric electronic tongue.

PubMed

Wei, Zhenbo; Wang, Jun; Ye, Linshuang

2011-08-15

A voltammetric electronic tongue (VE-tongue) was developed to discriminate the difference between Chinese rice wines in this research. Three types of Chinese rice wine with different marked ages (1, 3, and 5 years) were classified by the VE-tongue by principal component analysis (PCA) and cluster analysis (CA). The VE-tongue consisted of six working electrodes (gold, silver, platinum, palladium, tungsten, and titanium) in a standard three-electrode configuration. The multi-frequency large amplitude pulse voltammetry (MLAPV), which consisted of four segments of 1 Hz, 10 Hz, 100 Hz, and 1000 Hz, was applied as the potential waveform. The three types of Chinese rice wine could be classified accurately by PCA and CA, and some interesting regularity is shown in the score plots with the help of PCA. Two regression models, partial least squares (PLS) and back-error propagation-artificial neural network (BP-ANN), were used for wine age prediction. The regression results showed that the marked ages of the three types of Chinese rice wine were successfully predicted using PLS and BP-ANN. Copyright © 2011 Elsevier B.V. All rights reserved.

Case-control study on the associations between lifestyle-behavioral risk factors and phlegm-wetness constitution.

PubMed

Zhu, Yanbo; Wang, Qi; Dai, Zhaoyu; Origasa, Hideki; Di, Jie; Wang, Yangyang; Lin, Lin; Fan, Chunpok

2014-06-01

To explore the relationships between different lifestyle-behavioral factors and phlegm-wetness type of Traditional Chinese Medicine constitution, so as to provide health management strategies for phlegm-wetness constitution. A case-control study was conducted with the cases selected from the database of Chinese constitution survey in 9 provinces or municipalities of China. 1380 cases met the diagnostic criteria of phlegm-wetness type were taken as the case group, and 1380 cases were randomly selected from gentleness type as the control group. Using Chi-square test to compare the differences of lifestyle-behavior composition in each group; single factor and multiple logistic regression analysis were used to compare the relationships of lifestyle-behavioral factors and phlegm-wetness type. There were statistically significant differences between phlegm-wetness type group and gentleness type group in lifestyle behaviors (dietary habits, tobacco and liquor consumptions, exercise habits, sleeping habits). The results of single factor logistic regression analysis demonstrated that the risk of phlegm-wetness constitution decreased significantly in light diet (odds ratio, OR = 0.68); The risk factors of phlegm-wetness type were fatty food intake (OR = 2.36), sleeping early and getting up late (OR = 1.87), tobacco smoking (OR = 1.83), barbecued food intake (OR = 1.68), alcohol drinking (OR = 1.63), salty food intake (OR = 1.44), sleeping erratically (OR = 1.43), less physical activities (OR = 1.42), sweet food intake (OR = 1.29), sleeping and getting up late (OR = 1.26), and pungent food intake (OR = 1.21), respectively. Regardless of the interaction among lifestyle-behavioral factors, the results of the multiple logistic regression analysis revealed that the risk factors of phlegm-wetness type were sleeping early and getting up late (OR = 1.94), fatty food intake (OR = 1.80), tobacco smoking (OR = 1.50), sleeping erratically (OR = 1.50), barbecued food intake (OR = 1.40), sleeping and getting up late (OR = 1.40), less physical activities (OR = 1.31), sleeping late and getting up early (OR = 1.27), and sweet food intake (OR = 1.27, respectively, and the risk of phlegm-wetness type still decreased significantly in light food intake (OR = 0.79). Light diet can decrease the risk of being phlegm-wetness constitution, and bad lifestyle behaviors such as sleeping early and getting up late, sleeping erratically, fatty food, barbecued food or sweet food intake, tobacco and liquor consumptions, and less physical activities can increase the risks of becoming phlegm-wetness constitution.

Grey and white matter differences in brain energy metabolism in first episode schizophrenia: 31P-MRS chemical shift imaging at 4 Tesla.

PubMed

Jensen, J Eric; Miller, Jodi; Williamson, Peter C; Neufeld, Richard W J; Menon, Ravi S; Malla, Ashok; Manchanda, Rahul; Schaefer, Betsy; Densmore, Maria; Drost, Dick J

2006-03-31

Altered high energy and membrane metabolism, measured with phosphorus magnetic resonance spectroscopy (31P-MRS), has been inconsistently reported in schizophrenic patients in several anatomical brain regions implicated in the pathophysiology of this illness, with little attention to the effects of brain tissue type on the results. Tissue regression analysis correlates brain tissue type to measured metabolite levels, allowing for the extraction of "pure" estimated grey and white matter compartment metabolite levels. We use this tissue analysis technique on a clinical dataset of first episode schizophrenic patients and matched controls to investigate the effect of brain tissue specificity on altered energy and membrane metabolism. In vivo brain spectra from two regions, (a) the fronto-temporal-striatal region and (b) the frontal-lobes, were analyzed from 12 first episode schizophrenic patients and 11 matched controls from a (31)P chemical shift imaging (CSI) study at 4 Tesla (T) field strength. Tissue regression analyses using voxels from each region were performed relating metabolite levels to tissue content, examining phosphorus metabolite levels in grey and white matter compartments. Compared with controls, the first episode schizophrenic patient group showed significantly increased adenosine triphosphate levels (B-ATP) in white matter and decreased B-ATP levels in grey matter in the fronto-temporal-striatal region. No significant metabolite level differences were found in grey or white matter compartments in the frontal cortex. Tissue regression analysis reveals grey and white matter specific aberrations in high-energy phosphates in first episode schizophrenia. Although past studies report inconsistent regional differences in high-energy phosphate levels in schizophrenia, the present analysis suggests more widespread differences that seem to be strongly related to tissue type. Our data suggest that differences in grey and white matter tissue content between past studies may account for some of the variance in the literature.

Methods for scalar-on-function regression.

PubMed

Reiss, Philip T; Goldsmith, Jeff; Shang, Han Lin; Ogden, R Todd

2017-08-01

Recent years have seen an explosion of activity in the field of functional data analysis (FDA), in which curves, spectra, images, etc. are considered as basic functional data units. A central problem in FDA is how to fit regression models with scalar responses and functional data points as predictors. We review some of the main approaches to this problem, categorizing the basic model types as linear, nonlinear and nonparametric. We discuss publicly available software packages, and illustrate some of the procedures by application to a functional magnetic resonance imaging dataset.

Water quality and relation to taste-and-odor compounds in North Fork Ninnescah River and Cheney Reservoir, south-central Kansas, 1997-2003

USGS Publications Warehouse

Christensen, Victoria G.; Graham, Jennifer L.; Milligan, Chad R.; Pope, Larry M.; Ziegler, Andrew C.

2006-01-01

Regression models were developed between geosmin and the physical property measurements continuously recorded by water-quality monitors at each site. The geosmin regression model was applied to water-quality monitor measurements, providing a continuous estimate of geosmin for 2003. The city of Wichita will be able to use this type of analysis to determine the probability of when concentrations of geosmin are likely to be at or above the human detection level of 0.01 microgram per liter.

Risk factors for amendment in type, duration and setting of prescribed outpatient parenteral antimicrobial therapy (OPAT) for adult patients with cellulitis: a retrospective cohort study and CART analysis.

PubMed

Quirke, Michael; Curran, Emma May; O'Kelly, Patrick; Moran, Ruth; Daly, Eimear; Aylward, Seamus; McElvaney, Gerry; Wakai, Abel

2018-01-01

To measure the percentage rate and risk factors for amendment in the type, duration and setting of outpatient parenteral antimicrobial therapy ( OPAT) for the treatment of cellulitis. A retrospective cohort study of adult patients receiving OPAT for cellulitis was performed. Treatment amendment (TA) was defined as hospital admission or change in antibiotic therapy in order to achieve clinical response. Multivariable logistic regression (MVLR) and classification and regression tree (CART) analysis were performed. There were 307 patients enrolled. TA occurred in 36 patients (11.7%). Significant risk factors for TA on MVLR were increased age, increased Numerical Pain Scale Score (NPSS) and immunocompromise. The median OPAT duration was 7 days. Increased age, heart rate and C reactive protein were associated with treatment prolongation. CART analysis selected age <64.5 years, female gender and NPSS <2.5 in the final model, generating a low-sensitivity (27.8%), high-specificity (97.1%) decision tree. Increased age, NPSS and immunocompromise were associated with OPAT amendment. These identified risk factors can be used to support an evidence-based approach to patient selection for OPAT in cellulitis. The CART algorithm has good specificity but lacks sensitivity and is shown to be inferior in this study to logistic regression modelling. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Using cluster analysis and a classification and regression tree model to developed cover types in the Sky Islands of southeastern Arizona

Treesearch

Jose M. Iniguez; Joseph L. Ganey; Peter J. Daughtery; John D. Bailey

2005-01-01

The objective of this study was to develop a rule based cover type classification system for the forest and woodland vegetation in the Sky Islands of southeastern Arizona. In order to develop such a system we qualitatively and quantitatively compared a hierarchical (Ward’s) and a non-hierarchical (k-means) clustering method. Ecologically, unique groups represented by...

Using cluster analysis and a classification and regression tree model to developed cover types in the Sky Islands of southeastern Arizona [Abstract

Treesearch

Jose M. Iniguez; Joseph L. Ganey; Peter J. Daugherty; John D. Bailey

2005-01-01

The objective of this study was to develop a rule based cover type classification system for the forest and woodland vegetation in the Sky Islands of southeastern Arizona. In order to develop such system we qualitatively and quantitatively compared a hierarchical (Ward’s) and a non-hierarchical (k-means) clustering method. Ecologically, unique groups and plots...

Nonparametric methods for drought severity estimation at ungauged sites

NASA Astrophysics Data System (ADS)

Sadri, S.; Burn, D. H.

2012-12-01

The objective in frequency analysis is, given extreme events such as drought severity or duration, to estimate the relationship between that event and the associated return periods at a catchment. Neural networks and other artificial intelligence approaches in function estimation and regression analysis are relatively new techniques in engineering, providing an attractive alternative to traditional statistical models. There are, however, few applications of neural networks and support vector machines in the area of severity quantile estimation for drought frequency analysis. In this paper, we compare three methods for this task: multiple linear regression, radial basis function neural networks, and least squares support vector regression (LS-SVR). The area selected for this study includes 32 catchments in the Canadian Prairies. From each catchment drought severities are extracted and fitted to a Pearson type III distribution, which act as observed values. For each method-duration pair, we use a jackknife algorithm to produce estimated values at each site. The results from these three approaches are compared and analyzed, and it is found that LS-SVR provides the best quantile estimates and extrapolating capacity.

Fine and Gray competing risk regression model to study the cause-specific under-five child mortality in Bangladesh.

PubMed

Mohammad, Khandoker Akib; Fatima-Tuz-Zahura, Most; Bari, Wasimul

2017-01-28

The cause-specific under-five mortality of Bangladesh has been studied by fitting cumulative incidence function (CIF) based Fine and Gray competing risk regression model (1999). For the purpose of analysis, Bangladesh Demographic and Health Survey (BDHS), 2011 data set was used. Three types of mode of mortality for the under-five children are considered. These are disease, non-disease and other causes. Product-Limit survival probabilities for the under-five child mortality with log-rank test were used to select a set of covariates for the regression model. The covariates found to have significant association in bivariate analysis were only considered in the regression analysis. Potential determinants of under-five child mortality due to disease is size of child at birth, while gender of child, NGO (non-government organization) membership of mother, mother's education level, and size of child at birth are due to non-disease and age of mother at birth, NGO membership of mother, and mother's education level are for the mortality due to other causes. Female participation in the education programs needs to be increased because of the improvement of child health and government should arrange family and social awareness programs as well as health related programs for women so that they are aware of their child health.

Nonlinear models for estimating GSFC travel requirements

NASA Technical Reports Server (NTRS)

Buffalano, C.; Hagan, F. J.

1974-01-01

A methodology is presented for estimating travel requirements for a particular period of time. Travel models were generated using nonlinear regression analysis techniques on a data base of FY-72 and FY-73 information from 79 GSFC projects. Although the subject matter relates to GSFX activities, the type of analysis used and the manner of selecting the relevant variables would be of interest to other NASA centers, government agencies, private corporations and, in general, any organization with a significant travel budget. Models were developed for each of six types of activity: flight projects (in-house and out-of-house), experiments on non-GSFC projects, international projects, ART/SRT, data analysis, advanced studies, tracking and data, and indirects.

Correlation of worldwide incidence of type 1 diabetes (DiaMond) with prevalence of asthma and atopic eczema (ISAAC).

PubMed

Fsadni, Peter; Fsadni, Claudia; Fava, Stephen; Montefort, Stephen

2012-01-01

Environmental factors play a role in pathogenesis of both type 1 diabetes and atopic disease but they remain incompletely understood. T cell-mediated responses primarily of the T helper type 1 (Th1) are involved in type 1 diabetes while T helper type 2 (Th2) responses favour allergic disease. This TH 1/TH 2 paradigm is currently the source of much controversy in various studies. The aim of the study was to compare the reported country incidence of type 1 diabetes with the prevalence of atopic disease. The prevalence of wheeze, rhinitis, rhinoconjunctivitis and atopic eczema in the preceding 12 months in the 13- to 14-year-old age group was taken from The International Study of Asthma and Allergies in Childhood phase 1 study. These were compared to the age specific incidence of type 1 diabetes in children per 100 000 per year obtained from the Diabetes Mondiale Project Group study from those countries participating in both studies. Data collected from these 31 countries together with latitude was analysed using a Pearson correlation and significance analysis. A multiple regression analysis determined the confounding effect of latitude. The incidence of type 1 diabetes was found to have a positive correlation with both wheezing (P = 0.009) and atopic eczema (P < 0.01). There was a no correlation between the incidence of type 1 diabetes and the prevalance of rhinitis (r = 0.02, P = 0.88) or of rhinoconjunctivitis (r = 0.026, P = 0.88). Latitude correlated negatively with type 1 diabetes and positively with rhinitis and rhinoconjnctuvits; it was not significantly correlated with wheeze or eczema. Regression analysis showed that latitude is a significant confounding factor in the correlation of rhinitis (P value < 0.0008) and rhinoconjunctivitis (P value < 0.0003) with diabetes. The study suggests that common environmental and/or genetic factors predispose to type 1 diabetes, wheezing and atopic eczema while factors predisposing to rhinitis and rhinoconjunctivitis appear to be distinct from those predisposing to type 1 diabetes. © 2011 Blackwell Publishing Ltd.

Multinomial Logistic Regression Predicted Probability Map To Visualize The Influence Of Socio-Economic Factors On Breast Cancer Occurrence in Southern Karnataka

NASA Astrophysics Data System (ADS)

Madhu, B.; Ashok, N. C.; Balasubramanian, S.

2014-11-01

Multinomial logistic regression analysis was used to develop statistical model that can predict the probability of breast cancer in Southern Karnataka using the breast cancer occurrence data during 2007-2011. Independent socio-economic variables describing the breast cancer occurrence like age, education, occupation, parity, type of family, health insurance coverage, residential locality and socioeconomic status of each case was obtained. The models were developed as follows: i) Spatial visualization of the Urban- rural distribution of breast cancer cases that were obtained from the Bharat Hospital and Institute of Oncology. ii) Socio-economic risk factors describing the breast cancer occurrences were complied for each case. These data were then analysed using multinomial logistic regression analysis in a SPSS statistical software and relations between the occurrence of breast cancer across the socio-economic status and the influence of other socio-economic variables were evaluated and multinomial logistic regression models were constructed. iii) the model that best predicted the occurrence of breast cancer were identified. This multivariate logistic regression model has been entered into a geographic information system and maps showing the predicted probability of breast cancer occurrence in Southern Karnataka was created. This study demonstrates that Multinomial logistic regression is a valuable tool for developing models that predict the probability of breast cancer Occurrence in Southern Karnataka.

Force required for correcting the deformity of pectus carinatum and related multivariate analysis.

PubMed

Chen, Chenghao; Zeng, Qi; Li, Zhongzhi; Zhang, Na; Yu, Jie

2017-12-24

To measure the force required for correcting pectus carinatum to the desired position and investigate the correlations of the required force with patients' gender, age, deformity type, severity and body mass index (BMI). A total of 125 patients with pectus carinatum were enrolled in the study from August 2013 to August 2016. Their gender, age, deformity type, severity and BMI were recorded. A chest wall compressor was used to measure the force required for correcting the chest wall deformity. Multivariate linear regression was used for data analysis. Among the 125 patients, 112 were males and 13 were females. Their mean age was 13.7±1.5 years old, mean Haller index was 2.1±0.2, and mean BMI was 17.4±1.8 kg/m 2 . Multivariate linear regression analysis showed that the desirable force for correcting chest wall deformity was not correlated with gender and deformity type, but positively correlated with age and BMI and negatively correlated with Haller index. The desirable force measured for correcting chest wall deformities of patients with pectus carinatum positively correlates with age and BMI and negatively correlates with Haller index. The study provides valuable information for future improvement of implanted bar, bar fixation technique, and personalized surgery. Retrospective study. Level 3-4. Copyright © 2018. Published by Elsevier Inc.

Comparison between antegrade and retrograde cerebral perfusion or profound hypothermia as brain protection strategies during repair of type A aortic dissection.

PubMed

Stamou, Sotiris C; Rausch, Laura A; Kouchoukos, Nicholas T; Lobdell, Kevin W; Khabbaz, Kamal; Murphy, Edward; Hagberg, Robert C

2016-07-01

The goal of this study was to compare early postoperative outcomes and actuarial-free survival between patients who underwent repair of acute type A aortic dissection by the method of cerebral perfusion used. A total of 324 patients from five academic medical centers underwent repair of acute type A aortic dissection between January 2000 and December 2010. Of those, antegrade cerebral perfusion (ACP) was used for 84 patients, retrograde cerebral perfusion (RCP) was used for 55 patients, and deep hypothermic circulatory arrest (DHCA) was used for 184 patients during repair. Major morbidity, operative mortality, and 5-year actuarial survival were compared between groups. Multivariate logistic regression was used to determine predictors of operative mortality and Cox Regression hazard ratios were calculated to determine the predictors of long term mortality. Operative mortality was not influenced by the type of cerebral protection (19% for ACP, 14.5% for RCP and 19.1% for DHCA, P=0.729). In multivariable logistic regression analysis, hemodynamic instability [odds ratio (OR) =19.6, 95% confidence intervals (CI), 0.102-0.414, P<0.001] and CPB time >200 min(OR =4.7, 95% CI, 1.962-1.072, P=0.029) emerged as independent predictors of operative mortality. Actuarial 5-year survival was unchanged by cerebral protection modality (48.8% for ACP, 61.8% for RCP and 66.8% for no cerebral protection, log-rank P=0.844). During surgical repair of type A aortic dissection, ACP, RCP or DHCA are safe strategies for cerebral protection in selected patients with type A aortic dissection.

Creativity, Bipolar Disorder Vulnerability and Psychological Well-Being: A Preliminary Study

ERIC Educational Resources Information Center

Gostoli, Sara; Cerini, Veronica; Piolanti, Antonio; Rafanelli, Chiara

2017-01-01

The aim of this research was to investigate the relationships between creativity, subclinical bipolar disorder symptomatology, and psychological well-being. The study method was of descriptive, correlational type. Significant tests were performed using multivariate regression analysis. Students of the 4th grade of 6 different Italian colleges…

On the impact of relatedness on SNP association analysis.

PubMed

Gross, Arnd; Tönjes, Anke; Scholz, Markus

2017-12-06

When testing for SNP (single nucleotide polymorphism) associations in related individuals, observations are not independent. Simple linear regression assuming independent normally distributed residuals results in an increased type I error and the power of the test is also affected in a more complicate manner. Inflation of type I error is often successfully corrected by genomic control. However, this reduces the power of the test when relatedness is of concern. In the present paper, we derive explicit formulae to investigate how heritability and strength of relatedness contribute to variance inflation of the effect estimate of the linear model. Further, we study the consequences of variance inflation on hypothesis testing and compare the results with those of genomic control correction. We apply the developed theory to the publicly available HapMap trio data (N=129), the Sorbs (a self-contained population with N=977 characterised by a cryptic relatedness structure) and synthetic family studies with different sample sizes (ranging from N=129 to N=999) and different degrees of relatedness. We derive explicit and easily to apply approximation formulae to estimate the impact of relatedness on the variance of the effect estimate of the linear regression model. Variance inflation increases with increasing heritability. Relatedness structure also impacts the degree of variance inflation as shown for example family structures. Variance inflation is smallest for HapMap trios, followed by a synthetic family study corresponding to the trio data but with larger sample size than HapMap. Next strongest inflation is observed for the Sorbs, and finally, for a synthetic family study with a more extreme relatedness structure but with similar sample size as the Sorbs. Type I error increases rapidly with increasing inflation. However, for smaller significance levels, power increases with increasing inflation while the opposite holds for larger significance levels. When genomic control is applied, type I error is preserved while power decreases rapidly with increasing variance inflation. Stronger relatedness as well as higher heritability result in increased variance of the effect estimate of simple linear regression analysis. While type I error rates are generally inflated, the behaviour of power is more complex since power can be increased or reduced in dependence on relatedness and the heritability of the phenotype. Genomic control cannot be recommended to deal with inflation due to relatedness. Although it preserves type I error, the loss in power can be considerable. We provide a simple formula for estimating variance inflation given the relatedness structure and the heritability of a trait of interest. As a rule of thumb, variance inflation below 1.05 does not require correction and simple linear regression analysis is still appropriate.

Trochanteric entry femoral nails yield better femoral version and lower revision rates-A large cohort multivariate regression analysis.

PubMed

Yoon, Richard S; Gage, Mark J; Galos, David K; Donegan, Derek J; Liporace, Frank A

2017-06-01

Intramedullary nailing (IMN) has become the standard of care for the treatment of most femoral shaft fractures. Different IMN options include trochanteric and piriformis entry as well as retrograde nails, which may result in varying degrees of femoral rotation. The objective of this study was to analyze postoperative femoral version between three types of nails and to delineate any significant differences in femoral version (DFV) and revision rates. Over a 10-year period, 417 patients underwent IMN of a diaphyseal femur fracture (AO/OTA 32A-C). Of these patients, 316 met inclusion criteria and obtained postoperative computed tomography (CT) scanograms to calculate femoral version and were thus included in the study. In this study, our main outcome measure was the difference in femoral version (DFV) between the uninjured limb and the injured limb. The effect of the following variables on DFV and revision rates were determined via univariate, multivariate, and ordinal regression analyses: gender, age, BMI, ethnicity, mechanism of injury, operative side, open fracture, and table type/position. Statistical significance was set at p<0.05. A total of 316 patients were included. Piriformis entry nails made up the majority (n=141), followed by retrograde (n=108), then trochanteric entry nails (n=67). Univariate regression analysis revealed that a lower BMI was significantly associated with a lower DFV (p=0.006). Controlling for possible covariables, multivariate analysis yielded a significantly lower DFV for trochanteric entry nails than piriformis or retrograde nails (7.9±6.10 vs. 9.5±7.4 vs. 9.4±7.8°, p<0.05). Using revision as an endpoint, trochanteric entry nails also had a significantly lower revision rate, even when controlling for all other variables (p<0.05). Comparative, objective comparisons between DFV between different nails based on entry point revealed that trochanteric nails had a significantly lower DFV and a lower revision rate, even after regression analysis. However, this is not to state that the other nail types exhibited abnormal DFV. Translation to the clinical impact of a few degrees of DFV is also unknown. Future studies to more in-depth study the intricacies of femoral version may lead to improved technology in addition to potentially improved clinical outcomes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Characterization of the spatial variability of soil available zinc at various sampling densities using grouped soil type information.

PubMed

Song, Xiao-Dong; Zhang, Gan-Lin; Liu, Feng; Li, De-Cheng; Zhao, Yu-Guo

2016-11-01

The influence of anthropogenic activities and natural processes involved high uncertainties to the spatial variation modeling of soil available zinc (AZn) in plain river network regions. Four datasets with different sampling densities were split over the Qiaocheng district of Bozhou City, China. The difference of AZn concentrations regarding soil types was analyzed by the principal component analysis (PCA). Since the stationarity was not indicated and effective ranges of four datasets were larger than the sampling extent (about 400 m), two investigation tools, namely F3 test and stationarity index (SI), were employed to test the local non-stationarity. Geographically weighted regression (GWR) technique was performed to describe the spatial heterogeneity of AZn concentrations under the non-stationarity assumption. GWR based on grouped soil type information (GWRG for short) was proposed so as to benefit the local modeling of soil AZn within each soil-landscape unit. For reference, the multiple linear regression (MLR) model, a global regression technique, was also employed and incorporated the same predictors as in the GWR models. Validation results based on 100 times realization demonstrated that GWRG outperformed MLR and can produce similar or better accuracy than the GWR approach. Nevertheless, GWRG can generate better soil maps than GWR for limit soil data. Two-sample t test of produced soil maps also confirmed significantly different means. Variogram analysis of the model residuals exhibited weak spatial correlation, rejecting the use of hybrid kriging techniques. As a heuristically statistical method, the GWRG was beneficial in this study and potentially for other soil properties.

Retrospective chart review of children with type 2 diabetes mellitus evaluating the efficacy of metformin vs. insulin vs. combination insulin/metformin.

PubMed

Meyer, Stacy L; Hoffman, Robert P

2011-10-01

Type 2 diabetes mellitus is a growing problem in pediatrics and there is no consensus on the best treatment. We conducted this chart review on newly diagnosed pediatric patients with type 2 diabetes mellitus to compare the effect of treatment regimen on body mass index (BMI) and hemoglobin A1c over a 6-month period. We conducted a retrospective chart review on patients with type 2 DM who presented to Nationwide Children's Hospital. Data were collected on therapy type, BMI, and hemoglobin A1c over a 6-month follow-up. Therapy type was divided into metformin, insulin, or combination insulin and metformin. 1,997 charts were reviewed for inclusion based on ICD-9 codes consistent with a diagnosis of diabetes, abnormal oral glucose tolerance test, or insulin resistance. Of the 47 charts eligible for the review, 26 subjects were treated with metformin 1000-1500 mg daily, 14 patients were treated with insulin therapy, and 7 patients were treated with a combination of insulin and metformin therapy. At baseline, the only significant difference among groups was A1c (P = 0.012). In regression analysis with baseline A1c as a covariate, the only predictor of change in A1c over time was the A1c at onset (P < 0.001). Therapy type was not predictive of change (P = 0.905). Regression analysis showed a greater BMI at onset predicted a greater decrease in BMI (P = 0.006), but therapy type did not predict a change (P = 0.517). Metformin may be as effective as insulin or combination therapy for treatment of diabetes from onset to 6-month follow-up.

Composition, volume, and prices for major softwood lumber types in western Oregon and Washington, 1971-2020.

Treesearch

James F. Weigand

1998-01-01

An analysis of lumber prices provided regressions for price trends during the period 1971-95 for composite lumber grades of major timber species found in the Pacific Northwest west of the crest of the Cascade Range. The analysis included data for coastal Douglas-fir and hem-fir lumber; coastal and inland Pacific Northwest ponderosa, sugar, and western white pines; and...

Relative effect of socio-economic status on the health-related quality of life in type 2 diabetic patients in Iran.

PubMed

Hosseini Nejhad, Zahra; Molavi Vardanjani, Hossein; Abolhasani, Farid; Hadipour, Maryam; Sheikhzadeh, Khodadad

2013-01-01

Type II diabetes mellitus (T2DM) is a progressing epidemic and a major cause of mortality and morbidity worldwide. The quality of life (QoL) of diabetic patients has been strongly influenced by socioeconomic status (SES) in developed countries. Therefore, the QoL improvement is considered to be a major goal in diabetes control program. In this context, there is no reliable evidence for developing countries. In this study, the relative association of SES with health-related quality of life (HRQoL) was assessed in patients with T2DM in Iran. The "Cost estimation of Type 2 Diabetes in Iran" was used for secondary data analysis. The socio-economic status has been assessed by Categorical principal component analysis (CATPCA) techniques and HRQoL, using EQ-5D Visual Analog Scale, modified for digit preferences. Age, gender, education, occupation, SES, marital status, residency, education (T2DM related), diagnostic methods, number of annual care, type of treatment and Duration of disease awareness were used as independent variables in the multivariable linear regression model. Statistical analysis was performed using Stata software version 11.2. The response rate was 88.6%. Out of 3472 patients, 2128 were female and about 78.7% were from urban areas. All variables associated with T2DM were significant at the level of 0.05 except, the type of treatment, residency and education. Standardized regression coefficient for SES was estimated as 0.106 (p-value<0.0001). It seems that the SES of households in developing countries has a meaningful effect on the HRQoL of patients with T2DM as well as developed countries. Copyright © 2013 Diabetes India. Published by Elsevier Ltd. All rights reserved.

BMI and diabetes risk in Singaporean Chinese.

PubMed

Odegaard, Andrew O; Koh, Woon-Puay; Vazquez, Gabrielle; Arakawa, Kazuko; Lee, Hin-Peng; Yu, Mimi C; Pereira, Mark A

2009-06-01

Increased BMI is a robust risk factor for type 2 diabetes. Paradoxically, South Asians have relatively low BMIs despite their high prevalence of type 2 diabetes. We examined the association between BMI and incident type 2 diabetes because detailed prospective cohort data on this topic in Asians are scarce. This study was a prospective analysis of 37,091 men and women aged 45-74 years in the Singapore Chinese Health Study, using Cox regression analysis. Risk of incident type 2 diabetes significantly increased beginning with BMIs 18.5-23.0 kg/m(2)(relative risk 2.47 [95% CI 1.75-3.48]) and continued in a monotonic fashion across the spectrum of BMI. Results were stronger for younger than for older adults. BMIs considered lean and normal in Singaporean Chinese are strongly associated with increased risk of incident type 2 diabetes. This association weakened with advanced age but remained significant.

Analysis of Binary Adherence Data in the Setting of Polypharmacy: A Comparison of Different Approaches

PubMed Central

Esserman, Denise A.; Moore, Charity G.; Roth, Mary T.

2009-01-01

Older community dwelling adults often take multiple medications for numerous chronic diseases. Non-adherence to these medications can have a large public health impact. Therefore, the measurement and modeling of medication adherence in the setting of polypharmacy is an important area of research. We apply a variety of different modeling techniques (standard linear regression; weighted linear regression; adjusted linear regression; naïve logistic regression; beta-binomial (BB) regression; generalized estimating equations (GEE)) to binary medication adherence data from a study in a North Carolina based population of older adults, where each medication an individual was taking was classified as adherent or non-adherent. In addition, through simulation we compare these different methods based on Type I error rates, bias, power, empirical 95% coverage, and goodness of fit. We find that estimation and inference using GEE is robust to a wide variety of scenarios and we recommend using this in the setting of polypharmacy when adherence is dichotomously measured for multiple medications per person. PMID:20414358

Predicting tobacco sales in community pharmacies using population demographics and pharmacy type.

PubMed

Hickey, Lisa M; Farris, Karen B; Peterson, N Andrew; Aquilino, Mary L

2006-01-01

To determine whether the population demographics of the location of pharmacies were associated with tobacco sales in pharmacies, when controlling for pharmacy type. Retrospective analysis. Iowa. All retailers in Iowa that obtained tobacco licenses and all pharmacies registered with the Iowa Board of Pharmacy in 2003. MAIN OUTCOME MEASURE AND INTERVENTIONS: Percentage of pharmacies selling tobacco (examined by pharmacy type using chi-square analysis); median income and distribution of race/ethnicity in the county for pharmacies that did or did not sell tobacco (t tests); predictors of whether a pharmacy sold tobacco (logistic regression using the independent variables county-level demographic variables and pharmacy characteristics). County gender composition, race/ethnicity make-up, and income levels were different for tobacco-selling and -nonselling pharmacies. Logistic regression showed that whether a pharmacy sold tobacco was strongly dependent on the type of pharmacy; compared with independent pharmacies (of which only 5% sold tobacco products), chain pharmacies were 34 times more likely to sell tobacco products, mass merchandiser outlets were 47 times more likely to stock these goods, and grocery stores were 378 times more likely to do so. Pharmacies selling tobacco were more likely to be located in counties with significantly higher numbers of multiracial groups. The best predictor of whether an Iowa pharmacy sells tobacco products is type of pharmacy. In multivariable analyses, population demographics of the county in which pharmacies were located were generally not predictive of whether a pharmacy sold tobacco.

HIV prevention costs and program scale: data from the PANCEA project in five low and middle-income countries.

PubMed

Marseille, Elliot; Dandona, Lalit; Marshall, Nell; Gaist, Paul; Bautista-Arredondo, Sergio; Rollins, Brandi; Bertozzi, Stefano M; Coovadia, Jerry; Saba, Joseph; Lioznov, Dmitry; Du Plessis, Jo-Ann; Krupitsky, Evgeny; Stanley, Nicci; Over, Mead; Peryshkina, Alena; Kumar, S G Prem; Muyingo, Sowedi; Pitter, Christian; Lundberg, Mattias; Kahn, James G

2007-07-12

Economic theory and limited empirical data suggest that costs per unit of HIV prevention program output (unit costs) will initially decrease as small programs expand. Unit costs may then reach a nadir and start to increase if expansion continues beyond the economically optimal size. Information on the relationship between scale and unit costs is critical to project the cost of global HIV prevention efforts and to allocate prevention resources efficiently. The "Prevent AIDS: Network for Cost-Effectiveness Analysis" (PANCEA) project collected 2003 and 2004 cost and output data from 206 HIV prevention programs of six types in five countries. The association between scale and efficiency for each intervention type was examined for each country. Our team characterized the direction, shape, and strength of this association by fitting bivariate regression lines to scatter plots of output levels and unit costs. We chose the regression forms with the highest explanatory power (R2). Efficiency increased with scale, across all countries and interventions. This association varied within intervention and within country, in terms of the range in scale and efficiency, the best fitting regression form, and the slope of the regression. The fraction of variation in efficiency explained by scale ranged from 26-96%. Doubling in scale resulted in reductions in unit costs averaging 34.2% (ranging from 2.4% to 58.0%). Two regression trends, in India, suggested an inflection point beyond which unit costs increased. Unit costs decrease with scale across a wide range of service types and volumes. These country and intervention-specific findings can inform projections of the global cost of scaling up HIV prevention efforts.

Acceleration of the loss of the first-phase insulin response during the progression to type 1 diabetes in diabetes prevention trial-type 1 participants.

PubMed

Sosenko, Jay M; Skyler, Jay S; Beam, Craig A; Krischer, Jeffrey P; Greenbaum, Carla J; Mahon, Jeffrey; Rafkin, Lisa E; Matheson, Della; Herold, Kevan C; Palmer, Jerry P

2013-12-01

We studied the change in the first-phase insulin response (FPIR) during the progression to type 1 diabetes (T1D). Seventy-four oral insulin trial progressors to T1D from the Diabetes Prevention Trial-Type 1 with at least one FPIR measurement after baseline and before diagnosis were studied. The FPIR was examined longitudinally in 26 progressors who had FPIR measurements during each of the 3 years before diagnosis. The association between the change from the baseline FPIR to the last FPIR and time to diagnosis was studied in the remainder (n = 48). The 74 progressors had lower baseline FPIR values than nonprogressors (n = 270), with adjustments made for age and BMI. In the longitudinal analysis of the 26 progressors, there was a greater decline in the FPIR from 1.5 to 0.5 years before diagnosis than from 2.5 to 1.5 years before diagnosis. This accelerated decline was also evident in a regression analysis of the 48 remaining progressors in whom the rate of decline became more marked with the approaching diagnosis. The patterns of decline were similar between the longitudinal and regression analyses. There is an acceleration of decline in the FPIR during the progression to T1D, which becomes especially marked between 1.5 and 0.5 years before diagnosis.

Fasting insulin levels and metabolic risk factors in type 2 diabetic patients at the first visit in Japan: a 10-year, nationwide, observational study (JDDM 28).

PubMed

Matsuba, Ikuro; Saito, Kazumi; Takai, Masahiko; Hirao, Koichi; Sone, Hirohito

2012-09-01

To investigate the relationship between fasting insulin levels and metabolic risk factors (MRFs) in type 2 diabetic patients at the first clinic/hospital visit in Japan over the years 2000 to 2009. In total, 4,798 drug-naive Japanese patients with type 2 diabetes were registered on their first clinic/hospital visits. Conventional clinical factors and fasting insulin levels were observed at baseline within the Japan Diabetes Clinical Data Management (JDDM) study between consecutive 2-year groups. Multiple linear regression analysis was performed using a model in which the dependent variable was fasting insulin values using various clinical explanatory variables. Fasting insulin levels were found to be decreasing from 2000 to 2009. Multiple linear regression analysis with the fasting insulin levels as the dependent variable showed that waist circumference (WC), BMI, mean blood pressure, triglycerides, and HDL cholesterol were significant, with WC and BMI as the main factors. ANCOVA after adjustment for age and fasting plasma glucose clearly shows the decreasing trend in fasting insulin levels and the increasing trend in BMI. During the 10-year observation period, the decreasing trend in fasting insulin was related to the slight increase in WC/BMI in type 2 diabetes. Low pancreatic β-cell reserve on top of a lifestyle background might be dependent on an increase in MRFs.

Fasting Insulin Levels and Metabolic Risk Factors in Type 2 Diabetic Patients at the First Visit in Japan

PubMed Central

Matsuba, Ikuro; Saito, Kazumi; Takai, Masahiko; Hirao, Koichi; Sone, Hirohito

2012-01-01

OBJECTIVE To investigate the relationship between fasting insulin levels and metabolic risk factors (MRFs) in type 2 diabetic patients at the first clinic/hospital visit in Japan over the years 2000 to 2009. RESEARCH DESIGN AND METHODS In total, 4,798 drug-naive Japanese patients with type 2 diabetes were registered on their first clinic/hospital visits. Conventional clinical factors and fasting insulin levels were observed at baseline within the Japan Diabetes Clinical Data Management (JDDM) study between consecutive 2-year groups. Multiple linear regression analysis was performed using a model in which the dependent variable was fasting insulin values using various clinical explanatory variables. RESULTS Fasting insulin levels were found to be decreasing from 2000 to 2009. Multiple linear regression analysis with the fasting insulin levels as the dependent variable showed that waist circumference (WC), BMI, mean blood pressure, triglycerides, and HDL cholesterol were significant, with WC and BMI as the main factors. ANCOVA after adjustment for age and fasting plasma glucose clearly shows the decreasing trend in fasting insulin levels and the increasing trend in BMI. CONCLUSIONS During the 10-year observation period, the decreasing trend in fasting insulin was related to the slight increase in WC/BMI in type 2 diabetes. Low pancreatic β-cell reserve on top of a lifestyle background might be dependent on an increase in MRFs. PMID:22665215

Peripheral neuropathy, decreased muscle strength and obesity are strongly associated with walking in persons with type 2 diabetes without manifest mobility limitations.

PubMed

van Sloten, Thomas T; Savelberg, Hans H C M; Duimel-Peeters, Inge G P; Meijer, Kenneth; Henry, Ronald M A; Stehouwer, Coen D A; Schaper, Nicolaas C

2011-01-01

We evaluated the associations of diabetic complications and underlying pathology with daily walking activity in type 2 diabetic patients without manifest mobility limitations. 100 persons with type 2 diabetes (mean age 64.5 ± 9.4 years) were studied. Persons with manifest mobility limitations were excluded. Possible determinants measured: peripheral neuropathy, neuropathic pain, peripheral arterial disease, cardiovascular disease, decreased muscle strength (handgrip strength), BMI, depression, falls and fear of falling. Walking activity was measured during one week with a pedometer. Functional capacity was measured with the 6 min walk test, the timed "up and go" test and a stair climbing test. prevalence of neuropathy (40%) and obesity (53%) was high. Persons took a median of 6429 steps/day. In multivariate regression analysis, adjusted for age and sex, neuropathy was associated with a reduction of 1967 steps/day, decreased muscle strength with 1782 steps/day, and an increase in BMI of 1 kg/m(2) with a decrease of 210 steps/day (all p<0.05). Decreased muscle strength and BMI, but not neuropathy, were associated with outcome of functional capacity tests in multiple regression analysis. peripheral neuropathy, decreased muscle strength and obesity are strongly associated with walking in persons with type 2 diabetes without manifest mobility limitations. 2010 Elsevier Ireland Ltd. All rights reserved.

Determinants of orphan drugs prices in France: a regression analysis.

PubMed

Korchagina, Daria; Millier, Aurelie; Vataire, Anne-Lise; Aballea, Samuel; Falissard, Bruno; Toumi, Mondher

2017-04-21

The introduction of the orphan drug legislation led to the increase in the number of available orphan drugs, but the access to them is often limited due to the high price. Social preferences regarding funding orphan drugs as well as the criteria taken into consideration while setting the price remain unclear. The study aimed at identifying the determinant of orphan drug prices in France using a regression analysis. All drugs with a valid orphan designation at the moment of launch for which the price was available in France were included in the analysis. The selection of covariates was based on a literature review and included drug characteristics (Anatomical Therapeutic Chemical (ATC) class, treatment line, age of target population), diseases characteristics (severity, prevalence, availability of alternative therapeutic options), health technology assessment (HTA) details (actual benefit (AB) and improvement in actual benefit (IAB) scores, delay between the HTA and commercialisation), and study characteristics (type of study, comparator, type of endpoint). The main data sources were European public assessment reports, HTA reports, summaries of opinion on orphan designation of the European Medicines Agency, and the French insurance database of drugs and tariffs. A generalized regression model was developed to test the association between the annual treatment cost and selected covariates. A total of 68 drugs were included. The mean annual treatment cost was €96,518. In the univariate analysis, the ATC class (p = 0.01), availability of alternative treatment options (p = 0.02) and the prevalence (p = 0.02) showed a significant correlation with the annual cost. The multivariate analysis demonstrated significant association between the annual cost and availability of alternative treatment options, ATC class, IAB score, type of comparator in the pivotal clinical trial, as well as commercialisation date and delay between the HTA and commercialisation. The orphan drug pricing is a multivariate phenomenon. The complex association between drug prices and the studied attributes and shows that payers integrate multiple variables in decision making when setting orphan drug prices. The interpretation of the study results is limited by the small sample size and the complex data structure.

Procedures for adjusting regional regression models of urban-runoff quality using local data

USGS Publications Warehouse

Hoos, A.B.; Sisolak, J.K.

1993-01-01

Statistical operations termed model-adjustment procedures (MAP?s) can be used to incorporate local data into existing regression models to improve the prediction of urban-runoff quality. Each MAP is a form of regression analysis in which the local data base is used as a calibration data set. Regression coefficients are determined from the local data base, and the resulting `adjusted? regression models can then be used to predict storm-runoff quality at unmonitored sites. The response variable in the regression analyses is the observed load or mean concentration of a constituent in storm runoff for a single storm. The set of explanatory variables used in the regression analyses is different for each MAP, but always includes the predicted value of load or mean concentration from a regional regression model. The four MAP?s examined in this study were: single-factor regression against the regional model prediction, P, (termed MAP-lF-P), regression against P,, (termed MAP-R-P), regression against P, and additional local variables (termed MAP-R-P+nV), and a weighted combination of P, and a local-regression prediction (termed MAP-W). The procedures were tested by means of split-sample analysis, using data from three cities included in the Nationwide Urban Runoff Program: Denver, Colorado; Bellevue, Washington; and Knoxville, Tennessee. The MAP that provided the greatest predictive accuracy for the verification data set differed among the three test data bases and among model types (MAP-W for Denver and Knoxville, MAP-lF-P and MAP-R-P for Bellevue load models, and MAP-R-P+nV for Bellevue concentration models) and, in many cases, was not clearly indicated by the values of standard error of estimate for the calibration data set. A scheme to guide MAP selection, based on exploratory data analysis of the calibration data set, is presented and tested. The MAP?s were tested for sensitivity to the size of a calibration data set. As expected, predictive accuracy of all MAP?s for the verification data set decreased as the calibration data-set size decreased, but predictive accuracy was not as sensitive for the MAP?s as it was for the local regression models.

Comparison of two occurrence risk assessment methods for collapse gully erosion ——A case study in Guangdong province

NASA Astrophysics Data System (ADS)

Sun, K.; Cheng, D. B.; He, J. J.; Zhao, Y. L.

2018-02-01

Collapse gully erosion is a specific type of soil erosion in the red soil region of southern China, and early warning and prevention of the occurrence of collapse gully erosion is very important. Based on the idea of risk assessment, this research, taking Guangdong province as an example, adopt the information acquisition analysis and the logistic regression analysis, to discuss the feasibility for collapse gully erosion risk assessment in regional scale, and compare the applicability of the different risk assessment methods. The results show that in the Guangdong province, the risk degree of collapse gully erosion occurrence is high in northeastern and western area, and relatively low in southwestern and central part. The comparing analysis of the different risk assessment methods on collapse gully also indicated that the risk distribution patterns from the different methods were basically consistent. However, the accuracy of risk map from the information acquisition analysis method was slightly better than that from the logistic regression analysis method.

Robust Methods for Moderation Analysis with a Two-Level Regression Model.

PubMed

Yang, Miao; Yuan, Ke-Hai

2016-01-01

Moderation analysis has many applications in social sciences. Most widely used estimation methods for moderation analysis assume that errors are normally distributed and homoscedastic. When these assumptions are not met, the results from a classical moderation analysis can be misleading. For more reliable moderation analysis, this article proposes two robust methods with a two-level regression model when the predictors do not contain measurement error. One method is based on maximum likelihood with Student's t distribution and the other is based on M-estimators with Huber-type weights. An algorithm for obtaining the robust estimators is developed. Consistent estimates of standard errors of the robust estimators are provided. The robust approaches are compared against normal-distribution-based maximum likelihood (NML) with respect to power and accuracy of parameter estimates through a simulation study. Results show that the robust approaches outperform NML under various distributional conditions. Application of the robust methods is illustrated through a real data example. An R program is developed and documented to facilitate the application of the robust methods.

A difference in systolic blood pressure between arms is a novel predictor of the development and progression of diabetic nephropathy in patients with type 2 diabetes.

PubMed

Okada, Hiroshi; Fukui, Michiaki; Tanaka, Muhei; Matsumoto, Shinobu; Iwase, Hiroya; Kobayashi, Kanae; Asano, Mai; Yamazaki, Masahiro; Hasegawa, Goji; Nakamura, Naoto

2013-10-01

Recent studies have suggested that a difference in systolic blood pressure (SBP) between arms is associated with both vascular disease and mortality. The aim of this study was to investigate the relationship between a difference in SBP between arms and change in urinary albumin excretion or development of albuminuria in patients with type 2 diabetes. We measured SBP in 408 consecutive patients with type 2 diabetes, and calculated a difference in SBP between arms. We performed follow-up study to assess change in urinary albumin excretion or development of albuminuria, mean interval of which was 4.6 ± 1.7 years. We then evaluated the relationship of a difference in SBP between arms to diabetic nephropathy using multiple regression analysis and multiple Cox regression model. Multiple regression analyses demonstrated that a difference in SBP between arms was independently associated with change in urinary albumin excretion (β = 0.1869, P = 0.0010). Adjusted Cox regression analyses demonstrated that a difference in SBP between arms was associated with an increased hazard of development of albuminuria; hazard ratio was 1.215 (95% confidence interval 1.077-1.376). Moreover, the risk of development of albuminuria was increased in patients with a difference in SBP of equal to or more than 10 mmHg between arms; hazard ratio was 4.168 (95% confidence interval 1.478-11.70). A difference in SBP between arms could be a novel predictor of the development and progression of diabetic nephropathy in patients with type 2 diabetes. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Bayesian generalized least squares regression with application to log Pearson type 3 regional skew estimation

NASA Astrophysics Data System (ADS)

Reis, D. S.; Stedinger, J. R.; Martins, E. S.

2005-10-01

This paper develops a Bayesian approach to analysis of a generalized least squares (GLS) regression model for regional analyses of hydrologic data. The new approach allows computation of the posterior distributions of the parameters and the model error variance using a quasi-analytic approach. Two regional skew estimation studies illustrate the value of the Bayesian GLS approach for regional statistical analysis of a shape parameter and demonstrate that regional skew models can be relatively precise with effective record lengths in excess of 60 years. With Bayesian GLS the marginal posterior distribution of the model error variance and the corresponding mean and variance of the parameters can be computed directly, thereby providing a simple but important extension of the regional GLS regression procedures popularized by Tasker and Stedinger (1989), which is sensitive to the likely values of the model error variance when it is small relative to the sampling error in the at-site estimator.

Above-ground biomass of mangrove species. I. Analysis of models

NASA Astrophysics Data System (ADS)

Soares, Mário Luiz Gomes; Schaeffer-Novelli, Yara

2005-10-01

This study analyzes the above-ground biomass of Rhizophora mangle and Laguncularia racemosa located in the mangroves of Bertioga (SP) and Guaratiba (RJ), Southeast Brazil. Its purpose is to determine the best regression model to estimate the total above-ground biomass and compartment (leaves, reproductive parts, twigs, branches, trunk and prop roots) biomass, indirectly. To do this, we used structural measurements such as height, diameter at breast-height (DBH), and crown area. A combination of regression types with several compositions of independent variables generated 2.272 models that were later tested. Subsequent analysis of the models indicated that the biomass of reproductive parts, branches, and prop roots yielded great variability, probably because of environmental factors and seasonality (in the case of reproductive parts). It also indicated the superiority of multiple regression to estimate above-ground biomass as it allows researchers to consider several aspects that affect above-ground biomass, specially the influence of environmental factors. This fact has been attested to the models that estimated the biomass of crown compartments.

[An investigation on job burnout of medical personnel in a top three hospital].

PubMed

Li, Y Y; Li, L P

2016-05-20

To investigate job burnout status of medical Personnel in a top three hospitals, in order to provide basic data for intervention of the hospital management. A total of 549 doctors and nurses were assessed by Maslach Burnout Inventory-Human Service Survey (MBI-HSS). SPSS 19.0 software package was applied to data description and analysis, including univariate analysis and orderly classification Logistic regression analysis. The rate of high job burnout of doctors and nurses are 36.3% and 42.8% respectively. Female subjects got higher scores (29.4±13.5) on emotional exhaustion than male subjects (26.2±12.8) compared with.Doctors got lower scores (28.2±15.9) on emotional exhaustion and higher scores (31.4±9.3) on personal accomplishment than nurses.Compared with subjects with higher professional title, young subjects with primary professional title got lower scores on personal accomplishment.Subjects with 11-20 years working age got the highest scores on depersonalization.Among all the test departments, medical personnel of emergency department got the highest scores (31.9±12.6) on emotional exhaustion,while the lowest scores (28.1±8.0) on personal accomplishment. According to the results of orderly classification Logistic regression analysis, age, job type,professional qualifications and clinical departments type entered the regression model. Physical resources and emotional resources of medical personnel are overdraft so that they got some high degree of job burnout.Much more attention should be paid to professional mental health of nurses,and personnel who at low age,got low professional titles.Positive measures should be provided, including management mechanism,organizational culture, occupational protection and psychological intervention.

Predictors of adherence with self-care guidelines among persons with type 2 diabetes: results from a logistic regression tree analysis.

PubMed

Yamashita, Takashi; Kart, Cary S; Noe, Douglas A

2012-12-01

Type 2 diabetes is known to contribute to health disparities in the U.S. and failure to adhere to recommended self-care behaviors is a contributing factor. Intervention programs face difficulties as a result of patient diversity and limited resources. With data from the 2005 Behavioral Risk Factor Surveillance System, this study employs a logistic regression tree algorithm to identify characteristics of sub-populations with type 2 diabetes according to their reported frequency of adherence to four recommended diabetes self-care behaviors including blood glucose monitoring, foot examination, eye examination and HbA1c testing. Using Andersen's health behavior model, need factors appear to dominate the definition of which sub-groups were at greatest risk for low as well as high adherence. Findings demonstrate the utility of easily interpreted tree diagrams to design specific culturally appropriate intervention programs targeting sub-populations of diabetes patients who need to improve their self-care behaviors. Limitations and contributions of the study are discussed.

Sarcopenia is associated with incident albuminuria in patients with type 2 diabetes: A retrospective observational study.

PubMed

Bouchi, Ryotaro; Fukuda, Tatsuya; Takeuchi, Takato; Minami, Isao; Yoshimoto, Takanobu; Ogawa, Yoshihiro

2017-11-01

Sarcopenia, defined as age-related loss of skeletal muscle mass and function, increases the risk of albuminuria. However, it has still unknown whether sarcopenia could increase the risk for the progression of albuminuria. A total 238 patients with type 2 diabetes (mean age 64 ± 12 years; 39.2% women) were studied in the present retrospective observational study. The prevalence of sarcopenia was 17.6%. During the median follow-up period of 2.6 years, albuminuria was measured 5.8 ± 1.8 times, and progression of albuminuria was observed in 14.9% of patients with normoalbuminuria, as was 11.5% in those with microalbuminuria. Sarcopenia was significantly associated with both progression (hazard ratio 2.61, 95% confidence interval 1.08-6.31, P = 0.034) and regression (hazard ratio 0.23, 95% confidence interval 0.05-0.98, P = 0.048) of albuminuria by multivariate Cox regression analysis. The present data suggest that sarcopenia is an important determinant of both progression and regression of albuminuria in patients with type 2 diabetes. © 2017 The Authors. Journal of Diabetes Investigation published by Asian Association for the Study of Diabetes (AASD) and John Wiley & Sons Australia, Ltd.

Associations between content types of early media exposure and subsequent attentional problems.

PubMed

Zimmerman, Frederick J; Christakis, Dimitri A

2007-11-01

Television and video/DVD viewing among very young children has become both pervasive and heavy. Previous studies have reported an association between early media exposure and problems with attention regulation but did not have data on the content type that children watched. We tested the hypothesis that early television viewing of 3 content types is associated with subsequent attentional problems. The 3 different content types are educational, nonviolent entertainment, and violent entertainment. Participants were children in a nationally representative sample collected in 1997 and reassessed in 2002. The analysis was a logistic regression of a high score on a validated parent-reported measure of attentional problems, regressed on early television exposure by content and several important sociodemographic control variables. Viewing of educational television before age 3 was not associated with attentional problems 5 years later. However, viewing of either violent or non-violent entertainment television before age 3 was significantly associated with subsequent attentional problems, and the magnitude of the association was large. Viewing of any content type at ages 4 to 5 was not associated with subsequent problems. The association between early television viewing and subsequent attentional problems is specific to noneducational viewing and to viewing before age 3.

Mangrove vegetation structure in Southeast Brazil from phased array L-band synthetic aperture radar data

NASA Astrophysics Data System (ADS)

de Souza Pereira, Francisca Rocha; Kampel, Milton; Cunha-Lignon, Marilia

2016-07-01

The potential use of phased array type L-band synthetic aperture radar (PALSAR) data for discriminating distinct physiographic mangrove types with different forest structure developments in a subtropical mangrove forest located in Cananéia on the Southern coast of São Paulo, Brazil, is investigated. The basin and fringe physiographic types and the structural development of mangrove vegetation were identified with the application of the Kruskal-Wallis statistical test to the SAR backscatter values of 10 incoherent attributes. The best results to separate basin to fringe types were obtained using copolarized HH, cross-polarized HV, and the biomass index (BMI). Mangrove structural parameters were also estimated using multiple linear regressions. BMI and canopy structure index were used as explanatory variables for canopy height, mean height, and mean diameter at breast height regression models, with significant R2=0.69, 0.73, and 0.67, respectively. The current study indicates that SAR L-band images can be used as a tool to discriminate physiographic types and to characterize mangrove forests. The results are relevant considering the crescent availability of freely distributed SAR images that can be more utilized for analysis, monitoring, and conservation of the mangrove ecosystem.

Predictors of effects of lifestyle intervention on diabetes mellitus type 2 patients.

PubMed

Jacobsen, Ramune; Vadstrup, Eva; Røder, Michael; Frølich, Anne

2012-01-01

The main aim of the study was to identify predictors of the effects of lifestyle intervention on diabetes mellitus type 2 patients by means of multivariate analysis. Data from a previously published randomised clinical trial, which compared the effects of a rehabilitation programme including standardised education and physical training sessions in the municipality's health care centre with the same duration of individual counseling in the diabetes outpatient clinic, were used. Data from 143 diabetes patients were analysed. The merged lifestyle intervention resulted in statistically significant improvements in patients' systolic blood pressure, waist circumference, exercise capacity, glycaemic control, and some aspects of general health-related quality of life. The linear multivariate regression models explained 45% to 80% of the variance in these improvements. The baseline outcomes in accordance to the logic of the regression to the mean phenomenon were the only statistically significant and robust predictors in all regression models. These results are important from a clinical point of view as they highlight the more urgent need for and better outcomes following lifestyle intervention for those patients who have worse general and disease-specific health.

Novel insights into the lipidome of glioblastoma cells based on a combined PLSR and DD-HDS computational analysis

NASA Astrophysics Data System (ADS)

Lespinats, S.; Meyer-Bäse, Anke; He, Huan; Marshall, Alan G.; Conrad, Charles A.; Emmett, Mark R.

2009-05-01

Partial Least Square Regression (PLSR) and Data-Driven High Dimensional Scaling (DD-HDS) are employed for the prediction and the visualization of changes in polar lipid expression induced by different combinations of wild-type (wt) p53 gene therapy and SN38 chemotherapy of U87 MG glioblastoma cells. A very detailed analysis of the gangliosides reveals that certain gangliosides of GM3 or GD1-type have unique properties not shared by the others. In summary, this preliminary work shows that data mining techniques are able to determine the modulation of gangliosides by different treatment combinations.

Satisfaction of active duty soldiers with family dental care.

PubMed

Chisick, M C

1997-02-01

In the fall of 1992, a random, worldwide sample of 6,442 married and single parent soldiers completed a self-administered survey on satisfaction with 22 attributes of family dental care. Simple descriptive statistics for each attribute were derived, as was a composite overall satisfaction score using factor analysis. Composite scores were regressed on demographics, annual dental utilization, and access barriers to identify those factors having an impact on a soldier's overall satisfaction with family dental care. Separate regression models were constructed for single parents, childless couples, and couples with children. Results show below-average satisfaction with nearly all attributes of family dental care, with access attributes having the lowest average satisfaction scores. Factors influencing satisfaction with family dental care varied by family type with one exception: dependent dental utilization within the past year contributed positively to satisfaction across all family types.

Fertility is reduced in women and in men with type 1 diabetes: results from the Type 1 Diabetes Genetics Consortium (T1DGC).

PubMed

Wiebe, Julia C; Santana, Angelo; Medina-Rodríguez, Nathan; Hernández, Marta; Nóvoa, Javier; Mauricio, Dídac; Wägner, Ana M

2014-12-01

A recent Finnish study described reduced fertility in patients with childhood-onset type 1 diabetes. The Type 1 Diabetes Genetics Consortium (T1DGC) is an international programme studying the genetics and pathogenesis of type 1 diabetes that includes families with the disease. Our aim was to assess fertility, defined as number of offspring, in the affected and unaffected siblings included in the T1DGC. Clinical information from participants aged ≥18 years at the time of examination was included in the present analysis. The number of offspring of affected and unaffected siblings was compared (in families including both) and the influence of birth year, disease duration and age of onset was assessed, the last in affected siblings only, using Poisson regression models. A total of 3010 affected and 801 unaffected adult siblings that belonged to 1761 families were assessed. The mean number of offspring was higher in the unaffected than in the affected individuals, and the difference between the two groups was more pronounced in women than men. Poisson regression analysis showed that both sex and birth cohort significantly affected the differences between groups. In the affected siblings, adult onset (≥18 years), female sex and older birth cohort were associated with higher fertility. Patients with type 1 diabetes have fewer children than their unaffected siblings. This effect is more evident in women and in older birth cohorts. Onset of type 1 diabetes as an adult rather than a child is associated with a higher number of offspring, even after accounting for birth cohort and disease duration.

Extended cox regression model: The choice of timefunction

NASA Astrophysics Data System (ADS)

Isik, Hatice; Tutkun, Nihal Ata; Karasoy, Durdu

2017-07-01

Cox regression model (CRM), which takes into account the effect of censored observations, is one the most applicative and usedmodels in survival analysis to evaluate the effects of covariates. Proportional hazard (PH), requires a constant hazard ratio over time, is the assumptionofCRM. Using extended CRM provides the test of including a time dependent covariate to assess the PH assumption or an alternative model in case of nonproportional hazards. In this study, the different types of real data sets are used to choose the time function and the differences between time functions are analyzed and discussed.

Validation of a heteroscedastic hazards regression model.

PubMed

Wu, Hong-Dar Isaac; Hsieh, Fushing; Chen, Chen-Hsin

2002-03-01

A Cox-type regression model accommodating heteroscedasticity, with a power factor of the baseline cumulative hazard, is investigated for analyzing data with crossing hazards behavior. Since the approach of partial likelihood cannot eliminate the baseline hazard, an overidentified estimating equation (OEE) approach is introduced in the estimation procedure. It by-product, a model checking statistic, is presented to test for the overall adequacy of the heteroscedastic model. Further, under the heteroscedastic model setting, we propose two statistics to test the proportional hazards assumption. Implementation of this model is illustrated in a data analysis of a cancer clinical trial.

Lifestyle and clinical factors associated with elevated C-reactive protein among newly diagnosed Type 2 diabetes mellitus patients: a cross-sectional study from the nationwide DD2 cohort.

PubMed

Svensson, Elisabeth; Mor, Anil; Rungby, Jørgen; Berencsi, Klara; Nielsen, Jens Steen; Stidsen, Jacob V; Friborg, Søren; Brandslund, Ivan; Christiansen, Jens Sandahl; Beck-Nielsen, Henning; Sørensen, Henrik Toft; Thomsen, Reimar W

2014-08-28

We aimed to examine the prevalence of and modifiable factors associated with elevated C-reactive Protein (CRP), a marker of inflammation, in men and women with newly diagnosed Type 2 Diabetes mellitus (DM) in a population-based setting. CRP was measured in 1,037 patients (57% male) with newly diagnosed Type 2 DM included in the prospective nationwide Danish Centre for Strategic Research in Type 2 Diabetes (DD2) project. We assessed the prevalence of elevated CRP and calculated relative risks (RR) examining the association of CRP with lifestyle and clinical factors by Poisson regression, stratified by gender. We used linear regression to examine the association of CRP with other biomarkers. The median CRP value was 2.1 mg/L (interquartile range, 1.0 - 4.8 mg/L). In total, 405 out of the 1,037 Type 2 DM patients (40%) had elevated CRP levels (>3.0 mg/L). More women (46%) than men (34%) had elevated CRP. Among women, a lower risk of elevated CRP was observed in patients receiving statins (adjusted RR (aRR) 0.7 (95% confidence interval (CI) 0.6-0.9)), whereas a higher risk was seen in patients with central obesity (aRR 2.3 (95% CI 1.0-5.3)). For men, CRP was primarily elevated among patients with no regular physical activity (aRR 1.5 (95% CI 1.1-1.9)), previous cardiovascular disease (aRR1.5 (95% CI 1.2-1.9) and other comorbidity. For both genders, elevated CRP was 1.4-fold increased in those with weight gain >30 kg since age 20 years. Sensitivity analyses showed consistent results with the full analysis. The linear regression analysis conveyed an association between high CRP and increased fasting blood glucose. Among newly diagnosed Type 2 DM patients, 40% had elevated CRP levels. Important modifiable risk factors for elevated CRP may vary by gender, and include low physical activity for men and central obesity and absence of statin use for women.

Comparing transfusion reaction rates for various plasma types: a systematic review and meta-analysis/regression.

PubMed

Saadah, Nicholas H; van Hout, Fabienne M A; Schipperus, Martin R; le Cessie, Saskia; Middelburg, Rutger A; Wiersum-Osselton, Johanna C; van der Bom, Johanna G

2017-09-01

We estimated rates for common plasma-associated transfusion reactions and compared reported rates for various plasma types. We performed a systematic review and meta-analysis of peer-reviewed articles that reported plasma transfusion reaction rates. Random-effects pooled rates were calculated and compared between plasma types. Meta-regression was used to compare various plasma types with regard to their reported plasma transfusion reaction rates. Forty-eight studies reported transfusion reaction rates for fresh-frozen plasma (FFP; mixed-sex and male-only), amotosalen INTERCEPT FFP, methylene blue-treated FFP, and solvent/detergent-treated pooled plasma. Random-effects pooled average rates for FFP were: allergic reactions, 92/10 5 units transfused (95% confidence interval [CI], 46-184/10 5 units transfused); febrile nonhemolytic transfusion reactions (FNHTRs), 12/10 5 units transfused (95% CI, 7-22/10 5 units transfused); transfusion-associated circulatory overload (TACO), 6/10 5 units transfused (95% CI, 1-30/10 5 units transfused); transfusion-related acute lung injury (TRALI), 1.8/10 5 units transfused (95% CI, 1.2-2.7/10 5 units transfused); and anaphylactic reactions, 0.8/10 5 units transfused (95% CI, 0-45.7/10 5 units transfused). Risk differences between plasma types were not significant for allergic reactions, TACO, or anaphylactic reactions. Methylene blue-treated FFP led to fewer FNHTRs than FFP (risk difference = -15.3 FNHTRs/10 5 units transfused; 95% CI, -24.7 to -7.1 reactions/10 5 units transfused); and male-only FFP led to fewer cases of TRALI than mixed-sex FFP (risk difference = -0.74 TRALI/10 5 units transfused; 95% CI, -2.42 to -0.42 injuries/10 5 units transfused). Meta-regression demonstrates that the rate of FNHTRs is lower for methylene blue-treated compared with FFP, and the rate of TRALI is lower for male-only than for mixed-sex FFP; whereas no significant differences are observed between plasma types for allergic reactions, TACO, or anaphylactic reactions. Reported transfusion reaction rates suffer from high heterogeneity. © 2017 AABB.

Low temperature-induced circulating triiodothyronine accelerates seasonal testicular regression.

PubMed

Ikegami, Keisuke; Atsumi, Yusuke; Yorinaga, Eriko; Ono, Hiroko; Murayama, Itaru; Nakane, Yusuke; Ota, Wataru; Arai, Natsumi; Tega, Akinori; Iigo, Masayuki; Darras, Veerle M; Tsutsui, Kazuyoshi; Hayashi, Yoshitaka; Yoshida, Shosei; Yoshimura, Takashi

2015-02-01

In temperate zones, animals restrict breeding to specific seasons to maximize the survival of their offspring. Birds have evolved highly sophisticated mechanisms of seasonal regulation, and their testicular mass can change 100-fold within a few weeks. Recent studies on Japanese quail revealed that seasonal gonadal development is regulated by central thyroid hormone activation within the hypothalamus, depending on the photoperiodic changes. By contrast, the mechanisms underlying seasonal testicular regression remain unclear. Here we show the effects of short day and low temperature on testicular regression in quail. Low temperature stimulus accelerated short day-induced testicular regression by shutting down the hypothalamus-pituitary-gonadal axis and inducing meiotic arrest and germ cell apoptosis. Induction of T3 coincided with the climax of testicular regression. Temporal gene expression analysis over the course of apoptosis revealed the suppression of LH response genes and activation of T3 response genes involved in amphibian metamorphosis within the testis. Daily ip administration of T3 mimicked the effects of low temperature stimulus on germ cell apoptosis and testicular mass. Although type 2 deiodinase, a thyroid hormone-activating enzyme, in the brown adipose tissue generates circulating T3 under low-temperature conditions in mammals, there is no distinct brown adipose tissue in birds. In birds, type 2 deiodinase is induced by low temperature exclusively in the liver, which appears to be caused by increased food consumption. We conclude that birds use low temperature-induced circulating T3 not only for adaptive thermoregulation but also to trigger apoptosis to accelerate seasonal testicular regression.

Design of overlays based on pavement condition, roughness, and deflections : part 1 : tentative method for overlay design based on visual pavement distress.

DOT National Transportation Integrated Search

1978-01-01

Data collected on 111 interstate highway projects in Virginia were analyzed by multi-regression analysis and the rating coefficient for each type of distress determined. By this means, the total pavement distress and, hence, the maintenance rating of...

A Meta-Analysis of Trials Evaluating Patient Education and Counseling for Three Groups of Preventive Health Behaviors.

ERIC Educational Resources Information Center

Mullen, Patricia Dolan; Simons-Morton, Denise G.; Ramirez, Gilbert; Frankowski, Ralph F.; Green, Lawrence W.; Mains, Douglas A.

1997-01-01

The overall effectiveness of patient education and counseling on preventive health behaviors was examined across published clinical trials, 1971-1994. The effectiveness of various approaches for modifying specific types of behaviors among patients without diagnosed disease was assessed. Multiple regression models indicated differences among…

Sequential-Simultaneous Analysis of Japanese Children's Performance on the Japanese McCarthy.

ERIC Educational Resources Information Center

Ishikuma, Toshinori; And Others

This study explored the hypothesis that Japanese children perform significantly better on simultaneous processing than on sequential processing. The Kaufman Assessment Battery for Children (K-ABC) served as the criterion of the two types of mental processing. Regression equations to predict Sequential and Simultaneous processing from McCarthy…

Time Series Analysis of Soil Radon Data Using Multiple Linear Regression and Artificial Neural Network in Seismic Precursory Studies

NASA Astrophysics Data System (ADS)

Singh, S.; Jaishi, H. P.; Tiwari, R. P.; Tiwari, R. C.

2017-07-01

This paper reports the analysis of soil radon data recorded in the seismic zone-V, located in the northeastern part of India (latitude 23.73N, longitude 92.73E). Continuous measurements of soil-gas emission along Chite fault in Mizoram (India) were carried out with the replacement of solid-state nuclear track detectors at weekly interval. The present study was done for the period from March 2013 to May 2015 using LR-115 Type II detectors, manufactured by Kodak Pathe, France. In order to reduce the influence of meteorological parameters, statistical analysis tools such as multiple linear regression and artificial neural network have been used. Decrease in radon concentration was recorded prior to some earthquakes that occurred during the observation period. Some false anomalies were also recorded which may be attributed to the ongoing crustal deformation which was not major enough to produce an earthquake.

Sensitivity analysis, calibration, and testing of a distributed hydrological model using error‐based weighting and one objective function

USGS Publications Warehouse

Foglia, L.; Hill, Mary C.; Mehl, Steffen W.; Burlando, P.

2009-01-01

We evaluate the utility of three interrelated means of using data to calibrate the fully distributed rainfall‐runoff model TOPKAPI as applied to the Maggia Valley drainage area in Switzerland. The use of error‐based weighting of observation and prior information data, local sensitivity analysis, and single‐objective function nonlinear regression provides quantitative evaluation of sensitivity of the 35 model parameters to the data, identification of data types most important to the calibration, and identification of correlations among parameters that contribute to nonuniqueness. Sensitivity analysis required only 71 model runs, and regression required about 50 model runs. The approach presented appears to be ideal for evaluation of models with long run times or as a preliminary step to more computationally demanding methods. The statistics used include composite scaled sensitivities, parameter correlation coefficients, leverage, Cook's D, and DFBETAS. Tests suggest predictive ability of the calibrated model typical of hydrologic models.

Integrative eQTL analysis of tumor and host omics data in individuals with bladder cancer.

PubMed

Pineda, Silvia; Van Steen, Kristel; Malats, Núria

2017-09-01

Integrative analyses of several omics data are emerging. The data are usually generated from the same source material (i.e., tumor sample) representing one level of regulation. However, integrating different regulatory levels (i.e., blood) with those from tumor may also reveal important knowledge about the human genetic architecture. To model this multilevel structure, an integrative-expression quantitative trait loci (eQTL) analysis applying two-stage regression (2SR) was proposed. This approach first regressed tumor gene expression levels with tumor markers and the adjusted residuals from the previous model were then regressed with the germline genotypes measured in blood. Previously, we demonstrated that penalized regression methods in combination with a permutation-based MaxT method (Global-LASSO) is a promising tool to fix some of the challenges that high-throughput omics data analysis imposes. Here, we assessed whether Global-LASSO can also be applied when tumor and blood omics data are integrated. We further compared our strategy with two 2SR-approaches, one using multiple linear regression (2SR-MLR) and other using LASSO (2SR-LASSO). We applied the three models to integrate genomic, epigenomic, and transcriptomic data from tumor tissue with blood germline genotypes from 181 individuals with bladder cancer included in the TCGA Consortium. Global-LASSO provided a larger list of eQTLs than the 2SR methods, identified a previously reported eQTLs in prostate stem cell antigen (PSCA), and provided further clues on the complexity of APBEC3B loci, with a minimal false-positive rate not achieved by 2SR-MLR. It also represents an important contribution for omics integrative analysis because it is easy to apply and adaptable to any type of data. © 2017 WILEY PERIODICALS, INC.

Prevalence of treponema species detected in endodontic infections: systematic review and meta-regression analysis.

PubMed

Leite, Fábio R M; Nascimento, Gustavo G; Demarco, Flávio F; Gomes, Brenda P F A; Pucci, Cesar R; Martinho, Frederico C

2015-05-01

This systematic review and meta-regression analysis aimed to calculate a combined prevalence estimate and evaluate the prevalence of different Treponema species in primary and secondary endodontic infections, including symptomatic and asymptomatic cases. The MEDLINE/PubMed, Embase, Scielo, Web of Knowledge, and Scopus databases were searched without starting date restriction up to and including March 2014. Only reports in English were included. The selected literature was reviewed by 2 authors and classified as suitable or not to be included in this review. Lists were compared, and, in case of disagreements, decisions were made after a discussion based on inclusion and exclusion criteria. A pooled prevalence of Treponema species in endodontic infections was estimated. Additionally, a meta-regression analysis was performed. Among the 265 articles identified in the initial search, only 51 were included in the final analysis. The studies were classified into 2 different groups according to the type of endodontic infection and whether it was an exclusively primary/secondary study (n = 36) or a primary/secondary comparison (n = 15). The pooled prevalence of Treponema species was 41.5% (95% confidence interval, 35.9-47.0). In the multivariate model of meta-regression analysis, primary endodontic infections (P < .001), acute apical abscess, symptomatic apical periodontitis (P < .001), and concomitant presence of 2 or more species (P = .028) explained the heterogeneity regarding the prevalence rates of Treponema species. Our findings suggest that Treponema species are important pathogens involved in endodontic infections, particularly in cases of primary and acute infections. Copyright © 2015 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Comprehension of texts by deaf elementary school students: The role of grammatical understanding.

PubMed

Barajas, Carmen; González-Cuenca, Antonia M; Carrero, Francisco

2016-12-01

The aim of this study was to analyze how the reading process of deaf Spanish elementary school students is affected both by those components that explain reading comprehension according to the Simple View of Reading model: decoding and linguistic comprehension (both lexical and grammatical) and by other variables that are external to the reading process: the type of assistive technology used, the age at which it is implanted or fitted, the participant's socioeconomic status and school stage. Forty-seven students aged between 6 and 13 years participated in the study; all presented with profound or severe prelingual bilateral deafness, and all used digital hearing aids or cochlear implants. Students' text comprehension skills, decoding skills and oral comprehension skills (both lexical and grammatical) were evaluated. Logistic regression analysis indicated that neither the type of assistive technology, age at time of fitting or activation, socioeconomic status, nor school stage could predict the presence or absence of difficulties in text comprehension. Furthermore, logistic regression analysis indicated that neither decoding skills, nor lexical age could predict competency in text comprehension; however, grammatical age could explain 41% of the variance. Probing deeper into the effect of grammatical understanding, logistic regression analysis indicated that a participant's understanding of reversible passive object-verb-subject sentences and reversible predicative subject-verb-object sentences accounted for 38% of the variance in text comprehension. Based on these results, we suggest that it might be beneficial to devise and evaluate interventions that focus specifically on grammatical comprehension. Copyright © 2016 Elsevier Ltd. All rights reserved.

The relationship between burnout symptoms and Type D personality among health care professionals in Turkey.

PubMed

Tekin, Atilla; Karadağ, Hekim; Yayla, Sinan

2017-05-04

The aim of this study was to investigate the relationship between burnout and Type D personality in health care professionals. The study randomly included 120 health care professionals (73 nurses, 47 doctors). Sociodemographic data form, Maslach Burnout Inventory, and Type D Personality Scale were applied to each participant; 38.3% of the health care professionals (n = 46) had the Type D personality. Emotional exhaustion and depersonalization of health care professionals with Type D personality were higher than of those without Type D personality (p = .006 and p = .005). Stepwise regression analysis indicated that Type D personality was a predictor of emotional exhaustion and depersonalization (p = .005 and p = .001, respectively). Our results suggest that Type D personality is associated with higher burnout levels.

[Correspondence analysis of association between types of unintentional injuries and influential factors among rural rear pupils].

PubMed

Dou, Dongmei; Wang, Peixi

2015-07-01

To explore the association between types of unintentional injuries and influential factors among rural rear pupils. The multistage stratified sampling method was used to select the study participant and thus 594 rural pupils were sampled, 292 rear pupils were confirmed and measured with unintentional injuries and influential factors of rural rear pupils scale. Binary logistic regression analysis indicate that the risk facts related to unintentional injury were left-behind status (OR = 2.68, 95% CI 1.06-6.79), gender (OR = 5.12, 95% C2.68-9.79) and surrounding environment (OR = 3.44, 95% CI 1.37-8.70). Correspondence analysis showed living with father, middle personality and low age were related possibly with traffic accident injury. Living with grandparents, extrovert personality and elder pupils were related possibly with unintentional falls injury. Living with mother, introvert personality and middle-age pupils were related possibly with animmal injury. The personality, ages and guardian types of rural rear pupils are correlated with types of unintentional injuries.

Bacterial diversity in saliva and oral health-related conditions: the Hisayama Study

NASA Astrophysics Data System (ADS)

Takeshita, Toru; Kageyama, Shinya; Furuta, Michiko; Tsuboi, Hidenori; Takeuchi, Kenji; Shibata, Yukie; Shimazaki, Yoshihiro; Akifusa, Sumio; Ninomiya, Toshiharu; Kiyohara, Yutaka; Yamashita, Yoshihisa

2016-02-01

This population-based study determined the salivary microbiota composition of 2,343 adult residents of Hisayama town, Japan, using 16S rRNA gene next-generation high-throughput sequencing. Of 550 identified species-level operational taxonomic units (OTUs), 72 were common, in ≥75% of all individuals, as well as in ≥75% of the individuals in the lowest quintile of phylogenetic diversity (PD). These “core” OTUs constituted 90.9 ± 6.1% of each microbiome. The relative abundance profiles of 22 of the core OTUs with mean relative abundances ≥1% were stratified into community type I and community type II by partitioning around medoids clustering. Multiple regression analysis revealed that a lower PD was associated with better conditions for oral health, including a lower plaque index, absence of decayed teeth, less gingival bleeding, shallower periodontal pockets and not smoking, and was also associated with tooth loss. By contrast, multiple Poisson regression analysis demonstrated that community type II, as characterized by a higher ratio of the nine dominant core OTUs, including Neisseria flavescens, was implicated in younger age, lower body mass index, fewer teeth with caries experience, and not smoking. Our large-scale data analyses reveal variation in the salivary microbiome among Japanese adults and oral health-related conditions associated with the salivary microbiome.

Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.

PubMed

Zhang, Futao; Xie, Dan; Liang, Meimei; Xiong, Momiao

2016-04-01

To date, most genetic analyses of phenotypes have focused on analyzing single traits or analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power and improve our understanding of the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two genes in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large-scale simulations to calculate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare the power with multivariate pairwise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for epistasis analysis is applied to five phenotypes of exome sequence data from the NHLBI's Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 267 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has a much higher power to detect interaction than the interaction analysis of a single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes.

Factors influencing attendance at structured education for Type 1 diabetes in south London.

PubMed

Harris, S M; Shah, P; Mulnier, H; Healey, A; Thomas, S M; Amiel, S A; Hopkins, D

2017-06-01

To investigate the factors influencing uptake of structured education for people with Type 1 diabetes in our local population in order to understand why such uptake is low. We conducted a cross-sectional database study of adults with Type 1 diabetes in two south London boroughs, analysed according to Dose Adjustment For Normal Eating (DAFNE) attendance or non-attendance. Demographics, glycaemic control and service use, with subset analysis by ethnicity, were compared using univariate analysis. An exploratory regression model was used to identify influencing factors. The analysis showed that 73% of adults had not attended the DAFNE programme. For non-attenders vs attenders, male gender (59 vs 48%; P = 0.002), older age (39 vs 35 years; P < 0.001), non-white ethnicity (30 vs 20%; P = 0.001) and coming from an area of social deprivation (index of multiple deprivation score 31 vs 28; P < 0.001) were associated with non-attendance. The difference in gender (88% men vs 70% women; P < 0.001) and age (43 vs 34 years) persisted in the non-white group. Regression analysis showed that higher baseline HbA 1c level (odds ratio 1.96; P = 0.004), younger age (odds ratio 0.98; P = 0.001) and lower social deprivation (odds ratio 0.52; P = 0.001) was associated with attendance. Socio-economic status and factors perceived as indicating greater severity of disease (HbA 1c ) influence attendance at DAFNE. More work is necessary to understand the demography of non-attenders to aid future service design and alternative engagement strategies for these groups. © 2017 Diabetes UK.

Quantitative analysis of organizational culture in occupational health research: a theory-based validation in 30 workplaces of the organizational culture profile instrument.

PubMed

Marchand, Alain; Haines, Victor Y; Dextras-Gauthier, Julie

2013-05-04

This study advances a measurement approach for the study of organizational culture in population-based occupational health research, and tests how different organizational culture types are associated with psychological distress, depression, emotional exhaustion, and well-being. Data were collected over a sample of 1,164 employees nested in 30 workplaces. Employees completed the 26-item OCP instrument. Psychological distress was measured with the General Health Questionnaire (12-item); depression with the Beck Depression Inventory (21-item); and emotional exhaustion with five items from the Maslach Burnout Inventory general survey. Exploratory factor analysis evaluated the dimensionality of the OCP scale. Multilevel regression models estimated workplace-level variations, and the contribution of organizational culture factors to mental health and well-being after controlling for gender, age, and living with a partner. Exploratory factor analysis of OCP items revealed four factors explaining about 75% of the variance, and supported the structure of the Competing Values Framework. Factors were labeled Group, Hierarchical, Rational and Developmental. Cronbach's alphas were high (0.82-0.89). Multilevel regression analysis suggested that the four culture types varied significantly between workplaces, and correlated with mental health and well-being outcomes. The Group culture type best distinguished between workplaces and had the strongest associations with the outcomes. This study provides strong support for the use of the OCP scale for measuring organizational culture in population-based occupational health research in a way that is consistent with the Competing Values Framework. The Group organizational culture needs to be considered as a relevant factor in occupational health studies.

Type I error rates of rare single nucleotide variants are inflated in tests of association with non-normally distributed traits using simple linear regression methods.

PubMed

Schwantes-An, Tae-Hwi; Sung, Heejong; Sabourin, Jeremy A; Justice, Cristina M; Sorant, Alexa J M; Wilson, Alexander F

2016-01-01

In this study, the effects of (a) the minor allele frequency of the single nucleotide variant (SNV), (b) the degree of departure from normality of the trait, and (c) the position of the SNVs on type I error rates were investigated in the Genetic Analysis Workshop (GAW) 19 whole exome sequence data. To test the distribution of the type I error rate, 5 simulated traits were considered: standard normal and gamma distributed traits; 2 transformed versions of the gamma trait (log 10 and rank-based inverse normal transformations); and trait Q1 provided by GAW 19. Each trait was tested with 313,340 SNVs. Tests of association were performed with simple linear regression and average type I error rates were determined for minor allele frequency classes. Rare SNVs (minor allele frequency < 0.05) showed inflated type I error rates for non-normally distributed traits that increased as the minor allele frequency decreased. The inflation of average type I error rates increased as the significance threshold decreased. Normally distributed traits did not show inflated type I error rates with respect to the minor allele frequency for rare SNVs. There was no consistent effect of transformation on the uniformity of the distribution of the location of SNVs with a type I error.

Comparison between antegrade and retrograde cerebral perfusion or profound hypothermia as brain protection strategies during repair of type A aortic dissection

PubMed Central

Rausch, Laura A.; Kouchoukos, Nicholas T.; Lobdell, Kevin W.; Khabbaz, Kamal; Murphy, Edward; Hagberg, Robert C.

2016-01-01

Background The goal of this study was to compare early postoperative outcomes and actuarial-free survival between patients who underwent repair of acute type A aortic dissection by the method of cerebral perfusion used. Methods A total of 324 patients from five academic medical centers underwent repair of acute type A aortic dissection between January 2000 and December 2010. Of those, antegrade cerebral perfusion (ACP) was used for 84 patients, retrograde cerebral perfusion (RCP) was used for 55 patients, and deep hypothermic circulatory arrest (DHCA) was used for 184 patients during repair. Major morbidity, operative mortality, and 5-year actuarial survival were compared between groups. Multivariate logistic regression was used to determine predictors of operative mortality and Cox Regression hazard ratios were calculated to determine the predictors of long term mortality. Results Operative mortality was not influenced by the type of cerebral protection (19% for ACP, 14.5% for RCP and 19.1% for DHCA, P=0.729). In multivariable logistic regression analysis, hemodynamic instability [odds ratio (OR) =19.6, 95% confidence intervals (CI), 0.102–0.414, P<0.001] and CPB time >200 min(OR =4.7, 95% CI, 1.962–1.072, P=0.029) emerged as independent predictors of operative mortality. Actuarial 5-year survival was unchanged by cerebral protection modality (48.8% for ACP, 61.8% for RCP and 66.8% for no cerebral protection, log-rank P=0.844). Conclusions During surgical repair of type A aortic dissection, ACP, RCP or DHCA are safe strategies for cerebral protection in selected patients with type A aortic dissection. PMID:27563545

Paraoxonase 1: a better atherosclerotic risk predictor than HDL in type 2 diabetes mellitus.

PubMed

Patra, Surajeet Kumar; Singh, Kamna; Singh, Ritu

2013-01-01

Type 2 diabetes mellitus is a state of glycative stress and oxidative stress. Lower level of serum PON 1 has been correlated to higher morbidity and mortality related to cardiovascular complications in type 2 diabetes mellitus. To estimate and compare the serum PON 1 levels in type 2 diabetes mellitus and controls and to predict which one is the better atherosclerotic risk predictor among HDL and PON 1 in T2DM patients. An observational analytical case-control study was conducted with a sample size of 30 in two groups like group I (30 cases of type 2 diabetes mellitus diagnosed by ADA 2010 criteria) and group II (30 age and sex matched controls). Human serum paroxonase 1 levels were measured by ELISA. Both HDL and PON 1 were negatively correlated with the various atherogenic indices (AIP, AC, CRI I, CRI II) but the strength of negative correlation is always greater for PON 1. In multiple linear regression analysis, we found that the regression coefficient (β) is always higher for PON 1 than for HDL while taking the atherogenic indices as outcome variable. PON 1 can be a better predictor than HDL for atherosclerotic risk in type 2 diabetes mellitus. Copyright © 2013 Diabetes India. Published by Elsevier Ltd. All rights reserved.

BAYESIAN LARGE-SCALE MULTIPLE REGRESSION WITH SUMMARY STATISTICS FROM GENOME-WIDE ASSOCIATION STUDIES1

PubMed Central

Zhu, Xiang; Stephens, Matthew

2017-01-01

Bayesian methods for large-scale multiple regression provide attractive approaches to the analysis of genome-wide association studies (GWAS). For example, they can estimate heritability of complex traits, allowing for both polygenic and sparse models; and by incorporating external genomic data into the priors, they can increase power and yield new biological insights. However, these methods require access to individual genotypes and phenotypes, which are often not easily available. Here we provide a framework for performing these analyses without individual-level data. Specifically, we introduce a “Regression with Summary Statistics” (RSS) likelihood, which relates the multiple regression coefficients to univariate regression results that are often easily available. The RSS likelihood requires estimates of correlations among covariates (SNPs), which also can be obtained from public databases. We perform Bayesian multiple regression analysis by combining the RSS likelihood with previously proposed prior distributions, sampling posteriors by Markov chain Monte Carlo. In a wide range of simulations RSS performs similarly to analyses using the individual data, both for estimating heritability and detecting associations. We apply RSS to a GWAS of human height that contains 253,288 individuals typed at 1.06 million SNPs, for which analyses of individual-level data are practically impossible. Estimates of heritability (52%) are consistent with, but more precise, than previous results using subsets of these data. We also identify many previously unreported loci that show evidence for association with height in our analyses. Software is available at https://github.com/stephenslab/rss. PMID:29399241

Norrie gene product is necessary for regression of hyaloid vessels.

PubMed

Ohlmann, Anne V; Adamek, Edith; Ohlmann, Andreas; Lütjen-Drecoll, Elke

2004-07-01

To investigate the nature and origin of the vitreous membranes in mice with knock-out of the Norrie gene product (ND mice). Eighty-two eyes of ND mice of different age groups (postnatal day [P]0-13 months) and 95 age-matched wild-type control mice were investigated. In vitreoretinal wholemounts and in sagittal sections, vessels and free cells were visualized by labeling for lectin. In addition, staining with a marker for macrophages (F4/80) and collagen XVIII/endostatin known to be involved in regression of hyaloid vessels was performed for light and electron microscopic investigations. Endostatin expression was confirmed by Western blot analysis. Wild-type controls showed the typical pattern of hyaloid vessels, their regression and concomitantly retinal vasculogenesis and angiogenesis. Hyaloid vessels all stained for endostatin, whereas retinal vessels remained unstained. In ND mice, 1 to 5 days after birth, the hyaloid and retinal vasculatures were comparable to that in control mice. The hyaloid vessels also stained for endostatin. Numerous F4/80-positive cells were present adjacent to the vessels. With increasing age, only a few connecting branches of the hyaloid vessels regressed. Even in old mice most of the hyaloid vessels persisted. The vessels still stained for endostatin. Retinal angiogenesis was impaired. Retrolental membranes in ND mice consist of persistent hyaloid vessels, indicating that the ND gene product is important for the process of regression of these vessels. The ND gene product neither influences endostatin expression nor the presence of macrophages.

Accuracy of magnetic resonance venography in diagnosing cerebral venous sinus thrombosis.

PubMed

Gao, Liansheng; Xu, Weilin; Li, Tao; Yu, Xiaobo; Cao, Shenglong; Xu, Hangzhe; Yan, Feng; Chen, Gao

2018-05-17

The non-specific clinical manifestations and lack of effective diagnostic techniques have made cerebral venous sinus thrombosis (CVST) difficult to recognize and easy to misdiagnose. Several studies have suggested that different types of magnetic resonance venography (MRV) have advantages in diagnosing CVST. We conducted this meta-analysis to assess the accuracy of MRV in identifying CVST. We searched the Embase, PubMed, and Chinese Biomedical (CBM) databases comprehensively to retrieve eligible articles up to Mar 31, 2018. The methodological quality of each article was evaluated individually. The summary diagnostic accuracy of MRV for CVST was obtained from pooled analysis with random-effects models. Sensitivity analysis, subgroup analysis, and meta-regression were used to explore the sources of heterogeneity. A trim and fill analysis was conducted to correct the funnel plot asymmetry. The meta-analysis synthesized 12 articles containing 27 cohorts with a total of 1933 cases. The pooled sensitivity and specificity were 0.86 (95% CI: 0.83, 0.89) and 0.94 (95% CI: 0.93, 0.95), respectively. The pooled diagnostic odds ratio (DOR) was 75.24 (95% CI: 38.33, 147.72). The area under the curve (AUC) was 0.9472 (95% CI: 0.9229, 0.9715). Subgroup analysis and meta-regression analysis revealed the technical types of MRV and the methods of counting cases contributing to the heterogeneity. The trim and fill method confirmed that publication bias has little effect on our results. MRV has excellent diagnostic performance and is accurate in confirming CVST. Copyright © 2018 Elsevier Ltd. All rights reserved.

Risk factors associated with cognitive decline in the elderly with type 2 diabetes: baseline data analysis of the Japanese Elderly Diabetes Intervention Trial.

PubMed

Umegaki, Hiroyuki; Iimuro, Satoshi; Shinozaki, Tomohiro; Araki, Atsushi; Sakurai, Takashi; Iijima, Katsuya; Ohashi, Yasuo; Ito, Hideki

2012-04-01

Recent evidence has shown that type 2 diabetes mellitus (T2DM) in the elderly is a risk factor for cognitive dysfunction or dementia. However, the precise mechanisms have not yet been elucidated. In the current study, we attempted to elucidate the association of clinical indices and diabetic complications at baseline with cognitive declines after 6-year follow up in type 2 diabetic elderly. The subjects were 261 participants who were administered the Mini-Mental State Examination (MMSE) at baseline and after 6 years, at the end of the observation period. The cognitive decline was determined as a 5-point or greater decline in MMSE scores during the observation period. Logistic regression analysis to find the factors associated with cognitive decline, adjusted for age and sex, were carried out, and factors with P-values of less than 0.2 were included in four models of multiple logistic regression analysis. We found that the existence of diabetic nephropathy, higher systolic blood pressure and higher serum triglycerides (or lower high-density lipoprotein cholesterol) at baseline were significantly associated with cognitive declines after 6 years in Japanese elderly diabetics in all four models. The comorbidity of diabetic nephropathy, hypertension and hypertriglyceridemia at baseline were associated with more than 5-point declines in MMSE. Elucidation of the underlying mechanisms of this association is warranted. © 2012 Japan Geriatrics Society.

The 300 most cited articles published in periodontology.

PubMed

Faggion, Clovis Mariano; Málaga, Lilian; Monje, Alberto; Trescher, Anna-Lena; Listl, Stefan; Alarcón, Marco Antonio

2017-07-01

It is important to evaluate the characteristics of the most cited articles in any specialty. The number of citations may be a proxy for clinical and research activity. The objectives of the present methodological study were (1) to report the characteristics of the 300 most cited articles in periodontology and (2) to explore the association of these characteristics with the number of citations. We searched in the Web of Science database for the 300 most cited articles published in periodontology on June 15, 2015. We described characteristics of the articles such as type of study, type of scientific journal, topic reported, year of publication, affiliation of the first author of the article, and impact factor. Linear regression analysis was used to investigate associations of these variables with the number of citations. The search retrieved approximately 155,356 publications; out of the studies that met the eligibility criteria, the 300 most cited were included for analysis. Comprising more than 50 % of the included articles, basic biology and the detection of bacteria were the most prevalent topics. Narrative reviews were the most frequent type of article (27 % of the sample). Regression analysis demonstrated that some characteristics, for example "narrative reviews," are more prone to be cited than others. We conclude that scientific evolution in periodontology has been based more on narrative reviews than on reproducible systematic reviews. Future research is encouraged to elucidate the extent to which scientific progress is improved through systematic compared with narrative reviews.

A fully traits-based approach to modeling global vegetation distribution.

PubMed

van Bodegom, Peter M; Douma, Jacob C; Verheijen, Lieneke M

2014-09-23

Dynamic Global Vegetation Models (DGVMs) are indispensable for our understanding of climate change impacts. The application of traits in DGVMs is increasingly refined. However, a comprehensive analysis of the direct impacts of trait variation on global vegetation distribution does not yet exist. Here, we present such analysis as proof of principle. We run regressions of trait observations for leaf mass per area, stem-specific density, and seed mass from a global database against multiple environmental drivers, making use of findings of global trait convergence. This analysis explained up to 52% of the global variation of traits. Global trait maps, generated by coupling the regression equations to gridded soil and climate maps, showed up to orders of magnitude variation in trait values. Subsequently, nine vegetation types were characterized by the trait combinations that they possess using Gaussian mixture density functions. The trait maps were input to these functions to determine global occurrence probabilities for each vegetation type. We prepared vegetation maps, assuming that the most probable (and thus, most suited) vegetation type at each location will be realized. This fully traits-based vegetation map predicted 42% of the observed vegetation distribution correctly. Our results indicate that a major proportion of the predictive ability of DGVMs with respect to vegetation distribution can be attained by three traits alone if traits like stem-specific density and seed mass are included. We envision that our traits-based approach, our observation-driven trait maps, and our vegetation maps may inspire a new generation of powerful traits-based DGVMs.

Health utility of patients with Crohn's disease and ulcerative colitis: a systematic review and meta-analysis.

PubMed

Malinowski, Krzysztof Piotr; Kawalec, Paweł

2016-08-01

The aim of this systematic review was to collect and summarize the current data on the utilities of patients with Crohn's disease (CD) and ulcerative colitis (UC). A meta-analysis of the obtained utilities was performed using a random-effects model and meta-regression by the disease type and severity. A bootstrap analysis was performed as it does not require assumption on distribution of the data. The highest utility among patients with CD and UC was observed when the diseases were in remission. The meta-regression analysis showed that both disease severity and an instrument/method/questionnaire used to obtain utilities were significant predictors of utility. Utility was the lowest for severe disease and the highest for disease in remission, the association was more notable in patients with CD compared with UC. Expert commentary: The issue of patients' utility is important for healthcare decision makers but it has not been fully investigated and requires further study.

Spatial analysis of relative humidity during ungauged periods in a mountainous region

NASA Astrophysics Data System (ADS)

Um, Myoung-Jin; Kim, Yeonjoo

2017-08-01

Although atmospheric humidity influences environmental and agricultural conditions, thereby influencing plant growth, human health, and air pollution, efforts to develop spatial maps of atmospheric humidity using statistical approaches have thus far been limited. This study therefore aims to develop statistical approaches for inferring the spatial distribution of relative humidity (RH) for a mountainous island, for which data are not uniformly available across the region. A multiple regression analysis based on various mathematical models was used to identify the optimal model for estimating monthly RH by incorporating not only temperature but also location and elevation. Based on the regression analysis, we extended the monthly RH data from weather stations to cover the ungauged periods when no RH observations were available. Then, two different types of station-based data, the observational data and the data extended via the regression model, were used to form grid-based data with a resolution of 100 m. The grid-based data that used the extended station-based data captured the increasing RH trend along an elevation gradient. Furthermore, annual RH values averaged over the regions were examined. Decreasing temporal trends were found in most cases, with magnitudes varying based on the season and region.

Contributions of sociodemographic factors to criminal behavior

PubMed Central

Mundia, Lawrence; Matzin, Rohani; Mahalle, Salwa; Hamid, Malai Hayati; Osman, Ratna Suriani

2016-01-01

We explored the extent to which prisoner sociodemographic variables (age, education, marital status, employment, and whether their parents were married or not) influenced offending in 64 randomly selected Brunei inmates, comprising both sexes. A quantitative field survey design ideal for the type of participants used in a prison context was employed to investigate the problem. Hierarchical multiple regression analysis with backward elimination identified prisoner marital status and age groups as significantly related to offending. Furthermore, hierarchical multinomial logistic regression analysis with backward elimination indicated that prisoners’ age, primary level education, marital status, employment status, and parental marital status as significantly related to stealing offenses with high odds ratios. All 29 nonrecidivists were false negatives and predicted to reoffend upon release. Similarly, all 33 recidivists were projected to reoffend after release. Hierarchical binary logistic regression analysis revealed age groups (24–29 years and 30–35 years), employed prisoner, and primary level education as variables with high likelihood trends for reoffending. The results suggested that prisoner interventions (educational, counseling, and psychotherapy) in Brunei should treat not only antisocial personality, psychopathy, and mental health problems but also sociodemographic factors. The study generated offending patterns, trends, and norms that may inform subsequent investigations on Brunei prisoners. PMID:27382342

Controlling Type I Error Rates in Assessing DIF for Logistic Regression Method Combined with SIBTEST Regression Correction Procedure and DIF-Free-Then-DIF Strategy

ERIC Educational Resources Information Center

Shih, Ching-Lin; Liu, Tien-Hsiang; Wang, Wen-Chung

2014-01-01

The simultaneous item bias test (SIBTEST) method regression procedure and the differential item functioning (DIF)-free-then-DIF strategy are applied to the logistic regression (LR) method simultaneously in this study. These procedures are used to adjust the effects of matching true score on observed score and to better control the Type I error…

Social Network Type and Subjective Well-being in a National Sample of Older Americans

PubMed Central

Litwin, Howard; Shiovitz-Ezra, Sharon

2011-01-01

Purpose: The study considers the social networks of older Americans, a population for whom there have been few studies of social network type. It also examines associations between network types and well-being indicators: loneliness, anxiety, and happiness. Design and Methods: A subsample of persons aged 65 years and older from the first wave of the National Social Life, Health, and Aging Project was employed (N = 1,462). We applied K-means cluster analysis to derive social network types using 7 criterion variables. In the multivariate stage, the well-being outcomes were regressed on the network type construct and on background and health characteristics by means of logistic regression. Results: Five social network types were derived: “diverse,” “friend,” “congregant,” “family,” and “restricted.” Social network type was found to be associated with each of the well-being indicators after adjusting for demographic and health confounders. Respondents embedded in network types characterized by greater social capital tended to exhibit better well-being in terms of less loneliness, less anxiety, and greater happiness. Implications: Knowledge about differing network types should make gerontological practitioners more aware of the varying interpersonal milieus in which older people function. Adopting network type assessment as an integral part of intake procedures and tracing network shifts over time can serve as a basis for risk assessment as well as a means for determining the efficacy of interventions. PMID:21097553

Adolescent build plotting on body composition chart and the type of diabetes mellitus.

PubMed

Park, Hye Won; Kim, Yong Hyuk; Cho, Myunghyun; Kwak, Byung Ok; Kim, Kyo Sun; Chung, Sochung

2012-11-01

Although the prevalence of type 2 diabetes is increasing, there are cases difficult to categorize into certain type in pediatric diabetic patients. The aims of this study were to detect and choose a proper treatment modality for atypical cases of diabetes mellitus, using the body composition chart. We conducted a retrospective study from August 2005 to 2012 with patients who visited Konkuk University Medical Center, and were diagnosed with diabetes mellitus. The medical records were reviewed for the anthropometric data and indices of body composition. The subjects were grouped by the type of diabetes and gender. We constructed a body composition chart plotting fat free mass index and fat mass index (FMI). Body mass index and all body composition indices were higher in type 2 diabetes, in each gender in analysis with Mann-Whitney test. Significant determinant of diabetes type was revealed as FMI and contributing factors on FMI were analyzed with regression analysis. Six atypical cases were identified by a body composition chart including non-obese type 2 diabetes showing suboptimal growth with lower BMI related to relatively lower insulin secretion and type 1 diabetes with insulin resistance resulted from obesity. Body composition chart analysis might be useful in characterization of diabetes type and detection of atypical cases and early adjustment of diabetes management strategy.

Old and New Ideas for Data Screening and Assumption Testing for Exploratory and Confirmatory Factor Analysis

PubMed Central

Flora, David B.; LaBrish, Cathy; Chalmers, R. Philip

2011-01-01

We provide a basic review of the data screening and assumption testing issues relevant to exploratory and confirmatory factor analysis along with practical advice for conducting analyses that are sensitive to these concerns. Historically, factor analysis was developed for explaining the relationships among many continuous test scores, which led to the expression of the common factor model as a multivariate linear regression model with observed, continuous variables serving as dependent variables, and unobserved factors as the independent, explanatory variables. Thus, we begin our paper with a review of the assumptions for the common factor model and data screening issues as they pertain to the factor analysis of continuous observed variables. In particular, we describe how principles from regression diagnostics also apply to factor analysis. Next, because modern applications of factor analysis frequently involve the analysis of the individual items from a single test or questionnaire, an important focus of this paper is the factor analysis of items. Although the traditional linear factor model is well-suited to the analysis of continuously distributed variables, commonly used item types, including Likert-type items, almost always produce dichotomous or ordered categorical variables. We describe how relationships among such items are often not well described by product-moment correlations, which has clear ramifications for the traditional linear factor analysis. An alternative, non-linear factor analysis using polychoric correlations has become more readily available to applied researchers and thus more popular. Consequently, we also review the assumptions and data-screening issues involved in this method. Throughout the paper, we demonstrate these procedures using an historic data set of nine cognitive ability variables. PMID:22403561

Glomerular structural-functional relationship models of diabetic nephropathy are robust in type 1 diabetic patients.

PubMed

Mauer, Michael; Caramori, Maria Luiza; Fioretto, Paola; Najafian, Behzad

2015-06-01

Studies of structural-functional relationships have improved understanding of the natural history of diabetic nephropathy (DN). However, in order to consider structural end points for clinical trials, the robustness of the resultant models needs to be verified. This study examined whether structural-functional relationship models derived from a large cohort of type 1 diabetic (T1D) patients with a wide range of renal function are robust. The predictability of models derived from multiple regression analysis and piecewise linear regression analysis was also compared. T1D patients (n = 161) with research renal biopsies were divided into two equal groups matched for albumin excretion rate (AER). Models to explain AER and glomerular filtration rate (GFR) by classical DN lesions in one group (T1D-model, or T1D-M) were applied to the other group (T1D-test, or T1D-T) and regression analyses were performed. T1D-M-derived models explained 70 and 63% of AER variance and 32 and 21% of GFR variance in T1D-M and T1D-T, respectively, supporting the substantial robustness of the models. Piecewise linear regression analyses substantially improved predictability of the models with 83% of AER variance and 66% of GFR variance explained by classical DN glomerular lesions alone. These studies demonstrate that DN structural-functional relationship models are robust, and if appropriate models are used, glomerular lesions alone explain a major proportion of AER and GFR variance in T1D patients. © The Author 2014. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

An ecological analysis of food outlet density and prevalence of type II diabetes in South Carolina counties.

PubMed

AlHasan, Dana M; Eberth, Jan Marie

2016-01-05

Studies suggest that the built environment with high numbers of fast food restaurants and convenience stores and low numbers of super stores and grocery stores are related to obesity, type II diabetes mellitus, and other chronic diseases. Since few studies assess these relationships at the county level, we aim to examine fast food restaurant density, convenience store density, super store density, and grocery store density and prevalence of type II diabetes among counties in South Carolina. Pearson's correlation between four types of food outlet densities- fast food restaurants, convenience stores, super stores, and grocery stores- and prevalence of type II diabetes were computed. The relationship between each of these food outlet densities were mapped with prevalence of type II diabetes, and OLS regression analysis was completed adjusting for county-level rates of obesity, physical inactivity, density of recreation facilities, unemployment, households with no car and limited access to stores, education, and race. We showed a significant, negative relationship between fast food restaurant density and prevalence of type II diabetes, and a significant, positive relationship between convenience store density and prevalence of type II diabetes. In adjusted analysis, the food outlet densities (of any type) was not associated with prevalence of type II diabetes. This ecological analysis showed no associations between fast food restaurants, convenience stores, super stores, or grocery stores densities and the prevalence of type II diabetes. Consideration of environmental, social, and cultural determinants, as well as individual behaviors is needed in future research.

Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts.

PubMed

Iorgulescu, E; Voicu, V A; Sârbu, C; Tache, F; Albu, F; Medvedovici, A

2016-08-01

The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA. Copyright © 2016 Elsevier B.V. All rights reserved.

Evaluation of pharyngeal space and its correlation with mandible and hyoid bone in patients with different skeletal classes and facial types.

PubMed

Nejaim, Yuri; Aps, Johan K M; Groppo, Francisco Carlos; Haiter Neto, Francisco

2018-06-01

The purpose of this article was to evaluate the pharyngeal space volume, and the size and shape of the mandible and the hyoid bone, as well as their relationships, in patients with different facial types and skeletal classes. Furthermore, we estimated the volume of the pharyngeal space with a formula using only linear measurements. A total of 161 i-CAT Next Generation (Imaging Sciences International, Hatfield, Pa) cone-beam computed tomography images (80 men, 81 women; ages, 21-58 years; mean age, 27 years) were retrospectively studied. Skeletal class and facial type were determined for each patient from multiplanar reconstructions using the NemoCeph software (Nemotec, Madrid, Spain). Linear and angular measurements were performed using 3D imaging software (version 3.4.3; Carestream Health, Rochester, NY), and volumetric analysis of the pharyngeal space was carried out with ITK-SNAP (version 2.4.0; Cognitica, Philadelphia, Pa) segmentation software. For the statistics, analysis of variance and the Tukey test with a significance level of 0.05, Pearson correlation, and linear regression were used. The pharyngeal space volume, when correlated with mandible and hyoid bone linear and angular measurements, showed significant correlations with skeletal class or facial type. The linear regression performed to estimate the volume of the pharyngeal space showed an R of 0.92 and an adjusted R 2 of 0.8362. There were significant correlations between pharyngeal space volume, and the mandible and hyoid bone measurements, suggesting that the stomatognathic system should be evaluated in an integral and nonindividualized way. Furthermore, it was possible to develop a linear regression model, resulting in a useful formula for estimating the volume of the pharyngeal space. Copyright © 2018 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

Hip fractures are risky business: an analysis of the NSQIP data.

PubMed

Sathiyakumar, Vasanth; Greenberg, Sarah E; Molina, Cesar S; Thakore, Rachel V; Obremskey, William T; Sethi, Manish K

2015-04-01

Hip fractures are one of the most common types of orthopaedic injury with high rates of morbidity. Currently, no study has compared risk factors and adverse events following the different types of hip fracture surgeries. The purpose of this paper is to investigate the major and minor adverse events and risk factors for complication development associated with five common surgeries for the treatment of hip fractures using the NSQIP database. Using the ACS-NSQIP database, complications for five forms of hip surgeries were selected and categorized into major and minor adverse events. Demographics and clinical variables were collected and an unadjusted bivariate logistic regression analyses was performed to determine significant risk factors for adverse events. Five multivariate regressions were run for each surgery as well as a combined regression analysis. A total of 9640 patients undergoing surgery for hip fracture were identified with an adverse events rate of 25.2% (n=2433). Open reduction and internal fixation of a femoral neck fracture had the greatest percentage of all major events (16.6%) and total adverse events (27.4%), whereas partial hip hemiarthroplasty had the greatest percentage of all minor events (11.6%). Mortality was the most common major adverse event (44.9-50.6%). For minor complications, urinary tract infections were the most common minor adverse event (52.7-62.6%). Significant risk factors for development of any adverse event included age, BMI, gender, race, active smoking status, history of COPD, history of CHF, ASA score, dyspnoea, and functional status, with various combinations of these factors significantly affecting complication development for the individual surgeries. Hip fractures are associated with significantly high numbers of adverse events. The type of surgery affects the type of complications developed and also has an effect on what risk factors significantly predict the development of a complication. Concerted efforts from orthopaedists should be made to identify higher risk patients and prevent the most common adverse events that occur postoperatively. Copyright © 2014 Elsevier Ltd. All rights reserved.

Costs of hospitalization for stroke patients aged 18-64 years in the United States.

PubMed

Wang, Guijing; Zhang, Zefeng; Ayala, Carma; Dunet, Diane O; Fang, Jing; George, Mary G

2014-01-01

Estimates for the average cost of stroke have varied 20-fold in the United States. To provide a robust cost estimate, we conducted a comprehensive analysis of the hospitalization costs for stroke patients by diagnosis status and event type. Using the 2006-2008 MarketScan inpatient database, we identified 97,374 hospitalizations with a primary or secondary diagnosis of stroke. We analyzed the costs after stratifying the hospitalizations by stroke type (hemorrhagic, ischemic, and other strokes) and diagnosis status (primary and secondary). We employed regressions to estimate the impact of event type and diagnosis status on costs while controlling for major potential confounders. Among the 97,374 hospitalizations (average cost: $20,396 ± $23,256), the number with ischemic, hemorrhagic, or other strokes was 62,637, 16,331, and 48,208, respectively, with these types having average costs, in turn, of $18,963 ± $21,454, $32,035 ± $32,046, and $19,248 ± $21,703. A majority (62%) of the hospitalizations had stroke listed as a secondary diagnosis only. Regression analysis found that, overall, hemorrhagic stroke cost $14,499 more than ischemic stroke (P < .001). For hospitalizations with a primary diagnosis of ischemic stroke, those with a secondary diagnosis of ischemic heart disease (IHD) had costs that were $9836 higher (P < .001) than those without IHD. The costs of hospitalizations involving stroke are high and vary greatly by type of stroke, diagnosis status, and comorbidities. These findings should be incorporated into cost-effective strategies to reduce the impact of stroke. Published by Elsevier Inc.

Multivariate Boosting for Integrative Analysis of High-Dimensional Cancer Genomic Data

PubMed Central

Xiong, Lie; Kuan, Pei-Fen; Tian, Jianan; Keles, Sunduz; Wang, Sijian

2015-01-01

In this paper, we propose a novel multivariate component-wise boosting method for fitting multivariate response regression models under the high-dimension, low sample size setting. Our method is motivated by modeling the association among different biological molecules based on multiple types of high-dimensional genomic data. Particularly, we are interested in two applications: studying the influence of DNA copy number alterations on RNA transcript levels and investigating the association between DNA methylation and gene expression. For this purpose, we model the dependence of the RNA expression levels on DNA copy number alterations and the dependence of gene expression on DNA methylation through multivariate regression models and utilize boosting-type method to handle the high dimensionality as well as model the possible nonlinear associations. The performance of the proposed method is demonstrated through simulation studies. Finally, our multivariate boosting method is applied to two breast cancer studies. PMID:26609213

Does raising type 1 error rate improve power to detect interactions in linear regression models? A simulation study.

PubMed

Durand, Casey P

2013-01-01

Statistical interactions are a common component of data analysis across a broad range of scientific disciplines. However, the statistical power to detect interactions is often undesirably low. One solution is to elevate the Type 1 error rate so that important interactions are not missed in a low power situation. To date, no study has quantified the effects of this practice on power in a linear regression model. A Monte Carlo simulation study was performed. A continuous dependent variable was specified, along with three types of interactions: continuous variable by continuous variable; continuous by dichotomous; and dichotomous by dichotomous. For each of the three scenarios, the interaction effect sizes, sample sizes, and Type 1 error rate were varied, resulting in a total of 240 unique simulations. In general, power to detect the interaction effect was either so low or so high at α = 0.05 that raising the Type 1 error rate only served to increase the probability of including a spurious interaction in the model. A small number of scenarios were identified in which an elevated Type 1 error rate may be justified. Routinely elevating Type 1 error rate when testing interaction effects is not an advisable practice. Researchers are best served by positing interaction effects a priori and accounting for them when conducting sample size calculations.

Willingness to Exchange Health Information via Mobile Devices: Findings From a Population-Based Survey.

PubMed

Serrano, Katrina J; Yu, Mandi; Riley, William T; Patel, Vaishali; Hughes, Penelope; Marchesini, Kathryn; Atienza, Audie A

2016-01-01

The rapid proliferation of mobile devices offers unprecedented opportunities for patients and health care professionals to exchange health information electronically, but little is known about patients' willingness to exchange various types of health information using these devices. We examined willingness to exchange different types of health information via mobile devices, and assessed whether sociodemographic characteristics and trust in clinicians were associated with willingness in a nationally representative sample. We analyzed data for 3,165 patients captured in the 2013 Health Information National Trends Survey. Multinomial logistic regression analysis was conducted to test differences in willingness. Ordinal logistic regression analysis assessed correlates of willingness to exchange 9 types of information separately. Participants were very willing to exchange appointment reminders (odds ratio [OR] = 6.66; 95% CI, 5.68-7.81), general health tips (OR = 2.03; 95% CI, 1.74-2.38), medication reminders (OR = 2.73; 95% CI, 2.35-3.19), laboratory/test results (OR = 1.76; 95% CI, 1.62-1.92), vital signs (OR = 1.63; 95% CI, 1.48-1.80), lifestyle behaviors (OR = 1.40; 95% CI, 1.24-1.58), and symptoms (OR = 1.62; 95% CI, 1.46-1.79) as compared with diagnostic information. Older adults had lower odds of being more willing to exchange any type of information. Education, income, and trust in health care professional information correlated with willingness to exchange certain types of information. Respondents were less willing to exchange via mobile devices information that may be considered sensitive or complex. Age, socioeconomic factors, and trust in professional information were associated with willingness to engage in mobile health information exchange. Both information type and demographic group should be considered when developing and tailoring mobile technologies for patient-clinician communication. © 2016 Annals of Family Medicine, Inc.

The antagonistic effect between STAT1 and Survivin and its clinical significance in gastric cancer.

PubMed

Deng, Hao; Zhen, Hongyan; Fu, Zhengqi; Huang, Xuan; Zhou, Hongyan; Liu, Lijiang

2012-01-01

In previous studies, we observed that STAT1 and Survivin correlated negatively with gastric cancer tissues, and that the functions of the IFN-γ-STAT1 pathway and Survivin in gastric cancer are the same as those reported for other types of cancer. In this study, the SGC7901 gastric cancer cell line and 83 gastric cancer specimens were used to confirm the relationship between STAT1 and Survivin, as well as the clinical significance of this relationship in gastric cancer. IFN-γ and STAT1 and Survivin antisense oligonucleotides (ASONs) were used to knock down the expression in SGC7901 cells. The protein expression of STAT1 and Survivin was tested by immunocytochemical and image analysis methods. A gastric cancer tissue microarray was prepared and tested by immunohistochemical methods. Data were analyzed by the Spearman's rank correlation analysis, the χ(2) test and Cox's multivariate regression analysis. Upon knockdown of IFN-γ, STAT1 and Survivin expression by ASON in the SGC7901 cell line, an antagonistic effect was observed between STAT1 and Survivin. In gastric cancer tissues, STAT1 showed a negative correlation with depth of invasion (p<0.05) in gastric cancer tissues exhibiting a negative Survivin protein expression. Furthermore, in tissues exhibiting a negative STAT1 protein expression, Survivin correlated negatively with N stage (p<0.05). Pathological and molecular markers were used to conduct Cox's multivariate regression analysis, and depth of invasion and N stage were found to be prognostic factors (p<0.05). On the other hand, in tissues exhibiting a negative Survivin protein expression, Cox's multivariate regression analysis revealed that the differentiation type and STAT1 protein expression were prognostic factors (p<0.05). There is an antagonistic effect between STAT1 and Survivin in gastric cancer, and this antagonistic effect is of clinical significance in gastric cancer.

Potential redistribution of tree species habitat under five climate change scenarios in the eastern US

Treesearch

Louis R. Iverson; Anantha M. Prasad; Anantha M. Prasad

2002-01-01

Global climate change could have profound effects on the Earth's biota, including large redistributions of tree species and forest types. We used DISTRIB, a deterministic regression tree analysis model, to examine environmental drivers related to current forest-species distributions and then model potential suitable habitat under five climate change scenarios...

Cenesthopathy and Subjective Cognitive Complaints: An Exploratory Study in Schizophrenia.

PubMed

Jimeno, Natalia; Vargas, Martin L

2018-01-01

Cenesthopathy is mainly associated with schizophrenia; however, its neurobiological basis is nowadays unclear. The general objective was to explore clinical correlates of cenesthopathy and subjective cognitive complaints in schizophrenia. Participants (n = 30) meeting DSM-IV criteria for psychotic disorder were recruited from a psychiatry unit and assessed with: Association for Methodology and Documentation in Psychiatry (AMDP) system, Positive and Negative Syndrome Scale, Frankfurt Complaint Questionnaire (FCQ), and the Bonn Scale for the Assessment of Basic Symptoms (BSABS). For quantitative variables, means and Spearman correlation coefficients were calculated. Linear regression following backward method and principal component analysis with varimax rotation were used. 83.3% of subjects (73.3% male, mean age, 31.5 years) presented any type of cenesthopathy; all types of cenesthetic basic symptoms were found. Cenesthetic basic symptoms significantly correlated with the AMDP category "fear and anancasm," FCQ total score, and BSABS cognitive thought disturbances. In the regression analysis only 1 predictor, cognitive thought disturbances, entered the model. In the principal component analysis, a main component which accounted for 22.69% of the variance was found. Cenesthopathy, as assessed with the Bonn Scale (BSABS), is mainly associated with cog-nitive abnormalities including disturbances of thought initiative and mental intentionality, of receptive speech, and subjective retardation or pressure of thoughts. © 2018 S. Karger AG, Basel.

Evaluation of functional outcome of the floating knee injury using multivariate analysis.

PubMed

Yokoyama, Kazuhiko; Tsukamoto, Tatsuro; Aoki, Shinichi; Wakita, Ryuji; Uchino, Masataka; Noumi, Takashi; Fukushima, Nobuaki; Itoman, Moritoshi

2002-11-01

The objective of this study is to evaluate significant contributing factors affecting the functional prognosis of floating knee injuries using multivariate analysis. A total of 68 floating knee injuries (67 patients) were treated at Kitasato University Hospital from 1986 to 1999. Both the femoral fractures and the tibial fractures were managed surgically by various methods. The functional results of these injuries were evaluated using the grading system of Karlström and Olerud. Follow-up periods ranged from 2 to 19 years (mean 50.2 months) after the original injury. We defined satisfactory (S) outcomes as those cases with excellent or good results and unsatisfactory (US) outcomes as those cases with acceptable or poor results. Logistic regression analysis was used as a multivariate analysis, and the dependent variables were defined as a satisfactory outcome or as an unsatisfactory outcome. The explanatory variables were predicting factors influencing the functional outcome such as age at trauma, gender, severity of soft-tissue injury in the femur and the tibia, AO fracture grade in the femur and the tibia, Fraser type (type I or type II), Injury Severity Score (ISS), and fixation time after injury (less than 1 week or more than 1 week) in the femur and the tibia. The final functional results were as follows: 25 cases had excellent results, 15 cases good results, 16 cases acceptable results, and 12 cases poor results. The predictive logistic regression equation was as follows: Log 1-p/p = 3.12-1.52 x Fraser type - 1.65 x severity of soft-tissue injury in the tibia - 1.31 x fixation time after injury in the tibia - 0.821 x AO fracture grade in the tibia + 1.025 x fixation time after injury in the femur - 0.687 x AO fracture grade in the femur ( p=0.01). Among the variables, Fraser type and the severity of soft-tissue injury in the tibia were significantly related to the final result. The multivariate analysis showed that both the involvement of the knee joint and the severity grade of soft-tissue injury in the tibia represented significant risk factors of poor outcome in floating knee injuries in this study.

A Meta-Analysis of the Relative Risk of Mortality for Type 1 Diabetes Patients Compared to the General Population: Exploring Temporal Changes in Relative Mortality

PubMed Central

Lung, Tom W. C.; Hayes, Alison J.; Herman, William H.; Si, Lei; Palmer, Andrew J.; Clarke, Philip M.

2014-01-01

Aims Type 1 diabetes has been associated with an elevated relative risk (RR) of mortality compared to the general population. To review published studies on the RR of mortality of Type 1 diabetes patients compared to the general population, we conducted a meta-analysis and examined the temporal changes in the RR of mortality over time. Methods Systematic review of studies reporting RR of mortality for Type 1 diabetes compared to the general population. We conducted meta-analyses using a DerSimonian and Laird random effects model to obtain the average effect and the distribution of RR estimates. Sub-group meta-analyses and multivariate meta-regression analysis was performed to examine heterogeneity. Summary RR with 95% CIs was calculated using a random-effects model. Results 26 studies with a total of 88 subpopulations were included in the meta-analysis and overall RR of mortality was 3.82 (95% CI 3.41, 3.4.29) compared to the general population. Observations using data prior to 1971 had a much larger estimated RR (5.80 (95% CI 4.20, 8.01)) when compared to: data between; 1971 and 1980 (5.06 (95% CI 3.44, 7.45)); 1981–90 (3.59 (95% CI 3.15, 4.09)); and those after 1990 (3.11 (95% CI 2.47, 3.91)); suggesting mortality of Type 1 diabetes patients when compared to the general population have been improving over time. Similarly, females (4.54 (95% CI 3.79–5.45)) had a larger RR estimate when compared to males (3.25 (95% CI 2.82–3.73) and the meta-regression found evidence for temporal trends and sex (p<0.01) accounting for heterogeneity between studies. Conclusions Type 1 diabetes patients’ mortality has declined at a faster rate than the general population. However, the largest relative improvements have occurred prior to 1990. Emphasis on intensive blood glucose control alongside blood pressure control and statin therapy may translate into further reductions in mortality in coming years. PMID:25426948

Nocturnal hypoglycaemia in Type 1 diabetic patients, assessed with continuous glucose monitoring: frequency, duration and associations.

PubMed

Wentholt, I M E; Maran, A; Masurel, N; Heine, R J; Hoekstra, J B L; DeVries, J H

2007-05-01

We quantified the occurrence and duration of nocturnal hypoglycaemia in individuals with Type 1 diabetes treated with continuous subcutaneous insulin infusion (CSII) or multiple-injection therapy (MIT) using a continuous subcutaneous glucose sensor. A microdialysis sensor was worn at home by 24 patients on CSII (mean HbA(1c) 7.8 +/- 0.9%) and 33 patients on MIT (HbA(1c) 8.7 +/- 1.3%) for 48 h. Occurrence and duration of nocturnal hypoglycaemia were assessed and using multivariate regression analysis, the association between HbA(1c), diabetes duration, treatment type (CSII vs. MIT), fasting and bedtime blood glucose values, total daily insulin dose and mean nocturnal glucose concentrations, and hypoglycaemia occurrence and duration was investigated. Nocturnal hypoglycaemia < or = 3.9 mmol/l occurred in 33.3% of both the CSII- (8/24) and MIT-treated patients (11/33). Mean (+/- sd; median, interquartile range) duration of hypoglycaemia < or = 3.9 mmol/l was 78 (+/- 76; 57, 23-120) min per night for the CSII- and 98 (+/- 80; 81, 32-158) min per night for the MIT-treated group. Multivariate regression analysis showed that bedtime glucose value had the strongest association with the occurrence (P = 0.026) and duration (P = 0.032) of nocturnal hypoglycaemia. Microdialysis continuous glucose monitoring has enabled more precise quantification of nocturnal hypoglycaemia occurrence and duration in Type 1 diabetic patients. Occurrence and duration of nocturnal hypoglycaemia were mainly associated with bedtime glucose value.

Developing a predictive tropospheric ozone model for Tabriz

NASA Astrophysics Data System (ADS)

Khatibi, Rahman; Naghipour, Leila; Ghorbani, Mohammad A.; Smith, Michael S.; Karimi, Vahid; Farhoudi, Reza; Delafrouz, Hadi; Arvanaghi, Hadi

2013-04-01

Predictive ozone models are becoming indispensable tools by providing a capability for pollution alerts to serve people who are vulnerable to the risks. We have developed a tropospheric ozone prediction capability for Tabriz, Iran, by using the following five modeling strategies: three regression-type methods: Multiple Linear Regression (MLR), Artificial Neural Networks (ANNs), and Gene Expression Programming (GEP); and two auto-regression-type models: Nonlinear Local Prediction (NLP) to implement chaos theory and Auto-Regressive Integrated Moving Average (ARIMA) models. The regression-type modeling strategies explain the data in terms of: temperature, solar radiation, dew point temperature, and wind speed, by regressing present ozone values to their past values. The ozone time series are available at various time intervals, including hourly intervals, from August 2010 to March 2011. The results for MLR, ANN and GEP models are not overly good but those produced by NLP and ARIMA are promising for the establishing a forecasting capability.

Clinical, biomarker, and genetic predictors of specific types of atrial fibrillation in a community-based cohort: data of the PREVEND study.

PubMed

Hobbelt, Anne H; Siland, Joylene E; Geelhoed, Bastiaan; Van Der Harst, Pim; Hillege, Hans L; Van Gelder, Isabelle C; Rienstra, Michiel

2017-02-01

Atrial fibrillation (AF) may present variously in time, and AF may progress from self-terminating to non-self-terminating AF, and is associated with impaired prognosis. However, predictors of AF types are largely unexplored. We investigate the clinical, biomarker, and genetic predictors of development of specific types of AF in a community-based cohort. We included 8042 individuals (319 with incident AF) of the PREVEND study. Types of AF were compared, and multivariate multinomial regression analysis determined associations with specific types of AF. Mean age was 48.5 ± 12.4 years and 50% were men. The types of incident AF were ascertained based on electrocardiograms; 103(32%) were classified as AF without 2-year recurrence, 158(50%) as self-terminating AF, and 58(18%) as non-self-terminating AF. With multivariate multinomial logistic regression analysis, advancing age (P< 0.001 for all three types) was associated with all AF types, male sex was associated with AF without 2-year recurrence and self-terminating AF (P= 0.031 and P= 0.008, respectively). Increasing body mass index and MR-proANP were associated with both self-terminating (P= 0.009 and P< 0.001) and non-self-terminating AF (P= 0.003 and P< 0.001). The only predictor associated with solely self-terminating AF is prescribed anti-hypertensive treatment (P= 0.019). The following predictors were associated with non-self-terminating AF; lower heart rate (P= 0.018), lipid-lowering treatment prescribed (P= 0.009), and eGFR <60 mL/min/1.73 m2 (P= 0.006). Three known AF-genetic variants (rs6666258, rs6817105, and rs10821415) were associated with self-terminating AF. We found clinical, biomarker and genetic predictors of specific types of incident AF in a community-based cohort. The genetic background seems to play a more important role than modifiable risk factors in self-terminating AF.

A Comprehensive review of group level model performance in the presence of heteroscedasticity: Can a single model control Type I errors in the presence of outliers?

PubMed Central

Mumford, Jeanette A.

2017-01-01

Even after thorough preprocessing and a careful time series analysis of functional magnetic resonance imaging (fMRI) data, artifact and other issues can lead to violations of the assumption that the variance is constant across subjects in the group level model. This is especially concerning when modeling a continuous covariate at the group level, as the slope is easily biased by outliers. Various models have been proposed to deal with outliers including models that use the first level variance or that use the group level residual magnitude to differentially weight subjects. The most typically used robust regression, implementing a robust estimator of the regression slope, has been previously studied in the context of fMRI studies and was found to perform well in some scenarios, but a loss of Type I error control can occur for some outlier settings. A second type of robust regression using a heteroscedastic autocorrelation consistent (HAC) estimator, which produces robust slope and variance estimates has been shown to perform well, with better Type I error control, but with large sample sizes (500–1000 subjects). The Type I error control with smaller sample sizes has not been studied in this model and has not been compared to other modeling approaches that handle outliers such as FSL’s Flame 1 and FSL’s outlier de-weighting. Focusing on group level inference with a continuous covariate over a range of sample sizes and degree of heteroscedasticity, which can be driven either by the within- or between-subject variability, both styles of robust regression are compared to ordinary least squares (OLS), FSL’s Flame 1, Flame 1 with outlier de-weighting algorithm and Kendall’s Tau. Additionally, subject omission using the Cook’s Distance measure with OLS and nonparametric inference with the OLS statistic are studied. Pros and cons of these models as well as general strategies for detecting outliers in data and taking precaution to avoid inflated Type I error rates are discussed. PMID:28030782

Effects of greening and community reuse of vacant lots on crime

PubMed Central

Kondo, Michelle; Hohl, Bernadette; Han, SeungHoon; Branas, Charles

2016-01-01

The Youngstown Neighborhood Development Corporation initiated a ‘Lots of Green’ programme to reuse vacant land in 2010. We performed a difference-in-differences analysis of the effects of this programme on crime in and around newly treated lots, in comparison to crimes in and around randomly selected and matched, untreated vacant lot controls. The effects of two types of vacant lot treatments on crime were tested: a cleaning and greening ‘stabilisation’ treatment and a ‘community reuse’ treatment mostly involving community gardens. The combined effects of both types of vacant lot treatments were also tested. After adjustment for various sociodemographic factors, linear and Poisson regression models demonstrated statistically significant reductions in all crime classes for at least one lot treatment type. Regression models adjusted for spatial autocorrelation found the most consistent significant reductions in burglaries around stabilisation lots, and in assaults around community reuse lots. Spill-over crime reduction effects were found in contiguous areas around newly treated lots. Significant increases in motor vehicle thefts around both types of lots were also found after they had been greened. Community-initiated vacant lot greening may have a greater impact on reducing more serious, violent crimes. PMID:28529389

Robust logistic regression to narrow down the winner's curse for rare and recessive susceptibility variants.

PubMed

Kesselmeier, Miriam; Lorenzo Bermejo, Justo

2017-11-01

Logistic regression is the most common technique used for genetic case-control association studies. A disadvantage of standard maximum likelihood estimators of the genotype relative risk (GRR) is their strong dependence on outlier subjects, for example, patients diagnosed at unusually young age. Robust methods are available to constrain outlier influence, but they are scarcely used in genetic studies. This article provides a non-intimidating introduction to robust logistic regression, and investigates its benefits and limitations in genetic association studies. We applied the bounded Huber and extended the R package 'robustbase' with the re-descending Hampel functions to down-weight outlier influence. Computer simulations were carried out to assess the type I error rate, mean squared error (MSE) and statistical power according to major characteristics of the genetic study and investigated markers. Simulations were complemented with the analysis of real data. Both standard and robust estimation controlled type I error rates. Standard logistic regression showed the highest power but standard GRR estimates also showed the largest bias and MSE, in particular for associated rare and recessive variants. For illustration, a recessive variant with a true GRR=6.32 and a minor allele frequency=0.05 investigated in a 1000 case/1000 control study by standard logistic regression resulted in power=0.60 and MSE=16.5. The corresponding figures for Huber-based estimation were power=0.51 and MSE=0.53. Overall, Hampel- and Huber-based GRR estimates did not differ much. Robust logistic regression may represent a valuable alternative to standard maximum likelihood estimation when the focus lies on risk prediction rather than identification of susceptibility variants. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Evaluation of Denoising Strategies to Address Motion-Correlated Artifacts in Resting-State Functional Magnetic Resonance Imaging Data from the Human Connectome Project

PubMed Central

Kandala, Sridhar; Nolan, Dan; Laumann, Timothy O.; Power, Jonathan D.; Adeyemo, Babatunde; Harms, Michael P.; Petersen, Steven E.; Barch, Deanna M.

2016-01-01

Abstract Like all resting-state functional connectivity data, the data from the Human Connectome Project (HCP) are adversely affected by structured noise artifacts arising from head motion and physiological processes. Functional connectivity estimates (Pearson's correlation coefficients) were inflated for high-motion time points and for high-motion participants. This inflation occurred across the brain, suggesting the presence of globally distributed artifacts. The degree of inflation was further increased for connections between nearby regions compared with distant regions, suggesting the presence of distance-dependent spatially specific artifacts. We evaluated several denoising methods: censoring high-motion time points, motion regression, the FMRIB independent component analysis-based X-noiseifier (FIX), and mean grayordinate time series regression (MGTR; as a proxy for global signal regression). The results suggest that FIX denoising reduced both types of artifacts, but left substantial global artifacts behind. MGTR significantly reduced global artifacts, but left substantial spatially specific artifacts behind. Censoring high-motion time points resulted in a small reduction of distance-dependent and global artifacts, eliminating neither type. All denoising strategies left differences between high- and low-motion participants, but only MGTR substantially reduced those differences. Ultimately, functional connectivity estimates from HCP data showed spatially specific and globally distributed artifacts, and the most effective approach to address both types of motion-correlated artifacts was a combination of FIX and MGTR. PMID:27571276

Acupuncture for musculoskeletal pain: A meta-analysis and meta-regression of sham-controlled randomized clinical trials

PubMed Central

Yuan, Qi-ling; Wang, Peng; Liu, Liang; Sun, Fu; Cai, Yong-song; Wu, Wen-tao; Ye, Mao-lin; Ma, Jiang-tao; Xu, Bang-bang; Zhang, Yin-gang

2016-01-01

The aims of this systematic review were to study the analgesic effect of real acupuncture and to explore whether sham acupuncture (SA) type is related to the estimated effect of real acupuncture for musculoskeletal pain. Five databases were searched. The outcome was pain or disability immediately (≤1 week) following an intervention. Standardized mean differences (SMDs) with 95% confidence intervals were calculated. Meta-regression was used to explore possible sources of heterogeneity. Sixty-three studies (6382 individuals) were included. Eight condition types were included. The pooled effect size was moderate for pain relief (59 trials, 4980 individuals, SMD −0.61, 95% CI −0.76 to −0.47; P < 0.001) and large for disability improvement (31 trials, 4876 individuals, −0.77, −1.05 to −0.49; P < 0.001). In a univariate meta-regression model, sham needle location and/or depth could explain most or all heterogeneities for some conditions (e.g., shoulder pain, low back pain, osteoarthritis, myofascial pain, and fibromyalgia); however, the interactions between subgroups via these covariates were not significant (P < 0.05). Our review provided low-quality evidence that real acupuncture has a moderate effect (approximate 12-point reduction on the 100-mm visual analogue scale) on musculoskeletal pain. SA type did not appear to be related to the estimated effect of real acupuncture. PMID:27471137

Using regression analysis to predict emergency patient volume at the Indianapolis 500 mile race.

PubMed

Bowdish, G E; Cordell, W H; Bock, H C; Vukov, L F

1992-10-01

Emergency physicians often plan and provide on-site medical care for mass gatherings. Most of the mass gathering literature is descriptive. Only a few studies have looked at factors such as crowd size, event characteristics, or weather in predicting numbers and types of patients at mass gatherings. We used regression analysis to relate patient volume on Race Day at the Indianapolis Motor Speedway to weather conditions and race characteristics. Race Day weather data for the years 1983 to 1989 were obtained from the National Oceanic and Atmospheric Administration. Data regarding patients treated on 1983 to 1989 Race Days were obtained from the facility hospital (Hannah Emergency Medical Center) data base. Regression analysis was performed using weather factors and race characteristics as independent variables and number of patients seen as the dependent variable. Data from 1990 were used to test the validity of the model. There was a significant relationship between dew point (which is calculated from temperature and humidity) and patient load (P less than .01). Dew point, however, failed to predict patient load during the 1990 race. No relationships could be established between humidity, sunshine, wind, or race characteristics and number of patients. Although higher dew point was associated with higher patient load during the 1983 to 1989 races, dew point was a poor predictor of patient load during the 1990 race. Regression analysis may be useful in identifying relationships between event characteristics and patient load but is probably inadequate to explain the complexities of crowd behavior and too simplified to use as a prediction tool.

Spatial Bayesian Latent Factor Regression Modeling of Coordinate-based Meta-analysis Data

PubMed Central

Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D.; Nichols, Thomas E.

2017-01-01

Summary Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the paper are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to 1) identify areas of consistent activation; and 2) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterised as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. PMID:28498564

Comparing nouns and verbs in a lexical task.

PubMed

Cordier, Françoise; Croizet, Jean-Claude; Rigalleau, François

2013-02-01

We analyzed the differential processing of nouns and verbs in a lexical decision task. Moderate and high-frequency nouns and verbs were compared. The characteristics of our material were specified at the formal level (number of letters and syllables, number of homographs, orthographic neighbors, frequency and age of acquisition), and at the semantic level (imagery, number and strength of associations, number of meanings, context dependency). A regression analysis indicated a classical frequency effect and a word-type effect, with latencies for verbs being slower than for nouns. The regression analysis did not permit the conclusion that semantic effects were involved (particularly imageability). Nevertheless, the semantic opposition between nouns as prototypical representations of objects, and verbs as prototypical representation of actions was not tested in this experiment and remains a good candidate explanation of the response time discrepancies between verbs and nouns.

Prevalence of overactive bladder and its impact on quality of life in 1025 patients with type 2 diabetes in mainland China.

PubMed

Xu, Dongjuan; Gao, Jie; Wang, Xiaojuan; Huang, Liqun; Wang, Kefang

2017-08-01

This study examined the prevalence of overactive bladder (OAB) and investigated the impact of OAB on quality of life (QOL) in patients with type 2 diabetes in Mainland China. A total of 1025 patients with type 2 diabetes were surveyed. Patients were grouped into no OAB, dry OAB, and wet OAB groups according to the presence of OAB and urge incontinence. Descriptive analyses, one-way analysis of variance (ANOVA) and multivariable regression models were conducted to assess the prevalence of OAB and the effect of OAB on QOL. The prevalence of OAB among patients with type 2 diabetes was 13.9% (with dry OAB, 6.1%; with wet OAB, 7.8%). Multivariable regression models showed that OAB symptoms caused significant deterioration of the physical and mental aspects of QOL. Compared with dry OAB, wet OAB further decreased the mental aspect of QOL. Moreover, the effect sizes of the impacts of dry and wet OAB on QOL were larger than those of diabetic neuropathy or retinopathy, diabetes duration, or urinary tract infection history. OAB is more common in patients with type 2 diabetes than in the general population and substantially decreases patient QOL. Copyright © 2017 Elsevier Inc. All rights reserved.

Linear and non-linear regression analysis for the sorption kinetics of methylene blue onto activated carbon.

PubMed

Kumar, K Vasanth

2006-10-11

Batch kinetic experiments were carried out for the sorption of methylene blue onto activated carbon. The experimental kinetics were fitted to the pseudo first-order and pseudo second-order kinetics by linear and a non-linear method. The five different types of Ho pseudo second-order expression have been discussed. A comparison of linear least-squares method and a trial and error non-linear method of estimating the pseudo second-order rate kinetic parameters were examined. The sorption process was found to follow a both pseudo first-order kinetic and pseudo second-order kinetic model. Present investigation showed that it is inappropriate to use a type 1 and type pseudo second-order expressions as proposed by Ho and Blanachard et al. respectively for predicting the kinetic rate constants and the initial sorption rate for the studied system. Three correct possible alternate linear expressions (type 2 to type 4) to better predict the initial sorption rate and kinetic rate constants for the studied system (methylene blue/activated carbon) was proposed. Linear method was found to check only the hypothesis instead of verifying the kinetic model. Non-linear regression method was found to be the more appropriate method to determine the rate kinetic parameters.

Two-dimensional advective transport in ground-water flow parameter estimation

USGS Publications Warehouse

Anderman, E.R.; Hill, M.C.; Poeter, E.P.

1996-01-01

Nonlinear regression is useful in ground-water flow parameter estimation, but problems of parameter insensitivity and correlation often exist given commonly available hydraulic-head and head-dependent flow (for example, stream and lake gain or loss) observations. To address this problem, advective-transport observations are added to the ground-water flow, parameter-estimation model MODFLOWP using particle-tracking methods. The resulting model is used to investigate the importance of advective-transport observations relative to head-dependent flow observations when either or both are used in conjunction with hydraulic-head observations in a simulation of the sewage-discharge plume at Otis Air Force Base, Cape Cod, Massachusetts, USA. The analysis procedure for evaluating the probable effect of new observations on the regression results consists of two steps: (1) parameter sensitivities and correlations calculated at initial parameter values are used to assess the model parameterization and expected relative contributions of different types of observations to the regression; and (2) optimal parameter values are estimated by nonlinear regression and evaluated. In the Cape Cod parameter-estimation model, advective-transport observations did not significantly increase the overall parameter sensitivity; however: (1) inclusion of advective-transport observations decreased parameter correlation enough for more unique parameter values to be estimated by the regression; (2) realistic uncertainties in advective-transport observations had a small effect on parameter estimates relative to the precision with which the parameters were estimated; and (3) the regression results and sensitivity analysis provided insight into the dynamics of the ground-water flow system, especially the importance of accurate boundary conditions. In this work, advective-transport observations improved the calibration of the model and the estimation of ground-water flow parameters, and use of regression and related techniques produced significant insight into the physical system.

Evaluation of the magnitude and frequency of floods in urban watersheds in Phoenix and Tucson, Arizona

USGS Publications Warehouse

Kennedy, Jeffrey R.; Paretti, Nicholas V.

2014-01-01

Flooding in urban areas routinely causes severe damage to property and often results in loss of life. To investigate the effect of urbanization on the magnitude and frequency of flood peaks, a flood frequency analysis was carried out using data from urbanized streamgaging stations in Phoenix and Tucson, Arizona. Flood peaks at each station were predicted using the log-Pearson Type III distribution, fitted using the expected moments algorithm and the multiple Grubbs-Beck low outlier test. The station estimates were then compared to flood peaks estimated by rural-regression equations for Arizona, and to flood peaks adjusted for urbanization using a previously developed procedure for adjusting U.S. Geological Survey rural regression peak discharges in an urban setting. Only smaller, more common flood peaks at the 50-, 20-, 10-, and 4-percent annual exceedance probabilities (AEPs) demonstrate any increase in magnitude as a result of urbanization; the 1-, 0.5-, and 0.2-percent AEP flood estimates are predicted without bias by the rural-regression equations. Percent imperviousness was determined not to account for the difference in estimated flood peaks between stations, either when adjusting the rural-regression equations or when deriving urban-regression equations to predict flood peaks directly from basin characteristics. Comparison with urban adjustment equations indicates that flood peaks are systematically overestimated if the rural-regression-estimated flood peaks are adjusted upward to account for urbanization. At nearly every streamgaging station in the analysis, adjusted rural-regression estimates were greater than the estimates derived using station data. One likely reason for the lack of increase in flood peaks with urbanization is the presence of significant stormwater retention and detention structures within the watershed used in the study.

Landslide Hazard Mapping in Rwanda Using Logistic Regression

NASA Astrophysics Data System (ADS)

Piller, A.; Anderson, E.; Ballard, H.

2015-12-01

Landslides in the United States cause more than $1 billion in damages and 50 deaths per year (USGS 2014). Globally, figures are much more grave, yet monitoring, mapping and forecasting of these hazards are less than adequate. Seventy-five percent of the population of Rwanda earns a living from farming, mostly subsistence. Loss of farmland, housing, or life, to landslides is a very real hazard. Landslides in Rwanda have an impact at the economic, social, and environmental level. In a developing nation that faces challenges in tracking, cataloging, and predicting the numerous landslides that occur each year, satellite imagery and spatial analysis allow for remote study. We have focused on the development of a landslide inventory and a statistical methodology for assessing landslide hazards. Using logistic regression on approximately 30 test variables (i.e. slope, soil type, land cover, etc.) and a sample of over 200 landslides, we determine which variables are statistically most relevant to landslide occurrence in Rwanda. A preliminary predictive hazard map for Rwanda has been produced, using the variables selected from the logistic regression analysis.

Discrimination and characterization of strawberry juice based on electronic nose and tongue: comparison of different juice processing approaches by LDA, PLSR, RF, and SVM.

PubMed

Qiu, Shanshan; Wang, Jun; Gao, Liping

2014-07-09

An electronic nose (E-nose) and an electronic tongue (E-tongue) have been used to characterize five types of strawberry juices based on processing approaches (i.e., microwave pasteurization, steam blanching, high temperature short time pasteurization, frozen-thawed, and freshly squeezed). Juice quality parameters (vitamin C, pH, total soluble solid, total acid, and sugar/acid ratio) were detected by traditional measuring methods. Multivariate statistical methods (linear discriminant analysis (LDA) and partial least squares regression (PLSR)) and neural networks (Random Forest (RF) and Support Vector Machines) were employed to qualitative classification and quantitative regression. E-tongue system reached higher accuracy rates than E-nose did, and the simultaneous utilization did have an advantage in LDA classification and PLSR regression. According to cross-validation, RF has shown outstanding and indisputable performances in the qualitative and quantitative analysis. This work indicates that the simultaneous utilization of E-nose and E-tongue can discriminate processed fruit juices and predict quality parameters successfully for the beverage industry.

New robust statistical procedures for the polytomous logistic regression models.

PubMed

Castilla, Elena; Ghosh, Abhik; Martin, Nirian; Pardo, Leandro

2018-05-17

This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic regression model. Based on these estimators, a family of Wald-type test statistics for linear hypotheses is introduced. Robustness properties of both the proposed estimators and the test statistics are theoretically studied through the classical influence function analysis. Appropriate real life examples are presented to justify the requirement of suitable robust statistical procedures in place of the likelihood based inference for the polytomous logistic regression model. The validity of the theoretical results established in the article are further confirmed empirically through suitable simulation studies. Finally, an approach for the data-driven selection of the robustness tuning parameter is proposed with empirical justifications. © 2018, The International Biometric Society.

Studying Gene and Gene-Environment Effects of Uncommon and Common Variants on Continuous Traits: A Marker-Set Approach Using Gene-Trait Similarity Regression

PubMed Central

Tzeng, Jung-Ying; Zhang, Daowen; Pongpanich, Monnat; Smith, Chris; McCarthy, Mark I.; Sale, Michèle M.; Worrall, Bradford B.; Hsu, Fang-Chi; Thomas, Duncan C.; Sullivan, Patrick F.

2011-01-01

Genomic association analyses of complex traits demand statistical tools that are capable of detecting small effects of common and rare variants and modeling complex interaction effects and yet are computationally feasible. In this work, we introduce a similarity-based regression method for assessing the main genetic and interaction effects of a group of markers on quantitative traits. The method uses genetic similarity to aggregate information from multiple polymorphic sites and integrates adaptive weights that depend on allele frequencies to accomodate common and uncommon variants. Collapsing information at the similarity level instead of the genotype level avoids canceling signals that have the opposite etiological effects and is applicable to any class of genetic variants without the need for dichotomizing the allele types. To assess gene-trait associations, we regress trait similarities for pairs of unrelated individuals on their genetic similarities and assess association by using a score test whose limiting distribution is derived in this work. The proposed regression framework allows for covariates, has the capacity to model both main and interaction effects, can be applied to a mixture of different polymorphism types, and is computationally efficient. These features make it an ideal tool for evaluating associations between phenotype and marker sets defined by linkage disequilibrium (LD) blocks, genes, or pathways in whole-genome analysis. PMID:21835306

False Positives in Multiple Regression: Unanticipated Consequences of Measurement Error in the Predictor Variables

ERIC Educational Resources Information Center

Shear, Benjamin R.; Zumbo, Bruno D.

2013-01-01

Type I error rates in multiple regression, and hence the chance for false positive research findings, can be drastically inflated when multiple regression models are used to analyze data that contain random measurement error. This article shows the potential for inflated Type I error rates in commonly encountered scenarios and provides new…

Effect of Landscape Pattern on Insect Species Density within Urban Green Spaces in Beijing, China

PubMed Central

Su, Zhimin; Li, Xiaoma; Zhou, Weiqi; Ouyang, Zhiyun

2015-01-01

Urban green space is an important refuge of biodiversity in urban areas. Therefore, it is crucial to understand the relationship between the landscape pattern of green spaces and biodiversity to mitigate the negative effects of urbanization. In this study, we collected insects from 45 green patches in Beijing during July 2012 using suction sampling. The green patches were dominated by managed lawns, mixed with scattered trees and shrubs. We examined the effects of landscape pattern on insect species density using hierarchical partitioning analysis and partial least squares regression. The results of the hierarchical partitioning analysis indicated that five explanatory variables, i.e., patch area (with 19.9% independent effects), connectivity (13.9%), distance to nearest patch (13.8%), diversity for patch types (11.0%), and patch shape (8.3%), significantly contributed to insect species density. With the partial least squares regression model, we found species density was negatively related to patch area, shape, connectivity, diversity for patch types and proportion of impervious surface at the significance level of p < 0.05 and positively related to proportion of vegetated land. Regression tree analysis further showed that the highest species density was found in green patches with an area <500 m2. Our results indicated that improvement in habitat quality, such as patch area and connectivity that are typically thought to be important for conservation, did not actually increase species density. However, increasing compactness (low-edge) of patch shape and landscape composition did have the expected effect. Therefore, it is recommended that the composition of the surrounding landscape should be considered simultaneously with planned improvements in local habitat quality. PMID:25793897

Predictors of unsuccessful outcome in cemented femoral revisions using bone impaction grafting; Cox regression analysis of 208 cases.

PubMed

Te Stroet, Martijn A J; Rijnen, Wim H C; Gardeniers, Jean W M; Schreurs, B Willem; Hannink, Gerjon

2016-09-29

Despite improvements in the technique of femoral impaction bone grafting, reconstruction failures still can occur. Therefore, the aim of our study was to determine risk factors for the endpoint re-revision for any reason. We used prospectively collected demographic, clinical and surgical data of all 202 patients who underwent 208 femoral revisions using the X-change Femoral Revision System (Stryker-Howmedica), fresh-frozen morcellised allograft and a cemented polished Exeter stem in our department from 1991 to 2007. Univariable and multivariable Cox regression analyses were performed to identify potential factors associated with re-revision. The mean follow-up was 10.6 (5-21) years. The cumulative re-revision rate was 6.3% (13/208). After univariable selection, sex, age, body mass index (BMI), American Association of Anesthesiologists (ASA) classification, type of removed femoral component, and mesh used for reconstruction were included in multivariable regression analysis.In the multivariable analysis, BMI was the only factor that was significantly associated with the risk of re-revision after bone impaction grafting (BMI ≥30 vs. BMI <30, HR = 6.54 [95% CI 1.89-22.65]; p = 0.003). BMI was the only factor associated with the risk of re-revision for any reason. Besides BMI also other factors, such as Endoklinik score and the type of removed femoral component, can provide guidance in the process of preclinical decision making. With the knowledge obtained from this study, preoperative patient selection, informed consent, and treatment protocols can be better adjusted to the individual patient who needs to undergo a femoral revision with impaction bone grafting.

Effect of landscape pattern on insect species density within urban green spaces in Beijing, China.

PubMed

Su, Zhimin; Li, Xiaoma; Zhou, Weiqi; Ouyang, Zhiyun

2015-01-01

Urban green space is an important refuge of biodiversity in urban areas. Therefore, it is crucial to understand the relationship between the landscape pattern of green spaces and biodiversity to mitigate the negative effects of urbanization. In this study, we collected insects from 45 green patches in Beijing during July 2012 using suction sampling. The green patches were dominated by managed lawns, mixed with scattered trees and shrubs. We examined the effects of landscape pattern on insect species density using hierarchical partitioning analysis and partial least squares regression. The results of the hierarchical partitioning analysis indicated that five explanatory variables, i.e., patch area (with 19.9% independent effects), connectivity (13.9%), distance to nearest patch (13.8%), diversity for patch types (11.0%), and patch shape (8.3%), significantly contributed to insect species density. With the partial least squares regression model, we found species density was negatively related to patch area, shape, connectivity, diversity for patch types and proportion of impervious surface at the significance level of p < 0.05 and positively related to proportion of vegetated land. Regression tree analysis further showed that the highest species density was found in green patches with an area <500 m2. Our results indicated that improvement in habitat quality, such as patch area and connectivity that are typically thought to be important for conservation, did not actually increase species density. However, increasing compactness (low-edge) of patch shape and landscape composition did have the expected effect. Therefore, it is recommended that the composition of the surrounding landscape should be considered simultaneously with planned improvements in local habitat quality.

Impact of clinical status and salivary conditions on xerostomia and oral health-related quality of life of adolescents with type 1 diabetes mellitus.

PubMed

Busato, Ivana Maria Saes; Ignácio, Sérgio Aparecido; Brancher, João Armando; Moysés, Simone Tetu; Azevedo-Alanis, Luciana Reis

2012-02-01

To investigate the influence of clinical status and salivary conditions on the presence of xerostomia on adolescents with and without type 1 diabetes mellitus (DM1), and further to investigate the influence of clinical status, salivary conditions and xerostomia on oral health-related quality of life (OHQoL) of those with DM1. A cross-sectional study was performed on 102 adolescents, 51 with DM1 and 51 nondiabetics. Xerostomia was detected by asking a question about the sensation of having 'dry mouth', and Oral Health Impact Profile-14 was used to measure the impact of xerostomia on OHQoL. The clinical status was assessed by using decayed, missing or filled and Community Periodontal indices, and by evaluating oral manifestations; and the following salivary conditions were evaluated: stimulated salivary flow, pH, buffer capacity, total protein, amylase, urea, calcium, and glucose salivary concentrations. Multiple logistic regression analysis was used to evaluate the influence of clinical status and salivary conditions on xerostomia and the impact of xerostomia on the OHQoL of adolescents with DM1. Clinical status and salivary conditions was shown to have no influence on the presence of xerostomia. Bivariate (P = 0.00) and logistic regression (P = 0.01) analysis showed a significant association between DM1 and xerostomia. Logistic regression analysis showed association between xerostomia (P = 0.00) and OHQoL, and caries experience (P = 0.03) and OHQoL. DM1 showed to be predictive of a high prevalence of xerostomia in adolescents. Caries experience and xerostomia showed to have a negative impact on the OHQoL of adolescents with DM1. © 2011 John Wiley & Sons A/S.

Characteristics of cyclist crashes in Italy using latent class analysis and association rule mining

PubMed Central

De Angelis, Marco; Marín Puchades, Víctor; Fraboni, Federico; Pietrantoni, Luca

2017-01-01

The factors associated with severity of the bicycle crashes may differ across different bicycle crash patterns. Therefore, it is important to identify distinct bicycle crash patterns with homogeneous attributes. The current study aimed at identifying subgroups of bicycle crashes in Italy and analyzing separately the different bicycle crash types. The present study focused on bicycle crashes that occurred in Italy during the period between 2011 and 2013. We analyzed categorical indicators corresponding to the characteristics of infrastructure (road type, road signage, and location type), road user (i.e., opponent vehicle and cyclist’s maneuver, type of collision, age and gender of the cyclist), vehicle (type of opponent vehicle), and the environmental and time period variables (time of the day, day of the week, season, pavement condition, and weather). To identify homogenous subgroups of bicycle crashes, we used latent class analysis. Using latent class analysis, the bicycle crash data set was segmented into 19 classes, which represents 19 different bicycle crash types. Logistic regression analysis was used to identify the association between class membership and severity of the bicycle crashes. Finally, association rules were conducted for each of the latent classes to uncover the factors associated with an increased likelihood of severity. Association rules highlighted different crash characteristics associated with an increased likelihood of severity for each of the 19 bicycle crash types. PMID:28158296

Approximate median regression for complex survey data with skewed response.

PubMed

Fraser, Raphael André; Lipsitz, Stuart R; Sinha, Debajyoti; Fitzmaurice, Garrett M; Pan, Yi

2016-12-01

The ready availability of public-use data from various large national complex surveys has immense potential for the assessment of population characteristics using regression models. Complex surveys can be used to identify risk factors for important diseases such as cancer. Existing statistical methods based on estimating equations and/or utilizing resampling methods are often not valid with survey data due to complex survey design features. That is, stratification, multistage sampling, and weighting. In this article, we accommodate these design features in the analysis of highly skewed response variables arising from large complex surveys. Specifically, we propose a double-transform-both-sides (DTBS)'based estimating equations approach to estimate the median regression parameters of the highly skewed response; the DTBS approach applies the same Box-Cox type transformation twice to both the outcome and regression function. The usual sandwich variance estimate can be used in our approach, whereas a resampling approach would be needed for a pseudo-likelihood based on minimizing absolute deviations (MAD). Furthermore, the approach is relatively robust to the true underlying distribution, and has much smaller mean square error than a MAD approach. The method is motivated by an analysis of laboratory data on urinary iodine (UI) concentration from the National Health and Nutrition Examination Survey. © 2016, The International Biometric Society.

Approximate Median Regression for Complex Survey Data with Skewed Response

PubMed Central

Fraser, Raphael André; Lipsitz, Stuart R.; Sinha, Debajyoti; Fitzmaurice, Garrett M.; Pan, Yi

2016-01-01

Summary The ready availability of public-use data from various large national complex surveys has immense potential for the assessment of population characteristics using regression models. Complex surveys can be used to identify risk factors for important diseases such as cancer. Existing statistical methods based on estimating equations and/or utilizing resampling methods are often not valid with survey data due to complex survey design features. That is, stratification, multistage sampling and weighting. In this paper, we accommodate these design features in the analysis of highly skewed response variables arising from large complex surveys. Specifically, we propose a double-transform-both-sides (DTBS) based estimating equations approach to estimate the median regression parameters of the highly skewed response; the DTBS approach applies the same Box-Cox type transformation twice to both the outcome and regression function. The usual sandwich variance estimate can be used in our approach, whereas a resampling approach would be needed for a pseudo-likelihood based on minimizing absolute deviations (MAD). Furthermore, the approach is relatively robust to the true underlying distribution, and has much smaller mean square error than a MAD approach. The method is motivated by an analysis of laboratory data on urinary iodine (UI) concentration from the National Health and Nutrition Examination Survey. PMID:27062562

Mental ability and psychological work performance in Chinese workers.

PubMed

Zhong, Fei; Yano, Eiji; Lan, Yajia; Wang, Mianzhen; Wang, Zhiming; Wang, Xiaorong

2006-10-01

This study was to explore the relationship among mental ability, occupational stress, and psychological work performance in Chinese workers, and to identify relevant modifiers of mental ability and psychological work performance. Psychological Stress Intensity (PSI), psychological work performance, and mental ability (Mental Function Index, MFI) were determined among 485 Chinese workers (aged 33 to 62 yr, 65% of men) with varied work occupations. Occupational Stress Questionnaire (OSQ) and mental ability with 3 tests (including immediate memory, digit span, and cipher decoding) were used. The relationship between mental ability and psychological work performance was analyzed with multiple linear regression approach. PSI, MFI, or psychological work performance were significantly different among different work types and educational level groups (p<0.01). Multiple linear regression analysis showed that MFI was significantly related to gender, age, educational level, and work type. Higher MFI and lower PSI predicted a better psychological work performance, even after adjusted for gender, age, educational level, and work type. The study suggests that occupational stress and low mental ability are important predictors for poor psychological work performance, which is modified by both gender and educational level.

Soft sediment deformation structures in the Maastrichtian Ajali Formation Western Flank of Anambra Basin, Southern Nigeria

NASA Astrophysics Data System (ADS)

Olabode, Solomon Ojo

2014-01-01

Soft sediment deformation structures were recognized in the Maastrichtian shallow marine wave to tide influenced regressive sediments of Ajali Formation in the western flank of Anambra basin, southern Nigerian. The soft sediment deformation structures were in association with cross bedded sands, clay and silt and show different morphological types. Two main types recognised are plastic deformations represented by different types of recumbent folds and injection structure represented by clastic dykes. Other structures in association with the plastic deformation structures include distorted convolute lamination, subsidence lobes, pillars, cusps and sand balls. These structures are interpreted to have been formed by liquefaction and fluidization mechanisms. The driving forces inferred include gravitational instabilities and hydraulic processes. Facies analysis, detailed morphologic study of the soft sediment deformation structures and previous tectonic history of the basin indicate that the main trigger agent for deformation is earthquake shock. The soft sediment deformation structures recognised in the western part of Anambra basin provide a continuous record of the tectonic processes that acted on the regressive Ajali Formation during the Maastrichtian.

Using CART to Identify Thresholds and Hierarchies in the Determinants of Funding Decisions.

PubMed

Schilling, Chris; Mortimer, Duncan; Dalziel, Kim

2017-02-01

There is much interest in understanding decision-making processes that determine funding outcomes for health interventions. We use classification and regression trees (CART) to identify cost-effectiveness thresholds and hierarchies in the determinants of funding decisions. The hierarchical structure of CART is suited to analyzing complex conditional and nonlinear relationships. Our analysis uncovered hierarchies where interventions were grouped according to their type and objective. Cost-effectiveness thresholds varied markedly depending on which group the intervention belonged to: lifestyle-type interventions with a prevention objective had an incremental cost-effectiveness threshold of $2356, suggesting that such interventions need to be close to cost saving or dominant to be funded. For lifestyle-type interventions with a treatment objective, the threshold was much higher at $37,024. Lower down the tree, intervention attributes such as the level of patient contribution and the eligibility for government reimbursement influenced the likelihood of funding within groups of similar interventions. Comparison between our CART models and previously published results demonstrated concurrence with standard regression techniques while providing additional insights regarding the role of the funding environment and the structure of decision-maker preferences.

Gender differences in social support and leisure-time physical activity.

PubMed

Oliveira, Aldair J; Lopes, Claudia S; Rostila, Mikael; Werneck, Guilherme Loureiro; Griep, Rosane Härter; Leon, Antônio Carlos Monteiro Ponce de; Faerstein, Eduardo

2014-08-01

To identify gender differences in social support dimensions' effect on adults' leisure-time physical activity maintenance, type, and time. Longitudinal study of 1,278 non-faculty public employees at a university in Rio de Janeiro, RJ, Southeastern Brazil. Physical activity was evaluated using a dichotomous question with a two-week reference period, and further questions concerning leisure-time physical activity type (individual or group) and time spent on the activity. Social support was measured with the Medical Outcomes Study Social Support Scale. For the analysis, logistic regression models were adjusted separately by gender. A multinomial logistic regression showed an association between material support and individual activities among women (OR = 2.76; 95%CI 1.2;6.5). Affective support was associated with time spent on leisure-time physical activity only among men (OR = 1.80; 95%CI 1.1;3.2). All dimensions of social support that were examined influenced either the type of, or the time spent on, leisure-time physical activity. In some social support dimensions, the associations detected varied by gender. Future studies should attempt to elucidate the mechanisms involved in these gender differences.

ABO blood groups and susceptibility to brucellosis.

PubMed

Mohsenpour, Behzad; Hajibagheri, Katayon; Afrasiabian, Shahla; Ghaderi, Ebrahim; Ghasembegloo, Saeideh

2015-01-01

The relationship between blood groups and some infections such as norovirus, cholera, and malaria has been reported. Despite the importance of brucellosis, there is a lack of data on the relationship between blood groups and brucellosis. Thus, in this study, we examined the relationship between blood groups and brucellosis. In this case-control study, the blood groups of 100 patients with brucellosis and 200 healthy individuals were studied. Exclusion criteria for the control group consisted of a positive Coombs Wright test or a history of brucellosis. The chi-square test was used to compare qualitative variables between the two groups. The variables that met inclusion criteria for the regression model were entered into the logistic regression model. A total of 43% patients were female and 57% male; 27% were urban and 73% rural. Regression analysis showed that the likelihood of brucellosis infection was 6.26 times more in people with blood group AB than in those with blood group O (P<0.001). However, Rh type was not associated with brucellosis infection. Thus, there is a relationship between blood group and brucellosis. People with blood group AB were susceptible to brucellosis, but no difference was observed for brucellosis infection in terms of blood Rh type.

Co-morbidity but not dysglycaemia reduces quality of life in patients with type-2 diabetes treated with oral mono- or dual combination therapy – an analysis of the DiaRegis registry

PubMed Central

2013-01-01

Background Type-2 diabetes mellitus has a major impact on health related quality of life (HRQoL). We aimed to identify patient and treatment related variables having a major impact. Methods DiaRegis is a prospective diabetes registry. The EQ-5D was used to describe differences in HRQoL at baseline. Odds ratios (OR) with 95% confidence intervals (CI) were determined from univariable regression analysis. For the identification of independent predictors of a low score on the EQ-5D, multivariable unconditional logistic regression analysis was performed. Results A total of 2,760 patients were available for the present analysis (46.7% female, median age 66.2 years). Patients had considerable co-morbidity (18.3% coronary artery disease, 10.6% heart failure, 5.9% PAD and 5.0% stroke/TIA). Baseline HbA1c was 7.4%, fasting- and postprandial plasma glucose 139 mg/dl and 183 mg/dl. The median EQ-5D was 0.9 (interquartile range [IQR] 0.8–1.0). Independent predictors for a low EQ-5D were age > 66 years (OR 1.49; 95%CI 1.08–2.06), female gender (2.11; 1.55–2.86), hypertension (1.73; 1.03–2.93), peripheral neuropathy (1.62; 0.93–2.84) and clinically relevant depression (11.01; 3.97–30.50). There was no influence of dysglycaemia on the EQ-5D score. Conclusion The present study suggests, that co-morbidity but not average glycaemic control reduces health related quality of life in type 2 diabetes mellitus. PMID:23510200

Co-morbidity but not dysglycaemia reduces quality of life in patients with type-2 diabetes treated with oral mono- or dual combination therapy--an analysis of the DiaRegis registry.

PubMed

Wasem, Jürgen; Bramlage, Peter; Gitt, Anselm K; Binz, Christiane; Krekler, Michael; Deeg, Evelin; Tschöpe, Diethelm

2013-03-20

Type-2 diabetes mellitus has a major impact on health related quality of life (HRQoL). We aimed to identify patient and treatment related variables having a major impact. DiaRegis is a prospective diabetes registry. The EQ-5D was used to describe differences in HRQoL at baseline. Odds ratios (OR) with 95% confidence intervals (CI) were determined from univariable regression analysis. For the identification of independent predictors of a low score on the EQ-5D, multivariable unconditional logistic regression analysis was performed. A total of 2,760 patients were available for the present analysis (46.7% female, median age 66.2 years). Patients had considerable co-morbidity (18.3% coronary artery disease, 10.6% heart failure, 5.9% PAD and 5.0% stroke/TIA). Baseline HbA1c was 7.4%, fasting- and postprandial plasma glucose 139 mg/dl and 183 mg/dl.The median EQ-5D was 0.9 (interquartile range [IQR] 0.8-1.0). Independent predictors for a low EQ-5D were age > 66 years (OR 1.49; 95%CI 1.08-2.06), female gender (2.11; 1.55-2.86), hypertension (1.73; 1.03-2.93), peripheral neuropathy (1.62; 0.93-2.84) and clinically relevant depression (11.01; 3.97-30.50). There was no influence of dysglycaemia on the EQ-5D score. The present study suggests, that co-morbidity but not average glycaemic control reduces health related quality of life in type 2 diabetes mellitus.

Thermoreceptive innervation of human glabrous and hairy skin: a contact heat evoked potential analysis.

PubMed

Granovsky, Yelena; Matre, Dagfinn; Sokolik, Alexander; Lorenz, Jürgen; Casey, Kenneth L

2005-06-01

The human palm has a lower heat detection threshold and a higher heat pain threshold than hairy skin. Neurophysiological studies of monkeys suggest that glabrous skin has fewer low threshold heat nociceptors (AMH type 2) than hairy skin. Accordingly, we used a temperature-controlled contact heat evoked potential (CHEP) stimulator to excite selectively heat receptors with C fibers or Adelta-innervated AMH type 2 receptors in humans. On the dorsal hand, 51 degrees C stimulation produced painful pinprick sensations and 41 degrees C stimuli evoked warmth. On the glabrous thenar, 41 degrees C stimulation produced mild warmth and 51 degrees C evoked strong but painless heat sensations. We used CHEP responses to estimate the conduction velocities (CV) of peripheral fibers mediating these sensations. On hairy skin, 41 degrees C stimuli evoked an ultra-late potential (mean, SD; N wave latency: 455 (118) ms) mediated by C fibers (CV by regression analysis: 1.28 m/s, N=15) whereas 51 degrees C stimuli evoked a late potential (N latency: 267 (33) ms) mediated by Adelta afferents (CV by within-subject analysis: 12.9 m/s, N=6). In contrast, thenar responses to 41 and 51 degrees C were mediated by C fibers (average N wave latencies 485 (100) and 433 (73) ms, respectively; CVs 0.95-1.35 m/s by regression analysis, N=15; average CV=1.7 (0.41) m/s calculated from distal glabrous and proximal hairy skin stimulation, N=6). The exploratory range of the human and monkey palm is enhanced by the abundance of low threshold, C-innervated heat receptors and the paucity of low threshold AMH type 2 heat nociceptors.

Quantitative analysis of organizational culture in occupational health research: a theory-based validation in 30 workplaces of the organizational culture profile instrument

PubMed Central

2013-01-01

Background This study advances a measurement approach for the study of organizational culture in population-based occupational health research, and tests how different organizational culture types are associated with psychological distress, depression, emotional exhaustion, and well-being. Methods Data were collected over a sample of 1,164 employees nested in 30 workplaces. Employees completed the 26-item OCP instrument. Psychological distress was measured with the General Health Questionnaire (12-item); depression with the Beck Depression Inventory (21-item); and emotional exhaustion with five items from the Maslach Burnout Inventory general survey. Exploratory factor analysis evaluated the dimensionality of the OCP scale. Multilevel regression models estimated workplace-level variations, and the contribution of organizational culture factors to mental health and well-being after controlling for gender, age, and living with a partner. Results Exploratory factor analysis of OCP items revealed four factors explaining about 75% of the variance, and supported the structure of the Competing Values Framework. Factors were labeled Group, Hierarchical, Rational and Developmental. Cronbach’s alphas were high (0.82-0.89). Multilevel regression analysis suggested that the four culture types varied significantly between workplaces, and correlated with mental health and well-being outcomes. The Group culture type best distinguished between workplaces and had the strongest associations with the outcomes. Conclusions This study provides strong support for the use of the OCP scale for measuring organizational culture in population-based occupational health research in a way that is consistent with the Competing Values Framework. The Group organizational culture needs to be considered as a relevant factor in occupational health studies. PMID:23642223

Lipidomics study of plasma phospholipid metabolism in early type 2 diabetes rats with ancient prescription Huang-Qi-San intervention by UPLC/Q-TOF-MS and correlation coefficient.

PubMed

Wu, Xia; Zhu, Jian-Cheng; Zhang, Yu; Li, Wei-Min; Rong, Xiang-Lu; Feng, Yi-Fan

2016-08-25

Potential impact of lipid research has been increasingly realized both in disease treatment and prevention. An effective metabolomics approach based on ultra-performance liquid chromatography/quadrupole-time-of-flight mass spectrometry (UPLC/Q-TOF-MS) along with multivariate statistic analysis has been applied for investigating the dynamic change of plasma phospholipids compositions in early type 2 diabetic rats after the treatment of an ancient prescription of Chinese Medicine Huang-Qi-San. The exported UPLC/Q-TOF-MS data of plasma samples were subjected to SIMCA-P and processed by bioMark, mixOmics, Rcomdr packages with R software. A clear score plots of plasma sample groups, including normal control group (NC), model group (MC), positive medicine control group (Flu) and Huang-Qi-San group (HQS), were achieved by principal-components analysis (PCA), partial least-squares discriminant analysis (PLS-DA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). Biomarkers were screened out using student T test, principal component regression (PCR), partial least-squares regression (PLS) and important variable method (variable influence on projection, VIP). Structures of metabolites were identified and metabolic pathways were deduced by correlation coefficient. The relationship between compounds was explained by the correlation coefficient diagram, and the metabolic differences between similar compounds were illustrated. Based on KEGG database, the biological significances of identified biomarkers were described. The correlation coefficient was firstly applied to identify the structure and deduce the metabolic pathways of phospholipids metabolites, and the study provided a new methodological cue for further understanding the molecular mechanisms of metabolites in the process of regulating Huang-Qi-San for treating early type 2 diabetes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Serum sCD163 Levels Are Associated with Type 2 Diabetes Mellitus and Are Influenced by Coffee and Wine Consumption: Results of the Di@bet.es Study

PubMed Central

Rojo-Martínez, Gemma; Maymó-Masip, Elsa; Rodríguez, M. Mar; Solano, Esther; Goday, Albert; Soriguer, Federico; Valdés, Sergio; Chaves, Felipe Javier; Delgado, Elías; Colomo, Natalia; Hernández, Pilar

2014-01-01

Objective Serum levels of soluble TNF-like weak inducer of apoptosis (sTWEAK) and its scavenger receptor CD163 (sCD163) have been linked to insulin resistance. We analysed the usefulness of these cytokines as biomarkers of type 2 diabetes in a Spanish cohort, together with their relationship to food consumption in the setting of the Di@bet.es study. Research Design and Methods This is a cross-sectional, matched case-control study of 514 type 2 diabetes subjects and 517 controls with a Normal Oral Glucose Tolerance Test (NOGTT), using data from the Di@bet.es study. Study variables included clinical and demographic structured survey, food frequency questionnaire and physical examination. Serum concentrations of sTWEAK and sCD163 were measured by ELISA. Linear regression analysis determined which variables were related to sTWEAK and sCD163 levels. Logistic regression analysis was used to estimate odd ratios of presenting type 2 diabetes. Results sCD163 concentrations and sCD163/sTWEAK ratio were 11.0% and 15.0% higher, respectively, (P<0.001) in type 2 diabetes than in controls. Following adjustment for various confounders, the OR for presenting type 2 diabetes in subjects in the highest vs the lowest tertile of sCD163 was [(OR), 2,01 (95%CI, 1,46–2,97); P for trend <0.001]. Coffee and red wine consumption was negatively associated with serum levels of sCD163 (P = 0.0001 and; P = 0.002 for coffee and red wine intake, respectively). Conclusions High circulating levels of sCD163 are associated with type 2 diabetes in the Spanish population. The association between coffee and red wine intake and these biomarkers deserves further study to confirm its potential role in type 2 diabetes. PMID:24978196

Serum sCD163 levels are associated with type 2 diabetes mellitus and are influenced by coffee and wine consumption: results of the Di@bet.es study.

PubMed

Rojo-Martínez, Gemma; Maymó-Masip, Elsa; Rodríguez, M Mar; Solano, Esther; Goday, Albert; Soriguer, Federico; Valdés, Sergio; Chaves, Felipe Javier; Delgado, Elías; Colomo, Natalia; Hernández, Pilar; Vendrell, Joan; Chacón, Matilde R

2014-01-01

Serum levels of soluble TNF-like weak inducer of apoptosis (sTWEAK) and its scavenger receptor CD163 (sCD163) have been linked to insulin resistance. We analysed the usefulness of these cytokines as biomarkers of type 2 diabetes in a Spanish cohort, together with their relationship to food consumption in the setting of the Di@bet.es study. This is a cross-sectional, matched case-control study of 514 type 2 diabetes subjects and 517 controls with a Normal Oral Glucose Tolerance Test (NOGTT), using data from the Di@bet.es study. Study variables included clinical and demographic structured survey, food frequency questionnaire and physical examination. Serum concentrations of sTWEAK and sCD163 were measured by ELISA. Linear regression analysis determined which variables were related to sTWEAK and sCD163 levels. Logistic regression analysis was used to estimate odd ratios of presenting type 2 diabetes. sCD163 concentrations and sCD163/sTWEAK ratio were 11.0% and 15.0% higher, respectively, (P<0.001) in type 2 diabetes than in controls. Following adjustment for various confounders, the OR for presenting type 2 diabetes in subjects in the highest vs the lowest tertile of sCD163 was [(OR), 2,01 (95%CI, 1,46-2,97); P for trend <0.001]. Coffee and red wine consumption was negatively associated with serum levels of sCD163 (P = 0.0001 and; P = 0.002 for coffee and red wine intake, respectively). High circulating levels of sCD163 are associated with type 2 diabetes in the Spanish population. The association between coffee and red wine intake and these biomarkers deserves further study to confirm its potential role in type 2 diabetes.

Variables influencing allocation of capital expenditure in Indonesia

NASA Astrophysics Data System (ADS)

Muda, Iskandar; Naibaho, Revmianson

2018-03-01

The purpose of this study is to examine the factors affecting capital expenditure in Indonesia. The independent variables used are The Effects of Financing Surplus, Total Population and Regional Sizes and the dependent variable used is The Effects of Financing Surplus. This type of research is a causal associative research. The type of data used is secondary data in severals provinces in Indonesia with multiple regression analysis. The results show significantly the determinants of capital expenditure allocation in Indonesia are affected by Financing Surplus, Total Population and Regional Sizes.

Fatigue in Type 2 Diabetes: Impact on Quality of Life and Predictors.

PubMed

Singh, Rupali; Teel, Cynthia; Sabus, Carla; McGinnis, Patricia; Kluding, Patricia

2016-01-01

Fatigue is a persistent symptom, impacting quality of life (QoL) and functional status in people with type 2 diabetes, yet the symptom of fatigue has not been fully explored. The purpose of this study was to explore the relationship between fatigue, QoL functional status and to investigate the predictors of fatigue. These possible predictors included body mass index (BMI), Hemoglobin A1C (HbA1C), sleep quality, pain, number of complications from diabetes, years since diagnosis and depression. Forty-eight individuals with type 2 diabetes (22 females, 26 males; 59.66±7.24 years of age; 10.45 ±7.38 years since diagnosis) participated in the study. Fatigue was assessed by using Multidimensional Fatigue Inventory (MFI-20). Other outcomes included: QoL (Audit of Diabetes Dependent QoL), and functional status (6 minute walk test), BMI, HbA1c, sleep (Pittsburg sleep quality index, PSQI), pain (Visual Analog Scale), number of complications, years since diagnosis, and depression (Beck's depression Inventory-2). The Pearson correlation analysis followed by multivariable linear regression model was used. Fatigue was negatively related to quality of life and functional status. Multivariable linear regression analysis revealed sleep, pain and BMI as the independent predictors of fatigue signaling the presence of physiological (sleep, pain, BMI) phenomenon that could undermine health outcomes.

Fatigue in Type 2 Diabetes: Impact on Quality of Life and Predictors

PubMed Central

Teel, Cynthia; Sabus, Carla; McGinnis, Patricia; Kluding, Patricia

2016-01-01

Fatigue is a persistent symptom, impacting quality of life (QoL) and functional status in people with type 2 diabetes, yet the symptom of fatigue has not been fully explored. The purpose of this study was to explore the relationship between fatigue, QoL functional status and to investigate the predictors of fatigue. These possible predictors included body mass index (BMI), Hemoglobin A1C (HbA1C), sleep quality, pain, number of complications from diabetes, years since diagnosis and depression. Forty-eight individuals with type 2 diabetes (22 females, 26 males; 59.66±7.24 years of age; 10.45 ±7.38 years since diagnosis) participated in the study. Fatigue was assessed by using Multidimensional Fatigue Inventory (MFI-20). Other outcomes included: QoL (Audit of Diabetes Dependent QoL), and functional status (6 minute walk test), BMI, HbA1c, sleep (Pittsburg sleep quality index, PSQI), pain (Visual Analog Scale), number of complications, years since diagnosis, and depression (Beck’s depression Inventory-2). The Pearson correlation analysis followed by multivariable linear regression model was used. Fatigue was negatively related to quality of life and functional status. Multivariable linear regression analysis revealed sleep, pain and BMI as the independent predictors of fatigue signaling the presence of physiological (sleep, pain, BMI) phenomenon that could undermine health outcomes. PMID:27824886

The 11-year solar cycle in current reanalyses: a (non)linear attribution study of the middle atmosphere

NASA Astrophysics Data System (ADS)

Kuchar, A.; Sacha, P.; Miksovsky, J.; Pisoft, P.

2015-06-01

This study focusses on the variability of temperature, ozone and circulation characteristics in the stratosphere and lower mesosphere with regard to the influence of the 11-year solar cycle. It is based on attribution analysis using multiple nonlinear techniques (support vector regression, neural networks) besides the multiple linear regression approach. The analysis was applied to several current reanalysis data sets for the 1979-2013 period, including MERRA, ERA-Interim and JRA-55, with the aim to compare how these types of data resolve especially the double-peaked solar response in temperature and ozone variables and the consequent changes induced by these anomalies. Equatorial temperature signals in the tropical stratosphere were found to be in qualitative agreement with previous attribution studies, although the agreement with observational results was incomplete, especially for JRA-55. The analysis also pointed to the solar signal in the ozone data sets (i.e. MERRA and ERA-Interim) not being consistent with the observed double-peaked ozone anomaly extracted from satellite measurements. The results obtained by linear regression were confirmed by the nonlinear approach through all data sets, suggesting that linear regression is a relevant tool to sufficiently resolve the solar signal in the middle atmosphere. The seasonal evolution of the solar response was also discussed in terms of dynamical causalities in the winter hemispheres. The hypothetical mechanism of a weaker Brewer-Dobson circulation at solar maxima was reviewed together with a discussion of polar vortex behaviour.

Advantage of the modified Lunn-McNeil technique over Kalbfleisch-Prentice technique in competing risks

NASA Astrophysics Data System (ADS)

Lukman, Iing; Ibrahim, Noor A.; Daud, Isa B.; Maarof, Fauziah; Hassan, Mohd N.

2002-03-01

Survival analysis algorithm is often applied in the data mining process. Cox regression is one of the survival analysis tools that has been used in many areas, and it can be used to analyze the failure times of aircraft crashed. Another survival analysis tool is the competing risks where we have more than one cause of failure acting simultaneously. Lunn-McNeil analyzed the competing risks in the survival model using Cox regression with censored data. The modified Lunn-McNeil technique is a simplify of the Lunn-McNeil technique. The Kalbfleisch-Prentice technique is involving fitting models separately from each type of failure, treating other failure types as censored. To compare the two techniques, (the modified Lunn-McNeil and Kalbfleisch-Prentice) a simulation study was performed. Samples with various sizes and censoring percentages were generated and fitted using both techniques. The study was conducted by comparing the inference of models, using Root Mean Square Error (RMSE), the power tests, and the Schoenfeld residual analysis. The power tests in this study were likelihood ratio test, Rao-score test, and Wald statistics. The Schoenfeld residual analysis was conducted to check the proportionality of the model through its covariates. The estimated parameters were computed for the cause-specific hazard situation. Results showed that the modified Lunn-McNeil technique was better than the Kalbfleisch-Prentice technique based on the RMSE measurement and Schoenfeld residual analysis. However, the Kalbfleisch-Prentice technique was better than the modified Lunn-McNeil technique based on power tests measurement.

AGSuite: Software to conduct feature analysis of artificial grammar learning performance.

PubMed

Cook, Matthew T; Chubala, Chrissy M; Jamieson, Randall K

2017-10-01

To simplify the problem of studying how people learn natural language, researchers use the artificial grammar learning (AGL) task. In this task, participants study letter strings constructed according to the rules of an artificial grammar and subsequently attempt to discriminate grammatical from ungrammatical test strings. Although the data from these experiments are usually analyzed by comparing the mean discrimination performance between experimental conditions, this practice discards information about the individual items and participants that could otherwise help uncover the particular features of strings associated with grammaticality judgments. However, feature analysis is tedious to compute, often complicated, and ill-defined in the literature. Moreover, the data violate the assumption of independence underlying standard linear regression models, leading to Type I error inflation. To solve these problems, we present AGSuite, a free Shiny application for researchers studying AGL. The suite's intuitive Web-based user interface allows researchers to generate strings from a database of published grammars, compute feature measures (e.g., Levenshtein distance) for each letter string, and conduct a feature analysis on the strings using linear mixed effects (LME) analyses. The LME analysis solves the inflation of Type I errors that afflicts more common methods of repeated measures regression analysis. Finally, the software can generate a number of graphical representations of the data to support an accurate interpretation of results. We hope the ease and availability of these tools will encourage researchers to take full advantage of item-level variance in their datasets in the study of AGL. We moreover discuss the broader applicability of the tools for researchers looking to conduct feature analysis in any field.

[The role of inflammation factors in development of acute coronary syndrome in patients with type 2 diabetes mellitus and impaired glucose tolerance].

PubMed

Chazova, T E; Masenko, V P; Zykov, K A; Golitsyna, T Iu

2007-01-01

To study the levels of inflammatory markers in acute coronary syndrome (ACS) and 6 months after its regression in patients with diabetes mellitus (DM) type 2; to evaluate effects of anxiodepressive disorders on inflammatory markers. The levels of high-sensitive C-reactive protein (hsCRP), interleukin-6 (IL-6), interleukin-18 (IL-18), monocyte-chemmoattractant-protein-1 (MCP-1) and soluble vascular cell adhesion molecules (sVCAM) were measured in blood samples, severity of depressive symptoms and the level of glycated haemoglobin (HbA1c) were assessed in 58 patients with type 2 DM during ACS and in 54 patients 6 months after ACS regression. The levels of hsCRP and IL-18 correlate significantly with severity of myocardial lesion in ACS (p < 0.002; p < 0.009). Measurement of inflammatory markers 6 months after the discharge from hospital shows significant correlation between hsCRP, IL-18 and IL-6 levels; these levels were significantly lower in patients with HbA1c < 6.5% (tight glycemic control); there were associations between severity of depressive disorders and markers of inflammation (hsCRP, IL-18); analysis of MCP-1 and sVCAM levels 6 months after ACS regression shows a decrease of markers in 32-36% cases and an increase of markers in 64-67% cases. Complex immunological reactions, chronic hyperglycemia and depresssive disorders play an important role in development of latent inflammation of the vascular wall in patients with type 2 diabetes mellitus and ACS.

An Examination on the Effect of Prior Knowledge, Personal Goals, and Incentive in an Online Employee Training Program

ERIC Educational Resources Information Center

Zha, Shenghua; Adams, Andrea Harpine; Calcagno-Roach, Jamie Marie; Stringham, David A.

2017-01-01

This study explored factors that predicted learners' transformative learning in an online employee training program in a higher education institution in the U.S. A multivariate multiple regression analysis was conducted with a sample of 74 adult learners on their learning of a new learning management system. Four types of participants' behaviors…

Strain-gage bridge calibration and flight loads measurements on a low-aspect-ratio thin wing

NASA Technical Reports Server (NTRS)

Peele, E. L.; Eckstrom, C. V.

1975-01-01

Strain-gage bridges were used to make in-flight measurements of bending moment, shear, and torque loads on a low-aspect-ratio, thin, swept wing having a full depth honeycomb sandwich type structure. Standard regression analysis techniques were employed in the calibration of the strain bridges. Comparison of the measured loads with theoretical loads are included.

Solutions for Determining the Significance Region Using the Johnson-Neyman Type Procedure in Generalized Linear (Mixed) Models

ERIC Educational Resources Information Center

Lazar, Ann A.; Zerbe, Gary O.

2011-01-01

Researchers often compare the relationship between an outcome and covariate for two or more groups by evaluating whether the fitted regression curves differ significantly. When they do, researchers need to determine the "significance region," or the values of the covariate where the curves significantly differ. In analysis of covariance (ANCOVA),…

Analysis of the Magnitude and Frequency of Peak Discharge and Maximum Observed Peak Discharge in New Mexico and Surrounding Areas

USGS Publications Warehouse

Waltemeyer, Scott D.

2008-01-01

Estimates of the magnitude and frequency of peak discharges are necessary for the reliable design of bridges, culverts, and open-channel hydraulic analysis, and for flood-hazard mapping in New Mexico and surrounding areas. The U.S. Geological Survey, in cooperation with the New Mexico Department of Transportation, updated estimates of peak-discharge magnitude for gaging stations in the region and updated regional equations for estimation of peak discharge and frequency at ungaged sites. Equations were developed for estimating the magnitude of peak discharges for recurrence intervals of 2, 5, 10, 25, 50, 100, and 500 years at ungaged sites by use of data collected through 2004 for 293 gaging stations on unregulated streams that have 10 or more years of record. Peak discharges for selected recurrence intervals were determined at gaging stations by fitting observed data to a log-Pearson Type III distribution with adjustments for a low-discharge threshold and a zero skew coefficient. A low-discharge threshold was applied to frequency analysis of 140 of the 293 gaging stations. This application provides an improved fit of the log-Pearson Type III frequency distribution. Use of the low-discharge threshold generally eliminated the peak discharge by having a recurrence interval of less than 1.4 years in the probability-density function. Within each of the nine regions, logarithms of the maximum peak discharges for selected recurrence intervals were related to logarithms of basin and climatic characteristics by using stepwise ordinary least-squares regression techniques for exploratory data analysis. Generalized least-squares regression techniques, an improved regression procedure that accounts for time and spatial sampling errors, then were applied to the same data used in the ordinary least-squares regression analyses. The average standard error of prediction, which includes average sampling error and average standard error of regression, ranged from 38 to 93 percent (mean value is 62, and median value is 59) for the 100-year flood. The 1996 investigation standard error of prediction for the flood regions ranged from 41 to 96 percent (mean value is 67, and median value is 68) for the 100-year flood that was analyzed by using generalized least-squares regression analysis. Overall, the equations based on generalized least-squares regression techniques are more reliable than those in the 1996 report because of the increased length of record and improved geographic information system (GIS) method to determine basin and climatic characteristics. Flood-frequency estimates can be made for ungaged sites upstream or downstream from gaging stations by using a method that transfers flood-frequency data at the gaging station to the ungaged site by using a drainage-area ratio adjustment equation. The peak discharge for a given recurrence interval at the gaging station, drainage-area ratio, and the drainage-area exponent from the regional regression equation of the respective region is used to transfer the peak discharge for the recurrence interval to the ungaged site. Maximum observed peak discharge as related to drainage area was determined for New Mexico. Extreme events are commonly used in the design and appraisal of bridge crossings and other structures. Bridge-scour evaluations are commonly made by using the 500-year peak discharge for these appraisals. Peak-discharge data collected at 293 gaging stations and 367 miscellaneous sites were used to develop a maximum peak-discharge relation as an alternative method of estimating peak discharge of an extreme event such as a maximum probable flood.

Effect of candesartan on progression and regression of retinopathy in type 2 diabetes (DIRECT-Protect 2): a randomised placebo-controlled trial.

PubMed

Sjølie, Anne Katrin; Klein, Ronald; Porta, Massimo; Orchard, Trevor; Fuller, John; Parving, Hans Henrik; Bilous, Rudy; Chaturvedi, Nish

2008-10-18

Diabetic retinopathy remains a leading cause of visual loss in people of working age. We examined whether candesartan treatment could slow the progression and, secondly, induce regression of retinopathy in people with type 2 diabetes. We did a randomised, double-blind, parallel-group, placebo-controlled trial in 309 centres worldwide. We recruited normoalbuminuric, normotensive, or treated hypertensive people with type 2 diabetes with mild to moderately severe retinopathy and assigned them to candesartan 16 mg once a day or placebo. After a month, the dose was doubled to 32 mg once per day. Investigators and patients were unaware of the treatment allocation status. Progression of retinopathy was the primary endpoint, and regression was a secondary endpoint. Analysis was by intention to treat. The trial is registered with ClinicalTrials.gov, number NCT00252694. 1905 participants (aged 37-75 years) were randomised to candesartan (n=951) or placebo (n=954). 161 (17%) patients in the candesartan group and 182 (19%) in the placebo group had progression of retinopathy by three steps or more on the Early Treatment Diabetic Retinopathy Study scale. The risk of progression of retinopathy was non-significantly reduced by 13% in patients on candesartan compared with those on placebo (hazard ratio [HR] 0.87, 95% CI 0.70-1.08, p=0.20). Regression on active treatment was increased by 34% (1.34, 1.08-1.68, p=0.009). HRs were not attenuated by adjustment for baseline risk factors or changes in blood pressure during the trial. An overall change towards less severe retinopathy by the end of the trial was observed in the candesartan group (odds 1.17, 95% CI 1.05-1.30, p=0.003). Adverse events did not differ between the treatment groups. Treatment with candesartan in type 2 diabetic patients with mild to moderate retinopathy might induce improvement of retinopathy.

Noise in restaurants: levels and mathematical model.

PubMed

To, Wai Ming; Chung, Andy

2014-01-01

Noise affects the dining atmosphere and is an occupational hazard to restaurant service employees worldwide. This paper examines the levels of noise in dining areas during peak hours in different types of restaurants in Hong Kong SAR, China. A mathematical model that describes the noise level in a restaurant is presented. The 1-h equivalent continuous noise level (L(eq,1-h)) was measured using a Type-1 precision integral sound level meter while the occupancy density, the floor area of the dining area, and the ceiling height of each of the surveyed restaurants were recorded. It was found that the measured noise levels using Leq,1-h ranged from 67.6 to 79.3 dBA in Chinese restaurants, from 69.1 to 79.1 dBA in fast food restaurants, and from 66.7 to 82.6 dBA in Western restaurants. Results of the analysis of variance show that there were no significant differences between means of the measured noise levels among different types of restaurants. A stepwise multiple regression analysis was employed to determine the relationships between geometrical and operational parameters and the measured noise levels. Results of the regression analysis show that the measured noise levels depended on the levels of occupancy density only. By reconciling the measured noise levels and the mathematical model, it was found that people in restaurants increased their voice levels when the occupancy density increased. Nevertheless, the maximum measured hourly noise level indicated that the noise exposure experienced by restaurant service employees was below the regulated daily noise exposure value level of 85 dBA.

Construction and analysis of a modular model of caspase activation in apoptosis

PubMed Central

Harrington, Heather A; Ho, Kenneth L; Ghosh, Samik; Tung, KC

2008-01-01

Background A key physiological mechanism employed by multicellular organisms is apoptosis, or programmed cell death. Apoptosis is triggered by the activation of caspases in response to both extracellular (extrinsic) and intracellular (intrinsic) signals. The extrinsic and intrinsic pathways are characterized by the formation of the death-inducing signaling complex (DISC) and the apoptosome, respectively; both the DISC and the apoptosome are oligomers with complex formation dynamics. Additionally, the extrinsic and intrinsic pathways are coupled through the mitochondrial apoptosis-induced channel via the Bcl-2 family of proteins. Results A model of caspase activation is constructed and analyzed. The apoptosis signaling network is simplified through modularization methodologies and equilibrium abstractions for three functional modules. The mathematical model is composed of a system of ordinary differential equations which is numerically solved. Multiple linear regression analysis investigates the role of each module and reduced models are constructed to identify key contributions of the extrinsic and intrinsic pathways in triggering apoptosis for different cell lines. Conclusion Through linear regression techniques, we identified the feedbacks, dissociation of complexes, and negative regulators as the key components in apoptosis. The analysis and reduced models for our model formulation reveal that the chosen cell lines predominately exhibit strong extrinsic caspase, typical of type I cell, behavior. Furthermore, under the simplified model framework, the selected cells lines exhibit different modes by which caspase activation may occur. Finally the proposed modularized model of apoptosis may generalize behavior for additional cells and tissues, specifically identifying and predicting components responsible for the transition from type I to type II cell behavior. PMID:19077196

Principal component regression analysis with SPSS.

PubMed

Liu, R X; Kuang, J; Gong, Q; Hou, X L

2003-06-01

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

Local regression type methods applied to the study of geophysics and high frequency financial data

NASA Astrophysics Data System (ADS)

Mariani, M. C.; Basu, K.

2014-09-01

In this work we applied locally weighted scatterplot smoothing techniques (Lowess/Loess) to Geophysical and high frequency financial data. We first analyze and apply this technique to the California earthquake geological data. A spatial analysis was performed to show that the estimation of the earthquake magnitude at a fixed location is very accurate up to the relative error of 0.01%. We also applied the same method to a high frequency data set arising in the financial sector and obtained similar satisfactory results. The application of this approach to the two different data sets demonstrates that the overall method is accurate and efficient, and the Lowess approach is much more desirable than the Loess method. The previous works studied the time series analysis; in this paper our local regression models perform a spatial analysis for the geophysics data providing different information. For the high frequency data, our models estimate the curve of best fit where data are dependent on time.

Statistical relations among earthquake magnitude, surface rupture length, and surface fault displacement

USGS Publications Warehouse

Bonilla, M.G.; Mark, R.K.; Lienkaemper, J.J.

1984-01-01

In order to refine correlations of surface-wave magnitude, fault rupture length at the ground surface, and fault displacement at the surface by including the uncertainties in these variables, the existing data were critically reviewed and a new data base was compiled. Earthquake magnitudes were redetermined as necessary to make them as consistent as possible with the Gutenberg methods and results, which necessarily make up much of the data base. Measurement errors were estimated for the three variables for 58 moderate to large shallow-focus earthquakes. Regression analyses were then made utilizing the estimated measurement errors. The regression analysis demonstrates that the relations among the variables magnitude, length, and displacement are stochastic in nature. The stochastic variance, introduced in part by incomplete surface expression of seismogenic faulting, variation in shear modulus, and regional factors, dominates the estimated measurement errors. Thus, it is appropriate to use ordinary least squares for the regression models, rather than regression models based upon an underlying deterministic relation with the variance resulting from measurement errors. Significant differences exist in correlations of certain combinations of length, displacement, and magnitude when events are qrouped by fault type or by region, including attenuation regions delineated by Evernden and others. Subdivision of the data results in too few data for some fault types and regions, and for these only regressions using all of the data as a group are reported. Estimates of the magnitude and the standard deviation of the magnitude of a prehistoric or future earthquake associated with a fault can be made by correlating M with the logarithms of rupture length, fault displacement, or the product of length and displacement. Fault rupture area could be reliably estimated for about 20 of the events in the data set. Regression of MS on rupture area did not result in a marked improvement over regressions that did not involve rupture area. Because no subduction-zone earthquakes are included in this study, the reported results do not apply to such zones.

Analysis of the Einstein sample of early-type galaxies

NASA Technical Reports Server (NTRS)

Eskridge, Paul B.; Fabbiano, Giuseppina

1993-01-01

The EINSTEIN galaxy catalog contains x-ray data for 148 early-type (E and SO) galaxies. A detailed analysis of the global properties of this sample are studied. By comparing the x-ray properties with other tracers of the ISM, as well as with observables related to the stellar dynamics and populations of the sample, we expect to determine more clearly the physical relationships that determine the evolution of early-type galaxies. Previous studies with smaller samples have explored the relationships between x-ray luminosity (L(sub x)) and luminosities in other bands. Using our larger sample and the statistical techniques of survival analysis, a number of these earlier analyses were repeated. For our full sample, a strong statistical correlation is found between L(sub X) and L(sub B) (the probability that the null hypothesis is upheld is P less than 10(exp -4) from a variety of rank correlation tests. Regressions with several algorithms yield consistent results.

Regression equations for estimating flood flows for the 2-, 10-, 25-, 50-, 100-, and 500-Year recurrence intervals in Connecticut

USGS Publications Warehouse

Ahearn, Elizabeth A.

2004-01-01

Multiple linear-regression equations were developed to estimate the magnitudes of floods in Connecticut for recurrence intervals ranging from 2 to 500 years. The equations can be used for nonurban, unregulated stream sites in Connecticut with drainage areas ranging from about 2 to 715 square miles. Flood-frequency data and hydrologic characteristics from 70 streamflow-gaging stations and the upstream drainage basins were used to develop the equations. The hydrologic characteristics?drainage area, mean basin elevation, and 24-hour rainfall?are used in the equations to estimate the magnitude of floods. Average standard errors of prediction for the equations are 31.8, 32.7, 34.4, 35.9, 37.6 and 45.0 percent for the 2-, 10-, 25-, 50-, 100-, and 500-year recurrence intervals, respectively. Simplified equations using only one hydrologic characteristic?drainage area?also were developed. The regression analysis is based on generalized least-squares regression techniques. Observed flows (log-Pearson Type III analysis of the annual maximum flows) from five streamflow-gaging stations in urban basins in Connecticut were compared to flows estimated from national three-parameter and seven-parameter urban regression equations. The comparison shows that the three- and seven- parameter equations used in conjunction with the new statewide equations generally provide reasonable estimates of flood flows for urban sites in Connecticut, although a national urban flood-frequency study indicated that the three-parameter equations significantly underestimated flood flows in many regions of the country. Verification of the accuracy of the three-parameter or seven-parameter national regression equations using new data from Connecticut stations was beyond the scope of this study. A technique for calculating flood flows at streamflow-gaging stations using a weighted average also is described. Two estimates of flood flows?one estimate based on the log-Pearson Type III analyses of the annual maximum flows at the gaging station, and the other estimate from the regression equation?are weighted together based on the years of record at the gaging station and the equivalent years of record value determined from the regression. Weighted averages of flood flows for the 2-, 10-, 25-, 50-, 100-, and 500-year recurrence intervals are tabulated for the 70 streamflow-gaging stations used in the regression analysis. Generally, weighted averages give the most accurate estimate of flood flows at gaging stations. An evaluation of the Connecticut's streamflow-gaging network was performed to determine whether the spatial coverage and range of geographic and hydrologic conditions are adequately represented for transferring flood characteristics from gaged to ungaged sites. Fifty-one of 54 stations in the current (2004) network support one or more flood needs of federal, state, and local agencies. Twenty-five of 54 stations in the current network are considered high-priority stations by the U.S. Geological Survey because of their contribution to the longterm understanding of floods, and their application for regionalflood analysis. Enhancements to the network to improve overall effectiveness for regionalization can be made by increasing the spatial coverage of gaging stations, establishing stations in regions of the state that are not well-represented, and adding stations in basins with drainage area sizes not represented. Additionally, the usefulness of the network for characterizing floods can be maintained and improved by continuing operation at the current stations because flood flows can be more accurately estimated at stations with continuous, long-term record.

Prehospital management and fluid resuscitation in hypotensive trauma patients admitted to Karolinska University Hospital in Stockholm.

PubMed

Talving, Peep; Pålstedt, Joakim; Riddez, Louis

2005-01-01

Few previous studies have been conducted on the prehospital management of hypotensive trauma patients in Stockholm County. The aim of this study was to describe the prehospital management of hypotensive trauma patients admitted to the largest trauma center in Sweden, and to assess whether prehospital trauma life support (PHTLS) guidelines have been implemented regarding prehospital time intervals and fluid therapy. In addition, the effects of the age, type of injury, injury severity, prehospital time interval, blood pressure, and fluid therapy on outcome were investigated. This is a retrospective, descriptive study on consecutive, hypotensive trauma patients (systolic blood pressure < or = 90 mmHg on the scene of injury) admitted to Karolinska University Hospital in Stockholm, Sweden, during 2001-2003. The reported values are medians with interquartile ranges. Basic demographics, prehospital time intervals and interventions, injury severity scores (ISS), type and volumes of prehospital fluid resuscitation, and 30-day mortality were abstracted. The effects of the patient's age, gender, prehospital time interval, type of injury, injury severity, on-scene and emergency department blood pressure, and resuscitation fluid volumes on mortality were analyzed using the exact logistic regression model. In 102 (71 male) adult patients (age > or = 15 years) recruited, the median age was 35.5 years (range: 27-55 years) and 77 patients (75%) had suffered blunt injury. The predominant trauma mechanisms were falls between levels (24%) and motor vehicle crashes (22%) with an ISS of 28.5 (range: 16-50). The on-scene time interval was 19 minutes (range: 12-24 minutes). Fluid therapy was initiated at the scene of injury in the majority of patients (73%) regardless of the type of injury (77 blunt [75%] / 25 penetrating [25%]) or injury severity (ISS: 0-20; 21-40; 41-75). Age (odds ratio (OR) = 1.04), male gender (OR = 3.2), ISS 21-40 (OR = 13.6), and ISS >40 (OR = 43.6) were the significant factors affecting outcome in the exact logistic regression analysis. The time interval at the scene of injury exceeded PHTLS guidelines. The vast majority of the hypotensive trauma patients were fluid-resuscitated on-scene regardless of the type, mechanism, or severity of injury. A predefined fluid resuscitation regimen is not employed in hypotensive trauma victims with different types of injuries. The outcome was worsened by male gender, progressive age, and ISS > 20 in the exact multiple regression analysis.

Food insecurity is associated with high risk glycemic control and higher health care utilization among youth and young adults with type 1 diabetes.

PubMed

Mendoza, Jason A; Haaland, Wren; D'Agostino, Ralph B; Martini, Lauren; Pihoker, Catherine; Frongillo, Edward A; Mayer-Davis, Elizabeth J; Liu, Lenna L; Dabelea, Dana; Lawrence, Jean M; Liese, Angela D

2018-04-01

Household food insecurity (FI), i.e., limited availability of nutritionally adequate foods, is associated with poor glycemic control among adults with type 2 diabetes. We evaluated the association of FI among youth and young adults (YYA) with type 1 diabetes to inform recent clinical recommendations from the American Diabetes Association for providers to screen all patients with diabetes for FI. Using data from the Washington and South Carolina SEARCH for Diabetes in Youth Study sites, we conducted an observational, cross-sectional evaluation of associations between FI and glycemic control, hospitalizations, and emergency department (ED) visits among YYA with type 1 diabetes. FI was assessed using the Household Food Security Survey Module, which queries conditions and behaviors typical of households unable to meet basic food needs. Participants' HbA 1c were measured from blood drawn at the research visit; socio-demographics and medical history were collected by survey. The prevalence of FI was 19.5%. In adjusted logistic regression analysis, YYAs from food-insecure households had 2.37 higher odds (95% CI: 1.10, 5.09) of high risk glycemic control, i.e., HbA 1c >9.0%, vs. peers from food-secure households. In adjusted binomial regression analysis for ED visits, YYAs from food-insecure households had an adjusted prevalence rate that was 2.95 times (95% CI [1.17, 7.45]) as great as those from food secure households. FI was associated with high risk glycemic control and more ED visits. Targeted efforts should be developed and tested to alleviate FI among YYA with type 1 diabetes. Copyright © 2018 Elsevier B.V. All rights reserved.

Optimization of isotherm models for pesticide sorption on biopolymer-nanoclay composite by error analysis.

PubMed

Narayanan, Neethu; Gupta, Suman; Gajbhiye, V T; Manjaiah, K M

2017-04-01

A carboxy methyl cellulose-nano organoclay (nano montmorillonite modified with 35-45 wt % dimethyl dialkyl (C 14 -C 18 ) amine (DMDA)) composite was prepared by solution intercalation method. The prepared composite was characterized by infrared spectroscopy (FTIR), X-Ray diffraction spectroscopy (XRD) and scanning electron microscopy (SEM). The composite was utilized for its pesticide sorption efficiency for atrazine, imidacloprid and thiamethoxam. The sorption data was fitted into Langmuir and Freundlich isotherms using linear and non linear methods. The linear regression method suggested best fitting of sorption data into Type II Langmuir and Freundlich isotherms. In order to avoid the bias resulting from linearization, seven different error parameters were also analyzed by non linear regression method. The non linear error analysis suggested that the sorption data fitted well into Langmuir model rather than in Freundlich model. The maximum sorption capacity, Q 0 (μg/g) was given by imidacloprid (2000) followed by thiamethoxam (1667) and atrazine (1429). The study suggests that the degree of determination of linear regression alone cannot be used for comparing the best fitting of Langmuir and Freundlich models and non-linear error analysis needs to be done to avoid inaccurate results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Creep analysis of silicone for podiatry applications.

PubMed

Janeiro-Arocas, Julia; Tarrío-Saavedra, Javier; López-Beceiro, Jorge; Naya, Salvador; López-Canosa, Adrián; Heredia-García, Nicolás; Artiaga, Ramón

2016-10-01

This work shows an effective methodology to characterize the creep-recovery behavior of silicones before their application in podiatry. The aim is to characterize, model and compare the creep-recovery properties of different types of silicone used in podiatry orthotics. Creep-recovery phenomena of silicones used in podiatry orthotics is characterized by dynamic mechanical analysis (DMA). Silicones provided by Herbitas are compared by observing their viscoelastic properties by Functional Data Analysis (FDA) and nonlinear regression. The relationship between strain and time is modeled by fixed and mixed effects nonlinear regression to compare easily and intuitively podiatry silicones. Functional ANOVA and Kohlrausch-Willians-Watts (KWW) model with fixed and mixed effects allows us to compare different silicones observing the values of fitting parameters and their physical meaning. The differences between silicones are related to the variations of breadth of creep-recovery time distribution and instantaneous deformation-permanent strain. Nevertheless, the mean creep-relaxation time is the same for all the studied silicones. Silicones used in palliative orthoses have higher instantaneous deformation-permanent strain and narrower creep-recovery distribution. The proposed methodology based on DMA, FDA and nonlinear regression is an useful tool to characterize and choose the proper silicone for each podiatry application according to their viscoelastic properties. Copyright © 2016 Elsevier Ltd. All rights reserved.

Regarding to the Variance Analysis of Regression Equation of the Surface Roughness obtained by End Milling process of 7136 Aluminium Alloy

NASA Astrophysics Data System (ADS)

POP, A. B.; ȚÎȚU, M. A.

2016-11-01

In the metal cutting process, surface quality is intrinsically related to the cutting parameters and to the cutting tool geometry. At the same time, metal cutting processes are closely related to the machining costs. The purpose of this paper is to reduce manufacturing costs and processing time. A study was made, based on the mathematical modelling of the average of the absolute value deviation (Ra) resulting from the end milling process on 7136 aluminium alloy, depending on cutting process parameters. The novel element brought by this paper is the 7136 aluminium alloy type, chosen to conduct the experiments, which is a material developed and patented by Universal Alloy Corporation. This aluminium alloy is used in the aircraft industry to make parts from extruded profiles, and it has not been studied for the proposed research direction. Based on this research, a mathematical model of surface roughness Ra was established according to the cutting parameters studied in a set experimental field. A regression analysis was performed, which identified the quantitative relationships between cutting parameters and the surface roughness. Using the variance analysis ANOVA, the degree of confidence for the achieved results by the regression equation was determined, and the suitability of this equation at every point of the experimental field.

Development of the diabetes typology model for discerning Type 2 diabetes mellitus with national survey data.

PubMed

Bellatorre, Anna; Jackson, Sharon H; Choi, Kelvin

2017-01-01

To classify individuals with diabetes mellitus (DM) into DM subtypes using population-based studies. Population-based survey. Individuals participated in 2003-2004, 2005-2006, or 2009-2010 the National Health and Nutrition Examination Survey (NHANES), and 2010 Coronary Artery Risk Development in Young Adults (CARDIA) survey (research materials obtained from the National Heart, Lung, and Blood Institute Biologic Specimen and Data Repository Information Coordinating Center). 3084, 3040 and 3318 US adults from the 2003-2004, 2005-2006 and 2009-2010 NHANES samples respectively, and 5,115 US adults in the CARDIA cohort. We proposed the Diabetes Typology Model (DTM) through the use of six composite measures based on the Homeostatic Model Assessment (HOMA-IR, HOMA-%β, high HOMA-%S), insulin and glucose levels, and body mass index and conducted latent class analyses to empirically classify individuals into different classes. Three empirical latent classes consistently emerged across studies (entropy = 0.81-0.998). These three classes were likely Type 1 DM, likely Type 2 DM, and atypical DM. The classification has high sensitivity (75.5%), specificity (83.3%), and positive predictive value (97.4%) when validated against C-peptide level. Correlates of Type 2 DM were significantly associated with model-identified Type 2 DM. Compared to regression analysis on known correlates of Type 2 DM using all diabetes cases as outcomes, using DTM to remove likely Type 1 DM and atypical DM cases results in a 2.5-5.3% r-square improvement in the regression analysis, as well as model fits as indicated by significant improvement in -2 log likelihood (p<0.01). Lastly, model-defined likely Type 2 DM was significantly associated with known correlates of Type 2 DM (e.g., age, waist circumference), which provide additional validation of the DTM-defined classes. Our Diabetes Typology Model reflects a promising first step toward discerning likely DM types from population-based data. This novel tool will improve how large population-based studies can be used to examine behavioral and environmental factors associated with different types of DM.

Inverse expression of survivin and reprimo correlates with poor patient prognosis in gastric cancer

PubMed Central

Cerda-Opazo, Paulina; Valenzuela-Valderrama, Manuel; Wichmann, Ignacio; Rodríguez, Andrés; Contreras-Reyes, Daniel; Fernández, Elmer A.; Carrasco-Aviño, Gonzalo; Corvalán, Alejandro H.; Quest, Andrew F.G.

2018-01-01

BACKGROUND The objective of the study was to determine the relationship between Survivin and Reprimo transcript/protein expression levels, and gastric cancer outcome. METHODS In silico correlations between an agnostic set of twelve p53-dependent apoptosis and cell-cycle genes were explored in the gastric adenocarcinoma TCGA database, using cBioPortal. Findings were validated by regression analysis of RNAseq data. Separate regression analyses were performed to assess the impact of p53 status on Survivin and Reprimo. Quantitative reverse-transcription PCR (RT-qPCR) and immunohistochemistry confirmed in silico findings on fresh-frozen and paraffin-embedded gastric cancer tissues, respectively. Wild-type (AGS, SNU-1) and mutated p53 (NCI-N87) cell lines transfected with pEGFP-Survivin or pCMV6-Reprimo were evaluated by RT-qPCR and Western blotting. Kaplan-Meier method and Long-Rank test were used to assess differences in patient outcome. RESULTS cBioPortal analysis revealed an inverse correlation between Survivin and Reprimo expression (Pearson’s r= −0.3, Spearman’s ρ= −0.55). RNAseq analyses confirmed these findings (Spearman’s ρ= −0.37, p<4.2e-09) and revealed p53 dependence in linear regression models (p<0.05). mRNA and protein levels validated these observations in clinical samples (p<0.001). In vitro analysis in cell lines demonstrated that increasing Survivin reduced Reprimo, while increasing Reprimo reduced Survivin expression, but only did so in p53 wild-type gastric cells (p<0.05). Survivin-positive but Reprimo-negative patients displayed shorter overall survival rates (p=0.047, Long Rank Test) (HR=0.32; 95%IC: 0.11-0.97; p=0.044). CONCLUSIONS TCGA RNAseq data analysis, evaluation of clinical samples and studies in cell lines identified an inverse relationship between Survivin and Reprimo. Elevated Survivin and reduced Reprimo protein expression correlated with poor patient prognosis in gastric cancer. PMID:29560115

Inverse expression of survivin and reprimo correlates with poor patient prognosis in gastric cancer.

PubMed

Cerda-Opazo, Paulina; Valenzuela-Valderrama, Manuel; Wichmann, Ignacio; Rodríguez, Andrés; Contreras-Reyes, Daniel; Fernández, Elmer A; Carrasco-Aviño, Gonzalo; Corvalán, Alejandro H; Quest, Andrew F G

2018-02-27

The objective of the study was to determine the relationship between Survivin and Reprimo transcript/protein expression levels, and gastric cancer outcome. In silico correlations between an agnostic set of twelve p53-dependent apoptosis and cell-cycle genes were explored in the gastric adenocarcinoma TCGA database, using cBioPortal. Findings were validated by regression analysis of RNAseq data. Separate regression analyses were performed to assess the impact of p53 status on Survivin and Reprimo. Quantitative reverse-transcription PCR (RT-qPCR) and immunohistochemistry confirmed in silico findings on fresh-frozen and paraffin-embedded gastric cancer tissues, respectively. Wild-type (AGS, SNU-1) and mutated p53 (NCI-N87) cell lines transfected with pEGFP-Survivin or pCMV6-Reprimo were evaluated by RT-qPCR and Western blotting. Kaplan-Meier method and Long-Rank test were used to assess differences in patient outcome. cBioPortal analysis revealed an inverse correlation between Survivin and Reprimo expression (Pearson's r= -0.3, Spearman's ρ= -0.55). RNAseq analyses confirmed these findings (Spearman's ρ= -0.37, p<4.2e-09) and revealed p53 dependence in linear regression models (p<0.05). mRNA and protein levels validated these observations in clinical samples (p<0.001). In vitro analysis in cell lines demonstrated that increasing Survivin reduced Reprimo, while increasing Reprimo reduced Survivin expression, but only did so in p53 wild-type gastric cells (p<0.05). Survivin-positive but Reprimo-negative patients displayed shorter overall survival rates (p=0.047, Long Rank Test) (HR=0.32; 95%IC: 0.11-0.97; p=0.044). TCGA RNAseq data analysis, evaluation of clinical samples and studies in cell lines identified an inverse relationship between Survivin and Reprimo. Elevated Survivin and reduced Reprimo protein expression correlated with poor patient prognosis in gastric cancer.

Epistasis analysis for quantitative traits by functional regression model.

PubMed

Zhang, Futao; Boerwinkle, Eric; Xiong, Momiao

2014-06-01

The critical barrier in interaction analysis for rare variants is that most traditional statistical methods for testing interactions were originally designed for testing the interaction between common variants and are difficult to apply to rare variants because of their prohibitive computational time and poor ability. The great challenges for successful detection of interactions with next-generation sequencing (NGS) data are (1) lack of methods for interaction analysis with rare variants, (2) severe multiple testing, and (3) time-consuming computations. To meet these challenges, we shift the paradigm of interaction analysis between two loci to interaction analysis between two sets of loci or genomic regions and collectively test interactions between all possible pairs of SNPs within two genomic regions. In other words, we take a genome region as a basic unit of interaction analysis and use high-dimensional data reduction and functional data analysis techniques to develop a novel functional regression model to collectively test interactions between all possible pairs of single nucleotide polymorphisms (SNPs) within two genome regions. By intensive simulations, we demonstrate that the functional regression models for interaction analysis of the quantitative trait have the correct type 1 error rates and a much better ability to detect interactions than the current pairwise interaction analysis. The proposed method was applied to exome sequence data from the NHLBI's Exome Sequencing Project (ESP) and CHARGE-S study. We discovered 27 pairs of genes showing significant interactions after applying the Bonferroni correction (P-values < 4.58 × 10(-10)) in the ESP, and 11 were replicated in the CHARGE-S study. © 2014 Zhang et al.; Published by Cold Spring Harbor Laboratory Press.

Sperm Retrieval in Patients with Klinefelter Syndrome: A Skewed Regression Model Analysis.

PubMed

Chehrazi, Mohammad; Rahimiforoushani, Abbas; Sabbaghian, Marjan; Nourijelyani, Keramat; Sadighi Gilani, Mohammad Ali; Hoseini, Mostafa; Vesali, Samira; Yaseri, Mehdi; Alizadeh, Ahad; Mohammad, Kazem; Samani, Reza Omani

2017-01-01

The most common chromosomal abnormality due to non-obstructive azoospermia (NOA) is Klinefelter syndrome (KS) which occurs in 1-1.72 out of 500-1000 male infants. The probability of retrieving sperm as the outcome could be asymmetrically different between patients with and without KS, therefore logistic regression analysis is not a well-qualified test for this type of data. This study has been designed to evaluate skewed regression model analysis for data collected from microsurgical testicular sperm extraction (micro-TESE) among azoospermic patients with and without non-mosaic KS syndrome. This cohort study compared the micro-TESE outcome between 134 men with classic KS and 537 men with NOA and normal karyotype who were referred to Royan Institute between 2009 and 2011. In addition to our main outcome, which was sperm retrieval, we also used logistic and skewed regression analyses to compare the following demographic and hormonal factors: age, level of follicle stimulating hormone (FSH), luteinizing hormone (LH), and testosterone between the two groups. A comparison of the micro-TESE between the KS and control groups showed a success rate of 28.4% (38/134) for the KS group and 22.2% (119/537) for the control group. In the KS group, a significantly difference (P<0.001) existed between testosterone levels for the successful sperm retrieval group (3.4 ± 0.48 mg/mL) compared to the unsuccessful sperm retrieval group (2.33 ± 0.23 mg/mL). The index for quasi Akaike information criterion (QAIC) had a goodness of fit of 74 for the skewed model which was lower than logistic regression (QAIC=85). According to the results, skewed regression is more efficient in estimating sperm retrieval success when the data from patients with KS are analyzed. This finding should be investigated by conducting additional studies with different data structures.

Relation of Monocyte/High-Density Lipoprotein Cholesterol Ratio with Coronary Artery Disease in Type 2 Diabetes Mellitus.

PubMed

Ya, Gao; Qiu, Zhang; Tianrong, Pan

2018-06-01

Atherosclerotic cardiovascular disease is the leading cause of mortality of patients with type 2 diabetes mellitus, and both coronary artery disease (CAD) and diabetes mellitus are associated with inflammation. Emerging evidence suggests a relationship of the monocyte to high-density lipoprotein cholesterol ratio (MHR) with the incidence and severity of CAD. The aim of the present study was to examine the association of MHR with CAD in patients with type 2 diabetes mellitus. A total of 458 consecutive individuals were enrolled, comprising 178 type 2 diabetic patients, 124 type 2 diabetes with CAD, and 156 healthy volunteers as the controls. A multivariable logistic regression model was used to evaluate the relationship between the MHR and CAD in type 2 diabetes, and the receiver operating characteristic (ROC) curve of MHR was used for predicting the presence of CAD in type 2 diabetic patients. Values of MHR were significantly higher in type 2 diabetic patients with CAD compared with those without CAD and the control group. Moreover, multivariate logistic regression analysis showed that MHR was an independent predictor of the presence of CAD in type 2 diabetic patients (OR = 1.361, 95% CI 1.245 - 1.487, p < 0.0001). Based on the receiver operating characteristic (ROC) curve, the cutoff value of MHR (> 8.2) in predicting the presence of CAD in type 2 diabetic patients yields a sensitivity and specificity of 83.74% and 62.15%, respectively, with an area under the curve of 0.795 (95% CI: 0.745 - 0.840). The MHR is strongly associated with CAD in type 2 diabetes and might be a potential biomarker to predict the presence of CAD in type 2 diabetic patients.

Methods for estimating the magnitude and frequency of floods for urban and small, rural streams in Georgia, South Carolina, and North Carolina, 2011

USGS Publications Warehouse

Feaster, Toby D.; Gotvald, Anthony J.; Weaver, J. Curtis

2014-01-01

Reliable estimates of the magnitude and frequency of floods are essential for the design of transportation and water-conveyance structures, flood-insurance studies, and flood-plain management. Such estimates are particularly important in densely populated urban areas. In order to increase the number of streamflow-gaging stations (streamgages) available for analysis, expand the geographical coverage that would allow for application of regional regression equations across State boundaries, and build on a previous flood-frequency investigation of rural U.S Geological Survey streamgages in the Southeast United States, a multistate approach was used to update methods for determining the magnitude and frequency of floods in urban and small, rural streams that are not substantially affected by regulation or tidal fluctuations in Georgia, South Carolina, and North Carolina. The at-site flood-frequency analysis of annual peak-flow data for urban and small, rural streams (through September 30, 2011) included 116 urban streamgages and 32 small, rural streamgages, defined in this report as basins draining less than 1 square mile. The regional regression analysis included annual peak-flow data from an additional 338 rural streamgages previously included in U.S. Geological Survey flood-frequency reports and 2 additional rural streamgages in North Carolina that were not included in the previous Southeast rural flood-frequency investigation for a total of 488 streamgages included in the urban and small, rural regression analysis. The at-site flood-frequency analyses for the urban and small, rural streamgages included the expected moments algorithm, which is a modification of the Bulletin 17B log-Pearson type III method for fitting the statistical distribution to the logarithms of the annual peak flows. Where applicable, the flood-frequency analysis also included low-outlier and historic information. Additionally, the application of a generalized Grubbs-Becks test allowed for the detection of multiple potentially influential low outliers. Streamgage basin characteristics were determined using geographical information system techniques. Initial ordinary least squares regression simulations reduced the number of basin characteristics on the basis of such factors as statistical significance, coefficient of determination, Mallow’s Cp statistic, and ease of measurement of the explanatory variable. Application of generalized least squares regression techniques produced final predictive (regression) equations for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probability flows for urban and small, rural ungaged basins for three hydrologic regions (HR1, Piedmont–Ridge and Valley; HR3, Sand Hills; and HR4, Coastal Plain), which previously had been defined from exploratory regression analysis in the Southeast rural flood-frequency investigation. Because of the limited availability of urban streamgages in the Coastal Plain of Georgia, South Carolina, and North Carolina, additional urban streamgages in Florida and New Jersey were used in the regression analysis for this region. Including the urban streamgages in New Jersey allowed for the expansion of the applicability of the predictive equations in the Coastal Plain from 3.5 to 53.5 square miles. Average standard error of prediction for the predictive equations, which is a measure of the average accuracy of the regression equations when predicting flood estimates for ungaged sites, range from 25.0 percent for the 10-percent annual exceedance probability regression equation for the Piedmont–Ridge and Valley region to 73.3 percent for the 0.2-percent annual exceedance probability regression equation for the Sand Hills region.

The reproductive outcome of female patients with myotonic dystrophy type 1 (DM1) undergoing PGD is not affected by the size of the expanded CTG repeat tract

PubMed Central

Seneca, Sara; De Rademaeker, Marjan; Sermon, Karen; De Rycke, Martine; De Vos, Michel; Haentjens, Patrick; Devroey, Paul; Liebaers, Ingeborg

2010-01-01

Purpose This study aims to analyze the relationship between trinucleotide repeat length and reproductive outcome in a large cohort of DM1 patients undergoing ICSI and PGD. Methods Prospective cohort study. The effect of trinucleotide repeat length on reproductive outcome per patient was analyzed using bivariate analysis (T-test) and multivariate analysis using Kaplan-Meier and Cox regression analysis. Results Between 1995 and 2005, 205 cycles of ICSI and PGD were carried out for DM1 in 78 couples. The number of trinucleotide repeats does not have an influence on reproductive outcome when adjusted for age, BMI, basal FSH values, parity, infertility status and male or female affected. Cox regression analysis indicates that cumulative live birth rate is not influenced by the number of trinucleotide repeats. The only factor with a significant effect is age (p < 0.05). Conclusion There is no evidence of an effect of trinucleotide repeat length on reproductive outcome in patients undergoing ICSI and PGD. PMID:20221684

Evaluation of in-line Raman data for end-point determination of a coating process: Comparison of Science-Based Calibration, PLS-regression and univariate data analysis.

PubMed

Barimani, Shirin; Kleinebudde, Peter

2017-10-01

A multivariate analysis method, Science-Based Calibration (SBC), was used for the first time for endpoint determination of a tablet coating process using Raman data. Two types of tablet cores, placebo and caffeine cores, received a coating suspension comprising a polyvinyl alcohol-polyethylene glycol graft-copolymer and titanium dioxide to a maximum coating thickness of 80µm. Raman spectroscopy was used as in-line PAT tool. The spectra were acquired every minute and correlated to the amount of applied aqueous coating suspension. SBC was compared to another well-known multivariate analysis method, Partial Least Squares-regression (PLS) and a simpler approach, Univariate Data Analysis (UVDA). All developed calibration models had coefficient of determination values (R 2 ) higher than 0.99. The coating endpoints could be predicted with root mean square errors (RMSEP) less than 3.1% of the applied coating suspensions. Compared to PLS and UVDA, SBC proved to be an alternative multivariate calibration method with high predictive power. Copyright © 2017 Elsevier B.V. All rights reserved.

Features that exacerbate fatigue severity in joint hypermobility syndrome/Ehlers-Danlos syndrome - hypermobility type.

PubMed

Krahe, Anne Maree; Adams, Roger David; Nicholson, Leslie Lorenda

2018-08-01

To assess the prevalence, severity and impact of fatigue on individuals with joint hypermobility syndrome (JHS)/Ehlers-Danlos syndrome - hypermobility type (EDS-HT) and establish potential determinants of fatigue severity in this population. Questionnaires on symptoms and signs related to fatigue, quality of life, mental health, physical activity participation and sleep quality were completed by people with JHS/EDS-HT recruited through two social media sites. Multiple regression analysis was performed to identify predictors of fatigue in this population. Significant fatigue was reported by 79.5% of the 117 participants. Multiple regression analysis identified five predictors of fatigue severity, four being potentially modifiable, accounting for 52.3% of the variance in reported fatigue scores. Predictors of fatigue severity were: the self-perceived extent of joint hypermobility, orthostatic dizziness related to heat and exercise, levels of participation in personal relationships and community, current levels of physical activity and dissatisfaction with the diagnostic process and management options provided for their condition. Fatigue is a significant symptom associated with JHS/EDS-HT. Assessment of individuals with this condition should include measures of fatigue severity to enable targeted management of potentially modifiable factors associated with fatigue severity. Implications for rehabilitation Fatigue is a significant symptom reported by individuals affected by joint hypermobility syndrome/Ehlers-Danlos syndrome - hypermobility type. Potentially modifiable features that contribute to fatigue severity in this population have been identified. Targeted management of these features may decrease the severity and impact of fatigue in joint hypermobility syndrome/Ehlers-Danlos syndrome - hypermobility type.

Home blood pressure variability on one occasion is a novel factor associated with arterial stiffness in patients with type 2 diabetes.

PubMed

Fukui, Michiaki; Ushigome, Emi; Tanaka, Muhei; Hamaguchi, Masahide; Tanaka, Toru; Atsuta, Haruhiko; Ohnishi, Masayoshi; Oda, Yohei; Hasegawa, Goji; Nakamura, Naoto

2013-03-01

Recent studies have suggested that not only mean blood pressure but also variability in blood pressure might be related to cardiovascular disease. The aim of this study was to investigate the association between home blood pressure variability on one occasion and markers of arterial stiffness in patients with type 2 diabetes. We investigated the relationship between the s.d. of clinic- or home-measured systolic blood pressure on one occasion and pulse wave velocity (PWV) in 332 patients with type 2 diabetes, and we evaluated whether the SD of clinic- or home-measured systolic blood pressure on one occasion was an independent determinant of PWV by multivariate linear regression analysis, after adjustment for known risk factors for arterial stiffness, including sex, age, duration of diabetes, body mass index, hemoglobin A1c, serum total cholesterol, triglycerides, smoking status, drinking alcohol, presence of antihypertensive medication, average systolic blood pressure and heart rate. Age, average morning home-measured systolic blood pressure, heart rate and PWV (r=0.259, P<0.0001) were positively correlated with the s.d. of morning home blood pressure on one occasion. Multiple regression analysis demonstrated that age, average morning home-measured systolic blood pressure (P=0.0019), heart rate and the s.d. of morning home-measured systolic blood pressure on one occasion (P=0.0159) were independently associated with PWV. In conclusion, home blood pressure variability on one occasion was correlated with PWV, independent of other known risk factors, in Japanese patients with type 2 diabetes.

Regression Analysis by Example. 5th Edition

ERIC Educational Resources Information Center

Chatterjee, Samprit; Hadi, Ali S.

2012-01-01

Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…

[Relationship between dietary vitamin C and Type 2 diabetes].

PubMed

Li, Xiaoxiao; Wang, Xinliang; Wei, Jie; Yang, Tubao

2015-10-01

To examine the correlation between dietary vitamin C intake and Type 2 diabetes.
 A total of 5 168 participants from Xiangya Hospital, Central South University were randomly selected. According to the vitamin C intake, the participants were divided into 5 groups: a Q1 group (n=1 033), a Q2 group (n=1 034), a Q3 group (n=1 034), a Q4 group (n=1 034) and a Q5 group (n=1 033). They were also divided into a Type 2 diabetes group (n=502) and a non-diabetes group (n=4 666). The height, weight, and blood pressure were measured, and vitamin C intake and other dairy consumption were evaluated using a food frequency questionnaire and fasting plasma glucose (FPG). The analysis of variance (ANOVA), Chi-square test, Mann-Whitney U test and logistic regression model were used to analyze the relationship between dietary vitamin C and Type 2 diabetes.
 The univariate analysis showed that there were significant differences in the vitamin C consumption in energy intake, activity level, dietary fiber intake, nutritional supplementation status, drinking or not drinking, education level among the different vitamin C intake groups (all P<0.05). There were also significant differences in age, sex, body mass index (BMI), smoking status and vitamin C intake between the Type 2 diabetes group and the non-diabetes group (all P<0.05). After the adjustment for age, gender, hypertension, energy intake or smoking status, the multiple logistic regression model found that the multivariable adjusted OR was 0.610 (95% CI 0.428-0.870) for 
the highest level of vitamin C intake (>154.78 mg/d) in comparison with the lowest level (≤ 63.26 mg/d). The results suggested that the vitamin C intake was inversely associated with the Type 2 diabetes (r=-0.029, P<0.05).
 There is a significant negative correlation between the dietary vitamin C intake and the risk of Type 2 diabetes.

Do Working Hours and Type of Work Affect Obesity in South Korean Female Workers? Analysis of the Korean Community Health Survey.

PubMed

Yoon, Chang-Gyo; Kang, Mo-Yeol; Bae, Kyu-Jung; Yoon, Jin-Ha

2016-02-01

The prevalence of obesity and the female labor participation rate have been rapidly increasing in South Korea. To examine the relationship between these factors, we investigated the association between timing and type of work and obesity in the Korean female working population. Data collected by the 2008 Community Health Survey (CHS) were analyzed using a complex, stratified, multistage, probability cluster sampling method. Descriptive analysis of relevant variables was performed using the chi-square test, and work-related variables by work type were identified using multivariate logistic regression. The relationship between long working hours, night/shift work, and body-mass index in female workers and explanatory, stratifying, and dependent variables and covariates was analyzed using multiple logistic regression models. A total of 42,234 CHS participants were eligible for study inclusion. Among both manual and nonmanual workers, working less than 40 (adjusted odds ratio [aOR] 1.18, 95% confidence interval [CI] 1.07-1.31 and aOR 1.29; 95% CI 1.09-0.52, respectively) or more than 60 (aOR 1.18, 95% CI 1.06-1.30 and aOR 1.28, 95% CI 1.04-1.57, respectively) hours per week was significantly associated with obesity after controlling for covariates. However, working type (day or night/shift) was significantly associated with obesity only in nonmanual workers (aOR 1.20, 95% CI 1.01-1.42). When we controlled working type in the model, manual workers who work more than 60 hours show higher likelihood of being obese (OR 1.10, 95% CI 1.02-1.18). Working fewer (<40) or more than (>60) hours per week is significantly associated with obesity in the Korean female working population, regardless of the type of work. The type of work (day vs. night/shift work) was significantly associated with obesity only in only nonmanual workers.

Improving reliability of aggregation, numerical simulation and analysis of complex systems by empirical data

NASA Astrophysics Data System (ADS)

Dobronets, Boris S.; Popova, Olga A.

2018-05-01

The paper considers a new approach of regression modeling that uses aggregated data presented in the form of density functions. Approaches to Improving the reliability of aggregation of empirical data are considered: improving accuracy and estimating errors. We discuss the procedures of data aggregation as a preprocessing stage for subsequent to regression modeling. An important feature of study is demonstration of the way how represent the aggregated data. It is proposed to use piecewise polynomial models, including spline aggregate functions. We show that the proposed approach to data aggregation can be interpreted as the frequency distribution. To study its properties density function concept is used. Various types of mathematical models of data aggregation are discussed. For the construction of regression models, it is proposed to use data representation procedures based on piecewise polynomial models. New approaches to modeling functional dependencies based on spline aggregations are proposed.

Quantification of brain lipids by FTIR spectroscopy and partial least squares regression

NASA Astrophysics Data System (ADS)

Dreissig, Isabell; Machill, Susanne; Salzer, Reiner; Krafft, Christoph

2009-01-01

Brain tissue is characterized by high lipid content. Its content decreases and the lipid composition changes during transformation from normal brain tissue to tumors. Therefore, the analysis of brain lipids might complement the existing diagnostic tools to determine the tumor type and tumor grade. Objective of this work is to extract lipids from gray matter and white matter of porcine brain tissue, record infrared (IR) spectra of these extracts and develop a quantification model for the main lipids based on partial least squares (PLS) regression. IR spectra of the pure lipids cholesterol, cholesterol ester, phosphatidic acid, phosphatidylcholine, phosphatidylethanolamine, phosphatidylserine, phosphatidylinositol, sphingomyelin, galactocerebroside and sulfatide were used as references. Two lipid mixtures were prepared for training and validation of the quantification model. The composition of lipid extracts that were predicted by the PLS regression of IR spectra was compared with lipid quantification by thin layer chromatography.

Analysis and selection of magnitude relations for the Working Group on Utah Earthquake Probabilities

USGS Publications Warehouse

Duross, Christopher; Olig, Susan; Schwartz, David

2015-01-01

Prior to calculating time-independent and -dependent earthquake probabilities for faults in the Wasatch Front region, the Working Group on Utah Earthquake Probabilities (WGUEP) updated a seismic-source model for the region (Wong and others, 2014) and evaluated 19 historical regressions on earthquake magnitude (M). These regressions relate M to fault parameters for historical surface-faulting earthquakes, including linear fault length (e.g., surface-rupture length [SRL] or segment length), average displacement, maximum displacement, rupture area, seismic moment (Mo ), and slip rate. These regressions show that significant epistemic uncertainties complicate the determination of characteristic magnitude for fault sources in the Basin and Range Province (BRP). For example, we found that M estimates (as a function of SRL) span about 0.3–0.4 units (figure 1) owing to differences in the fault parameter used; age, quality, and size of historical earthquake databases; and fault type and region considered.

Regional regression equations to estimate peak-flow frequency at sites in North Dakota using data through 2009

USGS Publications Warehouse

Williams-Sether, Tara

2015-08-06

Annual peak-flow frequency data from 231 U.S. Geological Survey streamflow-gaging stations in North Dakota and parts of Montana, South Dakota, and Minnesota, with 10 or more years of unregulated peak-flow record, were used to develop regional regression equations for exceedance probabilities of 0.5, 0.20, 0.10, 0.04, 0.02, 0.01, and 0.002 using generalized least-squares techniques. Updated peak-flow frequency estimates for 262 streamflow-gaging stations were developed using data through 2009 and log-Pearson Type III procedures outlined by the Hydrology Subcommittee of the Interagency Advisory Committee on Water Data. An average generalized skew coefficient was determined for three hydrologic zones in North Dakota. A StreamStats web application was developed to estimate basin characteristics for the regional regression equation analysis. Methods for estimating a weighted peak-flow frequency for gaged sites and ungaged sites are presented.

TSS concentration in sewers estimated from turbidity measurements by means of linear regression accounting for uncertainties in both variables.

PubMed

Bertrand-Krajewski, J L

2004-01-01

In order to replace traditional sampling and analysis techniques, turbidimeters can be used to estimate TSS concentration in sewers, by means of sensor and site specific empirical equations established by linear regression of on-site turbidity Tvalues with TSS concentrations C measured in corresponding samples. As the ordinary least-squares method is not able to account for measurement uncertainties in both T and C variables, an appropriate regression method is used to solve this difficulty and to evaluate correctly the uncertainty in TSS concentrations estimated from measured turbidity. The regression method is described, including detailed calculations of variances and covariance in the regression parameters. An example of application is given for a calibrated turbidimeter used in a combined sewer system, with data collected during three dry weather days. In order to show how the established regression could be used, an independent 24 hours long dry weather turbidity data series recorded at 2 min time interval is used, transformed into estimated TSS concentrations, and compared to TSS concentrations measured in samples. The comparison appears as satisfactory and suggests that turbidity measurements could replace traditional samples. Further developments, including wet weather periods and other types of sensors, are suggested.

Exploring the links between macro-level contextual factors and their influence on nursing workforce composition.

PubMed

Squires, Allison; Beltrán-Sánchez, Hiram

2011-11-01

Research that links macro-level socioeconomic development variables to health care human resources workforce composition is scarce at best. The purpose of this study was to explore the links between nonnursing factors and nursing workforce composition through a secondary, descriptive analysis of year 2000, publicly available national nursing human resources data from Mexico. Building on previous research, the authors conducted multiple robust regression analysis by federal typing of nursing human resources from 31 Mexican states against macro-level socioeconomic development variables. Average education in a state was significantly associated in predicting all types of formally educated nurses in Mexico. Other results suggest that macro-level indicators have a different association with each type of nurse. Context may play a greater role in determining nursing workforce composition than previously thought. Further studies may help to explain differences both within and between countries.

Exploring the Links Between Macro-Level Contextual Factors and Their Influence on Nursing Workforce Composition

PubMed Central

Squires, Allison; Beltrán-Sánchez, Hiram

2012-01-01

Research that links macro-level socioeconomic development variables to healthcare human resources workforce composition is scarce at best. The purpose of this study was to explore the links between non-nursing factors and nursing workforce composition through a secondary, descriptive analysis of year 2000, publicly available national nursing human resources data from Mexico. Building on previous research, the authors conducted multiple robust regression analysis by federal typing of nursing human resources from 31 Mexican states against macro-level socioeconomic development variables. Average education in a state was significantly associated in predicting all types of formally educated nurses in Mexico. Other results suggest that macro level indicators have a different association with each type of nurse. Context may play a greater role in determining nursing workforce composition than previously thought. Further studies may help to explain differences both within and between countries. PMID:22513839

Designing pinhole vacancies in graphene towards functionalization: Effects on critical buckling load

NASA Astrophysics Data System (ADS)

Georgantzinos, S. K.; Markolefas, S.; Giannopoulos, G. I.; Katsareas, D. E.; Anifantis, N. K.

2017-03-01

The effect of size and placement of pinhole-type atom vacancies on Euler's critical load on free-standing, monolayer graphene, is investigated. The graphene is modeled by a structural spring-based finite element approach, in which every interatomic interaction is approached as a linear spring. The geometry of graphene and the pinhole size lead to the assembly of the stiffness matrix of the nanostructure. Definition of the boundary conditions of the problem leads to the solution of the eigenvalue problem and consequently to the critical buckling load. Comparison to results found in the literature illustrates the validity and accuracy of the proposed method. Parametric analysis regarding the placement and size of the pinhole-type vacancy, as well as the graphene geometry, depicts the effects on critical buckling load. Non-linear regression analysis leads to empirical-analytical equations for predicting the buckling behavior of graphene, with engineered pinhole-type atom vacancies.

Association between Serum Cystatin C and Diabetic Foot Ulceration in Patients with Type 2 Diabetes: A Cross-Sectional Study

PubMed Central

Zhao, Jie; Deng, Wuquan; Zhang, Yuping; Zheng, Yanling; Zhou, Lina; Boey, Johnson; Armstrong, David G.; Yang, Gangyi

2016-01-01

Serum cystatin C (CysC) has been identified as a possible potential biomarker in a variety of diabetic complications, including diabetic peripheral neuropathy and peripheral artery disease. We aimed to examine the association between CysC and diabetic foot ulceration (DFU) in patients with type 2 diabetes (T2D). 411 patients with T2D were enrolled in this cross-sectional study at a university hospital. Clinical manifestations and biochemical parameters were compared between DFU group and non-DFU group. The association between serum CysC and DFU was explored by binary logistic regression analysis. The cut point of CysC for DFU was also evaluated by receiver operating characteristic (ROC) curve. The prevalence of coronary artery disease, diabetic nephropathy (DN), and DFU dramatically increased with CysC (P < 0.01) in CysC quartiles. Multivariate logistic regression analysis indicated that the significant risk factors for DFU were serum CysC, coronary artery disease, hypertension, insulin use, the differences between supine and sitting TcPO2, and hypertension. ROC curve analysis revealed that the cut point of CysC for DFU was 0.735 mg/L. Serum CysC levels correlated with DFU and severity of tissue loss. Our study results indicated that serum CysC was associated with a high prevalence of DFU in Chinese T2D subjects. PMID:27668262

Parametric Analysis to Study the Influence of Aerogel-Based Renders' Components on Thermal and Mechanical Performance.

PubMed

Ximenes, Sofia; Silva, Ana; Soares, António; Flores-Colen, Inês; de Brito, Jorge

2016-05-04

Statistical models using multiple linear regression are some of the most widely used methods to study the influence of independent variables in a given phenomenon. This study's objective is to understand the influence of the various components of aerogel-based renders on their thermal and mechanical performance, namely cement (three types), fly ash, aerial lime, silica sand, expanded clay, type of aerogel, expanded cork granules, expanded perlite, air entrainers, resins (two types), and rheological agent. The statistical analysis was performed using SPSS (Statistical Package for Social Sciences), based on 85 mortar mixes produced in the laboratory and on their values of thermal conductivity and compressive strength obtained using tests in small-scale samples. The results showed that aerial lime assumes the main role in improving the thermal conductivity of the mortars. Aerogel type, fly ash, expanded perlite and air entrainers are also relevant components for a good thermal conductivity. Expanded clay can improve the mechanical behavior and aerogel has the opposite effect.

Parametric Analysis to Study the Influence of Aerogel-Based Renders’ Components on Thermal and Mechanical Performance

PubMed Central

Ximenes, Sofia; Silva, Ana; Soares, António; Flores-Colen, Inês; de Brito, Jorge

2016-01-01

Statistical models using multiple linear regression are some of the most widely used methods to study the influence of independent variables in a given phenomenon. This study’s objective is to understand the influence of the various components of aerogel-based renders on their thermal and mechanical performance, namely cement (three types), fly ash, aerial lime, silica sand, expanded clay, type of aerogel, expanded cork granules, expanded perlite, air entrainers, resins (two types), and rheological agent. The statistical analysis was performed using SPSS (Statistical Package for Social Sciences), based on 85 mortar mixes produced in the laboratory and on their values of thermal conductivity and compressive strength obtained using tests in small-scale samples. The results showed that aerial lime assumes the main role in improving the thermal conductivity of the mortars. Aerogel type, fly ash, expanded perlite and air entrainers are also relevant components for a good thermal conductivity. Expanded clay can improve the mechanical behavior and aerogel has the opposite effect. PMID:28773460

Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression

NASA Astrophysics Data System (ADS)

García-Rodríguez, M. J.; Malpica, J. A.; Benito, B.; Díaz, M.

2008-03-01

This work has evaluated the probability of earthquake-triggered landslide occurrence in the whole of El Salvador, with a Geographic Information System (GIS) and a logistic regression model. Slope gradient, elevation, aspect, mean annual precipitation, lithology, land use, and terrain roughness are the predictor variables used to determine the dependent variable of occurrence or non-occurrence of landslides within an individual grid cell. The results illustrate the importance of terrain roughness and soil type as key factors within the model — using only these two variables the analysis returned a significance level of 89.4%. The results obtained from the model within the GIS were then used to produce a map of relative landslide susceptibility.

Dependence of driving characteristics upon follower-leader combination

NASA Astrophysics Data System (ADS)

Nagahama, Akihito; Yanagisawa, Daichi; Nishinari, Katsuhiro

2017-10-01

The analysis of the microscopic view of mixed traffic offers a basis for resolving traffic jams which are inhomogeneous due to several types of vehicles. In this study, we research the dependence of driving characteristics upon vehicle order in a platoon. By focusing particularly upon the manner in which the driving characteristics of a follower are affected by both their own vehicle type and that of their leader, we measured the trajectories of platoons comprising two vehicles selected from motorcycles, passenger cars, and trucks on a test course. Analysis based on vehicle order showed that the vehicle types of both the leader and the follower as well as the leader's driving characteristics affected the velocity, acceleration, deceleration, operational delay of followers, and the distance gap between leaders and followers in different ways. In addition, we investigated the factors affecting driving characteristics by multiple regression analysis. We revealed that the operational delay and the maximum distance gap tend to be large when the length of leaders is large. Furthermore, as long as a follower can follow, we need not consider vehicle types among the parameters determining maximum velocity in car-following models. The vehicle types of the leader and the follower should be considered to determine maximum acceleration. When determining maximum deceleration, the vehicle types of the follower should be considered.

Are late-night eating habits and sleep duration associated with glycemic control in adult type 1 diabetes patients treated with insulin pumps?

PubMed

Matejko, Bartlomiej; Kiec-Wilk, Beata; Szopa, Magdalena; Trznadel Morawska, Iwona; Malecki, Maciej T; Klupa, Tomasz

2015-07-01

Little is known about the impact of sleep duration and late-night snacking on glycemic control in patients with type 1 diabetes using insulin pumps. The aim of the present study was to examine whether late-night eating habits and short sleep duration are associated with glycemic control in continuous subcutaneous insulin infusion-treated type 1 diabetic patients. We included 148 consecutive adult type 1 diabetic subjects using an insulin pump (100 women and 48 men). Participants completed a questionnaire regarding sleep duration (classified as short if ≤6 h) and late-night snacking. Other sources of information included medical records and data from blood glucose meters. Glycemic control was assessed by glycated hemoglobin (HbA1c) levels and mean self-monitoring of blood glucose (SMBG) readings. The mean age of patients was 26 years, mean type 1 diabetes duration was 13.4 years and mean HbA1c level was 7.2%. In a univariate regression analysis, sleep duration was a predictor of both HbA1c (β = 0.51, P = 0.01) and SMBG levels (β = 11.4, P = 0.02). Additionally, an association was found between frequent late-night snacking and higher SMBG readings (often snacking β = 18.1, P = 0.05), but not with increased HbA1c levels. In the multivariate linear regression, independent predictors for HbA1c and SMBG were sleep duration and patient age. In a univariate logistic regression, sleep duration and frequency of late-night snacking were not predictors of whether HbA1c target levels were achieved. Short sleep duration, but not late-night snacking, seems to be associated with poorer glycemic control in type 1 diabetic patients treated with continuous subcutaneous insulin infusion.

Correlation of concentration of modified cassava flour for banana fritter flour using simple linear regression

NASA Astrophysics Data System (ADS)

Herminiati, A.; Rahman, T.; Turmala, E.; Fitriany, C. G.

2017-12-01

The purpose of this study was to determine the correlation of different concentrations of modified cassava flour that was processed for banana fritter flour. The research method consists of two stages: (1) to determine the different types of flour: cassava flour, modified cassava flour-A (using the method of the lactid acid bacteria), and modified cassava flour-B (using the method of the autoclaving cooling cycle), then conducted on organoleptic test and physicochemical analysis; (2) to determine the correlation of concentration of modified cassava flour for banana fritter flour, by design was used simple linear regression. The factors were used different concentrations of modified cassava flour-B (y1) 40%, (y2) 50%, and (y3) 60%. The response in the study includes physical analysis (whiteness of flour, water holding capacity-WHC, oil holding capacity-OHC), chemical analysis (moisture content, ash content, crude fiber content, starch content), and organoleptic (color, aroma, taste, texture). The results showed that the type of flour selected from the organoleptic test was modified cassava flour-B. Analysis results of modified cassava flour-B component containing whiteness of flour 60.42%; WHC 41.17%; OHC 21.15%; moisture content 4.4%; ash content 1.75%; crude fiber content 1.86%; starch content 67.31%. The different concentrations of modified cassava flour-B with the results of the analysis provides correlation to the whiteness of flour, WHC, OHC, moisture content, ash content, crude fiber content, and starch content. The different concentrations of modified cassava flour-B does not affect the color, aroma, taste, and texture.

A Quantitative Study of the Relationship between the Distribution of Different Types of Collagen and the Mechanical Behavior of Rabbit Medial Collateral Ligaments

PubMed Central

Wan, Chao; Hao, Zhixiu; Wen, Shizhu; Leng, Huijie

2014-01-01

The mechanical properties of ligaments are key contributors to the stability and function of musculoskeletal joints. Ligaments are generally composed of ground substance, collagen (mainly type I and III collagen), and minimal elastin fibers. However, no consensus has been reached about whether the distribution of different types of collagen correlates with the mechanical behaviors of ligaments. The main objective of this study was to determine whether the collagen type distribution is correlated with the mechanical properties of ligaments. Using axial tensile tests and picrosirius red staining-polarization observations, the mechanical behaviors and the ratios of the various types of collagen were investigated for twenty-four rabbit medial collateral ligaments from twenty-four rabbits of different ages, respectively. One-way analysis of variance was used in the comparison of the Young's modulus in the linear region of the stress-strain curves and the ratios of type I and III collagen for the specimens (the mid-substance specimens of the ligaments) with different ages. A multiple linear regression was performed using the collagen contents (the ratios of type I and III collagen) and the Young's modulus of the specimens. During the maturation of the ligaments, the type I collagen content increased, and the type III collagen content decreased. A significant and strong correlation () was identified by multiple linear regression between the collagen contents (i.e., the ratios of type I and type III collagen) and the mechanical properties of the specimens. The collagen content of ligaments might provide a new perspective for evaluating the linear modulus of global stress-strain curves for ligaments and open a new door for studying the mechanical behaviors and functions of connective tissues. PMID:25062068

A quantitative study of the relationship between the distribution of different types of collagen and the mechanical behavior of rabbit medial collateral ligaments.

PubMed

Wan, Chao; Hao, Zhixiu; Wen, Shizhu; Leng, Huijie

2014-01-01

The mechanical properties of ligaments are key contributors to the stability and function of musculoskeletal joints. Ligaments are generally composed of ground substance, collagen (mainly type I and III collagen), and minimal elastin fibers. However, no consensus has been reached about whether the distribution of different types of collagen correlates with the mechanical behaviors of ligaments. The main objective of this study was to determine whether the collagen type distribution is correlated with the mechanical properties of ligaments. Using axial tensile tests and picrosirius red staining-polarization observations, the mechanical behaviors and the ratios of the various types of collagen were investigated for twenty-four rabbit medial collateral ligaments from twenty-four rabbits of different ages, respectively. One-way analysis of variance was used in the comparison of the Young's modulus in the linear region of the stress-strain curves and the ratios of type I and III collagen for the specimens (the mid-substance specimens of the ligaments) with different ages. A multiple linear regression was performed using the collagen contents (the ratios of type I and III collagen) and the Young's modulus of the specimens. During the maturation of the ligaments, the type I collagen content increased, and the type III collagen content decreased. A significant and strong correlation (R2 = 0.839, P < 0.05) was identified by multiple linear regression between the collagen contents (i.e., the ratios of type I and type III collagen) and the mechanical properties of the specimens. The collagen content of ligaments might provide a new perspective for evaluating the linear modulus of global stress-strain curves for ligaments and open a new door for studying the mechanical behaviors and functions of connective tissues.

A deeper look at two concepts of measuring gene-gene interactions: logistic regression and interaction information revisited.

PubMed

Mielniczuk, Jan; Teisseyre, Paweł

2018-03-01

Detection of gene-gene interactions is one of the most important challenges in genome-wide case-control studies. Besides traditional logistic regression analysis, recently the entropy-based methods attracted a significant attention. Among entropy-based methods, interaction information is one of the most promising measures having many desirable properties. Although both logistic regression and interaction information have been used in several genome-wide association studies, the relationship between them has not been thoroughly investigated theoretically. The present paper attempts to fill this gap. We show that although certain connections between the two methods exist, in general they refer two different concepts of dependence and looking for interactions in those two senses leads to different approaches to interaction detection. We introduce ordering between interaction measures and specify conditions for independent and dependent genes under which interaction information is more discriminative measure than logistic regression. Moreover, we show that for so-called perfect distributions those measures are equivalent. The numerical experiments illustrate the theoretical findings indicating that interaction information and its modified version are more universal tools for detecting various types of interaction than logistic regression and linkage disequilibrium measures. © 2017 WILEY PERIODICALS, INC.

Rapid and simultaneous analysis of five alkaloids in four parts of Coptidis Rhizoma by near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Jintao, Xue; Yufei, Liu; Liming, Ye; Chunyan, Li; Quanwei, Yang; Weiying, Wang; Yun, Jing; Minxiang, Zhang; Peng, Li

2018-01-01

Near-Infrared Spectroscopy (NIRS) was first used to develop a method for rapid and simultaneous determination of 5 active alkaloids (berberine, coptisine, palmatine, epiberberine and jatrorrhizine) in 4 parts (rhizome, fibrous root, stem and leaf) of Coptidis Rhizoma. A total of 100 samples from 4 main places of origin were collected and studied. With HPLC analysis values as calibration reference, the quantitative analysis of 5 marker components was performed by two different modeling methods, partial least-squares (PLS) regression as linear regression and artificial neural networks (ANN) as non-linear regression. The results indicated that the 2 types of models established were robust, accurate and repeatable for five active alkaloids, and the ANN models was more suitable for the determination of berberine, coptisine and palmatine while the PLS model was more suitable for the analysis of epiberberine and jatrorrhizine. The performance of the optimal models was achieved as follows: the correlation coefficient (R) for berberine, coptisine, palmatine, epiberberine and jatrorrhizine was 0.9958, 0.9956, 0.9959, 0.9963 and 0.9923, respectively; the root mean square error of validation (RMSEP) was 0.5093, 0.0578, 0.0443, 0.0563 and 0.0090, respectively. Furthermore, for the comprehensive exploitation and utilization of plant resource of Coptidis Rhizoma, the established NIR models were used to analysis the content of 5 active alkaloids in 4 parts of Coptidis Rhizoma and 4 main origin of places. This work demonstrated that NIRS may be a promising method as routine screening for off-line fast analysis or on-line quality assessment of traditional Chinese medicine (TCM).

Factors Contributing to Successful Employment Outcomes for Hispanic Women Who Are Deaf: Utilization of Chi-Squared Automatic Interaction Detector and Logistic Regression Analysis

ERIC Educational Resources Information Center

Feist, Amber M.

2013-01-01

Hispanic women who are deaf constitute a heterogeneous group of individuals with varying vocational needs. To understand the unique needs of this population, it is important to analyze how consumer characteristics, presence of public supports, and type of services provided influence employment outcomes for Hispanic women who are deaf. The purpose…

Use of Forest Inventory and Analysis information in wildlife habitat modeling: a process for linking multiple scales

Treesearch

Thomas C. Edwards; Gretchen G. Moisen; Tracey S. Frescino; Joshua L. Lawler

2002-01-01

We describe our collective efforts to develop and apply methods for using FIA data to model forest resources and wildlife habitat. Our work demonstrates how flexible regression techniques, such as generalized additive models, can be linked with spatially explicit environmental information for the mapping of forest type and structure. We illustrate how these maps of...

A Regression Analysis of South Carolina Algebra I End-of-Course Exam Scores by Schedule Type

ERIC Educational Resources Information Center

Smith, Dawn M.

2017-01-01

The purpose of this study was to examine the relationship between scheduling and first-year-high-school students' exam scores on the South Carolina Algebra I End-of-Course (EOC) assessment. The study compared existing empirical data from two southeastern high schools from the same school district using 4 X 4 block schedules from 2011-2014 and…

Factors that influence the efficacy of acarbose and metformin as initial therapy in Chinese patients with newly diagnosed type 2 diabetes: a subanalysis of the MARCH trial.

PubMed

Zhang, Jinping; Wang, Na; Xing, Xiaoyan; Yang, Zhaojun; Wang, Xin; Yang, Wenying

2016-01-01

To conduct a subanalysis of the randomized MARCH (Metformin and AcaRbose in Chinese as the initial Hypoglycemic treatment) trial to investigate whether specific characteristics are associated with the efficacy of either acarbose or metformin as initial therapy. A total of 657 type 2 diabetes patients who were randomly assigned to 48 weeks of therapy with either acarbose or metformin in the MARCH trial were divided into two groups based upon their hemoglobin A1c (HbA1c) levels at the end of follow-up: HbA1c <7% (<53 mmol/mol) and ≥7% (≥53 mmol/mol). Univariate, multivariate, and stepwise linear regression analyses were applied to identify the factors associated with treatment efficacy. Because this was a subanalysis, no measurement was performed. Univariate analysis showed that the efficacy of acarbose and metformin was influenced by HbA1c, fasting blood glucose (FBG), and 2 hour postprandial venous blood glucose (2hPPG) levels, as well as by changes in body mass index (BMI) (p ≤ 0.006). Multivariate analysis and stepwise linear regression analyses indicated that lower baseline 2hPPG values and greater changes in BMI were factors that positively influenced efficacy in both treatment groups (p ≤ 0.05). Stepwise regression model analysis also revealed that a lower baseline homeostasis model assessment-estimated insulin resistance (HOMA-IR) and higher serum insulin area under the curve (AUC) were factors positively influencing HbA1c normalization in all patients (p ≤ 0.032). Newly diagnosed type 2 diabetes patients with lower baseline 2hPPG and HOMA-IR values are more likely to achieve glucose control with acarbose or metformin treatment. Furthermore, the change in BMI after acarbose or metformin treatment is also a factor influencing HbA1c normalization. A prospective study with a larger sample size is necessary to confirm our results as well as measure β cell function and examine the influence of the patients' dietary habits.

Same Game, Different Rules? Gender Differences in Political Participation.

PubMed

Coffé, Hilde; Bolzendahl, Catherine

2010-03-01

We investigate gender gaps in political participation with 2004 ISSP data for 18 advanced Western democracies (N: 20,359) using linear and logistic regression models. Controlling for socio-economic characteristics and political attitudes reveals that women are more likely than men to have voted and engaged in 'private' activism, while men are more likely to have engaged in direct contact, collective types of actions and be (more active) members of political parties. Our analysis indicates that demographic and attitudinal characteristics influence participation differently among men and among women, as well as across types of participation. These results highlight the need to move toward a view of women engaging in differing types of participation and based on different characteristics.

Association Between Nonrestorative Sleep and Risk of Diabetes: A Cross-Sectional Study.

PubMed

Okamoto, Masaki; Kobayashi, Yasuki; Nakamura, Fumiaki; Musha, Terunaga

2017-01-01

Although insufficient sleep or poor sleep quality has been reported to be associated with the development of type 2 diabetes, the relation of type 2 diabetes with nonrestorative sleep (NRS), a subjective feeling, has been overlooked. We used a large-scale medical checkup database to investigate whether there is a cross-sectional association between NRS and type 2 diabetes risk in Japanese individuals. We extracted data for 14,476 individuals who were not receiving therapeutic drugs for diabetes. About 36.8% of individuals were identified as having NRS. In a multiple logistic regression analysis, NRS was significantly associated with the risk of developing diabetes. Thus, this line of research may have implications for diabetes prevention.

[Optimization of diagnosis indicator selection and inspection plan by 3.0T MRI in breast cancer].

PubMed

Jiang, Zhongbiao; Wang, Yunhua; He, Zhong; Zhang, Lejun; Zheng, Kai

2013-08-01

To optimize 3.0T MRI diagnosis indicator in breast cancer and to select the best MRI scan program. Totally 45 patients with breast cancers were collected, and another 35 patients with benign breast tumor served as the control group. All patients underwent 3.0T MRI, including T1- weighted imaging (T1WI), fat suppression of the T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), 1H magnetic resonance spectroscopy (1H-MRS) and dynamic contrast enhanced (DCE) sequence. With operation pathology results as the gold standard in the diagnosis of breast diseases, the pathological results of benign and malignant served as dependent variables, and the diagnostic indicators of MRI were taken as independent variables. We put all the indicators of MRI examination under Logistic regression analysis, established the Logistic model, and optimized the diagnosis indicators of MRI examination to further improve MRI scan of breast cancer. By Logistic regression analysis, some indicators were selected in the equation, including the edge feature of the tumor, the time-signal intensity curve (TIC) type and the apparent diffusion coefficient (ADC) value when b=500 s/mm2. The regression equation was Logit (P)=-21.936+20.478X6+3.267X7+ 21.488X3. Valuable indicators in the diagnosis of breast cancer are the edge feature of the tumor, the TIC type and the ADC value when b=500 s/mm2. Combining conventional MRI scan, DWI and dynamic enhanced MRI is a better examination program, while MRS is the complementary program when diagnosis is difficult.

Assessing the Associations Between Types of Green Space, Physical Activity, and Health Indicators Using GIS and Participatory Survey

NASA Astrophysics Data System (ADS)

Akpinar, A.

2017-11-01

This study explores whether specific types of green spaces (i.e. urban green spaces, forests, agricultural lands, rangelands, and wetlands) are associated with physical activity, quality of life, and cardiovascular disease prevalence. A sample of 8,976 respondents from the Behavioral Risk Factor Surveillance System, conducted in 2006 in Washington State across 291 zip-codes, was analyzed. Measures included physical activity status, quality of life, and cardiovascular disease prevalence (i.e. heart attack, angina, and stroke). Percentage of green spaces was derived from the National Land Cover Dataset and measured with Geographical Information System. Multilevel regression analyses were conducted to analyze the data while controlling for age, sex, race, weight, marital status, occupation, income, education level, and zip-code population and socio-economic situation. Regression results reveal that no green space types were associated with physical activity, quality of life, and cardiovascular disease prevalence. On the other hand, the analysis shows that physical activity was associated with general health, quality of life, and cardiovascular disease prevalence. The findings suggest that other factors such as size, structure and distribution (sprawled or concentrated, large or small), quality, and characteristics of green space might be important in general health, quality of life, and cardiovascular disease prevalence rather than green space types. Therefore, further investigations are needed.

A novel variational Bayes multiple locus Z-statistic for genome-wide association studies with Bayesian model averaging

PubMed Central

Logsdon, Benjamin A.; Carty, Cara L.; Reiner, Alexander P.; Dai, James Y.; Kooperberg, Charles

2012-01-01

Motivation: For many complex traits, including height, the majority of variants identified by genome-wide association studies (GWAS) have small effects, leaving a significant proportion of the heritable variation unexplained. Although many penalized multiple regression methodologies have been proposed to increase the power to detect associations for complex genetic architectures, they generally lack mechanisms for false-positive control and diagnostics for model over-fitting. Our methodology is the first penalized multiple regression approach that explicitly controls Type I error rates and provide model over-fitting diagnostics through a novel normally distributed statistic defined for every marker within the GWAS, based on results from a variational Bayes spike regression algorithm. Results: We compare the performance of our method to the lasso and single marker analysis on simulated data and demonstrate that our approach has superior performance in terms of power and Type I error control. In addition, using the Women's Health Initiative (WHI) SNP Health Association Resource (SHARe) GWAS of African-Americans, we show that our method has power to detect additional novel associations with body height. These findings replicate by reaching a stringent cutoff of marginal association in a larger cohort. Availability: An R-package, including an implementation of our variational Bayes spike regression (vBsr) algorithm, is available at http://kooperberg.fhcrc.org/soft.html. Contact: blogsdon@fhcrc.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22563072

The association between Type D personality and the metabolic syndrome: a cross-sectional study in a University-based outpatient lipid clinic

PubMed Central

2011-01-01

Background Type D personality has been associated in the past with increased cardiovascular mortality among patients with established coronary heart disease. Very few studies have investigated the association of type D personality with traditional cardiovascular risk factors. In this study, we assessed the association between type D personality and the metabolic syndrome. Findings New consecutive patients referred to an outpatient lipid clinic for evaluation of possible metabolic syndrome were eligible for inclusion in the study. The metabolic syndrome was defined according to the International Diabetes Federation (IDF) diagnostic criteria. Type D personality was assessed with the DS-14 scale. Multivariate regression techniques were used to investigate the association between personality and metabolic syndromes adjusting for a number of medical and psychiatric confounders. Three hundred and fifty-nine persons were screened of whom 206 met the diagnostic criteria for the metabolic syndrome ("cases") and 153 did not ("control group"). The prevalence of type D personality was significantly higher in the cases as compared to the control group (44% versus 15% respectively, p < 0.001). In multivariate logistic regression analysis the presence of Type D personality was significantly associated with metabolic syndrome independently of other clinical factors, anxiety and depressive symptoms (odds ratio 3.47; 95% Confidence Interval: 1.90 - 6.33). Conclusions Type D personality was independently associated with the metabolic syndrome in this cross-sectional study. The potential implications of this finding, especially from a clinical or preventive perspective, should be examined in future research. PMID:21466680

Varying coefficient subdistribution regression for left-truncated semi-competing risks data.

PubMed

Li, Ruosha; Peng, Limin

2014-10-01

Semi-competing risks data frequently arise in biomedical studies when time to a disease landmark event is subject to dependent censoring by death, the observation of which however is not precluded by the occurrence of the landmark event. In observational studies, the analysis of such data can be further complicated by left truncation. In this work, we study a varying co-efficient subdistribution regression model for left-truncated semi-competing risks data. Our method appropriately accounts for the specifical truncation and censoring features of the data, and moreover has the flexibility to accommodate potentially varying covariate effects. The proposed method can be easily implemented and the resulting estimators are shown to have nice asymptotic properties. We also present inference, such as Kolmogorov-Smirnov type and Cramér Von-Mises type hypothesis testing procedures for the covariate effects. Simulation studies and an application to the Denmark diabetes registry demonstrate good finite-sample performance and practical utility of the proposed method.

Physical activity, social network type, and depressive symptoms in late life: an analysis of data from the National Social Life, Health and Aging Project.

PubMed

Litwin, Howard

2012-01-01

To clarify whether physical activity among older Americans is associated with depressive symptoms, beyond the effects of social network type, physical health, and sociodemographic characteristics. The analysis used data from a sub-sample, aged 65–85, from the National Social Life, Health and Aging Project (N=1349). Hierarchical regressions examined the respective effects of selected network types and extent of engagement in physical activity on depressive symptoms, controlling for physical health and sociodemographic background. The findings showed that physical activity was correlated inversely with late life depressive symptoms. However, when interaction terms for the selected social network types and the extent of physical activity were also considered, the main effect of social network on depressive symptoms increased, while that of physical activity was eliminated. The results show that older American adults embedded in family network types are at risk of limited physical activity. However, interventions aimed to increase their engagement in physical activity might help to reduce depressive symptoms within this group.

Spatial Bayesian latent factor regression modeling of coordinate-based meta-analysis data.

PubMed

Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D; Nichols, Thomas E

2018-03-01

Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the article are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to (i) identify areas of consistent activation; and (ii) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterized as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. © 2017, The International Biometric Society.

Type D personality, self-efficacy, and medication adherence in patients with heart failure-A mediation analysis.

PubMed

Wu, Jia-Rong; Song, Eun Kyeung; Moser, Debra K

2015-01-01

Type D personality is associated with medication non-adherence. Both Type D personality and non-adherence are predictors of poor outcomes. Self-efficacy, which is modifiable, is also associated with medication adherence. To determine the relationships among Type D personality, self-efficacy, and medication adherence in 84 heart failure patients. Self-efficacy, Type D personality, medication adherence, demographic and clinical data were collected. Hierarchical linear regression was used. Type D patients were more likely to have lower self-efficacy (p = .023) and medication non-adherence (p = .027) than non-Type D patients. Low self-efficacy was associated with medication non-adherence (p < .001). Type D personality didn't predict medication adherence after entering self-efficacy in the model (p = .422), demonstrating mediation. Self-efficacy mediates the relationship between Type D personality and medication adherence. Developing and applying interventions to enhance self-efficacy may help to sever the link between Type D personality and poor outcomes. Copyright © 2015 Elsevier Inc. All rights reserved.

Epstein-Barr Virus and Gastric Cancer Risk: A Meta-analysis With Meta-regression of Case-control Studies.

PubMed

Bae, Jong-Myon; Kim, Eun Hee

2016-03-01

Research on how the risk of gastric cancer increases with Epstein-Barr virus (EBV) infection is lacking. In a systematic review that investigated studies published until September 2014, the authors did not calculate the summary odds ratio (SOR) due to heterogeneity across studies. Therefore, we include here additional studies published until October 2015 and conduct a meta-analysis with meta-regression that controls for the heterogeneity among studies. Using the studies selected in the previously published systematic review, we formulated lists of references, cited articles, and related articles provided by PubMed. From the lists, only case-control studies that detected EBV in tissue samples were selected. In order to control for the heterogeneity among studies, subgroup analysis and meta-regression were performed. In the 33 case-control results with adjacent non-cancer tissue, the total number of test samples in the case and control groups was 5280 and 4962, respectively. In the 14 case-control results with normal tissue, the total number of test samples in case and control groups was 1393 and 945, respectively. Upon meta-regression, the type of control tissue was found to be a statistically significant variable with regard to heterogeneity. When the control tissue was normal tissue of healthy individuals, the SOR was 3.41 (95% CI, 1.78 to 6.51; I-squared, 65.5%). The results of the present study support the argument that EBV infection increases the risk of gastric cancer. In the future, age-matched and sex-matched case-control studies should be conducted.

Measuring the statistical validity of summary meta‐analysis and meta‐regression results for use in clinical practice

PubMed Central

Riley, Richard D.

2017-01-01

An important question for clinicians appraising a meta‐analysis is: are the findings likely to be valid in their own practice—does the reported effect accurately represent the effect that would occur in their own clinical population? To this end we advance the concept of statistical validity—where the parameter being estimated equals the corresponding parameter for a new independent study. Using a simple (‘leave‐one‐out’) cross‐validation technique, we demonstrate how we may test meta‐analysis estimates for statistical validity using a new validation statistic, Vn, and derive its distribution. We compare this with the usual approach of investigating heterogeneity in meta‐analyses and demonstrate the link between statistical validity and homogeneity. Using a simulation study, the properties of Vn and the Q statistic are compared for univariate random effects meta‐analysis and a tailored meta‐regression model, where information from the setting (included as model covariates) is used to calibrate the summary estimate to the setting of application. Their properties are found to be similar when there are 50 studies or more, but for fewer studies Vn has greater power but a higher type 1 error rate than Q. The power and type 1 error rate of Vn are also shown to depend on the within‐study variance, between‐study variance, study sample size, and the number of studies in the meta‐analysis. Finally, we apply Vn to two published meta‐analyses and conclude that it usefully augments standard methods when deciding upon the likely validity of summary meta‐analysis estimates in clinical practice. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:28620945

Measuring the statistical validity of summary meta-analysis and meta-regression results for use in clinical practice.

PubMed

Willis, Brian H; Riley, Richard D

2017-09-20

An important question for clinicians appraising a meta-analysis is: are the findings likely to be valid in their own practice-does the reported effect accurately represent the effect that would occur in their own clinical population? To this end we advance the concept of statistical validity-where the parameter being estimated equals the corresponding parameter for a new independent study. Using a simple ('leave-one-out') cross-validation technique, we demonstrate how we may test meta-analysis estimates for statistical validity using a new validation statistic, Vn, and derive its distribution. We compare this with the usual approach of investigating heterogeneity in meta-analyses and demonstrate the link between statistical validity and homogeneity. Using a simulation study, the properties of Vn and the Q statistic are compared for univariate random effects meta-analysis and a tailored meta-regression model, where information from the setting (included as model covariates) is used to calibrate the summary estimate to the setting of application. Their properties are found to be similar when there are 50 studies or more, but for fewer studies Vn has greater power but a higher type 1 error rate than Q. The power and type 1 error rate of Vn are also shown to depend on the within-study variance, between-study variance, study sample size, and the number of studies in the meta-analysis. Finally, we apply Vn to two published meta-analyses and conclude that it usefully augments standard methods when deciding upon the likely validity of summary meta-analysis estimates in clinical practice. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

[Association of cardiovascular autonomic neuropathy and prolonged QT interval with cardiovascular morbidity and mortality in patients with type 2 diabetes mellitus].

PubMed

Ticse Aguirre, Ray; Villena, Jaime E

2011-03-01

In order to evaluate the relationship between cardiovascular autonomic neuropathy and corrected QT interval (QTc) with cardiovascular morbidity and mortality in patients with type 2 diabetes mellitus, we followed up for 5 years 67 patients attending the outpatient Endocrinology Service. 82% completed follow-up and cardiovascular events occurred in 16 patients. We found that long QTc interval was the only variable significantly associated with cardiovascular morbidity and mortality in the multiple logistic regression analysis (RR: 13.56, 95% CI: 2.01-91.36) (p = 0.0074).

Genetics and epidemiology, congenital anomalies and cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Friedman, J.M.

1997-03-01

Many of the basic statistical methods used in epidemiology - regression, analysis of variance, and estimation of relative risk, for example - originally were developed for the genetic analysis of biometric data. The familiarity that many geneticists have with this methodology has helped geneticists to understand and accept genetic epidemiology as a scientific discipline. It worth noting, however, that most of the work in genetic epidemiology during the past decade has been devoted to linkage and other family studies, rather than to population-based investigations of the type that characterize much of mainstream epidemiology. 30 refs., 2 tabs.

Categorical Data Analysis Using a Skewed Weibull Regression Model

NASA Astrophysics Data System (ADS)

Caron, Renault; Sinha, Debajyoti; Dey, Dipak; Polpo, Adriano

2018-03-01

In this paper, we present a Weibull link (skewed) model for categorical response data arising from binomial as well as multinomial model. We show that, for such types of categorical data, the most commonly used models (logit, probit and complementary log-log) can be obtained as limiting cases. We further compare the proposed model with some other asymmetrical models. The Bayesian as well as frequentist estimation procedures for binomial and multinomial data responses are presented in details. The analysis of two data sets to show the efficiency of the proposed model is performed.

Multiple Types of Childhood and Adult Violence Among Homeless and Unstably Housed Women in San Francisco.

PubMed

Wong, Lauren H; Shumway, Martha; Flentje, Annesa; Riley, Elise D

2016-12-01

This study examined the relationship between different forms of childhood violence (emotional, physical, and sexual) and these same forms of violence in adulthood, using a crosssectional baseline survey of 298 homeless and unstably housed women in San Francisco, California. We also examined other related factors, including mental illnesses diagnosis, sex exchange, jail time, HIV status, and sociodemographic information. Regression analysis indicated that although several of these factors were associated with experiences of violence as an adult, specific types of child violence (e.g., sexual violence) predicted instances of that same type of violence as an adult but not necessarily other types. Thus, risk of adult violence among low-income women may be better predicted and addressed through histories of same-type childhood violence, despite years of intervening exposures and stressors.

Multiple types of childhood and adult violence among homeless and unstably housed women in San Francisco

PubMed Central

Wong, Lauren H.; Shumway, Martha; Flentje, Annesa; Riley, Elise D.

2017-01-01

The present study examined the relationship between different forms of childhood violence (emotional, physical, and sexual) and these same forms of violence in adulthood, using a cross-sectional baseline survey of 298 homeless and unstably housed women in San Francisco, California. We also examined other related factors, including mental illnesses diagnosis, sex exchange, jail time, HIV status, and sociodemographic information. Regression analysis indicated that while several of these factors were associated with experiences of violence as an adult, specific types of child violence (e.g. sexual violence) predicted instances of that same type of violence as an adult, but not necessarily other types. Thus, risk of adult violence among low-income women may be better predicted and addressed through histories of same-type childhood violence, despite competing current stressors. PMID:27640925

Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression.

PubMed

Chen, Yanguang

2016-01-01

In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson's statistic depends on the sequence of data points. This paper develops two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran's index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then by analogy with the Durbin-Watson statistic, two types of new serial correlation indices are constructed. As a case study, the two newly presented statistics are applied to a spatial sample of 29 China's regions. These results show that the new spatial autocorrelation models can be used to test the serial correlation of residuals from regression analysis. In practice, the new statistics can make up for the deficiencies of the Durbin-Watson test.

Gastroduodenal Ulcers and ABO Blood Group: the Japan Nurses' Health Study (JNHS).

PubMed

Alkebsi, Lobna; Ideno, Yuki; Lee, Jung-Su; Suzuki, Shosuke; Nakajima-Shimada, Junko; Ohnishi, Hiroshi; Sato, Yasunori; Hayashi, Kunihiko

2018-01-05

Although several studies have shown that blood type O is associated with increased risk of peptic ulcer, few studies have investigated these associations in Japan. We sought to investigate the association between the ABO blood group and risk of gastroduodenal ulcers (GDU) using combined analysis of both retrospective and prospective data from a large cohort study of Japanese women, the Japan Nurses' Health Study (JNHS; n = 15,019). The impact of the ABO blood group on GDU risk was examined using Cox regression analysis to estimate hazard ratios (HRs) and 95% confidence intervals (CI), with adjustment for potential confounders. Compared with women with non-O blood types (A, B, and AB), women with blood type O had a significantly increased risk of GDU from birth (multivariable-adjusted HR 1.18; 95% CI, 1.04-1.34). Moreover, the highest cumulative incidence of GDU was observed in women born pre-1956 with blood type O. In a subgroup analysis stratified by birth year (pre-1956 or post-1955), the multivariable-adjusted HR of women with blood type O was 1.22 (95% CI, 1.00-1.49) and 1.15 (95% CI, 0.98-1.35) in the pre-1956 and post-1955 groups, respectively. In this large, combined, ambispective cohort study of Japanese women, older women with blood type O had a higher risk of developing GDU than those with other blood types.

Kepler AutoRegressive Planet Search: Motivation & Methodology

NASA Astrophysics Data System (ADS)

Caceres, Gabriel; Feigelson, Eric; Jogesh Babu, G.; Bahamonde, Natalia; Bertin, Karine; Christen, Alejandra; Curé, Michel; Meza, Cristian

2015-08-01

The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Auto-Regressive Moving-Average (ARMA) models, Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH), and related models are flexible, phenomenological methods used with great success to model stochastic temporal behaviors in many fields of study, particularly econometrics. Powerful statistical methods are implemented in the public statistical software environment R and its many packages. Modeling involves maximum likelihood fitting, model selection, and residual analysis. These techniques provide a useful framework to model stellar variability and are used in KARPS with the objective of reducing stellar noise to enhance opportunities to find as-yet-undiscovered planets. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; ARMA-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. We apply the procedures to simulated Kepler-like time series with known stellar and planetary signals to evaluate the effectiveness of the KARPS procedures. The ARMA-type modeling is effective at reducing stellar noise, but also reduces and transforms the transit signal into ingress/egress spikes. A periodogram based on the TCF is constructed to concentrate the signal of these periodic spikes. When a periodic transit is found, the model is displayed on a standard period-folded averaged light curve. We also illustrate the efficient coding in R.

Logistic Regression Likelihood Ratio Test Analysis for Detecting Signals of Adverse Events in Post-market Safety Surveillance.

PubMed

Nam, Kijoeng; Henderson, Nicholas C; Rohan, Patricia; Woo, Emily Jane; Russek-Cohen, Estelle

2017-01-01

The Vaccine Adverse Event Reporting System (VAERS) and other product surveillance systems compile reports of product-associated adverse events (AEs), and these reports may include a wide range of information including age, gender, and concomitant vaccines. Controlling for possible confounding variables such as these is an important task when utilizing surveillance systems to monitor post-market product safety. A common method for handling possible confounders is to compare observed product-AE combinations with adjusted baseline frequencies where the adjustments are made by stratifying on observable characteristics. Though approaches such as these have proven to be useful, in this article we propose a more flexible logistic regression approach which allows for covariates of all types rather than relying solely on stratification. Indeed, a main advantage of our approach is that the general regression framework provides flexibility to incorporate additional information such as demographic factors and concomitant vaccines. As part of our covariate-adjusted method, we outline a procedure for signal detection that accounts for multiple comparisons and controls the overall Type 1 error rate. To demonstrate the effectiveness of our approach, we illustrate our method with an example involving febrile convulsion, and we further evaluate its performance in a series of simulation studies.

Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle.

PubMed

Ruuska, Salla; Hämäläinen, Wilhelmiina; Kajava, Sari; Mughal, Mikaela; Matilainen, Pekka; Mononen, Jaakko

2018-03-01

The aim of the present study was to evaluate empirically confusion matrices in device validation. We compared the confusion matrix method to linear regression and error indices in the validation of a device measuring feeding behaviour of dairy cattle. In addition, we studied how to extract additional information on classification errors with confusion probabilities. The data consisted of 12 h behaviour measurements from five dairy cows; feeding and other behaviour were detected simultaneously with a device and from video recordings. The resulting 216 000 pairs of classifications were used to construct confusion matrices and calculate performance measures. In addition, hourly durations of each behaviour were calculated and the accuracy of measurements was evaluated with linear regression and error indices. All three validation methods agreed when the behaviour was detected very accurately or inaccurately. Otherwise, in the intermediate cases, the confusion matrix method and error indices produced relatively concordant results, but the linear regression method often disagreed with them. Our study supports the use of confusion matrix analysis in validation since it is robust to any data distribution and type of relationship, it makes a stringent evaluation of validity, and it offers extra information on the type and sources of errors. Copyright © 2018 Elsevier B.V. All rights reserved.

Correlation between increased urinary sodium excretion and decreased left ventricular diastolic function in patients with type 2 diabetes mellitus.

PubMed

Kagiyama, Shuntaro; Koga, Tokushi; Kaseda, Shigeru; Ishihara, Shiro; Kawazoe, Nobuyuki; Sadoshima, Seizo; Matsumura, Kiyoshi; Takata, Yutaka; Tsuchihashi, Takuya; Iida, Mitsuo

2009-10-01

Increased salt intake may induce hypertension, lead to cardiac hypertrophy, and exacerbate heart failure. When elderly patients develop heart failure, diastolic dysfunction is often observed, although the ejection fraction has decreased. Diabetes mellitus (DM) is an established risk factor for heart failure. However, little is known about the relationship between cardiac function and urinary sodium excretion (U-Na) in patients with DM. We measured 24-hour U-Na; cardiac function was evaluated directly during coronary catheterization in type 2 DM (n = 46) or non-DM (n = 55) patients with preserved cardiac systolic function (ejection fraction > or = 60%). Cardiac diastolic and systolic function was evaluated as - dp/dt and + dp/dt, respectively. The average of U-Na was 166.6 +/- 61.2 mEq/24 hour (mean +/- SD). In all patients, stepwise multivariate regression analysis revealed that - dp/dt had a negative correlation with serum B-type natriuretic peptide (BNP; beta = - 0.23, P = .021) and U-Na (beta = - 0.24, P = .013). On the other hand, + dp/dt negatively correlated with BNP (beta = - 0.30, P < .001), but did not relate to U-Na. In the DM-patients, stepwise multivariate regression analysis showed that - dp/dt still had a negative correlation with U-Na (beta = - 0.33, P = .025). The results indicated that increased urinary sodium excretion is associated with an impairment of cardiac diastolic function, especially in patients with DM, suggesting that a reduction of salt intake may improve cardiac diastolic function.

Meta-analysis of haplotype-association studies: comparison of methods and empirical evaluation of the literature

PubMed Central

2011-01-01

Background Meta-analysis is a popular methodology in several fields of medical research, including genetic association studies. However, the methods used for meta-analysis of association studies that report haplotypes have not been studied in detail. In this work, methods for performing meta-analysis of haplotype association studies are summarized, compared and presented in a unified framework along with an empirical evaluation of the literature. Results We present multivariate methods that use summary-based data as well as methods that use binary and count data in a generalized linear mixed model framework (logistic regression, multinomial regression and Poisson regression). The methods presented here avoid the inflation of the type I error rate that could be the result of the traditional approach of comparing a haplotype against the remaining ones, whereas, they can be fitted using standard software. Moreover, formal global tests are presented for assessing the statistical significance of the overall association. Although the methods presented here assume that the haplotypes are directly observed, they can be easily extended to allow for such an uncertainty by weighting the haplotypes by their probability. Conclusions An empirical evaluation of the published literature and a comparison against the meta-analyses that use single nucleotide polymorphisms, suggests that the studies reporting meta-analysis of haplotypes contain approximately half of the included studies and produce significant results twice more often. We show that this excess of statistically significant results, stems from the sub-optimal method of analysis used and, in approximately half of the cases, the statistical significance is refuted if the data are properly re-analyzed. Illustrative examples of code are given in Stata and it is anticipated that the methods developed in this work will be widely applied in the meta-analysis of haplotype association studies. PMID:21247440

Serum Irisin Predicts Mortality Risk in Acute Heart Failure Patients.

PubMed

Shen, Shutong; Gao, Rongrong; Bei, Yihua; Li, Jin; Zhang, Haifeng; Zhou, Yanli; Yao, Wenming; Xu, Dongjie; Zhou, Fang; Jin, Mengchao; Wei, Siqi; Wang, Kai; Xu, Xuejuan; Li, Yongqin; Xiao, Junjie; Li, Xinli

2017-01-01

Irisin is a peptide hormone cleaved from a plasma membrane protein fibronectin type III domain containing protein 5 (FNDC5). Emerging studies have indicated association between serum irisin and many major chronic diseases including cardiovascular diseases. However, the role of serum irisin as a predictor for mortality risk in acute heart failure (AHF) patients is not clear. AHF patients were enrolled and serum was collected at the admission and all patients were followed up for 1 year. Enzyme-linked immunosorbent assay was used to measure serum irisin levels. To explore predictors for AHF mortality, the univariate and multivariate logistic regression analysis, and receiver-operator characteristic (ROC) curve analysis were used. To determine the role of serum irisin levels in predicting survival, Kaplan-Meier survival analysis was used. In this study, 161 AHF patients were enrolled and serum irisin level was found to be significantly higher in patients deceased in 1-year follow-up. The univariate logistic regression analysis identified 18 variables associated with all-cause mortality in AHF patients, while the multivariate logistic regression analysis identified 2 variables namely blood urea nitrogen and serum irisin. ROC curve analysis indicated that blood urea nitrogen and the most commonly used biomarker, NT-pro-BNP, displayed poor prognostic value for AHF (AUCs ≤ 0.700) compared to serum irisin (AUC = 0.753). Kaplan-Meier survival analysis demonstrated that AHF patients with higher serum irisin had significantly higher mortality (P<0.001). Collectively, our study identified serum irisin as a predictive biomarker for 1-year all-cause mortality in AHF patients though large multicenter studies are highly needed. © 2017 The Author(s). Published by S. Karger AG, Basel.

Drivers of wetland conversion: a global meta-analysis.

PubMed

van Asselen, Sanneke; Verburg, Peter H; Vermaat, Jan E; Janse, Jan H

2013-01-01

Meta-analysis of case studies has become an important tool for synthesizing case study findings in land change. Meta-analyses of deforestation, urbanization, desertification and change in shifting cultivation systems have been published. This present study adds to this literature, with an analysis of the proximate causes and underlying forces of wetland conversion at a global scale using two complementary approaches of systematic review. Firstly, a meta-analysis of 105 case-study papers describing wetland conversion was performed, showing that different combinations of multiple-factor proximate causes, and underlying forces, drive wetland conversion. Agricultural development has been the main proximate cause of wetland conversion, and economic growth and population density are the most frequently identified underlying forces. Secondly, to add a more quantitative component to the study, a logistic meta-regression analysis was performed to estimate the likelihood of wetland conversion worldwide, using globally-consistent biophysical and socioeconomic location factor maps. Significant factors explaining wetland conversion, in order of importance, are market influence, total wetland area (lower conversion probability), mean annual temperature and cropland or built-up area. The regression analyses results support the outcomes of the meta-analysis of the processes of conversion mentioned in the individual case studies. In other meta-analyses of land change, similar factors (e.g., agricultural development, population growth, market/economic factors) are also identified as important causes of various types of land change (e.g., deforestation, desertification). Meta-analysis helps to identify commonalities across the various local case studies and identify which variables may lead to individual cases to behave differently. The meta-regression provides maps indicating the likelihood of wetland conversion worldwide based on the location factors that have determined historic conversions.

Drivers of Wetland Conversion: a Global Meta-Analysis

PubMed Central

van Asselen, Sanneke; Verburg, Peter H.; Vermaat, Jan E.; Janse, Jan H.

2013-01-01

Meta-analysis of case studies has become an important tool for synthesizing case study findings in land change. Meta-analyses of deforestation, urbanization, desertification and change in shifting cultivation systems have been published. This present study adds to this literature, with an analysis of the proximate causes and underlying forces of wetland conversion at a global scale using two complementary approaches of systematic review. Firstly, a meta-analysis of 105 case-study papers describing wetland conversion was performed, showing that different combinations of multiple-factor proximate causes, and underlying forces, drive wetland conversion. Agricultural development has been the main proximate cause of wetland conversion, and economic growth and population density are the most frequently identified underlying forces. Secondly, to add a more quantitative component to the study, a logistic meta-regression analysis was performed to estimate the likelihood of wetland conversion worldwide, using globally-consistent biophysical and socioeconomic location factor maps. Significant factors explaining wetland conversion, in order of importance, are market influence, total wetland area (lower conversion probability), mean annual temperature and cropland or built-up area. The regression analyses results support the outcomes of the meta-analysis of the processes of conversion mentioned in the individual case studies. In other meta-analyses of land change, similar factors (e.g., agricultural development, population growth, market/economic factors) are also identified as important causes of various types of land change (e.g., deforestation, desertification). Meta-analysis helps to identify commonalities across the various local case studies and identify which variables may lead to individual cases to behave differently. The meta-regression provides maps indicating the likelihood of wetland conversion worldwide based on the location factors that have determined historic conversions. PMID:24282580

Web usage mining at an academic health sciences library: an exploratory study.

PubMed

Bracke, Paul J

2004-10-01

This paper explores the potential of multinomial logistic regression analysis to perform Web usage mining for an academic health sciences library Website. Usage of database-driven resource gateway pages was logged for a six-month period, including information about users' network addresses, referring uniform resource locators (URLs), and types of resource accessed. It was found that referring URL did vary significantly by two factors: whether a user was on-campus and what type of resource was accessed. Although the data available for analysis are limited by the nature of the Web and concerns for privacy, this method demonstrates the potential for gaining insight into Web usage that supplements Web log analysis. It can be used to improve the design of static and dynamic Websites today and could be used in the design of more advanced Web systems in the future.

An Exploratory Analysis of Personality, Attitudes, and Study Skills on the Learning Curve within a Team-based Learning Environment

PubMed Central

Henry, Teague; Campbell, Ashley

2015-01-01

Objective. To examine factors that determine the interindividual variability of learning within a team-based learning environment. Methods. Students in a pharmacokinetics course were given 4 interim, low-stakes cumulative assessments throughout the semester and a cumulative final examination. Students’ Myers-Briggs personality type was assessed, as well as their study skills, motivations, and attitudes towards team-learning. A latent curve model (LCM) was applied and various covariates were assessed to improve the regression model. Results. A quadratic LCM was applied for the first 4 assessments to predict final examination performance. None of the covariates examined significantly impacted the regression model fit except metacognitive self-regulation, which explained some of the variability in the rate of learning. There were some correlations between personality type and attitudes towards team learning, with introverts having a lower opinion of team-learning than extroverts. Conclusion. The LCM could readily describe the learning curve. Extroverted and introverted personality types had the same learning performance even though preference for team-learning was lower in introverts. Other personality traits, study skills, or practice did not significantly contribute to the learning variability in this course. PMID:25861101

An exploratory analysis of personality, attitudes, and study skills on the learning curve within a team-based learning environment.

PubMed

Persky, Adam M; Henry, Teague; Campbell, Ashley

2015-03-25

To examine factors that determine the interindividual variability of learning within a team-based learning environment. Students in a pharmacokinetics course were given 4 interim, low-stakes cumulative assessments throughout the semester and a cumulative final examination. Students' Myers-Briggs personality type was assessed, as well as their study skills, motivations, and attitudes towards team-learning. A latent curve model (LCM) was applied and various covariates were assessed to improve the regression model. A quadratic LCM was applied for the first 4 assessments to predict final examination performance. None of the covariates examined significantly impacted the regression model fit except metacognitive self-regulation, which explained some of the variability in the rate of learning. There were some correlations between personality type and attitudes towards team learning, with introverts having a lower opinion of team-learning than extroverts. The LCM could readily describe the learning curve. Extroverted and introverted personality types had the same learning performance even though preference for team-learning was lower in introverts. Other personality traits, study skills, or practice did not significantly contribute to the learning variability in this course.

Gender differences in social support and leisure-time physical activity

PubMed Central

Oliveira, Aldair J; Lopes, Claudia S; Rostila, Mikael; Werneck, Guilherme Loureiro; Griep, Rosane Härter; de Leon, Antônio Carlos Monteiro Ponce; Faerstein, Eduardo

2014-01-01

OBJECTIVE To identify gender differences in social support dimensions’ effect on adults’ leisure-time physical activity maintenance, type, and time. METHODS Longitudinal study of 1,278 non-faculty public employees at a university in Rio de Janeiro, RJ, Southeastern Brazil. Physical activity was evaluated using a dichotomous question with a two-week reference period, and further questions concerning leisure-time physical activity type (individual or group) and time spent on the activity. Social support was measured with the Medical Outcomes Study Social Support Scale. For the analysis, logistic regression models were adjusted separately by gender. RESULTS A multinomial logistic regression showed an association between material support and individual activities among women (OR = 2.76; 95%CI 1.2;6.5). Affective support was associated with time spent on leisure-time physical activity only among men (OR = 1.80; 95%CI 1.1;3.2). CONCLUSIONS All dimensions of social support that were examined influenced either the type of, or the time spent on, leisure-time physical activity. In some social support dimensions, the associations detected varied by gender. Future studies should attempt to elucidate the mechanisms involved in these gender differences. PMID:25210819

Satellite-based prediction of rainfall interception by tropical forest stands of a human-dominated landscape in Central Sulawesi, Indonesia

NASA Astrophysics Data System (ADS)

Nieschulze, Jens; Erasmi, Stefan; Dietz, Johannes; Hölscher, Dirk

2009-01-01

SummaryRainforest conversion to other land use types drastically alters the hydrological cycle in which changes in rainfall interception contribute significantly to the observed differences. However, little is known about the effects of more gradual changes in forest structure and at regional scales. We studied land use types ranging from natural forest over selectively-logged forest to cacao agroforest in a lower montane region in Central Sulawesi, Indonesia, and tested the suitability of high-resolution optical satellite imagery for modeling observed interception patterns. Investigated characteristics indicating canopy structure were mean and standard deviation of reflectance values, local maxima, and self-similarity measures based on the grey level co-occurrence matrix and geostatistical variogram analysis. Previously studied and published rainfall interception data comprised twelve plots and median values per land use type ranged from 30% in natural forest to 18% in cacao agroforests. A linear regression model with local maxima, mean contrast and normalized digital vegetation index (NDVI) as regressors was able to explain more than 84% ( Radj2) of the variation encountered in the data. Other investigated characteristics did not prove significant in the regression analysis. The model yielded stable results with respect to cross-validation and also produced realistic values and spatial patterns when applied at the landscape level (783.6 ha). High values of interception were rare and localized in natural forest stands distant to villages, whereas low interception characterized the intensively used sites close to settlements. We conclude that forest use intensity significantly reduced rainfall interception and satellite image analysis can successfully be applied for its regional prediction, and most forest in the study region has already been subject to human-induced structural changes.

Willingness to Exchange Health Information via Mobile Devices: Findings From a Population-Based Survey

PubMed Central

Serrano, Katrina J.; Yu, Mandi; Riley, William T.; Patel, Vaishali; Hughes, Penelope; Marchesini, Kathryn; Atienza, Audie A.

2016-01-01

PURPOSE The rapid proliferation of mobile devices offers unprecedented opportunities for patients and health care professionals to exchange health information electronically, but little is known about patients’ willingness to exchange various types of health information using these devices. We examined willingness to exchange different types of health information via mobile devices, and assessed whether sociodemographic characteristics and trust in clinicians were associated with willingness in a nationally representative sample. METHODS We analyzed data for 3,165 patients captured in the 2013 Health Information National Trends Survey. Multinomial logistic regression analysis was conducted to test differences in willingness. Ordinal logistic regression analysis assessed correlates of willingness to exchange 9 types of information separately. RESULTS Participants were very willing to exchange appointment reminders (odds ratio [OR] = 6.66; 95% CI, 5.68–7.81), general health tips (OR = 2.03; 95% CI, 1.74–2.38), medication reminders (OR = 2.73; 95% CI, 2.35–3.19), laboratory/test results (OR = 1.76; 95% CI, 1.62–1.92), vital signs (OR = 1.63; 95% CI, 1.48–1.80), lifestyle behaviors (OR = 1.40; 95% CI, 1.24–1.58), and symptoms (OR = 1.62; 95% CI, 1.46–1.79) as compared with diagnostic information. Older adults had lower odds of being more willing to exchange any type of information. Education, income, and trust in health care professional information correlated with willingness to exchange certain types of information. CONCLUSIONS Respondents were less willing to exchange via mobile devices information that may be considered sensitive or complex. Age, socioeconomic factors, and trust in professional information were associated with willingness to engage in mobile health information exchange. Both information type and demographic group should be considered when developing and tailoring mobile technologies for patient-clinician communication. PMID:26755781

Type 2 diabetes mellitus and non-alcoholic fatty liver disease: a systematic review and meta-analysis.

PubMed

Amiri Dash Atan, Nasrin; Koushki, Mehdi; Motedayen, Morteza; Dousti, Majid; Sayehmiri, Fatemeh; Vafaee, Reza; Norouzinia, Mohsen; Gholami, Reza

2017-01-01

The aim of this study was the evaluation of the prevalence of NAFLD in patients with type 2 diabetes mellitus. Non-alcoholic fatty liver disease (NAFLD) is an emerging disease with high prevalence in patients with type 2 diabetes mellitus (T2DM). Many studies have reported the prevalence of NAFLD in type 2 diabetes mellitus patients. However, these results are inconsistent. A Literature search was conducted in PubMed, Scopus, web of science and Science Direct from 2005 to August 2017. The necessary information was extracted. Heterogeneity was evaluated using I 2 statistic. Meta-regression analyses were performed to the estimation of the relationship between the year of study and sample size with the prevalence of NAFLD. Publication bias was assessed by both Begg rank correlation and Egger tests. Subgroup analysis was performed for identification of sources heterogeneity. Seventeen studies involving 10897 type 2 diabetes mellitus patients with NAFLD were included in this meta-analysis. The overall prevalence of NAFLD in type 2 diabetes mellitus patients by random effects models was 54% (95% CI, 45%- 64%). There is a significant heterogeneity across studies with (I 2 = 99%, p> 0.01). The funnel plot as graphically and Begg and Egger as statistically showed no publication bias among studies. Subgroup analysis indicated that the prevalence of NAFLD in type 2 diabetes mellitus patients differed in predictive factors such as lipid profile, BMI, HbA1c, AST, and ALT. This finding in spite of heterogeneity of documents is corresponding to the positive correlation between NAFLD and type 2 diabetes mellitus. The findings indicated that the overall prevalence of NAFLD among type 2 diabetes mellitus patients is significantly higher. It can be concluded that type 2 diabetes mellitus patients should be managed to prevent NAFLD.

Integrated analysis of DNA-methylation and gene expression using high-dimensional penalized regression: a cohort study on bone mineral density in postmenopausal women.

PubMed

Lien, Tonje G; Borgan, Ørnulf; Reppe, Sjur; Gautvik, Kaare; Glad, Ingrid Kristine

2018-03-07

Using high-dimensional penalized regression we studied genome-wide DNA-methylation in bone biopsies of 80 postmenopausal women in relation to their bone mineral density (BMD). The women showed BMD varying from severely osteoporotic to normal. Global gene expression data from the same individuals was available, and since DNA-methylation often affects gene expression, the overall aim of this paper was to include both of these omics data sets into an integrated analysis. The classical penalized regression uses one penalty, but we incorporated individual penalties for each of the DNA-methylation sites. These individual penalties were guided by the strength of association between DNA-methylations and gene transcript levels. DNA-methylations that were highly associated to one or more transcripts got lower penalties and were therefore favored compared to DNA-methylations showing less association to expression. Because of the complex pathways and interactions among genes, we investigated both the association between DNA-methylations and their corresponding cis gene, as well as the association between DNA-methylations and trans-located genes. Two integrating penalized methods were used: first, an adaptive group-regularized ridge regression, and secondly, variable selection was performed through a modified version of the weighted lasso. When information from gene expressions was integrated, predictive performance was considerably improved, in terms of predictive mean square error, compared to classical penalized regression without data integration. We found a 14.7% improvement in the ridge regression case and a 17% improvement for the lasso case. Our version of the weighted lasso with data integration found a list of 22 interesting methylation sites. Several corresponded to genes that are known to be important in bone formation. Using BMD as response and these 22 methylation sites as covariates, least square regression analyses resulted in R 2 =0.726, comparable to an average R 2 =0.438 for 10000 randomly selected groups of DNA-methylations with group size 22. Two recent types of penalized regression methods were adapted to integrate DNA-methylation and their association to gene expression in the analysis of bone mineral density. In both cases predictions clearly benefit from including the additional information on gene expressions.

The use of segmented regression in analysing interrupted time series studies: an example in pre-hospital ambulance care.

PubMed

Taljaard, Monica; McKenzie, Joanne E; Ramsay, Craig R; Grimshaw, Jeremy M

2014-06-19

An interrupted time series design is a powerful quasi-experimental approach for evaluating effects of interventions introduced at a specific point in time. To utilize the strength of this design, a modification to standard regression analysis, such as segmented regression, is required. In segmented regression analysis, the change in intercept and/or slope from pre- to post-intervention is estimated and used to test causal hypotheses about the intervention. We illustrate segmented regression using data from a previously published study that evaluated the effectiveness of a collaborative intervention to improve quality in pre-hospital ambulance care for acute myocardial infarction (AMI) and stroke. In the original analysis, a standard regression model was used with time as a continuous variable. We contrast the results from this standard regression analysis with those from segmented regression analysis. We discuss the limitations of the former and advantages of the latter, as well as the challenges of using segmented regression in analysing complex quality improvement interventions. Based on the estimated change in intercept and slope from pre- to post-intervention using segmented regression, we found insufficient evidence of a statistically significant effect on quality of care for stroke, although potential clinically important effects for AMI cannot be ruled out. Segmented regression analysis is the recommended approach for analysing data from an interrupted time series study. Several modifications to the basic segmented regression analysis approach are available to deal with challenges arising in the evaluation of complex quality improvement interventions.

Standardized Regression Coefficients as Indices of Effect Sizes in Meta-Analysis

ERIC Educational Resources Information Center

Kim, Rae Seon

2011-01-01

When conducting a meta-analysis, it is common to find many collected studies that report regression analyses, because multiple regression analysis is widely used in many fields. Meta-analysis uses effect sizes drawn from individual studies as a means of synthesizing a collection of results. However, indices of effect size from regression analyses…

Analysis of training sample selection strategies for regression-based quantitative landslide susceptibility mapping methods

NASA Astrophysics Data System (ADS)

Erener, Arzu; Sivas, A. Abdullah; Selcuk-Kestel, A. Sevtap; Düzgün, H. Sebnem

2017-07-01

All of the quantitative landslide susceptibility mapping (QLSM) methods requires two basic data types, namely, landslide inventory and factors that influence landslide occurrence (landslide influencing factors, LIF). Depending on type of landslides, nature of triggers and LIF, accuracy of the QLSM methods differs. Moreover, how to balance the number of 0 (nonoccurrence) and 1 (occurrence) in the training set obtained from the landslide inventory and how to select which one of the 1's and 0's to be included in QLSM models play critical role in the accuracy of the QLSM. Although performance of various QLSM methods is largely investigated in the literature, the challenge of training set construction is not adequately investigated for the QLSM methods. In order to tackle this challenge, in this study three different training set selection strategies along with the original data set is used for testing the performance of three different regression methods namely Logistic Regression (LR), Bayesian Logistic Regression (BLR) and Fuzzy Logistic Regression (FLR). The first sampling strategy is proportional random sampling (PRS), which takes into account a weighted selection of landslide occurrences in the sample set. The second method, namely non-selective nearby sampling (NNS), includes randomly selected sites and their surrounding neighboring points at certain preselected distances to include the impact of clustering. Selective nearby sampling (SNS) is the third method, which concentrates on the group of 1's and their surrounding neighborhood. A randomly selected group of landslide sites and their neighborhood are considered in the analyses similar to NNS parameters. It is found that LR-PRS, FLR-PRS and BLR-Whole Data set-ups, with order, yield the best fits among the other alternatives. The results indicate that in QLSM based on regression models, avoidance of spatial correlation in the data set is critical for the model's performance.

Quantifying scaling effects on satellite-derived forest area estimates for the conterminous USA

Treesearch

Daolan Zheng; L.S. Heath; M.J. Ducey; J.E. Smith

2009-01-01

We quantified the scaling effects on forest area estimates for the conterminous USA using regression analysis and the National Land Cover Dataset 30m satellite-derived maps in 2001 and 1992. The original data were aggregated to: (1) broad cover types (forest vs. non-forest); and (2) coarser resolutions (1km and 10 km). Standard errors of the model estimates were 2.3%...

Team Performance Improvement: Mediating Roles of Employee Job Autonomy and Quality of Team Leader-Member Relations in Supportive Organizations in the Korean Business Context

ERIC Educational Resources Information Center

Song, Ji Hoon

2011-01-01

The purpose of this research was to examine the mediating roles of job autonomy and the quality of the leader-member relationship to explain the impact of organizational support on team performance. A total of 228 cases collected from Korean business organizations were used for data analysis. Hierarchical multiple regression, Type 1 SS-based…

Cost Differences in Public and Private Shipyards

DTIC Science & Technology

1981-01-01

block number) coefficients, costs, maintenance, naval shore facilities, naval vessels, nuclear powered ships, regression analysis, repair, salaries...of overhauls of nucler submarines, we mnight exp.,_t to find both production costs and the price of labor to be higher in naval shipyardi than in...about 18 months; in addition to the type of work done during regular overhauls, they include replacement of the nuclear core which powers the submarine

Social Impact of Stigma Regarding Tuberculosis Hindering Adherence to Treatment: A Cross Sectional Study Involving Tuberculosis Patients in Rajshahi City, Bangladesh.

PubMed

Chowdhury, Md Rocky Khan; Rahman, Md Shafiur; Mondal, Md Nazrul Islam; Sayem, Abu; Billah, Baki

2015-01-01

Stigma, considered a social disease, is more apparent in developing societies which are driven by various social affairs, and influences adherence to treatment. The aim of the present study was to examine levels of social stigma related to tuberculosis (TB) in sociodemographic context and identify the effects of sociodemographic factors on stigma. The study sample consisted of 372 TB patients. Data were collected using stratified sampling with simple random sampling techniques. T tests, chi-square tests, and binary logistic regression analysis were performed to examine correlations between stigma and sociodemographic variables. Approximately 85.9% of patients had experienced stigma. The most frequent indicator of the stigma experienced by patients involved problems taking part in social programs (79.5%). Mean levels of stigma were significantly higher in women (55.5%), illiterate individuals (60.8%), and villagers (60.8%) relative to those of other groups. Chi-square tests revealed that education, monthly family income, and type of patient (pulmonary and extrapulmonary) were significantly associated with stigma. Binary logistic regression analysis demonstrated that stigma was influenced by sex, education, and type of patient. Stigma is one of the most important barriers to treatment adherence. Therefore, in interventions that aim to reduce stigma, strong collaboration between various institutions is essential.

DIAGNOSIS OF ENDOCRINE DISEASE: Prevalence of hypercortisolism in type 2 diabetes patients: a systematic review and meta-analysis.

PubMed

Steffensen, Charlotte; Pereira, Alberto M; Dekkers, Olaf M; Jørgensen, Jens Otto L

2016-12-01

Type 2 diabetes (T2D) and Cushing's syndrome (CS) share clinical characteristics, and several small studies have recorded a high prevalence of hypercortisolism in T2D, which could have therapeutic implications. We aimed to assess the prevalence of endogenous hypercortisolism in T2D patients. Systematic review and meta-analysis of the literature. A search was performed in SCOPUS, MEDLINE, and EMBASE for original articles assessing the prevalence of endogenous hypercortisolism and CS in T2D. Data were pooled in a random-effect logistic regression model and reported with 95% confidence intervals (95% CI). Fourteen articles were included, with a total of 2827 T2D patients. The pooled prevalence of hypercortisolism and CS was 3.4% (95% CI: 1.5-5.9) and 1.4% (95 CI: 0.4-2.9) respectively. The prevalence did not differ between studies of unselected patients and patients selected based on the presence of metabolic features such as obesity or poor glycemic control (P = 0.41 from meta-regression). Imaging in patients with hypercortisolism (n = 102) revealed adrenal tumors and pituitary tumors in 52 and 14% respectively. Endogenous hypercortisolism is a relatively frequent finding in T2D, which may have therapeutic implications. © 2016 European Society of Endocrinology.

Statistical analysis of mixed recurrent event data with application to cancer survivor study

PubMed Central

Zhu, Liang; Tong, Xingwei; Zhao, Hui; Sun, Jianguo; Srivastava, Deo Kumar; Leisenring, Wendy; Robison, Leslie L.

2014-01-01

Event history studies occur in many fields including economics, medical studies and social science. In such studies concerning some recurrent events, two types of data have been extensively discussed in the literature. One is recurrent event data that arise if study subjects are monitored or observed continuously. In this case, the observed information provides the times of all occurrences of the recurrent events of interest. The other is panel count data, which occur if the subjects are monitored or observed only periodically. This can happen if the continuous observation is too expensive or not practical and in this case, only the numbers of occurrences of the events between subsequent observation times are available. In this paper, we discuss a third type of data, which is a mixture of recurrent event and panel count data and for which there exists little literature. For regression analysis of such data, a marginal mean model is presented and we propose an estimating equation-based approach for estimation of regression parameters. A simulation study is conducted to assess the finite sample performance of the proposed methodology and indicates that it works well for practical situations. Finally it is applied to a motivating study on childhood cancer survivors. PMID:23139023

[Relationship between Working Schedule and Sleeping Hours with Overweight and Obesity in Spanish Adult Population According to Data from the National Health Survey 2012].

PubMed

Marqueta de Salas, María; Rodríguez Gómez, Lorena; Enjuto Martínez, Diego; Juárez Soto, José Juan; Martín-Ramiro, José Javier

2017-03-01

Obesity is a public health problem worldwide. The aim of the present study was to determine the association between the type of working schedule and the sleeping hours per day with obesity and overweight. Cross-sectional study of the National Health Survey in 2012. We conducted an analysis of multinomial logistic regression and estimated the rates of possible risk of obesity and overweight versus the normal weight in relation to the type of working schedule and sleeping hours. Obesity among those who worked at night was 17,50% and those who had irregular works was 17,92%. Overweight among those who performed part-time works was 40,81% and 39,17% in night works. The obesity and overweight among those who slept less than six hours a day were 24,42% and 40,99% respectively. Regression analysis logistic showed OR=1,83 (IC95% 1,15-1,75) in irregular works and OR= 1,83 (IC95% 1,59-2,11) in people who slept less than six hours. Whenever overweight and obesity are present, a positive association between irregular jobs and short patterns of rest has been found, but stadistical significance is lost when estimating the OR adjusting the confounding factors.

Mortality in the Children of Atomic Bomb Survivors and Controls

PubMed Central

Neel, James V.; Kato, Hiroo; Schull, William J.

1974-01-01

A continuing study of mortality rates among children born to survivors of the atomic bombings and a suitable group of controls has been updated; the average interval between birth and verification of death or survival is 17 years. The mortality experience is now based on 18,946 children liveborn to parents one or both of whom were proximally exposed, receiving jointly an estimated dose of 117 rem; 16,516 children born to distally exposed parents receiving essentially no radiation; and 17,263 children born to parents not in Hiroshima or Nagasaki at the time of the bombings. No clearly significant effect of parental exposure on child's survival can be demonstrated either by a contingency χ2 type of analysis or regression analysis. On the basis of the regression data, the minimal gametic doubling dose of radiation of this type for mutations resulting in death during (on the average) the first 17 years of life among liveborn infants conceived 0–13 years after parental exposure is estimated at 46 rem for fathers and 125 rem for mothers. On the basis of experimental data, the gametic doubling dose for chronic, low-level radiation would be expected to be three to four times this value for males and as much as 1000 rem for females. PMID:4822470

MAGMA: Generalized Gene-Set Analysis of GWAS Data

PubMed Central

de Leeuw, Christiaan A.; Mooij, Joris M.; Heskes, Tom; Posthuma, Danielle

2015-01-01

By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and gene-set analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn’s Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn’s Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn’s Disease data was found to be considerably faster as well. PMID:25885710

MAGMA: generalized gene-set analysis of GWAS data.

PubMed

de Leeuw, Christiaan A; Mooij, Joris M; Heskes, Tom; Posthuma, Danielle

2015-04-01

By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and gene-set analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn's Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn's Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn's Disease data was found to be considerably faster as well.

From concepts, theory, and evidence of heterogeneity of treatment effects to methodological approaches: a primer.

PubMed

Willke, Richard J; Zheng, Zhiyuan; Subedi, Prasun; Althin, Rikard; Mullins, C Daniel

2012-12-13

Implicit in the growing interest in patient-centered outcomes research is a growing need for better evidence regarding how responses to a given intervention or treatment may vary across patients, referred to as heterogeneity of treatment effect (HTE). A variety of methods are available for exploring HTE, each associated with unique strengths and limitations. This paper reviews a selected set of methodological approaches to understanding HTE, focusing largely but not exclusively on their uses with randomized trial data. It is oriented for the "intermediate" outcomes researcher, who may already be familiar with some methods, but would value a systematic overview of both more and less familiar methods with attention to when and why they may be used. Drawing from the biomedical, statistical, epidemiological and econometrics literature, we describe the steps involved in choosing an HTE approach, focusing on whether the intent of the analysis is for exploratory, initial testing, or confirmatory testing purposes. We also map HTE methodological approaches to data considerations as well as the strengths and limitations of each approach. Methods reviewed include formal subgroup analysis, meta-analysis and meta-regression, various types of predictive risk modeling including classification and regression tree analysis, series of n-of-1 trials, latent growth and growth mixture models, quantile regression, and selected non-parametric methods. In addition to an overview of each HTE method, examples and references are provided for further reading.By guiding the selection of the methods and analysis, this review is meant to better enable outcomes researchers to understand and explore aspects of HTE in the context of patient-centered outcomes research.

A points-based algorithm for prognosticating clinical outcome of Chiari malformation Type I with syringomyelia: results from a predictive model analysis of 82 surgically managed adult patients.

PubMed

Thakar, Sumit; Sivaraju, Laxminadh; Jacob, Kuruthukulangara S; Arun, Aditya Atal; Aryan, Saritha; Mohan, Dilip; Sai Kiran, Narayanam Anantha; Hegde, Alangar S

2018-01-01

OBJECTIVE Although various predictors of postoperative outcome have been previously identified in patients with Chiari malformation Type I (CMI) with syringomyelia, there is no known algorithm for predicting a multifactorial outcome measure in this widely studied disorder. Using one of the largest preoperative variable arrays used so far in CMI research, the authors attempted to generate a formula for predicting postoperative outcome. METHODS Data from the clinical records of 82 symptomatic adult patients with CMI and altered hindbrain CSF flow who were managed with foramen magnum decompression, C-1 laminectomy, and duraplasty over an 8-year period were collected and analyzed. Various preoperative clinical and radiological variables in the 57 patients who formed the study cohort were assessed in a bivariate analysis to determine their ability to predict clinical outcome (as measured on the Chicago Chiari Outcome Scale [CCOS]) and the resolution of syrinx at the last follow-up. The variables that were significant in the bivariate analysis were further analyzed in a multiple linear regression analysis. Different regression models were tested, and the model with the best prediction of CCOS was identified and internally validated in a subcohort of 25 patients. RESULTS There was no correlation between CCOS score and syrinx resolution (p = 0.24) at a mean ± SD follow-up of 40.29 ± 10.36 months. Multiple linear regression analysis revealed that the presence of gait instability, obex position, and the M-line-fourth ventricle vertex (FVV) distance correlated with CCOS score, while the presence of motor deficits was associated with poor syrinx resolution (p ≤ 0.05). The algorithm generated from the regression model demonstrated good diagnostic accuracy (area under curve 0.81), with a score of more than 128 points demonstrating 100% specificity for clinical improvement (CCOS score of 11 or greater). The model had excellent reliability (κ = 0.85) and was validated with fair accuracy in the validation cohort (area under the curve 0.75). CONCLUSIONS The presence of gait imbalance and motor deficits independently predict worse clinical and radiological outcomes, respectively, after decompressive surgery for CMI with altered hindbrain CSF flow. Caudal displacement of the obex and a shorter M-line-FVV distance correlated with good CCOS scores, indicating that patients with a greater degree of hindbrain pathology respond better to surgery. The proposed points-based algorithm has good predictive value for postoperative multifactorial outcome in these patients.

Least Squares Moving-Window Spectral Analysis.

PubMed

Lee, Young Jong

2017-08-01

Least squares regression is proposed as a moving-windows method for analysis of a series of spectra acquired as a function of external perturbation. The least squares moving-window (LSMW) method can be considered an extended form of the Savitzky-Golay differentiation for nonuniform perturbation spacing. LSMW is characterized in terms of moving-window size, perturbation spacing type, and intensity noise. Simulation results from LSMW are compared with results from other numerical differentiation methods, such as single-interval differentiation, autocorrelation moving-window, and perturbation correlation moving-window methods. It is demonstrated that this simple LSMW method can be useful for quantitative analysis of nonuniformly spaced spectral data with high frequency noise.

Assessment of the relative merits of a few methods to detect evolutionary trends.

PubMed

Laurin, Michel

2010-12-01

Some of the most basic questions about the history of life concern evolutionary trends. These include determining whether or not metazoans have become more complex over time, whether or not body size tends to increase over time (the Cope-Depéret rule), or whether or not brain size has increased over time in various taxa, such as mammals and birds. Despite the proliferation of studies on such topics, assessment of the reliability of results in this field is hampered by the variability of techniques used and the lack of statistical validation of these methods. To solve this problem, simulations are performed using a variety of evolutionary models (gradual Brownian motion, speciational Brownian motion, and Ornstein-Uhlenbeck), with or without a drift of variable amplitude, with variable variance of tips, and with bounds placed close or far from the starting values and final means of simulated characters. These are used to assess the relative merits (power, Type I error rate, bias, and mean absolute value of error on slope estimate) of several statistical methods that have recently been used to assess the presence of evolutionary trends in comparative data. Results show widely divergent performance of the methods. The simple, nonphylogenetic regression (SR) and variance partitioning using phylogenetic eigenvector regression (PVR) with a broken stick selection procedure have greatly inflated Type I error rate (0.123-0.180 at a 0.05 threshold), which invalidates their use in this context. However, they have the greatest power. Most variants of Felsenstein's independent contrasts (FIC; five of which are presented) have adequate Type I error rate, although two have a slightly inflated Type I error rate with at least one of the two reference trees (0.064-0.090 error rate at a 0.05 threshold). The power of all contrast-based methods is always much lower than that of SR and PVR, except under Brownian motion with a strong trend and distant bounds. Mean absolute value of error on slope of all FIC methods is slightly higher than that of phylogenetic generalized least squares (PGLS), SR, and PVR. PGLS performs well, with low Type I error rate, low error on regression coefficient, and power comparable with some FIC methods. Four variants of skewness analysis are examined, and a new method to assess significance of results is presented. However, all have consistently low power, except in rare combinations of trees, trend strength, and distance between final means and bounds. Globally, the results clearly show that FIC-based methods and PGLS are globally better than nonphylogenetic methods and variance partitioning with PVR. FIC methods and PGLS are sensitive to the model of evolution (and, hence, to branch length errors). Our results suggest that regressing raw character contrasts against raw geological age contrasts yields a good combination of power and Type I error rate. New software to facilitate batch analysis is presented.

Antidiabetic pharmacotherapy and anamnestic hypoglycemia in a large cohort of type 2 diabetic patients - an analysis of the DiaRegis registry

PubMed Central

2011-01-01

Background We aimed to identify predictors of anamnestic hypoglycaemia in type-2 diabetic patients on oral mono- or dual oral combination antidiabetic pharmacotherapy. Methods DiaRegis is a prospective registry in type-2 diabetic patients in primary care. Odds ratios (OR) with 95% confidence intervals were determined from univariate logistic regression. Using multivariate logistic regression analysis with stepwise backward selection at an alpha of 0.05 independent predictors of hypoglycaemia were determined. Results 3,808 patients had data on hypoglycaemia available (median age 65.9 years, 46.6% female). 10.8% had at least one anamnestic hypoglycaemic episode within the previous 12 months. Patients with hypoglycaemia received more sulfonylureas (OR 2.16; 95%CI 1.75-2.67) and less metformin (OR 0.64; 95%CI 0.50-0.82). On top of metformin, patients with thiazolidine (OR 0.50; 95%CI 0.28-0.89) and DPP-4 inhibitor use (OR 0.34; 95%CI 0.16-0.70) had a decreased risk for hypoglycaemia while it was again increased with sulfonylureas (OR 2.08; 95%CI 1.44-2.99). Age < 65 years was an independent predictor of a reduced hypoglycaemia incidence (OR 0.76; 95%CI 0.59-0.96), low HbA1c (OR 1.68; 95%CI 1.31-2.14), stroke/TIA (OR 1.72; 95%CI 1.08-2.72), heart failure (OR 1.77; 95%CI 1.28-2.45), and the use of sulfonylureas (OR 2.58; 95%CI 2.03-3.29) were independent predictors of increased risk. Conclusions The results indicate that the risk of hypoglycaemia might be substantially reduced by carefully selecting antidiabetic pharmacotherapy in patients with type-2 diabets in primary care. PMID:21756359

Friendship networks of inner-city adults: a latent class analysis and multi-level regression of supporter types and the association of supporter latent class membership with supporter and recipient drug use.

PubMed

Bohnert, Amy S B; German, Danielle; Knowlton, Amy R; Latkin, Carl A

2010-03-01

Social support is a multi-dimensional construct that is important to drug use cessation. The present study identified types of supportive friends among the social network members in a community-based sample and examined the relationship of supporter-type classes with supporter, recipient, and supporter-recipient relationship characteristics. We hypothesized that the most supportive network members and their support recipients would be less likely to be current heroin/cocaine users. Participants (n=1453) were recruited from low-income neighborhoods with a high prevalence of drug use. Participants identified their friends via a network inventory, and all nominated friends were included in a latent class analysis and grouped based on their probability of providing seven types of support. These latent classes were included as the dependent variable in a multi-level regression of supporter drug use, recipient drug use, and other characteristics. The best-fitting latent class model identified five support patterns: friends who provided Little/No Support, Low/Moderate Support, High Support, Socialization Support, and Financial Support. In bivariate models, friends in the High, Low/Moderate, and Financial Support were less likely to use heroin or cocaine and had less conflict with and were more trusted by the support recipient than friends in the Low/No Support class. Individuals with supporters in those same support classes compared to the Low/No Support class were less likely to use heroin or cocaine, or to be homeless or female. Multivariable models suggested similar trends. Those with current heroin/cocaine use were less likely to provide or receive comprehensive support from friends. Published by Elsevier Ireland Ltd.

Relationships between type of pain and work participation in people with long-standing spinal cord injury: results from a cross-sectional study.

PubMed

Roels, Ellen H; Reneman, Michiel F; Stolwijk-Swuste, Janneke; van Laake-Geelen, Charlotte C; de Groot, Sonja; Adriaansen, Jacinthe J E; Post, Marcel W M

2018-05-01

Multicentre, cross-sectional study. To describe the relationships between the presence of (different types of) pain and participation in paid work in people with long-standing spinal cord injury (SCI). Furthermore, the associations of pain-related work limitations, age, gender, relationship, education, lesion level, and time since injury (TSI) with work participation (WP) were investigated. The Netherlands. Individuals (n = 265) with SCI for ≥ 10 years were included. Data were collected through a structured consultation with a rehabilitation physician and self-report questionnaire. Descriptive statistics and logistic regression analysis were performed. Median age of participants was 47.9 years, median time since injury was 22 years, 73% were male, 69% had complete SCI and 59% had paraplegia, 50% had paid work, 63% reported musculoskeletal pain, 49% reported neuropathic pain, and 31% reported other pain. Self-reported pain-related work limitations were significantly (V = 0.26 and V = 0.27) related to WP. In bivariable logistic regression analyses, no statistically significant relationships between type of pain and WP were observed. Younger age (OR=0.96), male gender (OR=0.52), a stable relationship (OR = 1.70), and shorter time since SCI (OR = 0.97) were significantly associated with a higher chance of being employed. Multivariable analysis confirmed these findings and in addition showed a higher level of education to be positively related with WP. Age, gender, relationship, education, TSI and self-reported work limitations showed a relationship with WP. Different types of pain were unrelated to WP. Fonds NutsOHRA through the Dutch Organization for Health Research and Development (ZonMw), Project number 89000006.

High prevalence of morphometric vertebral deformities in patients with inflammatory bowel disease.

PubMed

Heijckmann, Anna Caroline; Huijberts, Maya S P; Schoon, Erik J; Geusens, Piet; de Vries, Jolanda; Menheere, Paul P C A; van der Veer, Eveline; Wolffenbuttel, Bruce H R; Stockbrugger, Reinhold W; Dumitrescu, Bianca; Nieuwenhuijzen Kruseman, Arie C

2008-08-01

Earlier studies have documented that the prevalence of decreased bone mineral density (BMD) is elevated in patients with inflammatory bowel disease. The objective of this study was to investigate the prevalence of vertebral deformities in inflammatory bowel disease patients and their relation with BMD and bone turnover. One hundred and nine patients with Crohn's disease (CD) and 72 with ulcerative colitis (UC) (age 44.5+/-14.2 years) were studied. BMD of the hip (by dual X-ray absorptiometry) was measured and a lateral single energy densitometry of the spine for assessment of vertebral deformities was performed. Serum markers of bone resorption (carboxy-terminal cross-linked telopeptide of type I collagen) and formation (procollagen type I amino-terminal propeptide) were measured, and determinants of prevalent vertebral deformities were assessed using logistic regression analysis. Vertebral deformities were found in 25% of both CD and UC patients. Comparing patients with and without vertebral deformities, no significant difference was found between Z-scores and T-scores of BMD, or levels of serum carboxy-terminal cross-linked telopeptide of type I collagen and serum procollagen type I amino-terminal propeptide. Using logistic regression analysis the only determinant of any morphometric vertebral deformity was sex. The presence of multiple vertebral deformities was associated with older age and glucocorticoid use. The prevalence of morphometric vertebral deformities is high in CD and UC. Male sex, but neither disease activity, bone turnover markers, clinical risk factors, nor BMD predicted their presence. The determinants for having more than one vertebral deformity were age and glucocorticoid use. This implies that in addition to screening for low BMD, morphometric assessment of vertebral deformities is warranted in CD and UC.

Evidence Linking Hypoglycemic Events to an Increased Risk of Acute Cardiovascular Events in Patients With Type 2 Diabetes

PubMed Central

Johnston, Stephen S.; Conner, Christopher; Aagren, Mark; Smith, David M.; Bouchard, Jonathan; Brett, Jason

2011-01-01

OBJECTIVE This retrospective study examined the association between ICD-9-CM–coded outpatient hypoglycemic events (HEs) and acute cardiovascular events (ACVEs), i.e., acute myocardial infarction, coronary artery bypass grafting, revascularization, percutaneous coronary intervention, and incident unstable angina, in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS Data were derived from healthcare claims for individuals with employer-sponsored primary or Medicare supplemental insurance. A baseline period (30 September 2006 to 30 September 2007) was used to identify eligible patients and collect information on their clinical and demographic characteristics. An evaluation period (1 October 2007 to 30 September 2008) was used to identify HEs and ACVEs. Patients aged ≥18 years with type 2 diabetes were selected for analysis by a modified Healthcare Effectiveness Data and Information Set algorithm. Data were analyzed with multiple logistic regression and backward stepwise selection (maximum P = 0.01) with adjustment for important confounding variables, including age, sex, geography, insurance type, comorbidity scores, cardiovascular risk factors, diabetes complications, total baseline medical expenditures, and prior ACVEs. RESULTS Of the 860,845 patients in the analysis set, 27,065 (3.1%) had ICD-9-CM–coded HEs during the evaluation period. The main model retained 17 significant independent variables. Patients with HEs had 79% higher regression-adjusted odds (HE odds ratio [OR] 1.79; 95% CI 1.69–1.89) of ACVEs than patients without HEs; results in patients aged ≥65 years were similar to those for the entire population (HE OR 1.78, 95% CI 1.65–1.92). CONCLUSIONS ICD-9-CM–coded HEs were independently associated with an increased risk of ACVEs. Further studies of the relationship between hypoglycemia and the risk of ACVEs are warranted. PMID:21421802

Type of alcohol drink and exposure to violence: an emergency department study.

PubMed

Chavira, Cynthia; Bazargan-Hejazi, Shahrzad; Lin, Johnny; del Pino, Homero E; Bazargan, Mohsen

2011-08-01

We compared the prevalence of exposure to violence across different types of alcohol consumed and the association between the type of alcohol consumed and exposure to violence. A cross-sectional analysis of data collected from a sample of 295 Emergency Department (ED) patients identified as having an alcohol problem. Outcome measure include exposure to violence, and the main study predictor was "type of alcoholic drink" including: malt liquor beer (MLB), regular beer, wine cooler, wine, fortified wine or hard liquor. Using logistic regression analysis, ED patients who drank MLB in combination with other types of alcohol increased their odds of being both threatened and physically attacked by 8.5 compared to ED patients who drank other types of alcohol. Being female increased the odds of being both threatened and physically attacked by 2.5 and using illicit drugs increased the odds by 3.8. Analysis of covariance and estimated marginal means revealed that ED patients who only drank MLB had a higher exposure to violence compared to non-MLB drinkers, and that female illicit drug users who drank MLB in combination with other types of alcohol had the highest exposure to violence. MLB was identified as a predictor of the amount of exposure to violence and in particular, that the use of malt liquor beer in combination with other types of alcohol increased the risk of being both threatened and physically attacked. Implications for ED and community interventions are suggested.

Using Dominance Analysis to Determine Predictor Importance in Logistic Regression

ERIC Educational Resources Information Center

Azen, Razia; Traxel, Nicole

2009-01-01

This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…

Estimation of Magnitude and Frequency of Floods for Streams on the Island of Oahu, Hawaii

USGS Publications Warehouse

Wong, Michael F.

1994-01-01

This report describes techniques for estimating the magnitude and frequency of floods for the island of Oahu. The log-Pearson Type III distribution and methodology recommended by the Interagency Committee on Water Data was used to determine the magnitude and frequency of floods at 79 gaging stations that had 11 to 72 years of record. Multiple regression analysis was used to construct regression equations to transfer the magnitude and frequency information from gaged sites to ungaged sites. Oahu was divided into three hydrologic regions to define relations between peak discharge and drainage-basin and climatic characteristics. Regression equations are provided to estimate the 2-, 5-, 10-, 25-, 50-, and 100-year peak discharges at ungaged sites. Significant basin and climatic characteristics included in the regression equations are drainage area, median annual rainfall, and the 2-year, 24-hour rainfall intensity. Drainage areas for sites used in this study ranged from 0.03 to 45.7 square miles. Standard error of prediction for the regression equations ranged from 34 to 62 percent. Peak-discharge data collected through water year 1988, geographic information system (GIS) technology, and generalized least-squares regression were used in the analyses. The use of GIS seems to be a more flexible and consistent means of defining and calculating basin and climatic characteristics than using manual methods. Standard errors of estimate for the regression equations in this report are an average of 8 percent less than those published in previous studies.

Analysis of the Magnitude and Frequency of Peak Discharges for the Navajo Nation in Arizona, Utah, Colorado, and New Mexico

USGS Publications Warehouse

Waltemeyer, Scott D.

2006-01-01

Estimates of the magnitude and frequency of peak discharges are necessary for the reliable flood-hazard mapping in the Navajo Nation in Arizona, Utah, Colorado, and New Mexico. The Bureau of Indian Affairs, U.S. Army Corps of Engineers, and Navajo Nation requested that the U.S. Geological Survey update estimates of peak discharge magnitude for gaging stations in the region and update regional equations for estimation of peak discharge and frequency at ungaged sites. Equations were developed for estimating the magnitude of peak discharges for recurrence intervals of 2, 5, 10, 25, 50, 100, and 500 years at ungaged sites using data collected through 1999 at 146 gaging stations, an additional 13 years of peak-discharge data since a 1997 investigation, which used gaging-station data through 1986. The equations for estimation of peak discharges at ungaged sites were developed for flood regions 8, 11, high elevation, and 6 and are delineated on the basis of the hydrologic codes from the 1997 investigation. Peak discharges for selected recurrence intervals were determined at gaging stations by fitting observed data to a log-Pearson Type III distribution with adjustments for a low-discharge threshold and a zero skew coefficient. A low-discharge threshold was applied to frequency analysis of 82 of the 146 gaging stations. This application provides an improved fit of the log-Pearson Type III frequency distribution. Use of the low-discharge threshold generally eliminated the peak discharge having a recurrence interval of less than 1.4 years in the probability-density function. Within each region, logarithms of the peak discharges for selected recurrence intervals were related to logarithms of basin and climatic characteristics using stepwise ordinary least-squares regression techniques for exploratory data analysis. Generalized least-squares regression techniques, an improved regression procedure that accounts for time and spatial sampling errors, then was applied to the same data used in the ordinary least-squares regression analyses. The average standard error of prediction for a peak discharge have a recurrence interval of 100-years for region 8 was 53 percent (average) for the 100-year flood. The average standard of prediction, which includes average sampling error and average standard error of regression, ranged from 45 to 83 percent for the 100-year flood. Estimated standard error of prediction for a hybrid method for region 11 was large in the 1997 investigation. No distinction of floods produced from a high-elevation region was presented in the 1997 investigation. Overall, the equations based on generalized least-squares regression techniques are considered to be more reliable than those in the 1997 report because of the increased length of record and improved GIS method. Techniques for transferring flood-frequency relations to ungaged sites on the same stream can be estimated at an ungaged site by a direct application of the regional regression equation or at an ungaged site on a stream that has a gaging station upstream or downstream by using the drainage-area ratio and the drainage-area exponent from the regional regression equation of the respective region.

Factor Analysis of Linear Type Traits and Their Relation with Longevity in Brazilian Holstein Cattle

PubMed Central

Kern, Elisandra Lurdes; Cobuci, Jaime Araújo; Costa, Cláudio Napolis; Pimentel, Concepta Margaret McManus

2014-01-01

In this study we aimed to evaluate the reduction in dimensionality of 20 linear type traits and more final score in 14,943 Holstein cows in Brazil using factor analysis, and indicate their relationship with longevity and 305 d first lactation milk production. Low partial correlations (−0.19 to 0.38), the medium to high Kaiser sampling mean (0.79) and the significance of the Bartlett sphericity test (p<0.001), indicated correlations between type traits and the suitability of these data for a factor analysis, after the elimination of seven traits. Two factors had autovalues greater than one. The first included width and height of posterior udder, udder texture, udder cleft, loin strength, bone quality and final score. The second included stature, top line, chest width, body depth, fore udder attachment, angularity and final score. The linear regression of the factors on several measures of longevity and 305 d milk production showed that selection considering only the first factor should lead to improvements in longevity and 305 milk production. PMID:25050015

Investigation of the aroma of commercial peach (Prunus persica L. Batsch) types by Proton Transfer Reaction-Mass Spectrometry (PTR-MS) and sensory analysis.

PubMed

Bianchi, Tiago; Weesepoel, Yannick; Koot, Alex; Iglesias, Ignasi; Eduardo, Iban; Gratacós-Cubarsí, Marta; Guerrero, Luis; Hortós, Maria; van Ruth, Saskia

2017-09-01

The aim of this study was to investigate the aroma and sensory profiles of various types of peaches (Prunus persica L. Batsch.). Forty-three commercial cultivars comprising peaches, flat peaches, nectarines, and canning peaches (pavías) were grown over two consecutive harvest years. Fruits were assessed for chemical aroma and sensory profiles. Chemical aroma profile was obtained by proton transfer reaction-mass spectrometry (PTR-MS) and spectral masses were tentatively identified with PTR-Time of Flight-MS (PTR-Tof-MS). Sensory analysis was performed at commercial maturity considering seven aroma/flavor attributes. The four types of peaches showed both distinct chemical aroma and sensory profiles. Flat peaches and canning peaches showed most distinct patterns according to discriminant analysis. The sensory data were related to the volatile compounds by partial least square regression. γ-Hexalactone, γ-octalactone, hotrienol, acetic acid and ethyl acetate correlated positively, and benzeneacetaldehyde, trimethylbenzene and acetaldehyde negatively to the intensities of aroma and ripe fruit sensory scores. Copyright © 2017 Elsevier Ltd. All rights reserved.

Identification of milk origin and process-induced changes in milk by stable isotope ratio mass spectrometry.

PubMed

Scampicchio, Matteo; Mimmo, Tanja; Capici, Calogero; Huck, Christian; Innocente, Nadia; Drusch, Stephan; Cesco, Stefano

2012-11-14

Stable isotope values were used to develop a new analytical approach enabling the simultaneous identification of milk samples either processed with different heating regimens or from different geographical origins. The samples consisted of raw, pasteurized (HTST), and ultrapasteurized (UHT) milk from different Italian origins. The approach consisted of the analysis of the isotope ratio of δ¹³C and δ¹⁵N for the milk samples and their fractions (fat, casein, and whey). The main finding of this work is that as the heat processing affects the composition of the milk fractions, changes in δ¹³C and δ¹⁵N were also observed. These changes were used as markers to develop pattern recognition maps based on principal component analysis and supervised classification models, such as linear discriminant analysis (LDA), multivariate regression (MLR), principal component regression (PCR), and partial least-squares (PLS). The results give proof of the concept that isotope ratio mass spectroscopy can discriminate simultaneously between milk samples according to their geographical origin and type of processing.

[Long-term outcome analysis of subjective and objective parameters after breast reduction in 159 cases: Patients judge differently from plastic surgeons].

PubMed

Osinga, Rik; Babst, Doris; Bodmer, Elvira S; Link, Bjoern C; Fritsche, Elmar; Hug, Urs

2017-12-01

This work assessed both subjective and objective postoperative parameters after breast reduction surgery and compared between patients and plastic surgeons. After an average postoperative observation period of 6.7 ± 2.7 (2 - 13) years, 159 out of 259 patients (61 %) were examined. The mean age at the time of surgery was 37 ± 14 (15 - 74) years. The postoperative anatomy of the breast and other anthropometric parameters were measured in cm with the patient in an upright position. The visual analogue scale (VAS) values for symmetry, size, shape, type of scar and overall satisfaction both from the patient's and from four plastic surgeons' perspectives were assessed and compared. Patients rated the postoperative result significantly better than surgeons. Good subjective ratings by patients for shape, symmetry and sensitivity correlated with high scores for overall assessment. Shape had the strongest influence on overall satisfaction (regression coefficient 0.357; p < 0.001), followed by symmetry (regression coefficient 0.239; p < 0.001) and sensitivity (regression coefficient 0.109; p = 0.040) of the breast. The better the subjective rating for symmetry by the patient, the smaller the measured difference of the jugulum-mamillary distance between left and right (regression coefficient -0.773; p = 0.002) and the smaller the difference in height of the lowest part of the breast between left and right (regression coefficient -0.465; p = 0.035). There was no significant correlation between age, weight, height, BMI, resected weight of the breast, postoperative breast size or type of scar with overall satisfaction. After breast reduction surgery, long-term outcome is rated significantly better by patients than by plastic surgeons. Good subjective ratings by patients for shape, symmetry and sensitivity correlated with high scores for overall assessment. Shape had the strongest influence on overall satisfaction, followed by symmetry and sensitivity of the breast. Postoperative size of the breast, resection weight, type of scar, age or BMI was not of significant influence. Symmetry was the only assessed subjective parameter of this study that could be objectified by postoperative measurements. Georg Thieme Verlag KG Stuttgart · New York.

Same Game, Different Rules? Gender Differences in Political Participation

PubMed Central

Bolzendahl, Catherine

2010-01-01

We investigate gender gaps in political participation with 2004 ISSP data for 18 advanced Western democracies (N: 20,359) using linear and logistic regression models. Controlling for socio-economic characteristics and political attitudes reveals that women are more likely than men to have voted and engaged in ‘private’ activism, while men are more likely to have engaged in direct contact, collective types of actions and be (more active) members of political parties. Our analysis indicates that demographic and attitudinal characteristics influence participation differently among men and among women, as well as across types of participation. These results highlight the need to move toward a view of women engaging in differing types of participation and based on different characteristics. PMID:20407575

Independent and combined effects of physical activity and body mass index on the development of Type 2 Diabetes - a meta-analysis of 9 prospective cohort studies.

PubMed

Cloostermans, Laura; Wendel-Vos, Wanda; Doornbos, Gerda; Howard, Bethany; Craig, Cora Lynn; Kivimäki, Mika; Tabak, Adam G; Jefferis, Barbara J; Ronkainen, Kimmo; Brown, Wendy J; Picavet, Susan H S J; Ben-Shlomo, Yoav; Laukkanen, Jari Antero; Kauhanen, Jussi; Bemelmans, Wanda J E

2015-12-01

The aim of this harmonized meta-analysis was to examine the independent and combined effects of physical activity and BMI on the incidence of type 2 diabetes. Our systematic literature review in 2011 identified 127 potentially relevant prospective studies of which 9 fulfilled the inclusion criteria (total N = 117,878, 56.2 % female, mean age = 50.0 years, range = 25-65 years). Measures of baseline physical activity (low, intermediate, high), BMI-category [BMI < 18.4 (underweight), 18.5-24.9 (normal weight), 25.0-29.9 (overweight), 30+ (obese)] and incident type 2 diabetes were harmonized across studies. The associations between physical activity, BMI and incident type 2 diabetes were analyzed using Cox regression with a standardized analysis protocol including adjustments for age, gender, educational level, and smoking. Hazard ratios from individual studies were combined in a random-effects meta-analysis. Mean follow-up time was 9.1 years. A total of 11,237 incident type 2 diabetes cases were recorded. In mutually adjusted models, being overweight or obese (compared with normal weight) and having low physical activity (compared with high physical activity) were associated with an increased risk of incident type 2 diabetes (hazard ratios 2.33, 95 % CI 1.95-2.78; 6.10, 95 % CI: 4.63-8.04, and 1.23, 95 % CI: 1.09-1.39, respectively). Individuals who were both obese and had low physical activity had 7.4-fold (95 % CI 3.47-15.89) increased risk of type 2 diabetes compared with normal weight, high physically active participants. This harmonized meta-analysis shows the importance of maintaining a healthy weight and being physically active in diabetes prevention.

Association between omega-3 fatty acids consumption and the risk of type 2 diabetes: A meta-analysis of cohort studies.

PubMed

Chen, Cai; Yang, Yan; Yu, Xuefeng; Hu, Shuhong; Shao, Shiying

2017-07-01

Epidemiological evidence for the effect of omega-3 fatty acids on the risk of type 2 diabetes is controversial. A meta-analysis based on prospective cohorts was carried out to evaluate this issue. Pooled diabetic risk was calculated using a fixed or random effects model. The dose-response relationship was assessed by meta-regression analysis. The study showed that consumption of single omega-3 was associated with an increased risk of type 2 diabetes (relative risk [RR] = 1.45, P < 0.001); whereas the RR for mixed omega-3 was statistically insignificant. The dose-response curve presented an inverted U-shape of diabetes risk corresponding to the dose of omega-3 consumption. Subanalysis showed that omega-3 was inversely associated with type 2 diabetes risk in Asians (RR = 0.82, P < 0.001); whereas the risk was increased in Westerners (RR = 1.30, P < 0.001). Studies with follow-up duration ≥16 years and baseline age ≥54 years showed a positive association between type 2 diabetes risk and omega-3 intake. The present findings suggest that dosage and composition of omega-3, ethnicity, trial duration, and age could influence the effect of omega-3 on type 2 diabetes progression. © 2016 The Authors. Journal of Diabetes Investigation published by Asian Association for the Study of Diabetes (AASD) and John Wiley & Sons Australia, Ltd.

Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers

PubMed Central

García-Gonzalo, Esperanza; Fernández-Muñiz, Zulima; García Nieto, Paulino José; Bernardo Sánchez, Antonio; Menéndez Fernández, Marta

2016-01-01

The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine. PMID:28773653

Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers.

PubMed

García-Gonzalo, Esperanza; Fernández-Muñiz, Zulima; García Nieto, Paulino José; Bernardo Sánchez, Antonio; Menéndez Fernández, Marta

2016-06-29

The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine.

Price of gasoline: forecasting comparisons. [Box-Jenkins, econometric, and regression methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bopp, A.E.; Neri, J.A.

Gasoline prices are simulated using three popular forecasting methodologies: A Box--Jenkins type method, an econometric method, and a regression method. One-period-ahead and 18-period-ahead comparisons are made. For the one-period-ahead method, a Box--Jenkins type time-series model simulated best, although all do well. However, for the 18-period simulation, the econometric and regression methods perform substantially better than the Box-Jenkins formulation. A rationale for and implications of these results ae discussed. 11 references.

Breast cancer screening among shift workers: a nationwide population-based survey in Korea.

PubMed

Son, Heesook; Kang, Youngmi

2017-04-01

We aimed to examine the association between shift work types and participation in breast cancer screening (BCS) programs by comparing rates of participation for BCS among regular daytime workers and alternative shift workers using data from a nationally representative, population-based survey conducted in Korea. In addition, the results were analyzed according to sociodemographic factors, including occupation, education, income, private health insurance, age, and number of working hours a week. This secondary cross-sectional analysis used data from the 2012 Korean National Health and Nutritional Examination Survey. The target population included women aged ≥ 40 years who responded as to whether they had undergone BCS in the previous year. Accordingly, we analyzed survey data for a total of 1,193 women and used a multivariate logistic regression analysis to evaluate the differences in factors affecting BCS between regular daytime and alternative shift workers. A logistic regression analysis was performed considering private health insurance as a significant sociodemographic factor for BCS among regular daytime shift workers. In contrast, none of the tested variables could significantly predict adherence to BCS among alternative shift workers. The results of this study suggest the need for the development of comprehensive workplace breast cancer prevention programs by considering shift work types. More attention should be given to female workers with low education levels, those who are uninsured, and young workers to improve the participation rate for BCS at the workplace.

HbA1c is significantly associated with arterial stiffness but not with carotid atherosclerosis in a community-based population without type 2 diabetes: The Dong-gu study.

PubMed

Lee, Young-Hoon; Shin, Min-Ho; Choi, Jin-Su; Rhee, Jung-Ae; Nam, Hae-Sung; Jeong, Seul-Ki; Park, Kyeong-Soo; Ryu, So-Yeon; Choi, Seong-Woo; Kim, Bok-Hee; Oh, Gyung-Jae; Kweon, Sun-Seog

2016-04-01

We examined the associations between HbA1c levels and various atherosclerotic vascular parameters among adults without diabetes from the general population. A total of 6500 community-dwelling adults, who were free of type 2 diabetes and ≥50 years of age, were included. High-resolution B-mode ultrasound was used to evaluate carotid artery structure, including intima-media thickness (IMT), plaque, and luminal diameter. Brachial-ankle pulse wave velocity (baPWV), which is a useful indicator of systemic arterial stiffness, was determined using an automatic waveform analysis device. No significant associations were observed between HbA1c, carotid IMT, plaque, or luminal diameter in a fully adjusted model. However, the odds ratio (95% confidence interval) for high baPWV (defined as the highest quartile) increased by 1.43 (1.19-1.71) per 1% HbA1c increase after adjusting for conventional risk factors in a multivariate logistic regression analysis. In addition, HbA1c was independently associated with baPWV in a multivariate linear regression analysis. High-normal HbA1c level was independently associated with arterial stiffness, but not with carotid atherosclerotic parameters, in the general population without diabetes. Our results suggest that the functional atherosclerotic process may already be accelerated according to HbA1c level, even at a level below the diagnostic threshold for diabetes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Socioeconomic and Demographic Disparities in Knowledge of Reproductive Healthcare among Female University Students in Bangladesh

PubMed Central

Islam Mondal, Md. Nazrul; Nasir Ullah, Md. Monzur Morshad; Khan, Md. Nuruzzaman; Islam, Mohammad Zamirul; Islam, Md. Nurul; Moni, Sabiha Yasmin; Hoque, Md. Nazrul; Rahman, Md. Mashiur

2015-01-01

Background: Reproductive health (RH) is a critical component of women’s health and overall well-being around the world, especially in developing countries. We examine the factors that determine knowledge of RH care among female university students in Bangladesh. Methods: Data on 300 female students were collected from Rajshahi University, Bangladesh through a structured questionnaire using purposive sampling technique. The data were used for univariate analysis, to carry out the description of the variables; bivariate analysis was used to examine the associations between the variables; and finally, multivariate analysis (binary logistic regression model) was used to examine and fit the model and interpret the parameter estimates, especially in terms of odds ratios. Results: The results revealed that more than one-third (34.3%) respondents do not have sufficient knowledge of RH care. The χ2-test identified the significant (p < 0.05) associations between respondents’ knowledge of RH care with respondents’ age, education, family type, watching television; and knowledge about pregnancy, family planning, and contraceptive use. Finally, the binary logistic regression model identified respondents’ age, education, family type; and knowledge about family planning, and contraceptive use as the significant (p < 0.05) predictors of RH care. Conclusions and Global Health Implications: Knowledge of RH care among female university students was found unsatisfactory. Government and concerned organizations should promote and strengthen various health education programs to focus on RH care especially for the female university students in Bangladesh. PMID:27622005

Diabetes and Risk of Surgical Site Infection: A systematic review and meta-analysis

PubMed Central

Kaye, Keith S.; Knott, Caitlin; Nguyen, Huong; Santarossa, Maressa; Evans, Richard; Bertran, Elizabeth; Jaber, Linda

2016-01-01

Objective To determine the independent association between diabetes and SSI across multiple surgical procedures. Design Systematic review and meta-analysis. Methods Studies indexed in PubMed published between December 1985 and through July 2015 were identified through the search terms “risk factors” or “glucose” and “surgical site infection”. A total of 3,631 abstracts were identified through the initial search terms. Full texts were reviewed for 522 articles. Of these, 94 articles met the criteria for inclusion. Standardized data collection forms were used to extract study-specific estimates for diabetes, blood glucose levels, and body mass index (BMI). Random-effects meta-analysis was used to generate pooled estimates and meta-regression was used to evaluate specific hypothesized sources of heterogeneity. Results The primary outcome was SSI, as defined by the Centers for Disease Control and Prevention surveillance criteria. The overall effect size for the association between diabetes and SSI was OR=1.53 (95% Predictive Interval 1.11, 2.12, I2: 57.2%). SSI class, study design, or patient BMI did not significantly impact study results in a meta-regression model. The association was higher for cardiac surgery 2.03 (95% Predictive Interval 1.13, 4.05) compared to surgeries of other types (p=0.001). Conclusion These results support the consideration of diabetes as an independent risk factor for SSIs for multiple surgical procedure types. Continued efforts are needed to improve surgical outcomes for diabetic patients. PMID:26503187

Orthodontic bracket bonding without previous adhesive priming: A meta-regression analysis.

PubMed

Altmann, Aline Segatto Pires; Degrazia, Felipe Weidenbach; Celeste, Roger Keller; Leitune, Vicente Castelo Branco; Samuel, Susana Maria Werner; Collares, Fabrício Mezzomo

2016-05-01

To determine the consensus among studies that adhesive resin application improves the bond strength of orthodontic brackets and the association of methodological variables on the influence of bond strength outcome. In vitro studies were selected to answer whether adhesive resin application increases the immediate shear bond strength of metal orthodontic brackets bonded with a photo-cured orthodontic adhesive. Studies included were those comparing a group having adhesive resin to a group without adhesive resin with the primary outcome measurement shear bond strength in MPa. A systematic electronic search was performed in PubMed and Scopus databases. Nine studies were included in the analysis. Based on the pooled data and due to a high heterogeneity among studies (I(2)  =  93.3), a meta-regression analysis was conducted. The analysis demonstrated that five experimental conditions explained 86.1% of heterogeneity and four of them had significantly affected in vitro shear bond testing. The shear bond strength of metal brackets was not significantly affected when bonded with adhesive resin, when compared to those without adhesive resin. The adhesive resin application can be set aside during metal bracket bonding to enamel regardless of the type of orthodontic adhesive used.

[Factors associated with physical activity among Chinese immigrant women].

PubMed

Cho, Sung-Hye; Lee, Hyeonkyeong

2013-12-01

This study was done to assess the level of physical activity among Chinese immigrant women and to determine the relationships of physical activity with individual characteristics and behavior-specific cognition. A cross-sectional descriptive study was conducted with 161 Chinese immigrant women living in Busan. A health promotion model of physical activity adapted from Pender's Health Promotion Model was used. Self-administered questionnaires were used to collect data during the period from September 25 to November 20, 2012. Using SPSS 18.0 program, descriptive statistics, t-test, analysis of variance, correlation analysis, and multiple regression analysis were done. The average level of physical activity of the Chinese immigrant women was 1,050.06 ± 686.47 MET-min/week and the minimum activity among types of physical activity was most dominant (59.6%). As a result of multiple regression analysis, it was confirmed that self-efficacy and acculturation were statistically significant variables in the model (p<.001), with an explanatory power of 23.7%. The results indicate that the development and application of intervention strategies to increase acculturation and self-efficacy for immigrant women will aid in increasing the physical activity in Chinese immigrant women.

Soil Particle Size Analysis by Laser Diffractometry: Result Comparison with Pipette Method

NASA Astrophysics Data System (ADS)

Šinkovičová, Miroslava; Igaz, Dušan; Kondrlová, Elena; Jarošová, Miriam

2017-10-01

Soil texture as the basic soil physical property provides a basic information on the soil grain size distribution as well as grain size fraction representation. Currently, there are several methods of particle dimension measurement available that are based on different physical principles. Pipette method based on the different sedimentation velocity of particles with different diameter is considered to be one of the standard methods of individual grain size fraction distribution determination. Following the technical advancement, optical methods such as laser diffraction can be also used nowadays for grain size distribution determination in the soil. According to the literature review of domestic as well as international sources related to this topic, it is obvious that the results obtained by laser diffractometry do not correspond with the results obtained by pipette method. The main aim of this paper was to analyse 132 samples of medium fine soil, taken from the Nitra River catchment in Slovakia, from depths of 15-20 cm and 40-45 cm, respectively, using laser analysers: ANALYSETTE 22 MicroTec plus (Fritsch GmbH) and Mastersizer 2000 (Malvern Instruments Ltd). The results obtained by laser diffractometry were compared with pipette method and the regression relationships using linear, exponential, power and polynomial trend were derived. Regressions with the three highest regression coefficients (R2) were further investigated. The fit with the highest tightness was observed for the polynomial regression. In view of the results obtained, we recommend using the estimate of the representation of the clay fraction (<0.01 mm) polynomial regression, to achieve a highest confidence value R2 at the depths of 15-20 cm 0.72 (Analysette 22 MicroTec plus) and 0.95 (Mastersizer 2000), from a depth of 40-45 cm 0.90 (Analysette 22 MicroTec plus) and 0.96 (Mastersizer 2000). Since the percentage representation of clayey particles (2nd fraction according to the methodology of Complex Soil Survey done in Slovakia) in soil is the determinant for soil type specification, we recommend using the derived relationships in soil science when the soil texture analysis is done according to laser diffractometry. The advantages of laser diffraction method comprise the short analysis time, usage of small sample amount, application for the various grain size fraction and soil type classification systems, and a wide range of determined fractions. Therefore, it is necessary to focus on this issue further to address the needs of soil science research and attempt to replace the standard pipette method with more progressive laser diffraction method.

Peak-flow characteristics of Virginia streams

USGS Publications Warehouse

Austin, Samuel H.; Krstolic, Jennifer L.; Wiegand, Ute

2011-01-01

Peak-flow annual exceedance probabilities, also called probability-percent chance flow estimates, and regional regression equations are provided describing the peak-flow characteristics of Virginia streams. Statistical methods are used to evaluate peak-flow data. Analysis of Virginia peak-flow data collected from 1895 through 2007 is summarized. Methods are provided for estimating unregulated peak flow of gaged and ungaged streams. Station peak-flow characteristics identified by fitting the logarithms of annual peak flows to a Log Pearson Type III frequency distribution yield annual exceedance probabilities of 0.5, 0.4292, 0.2, 0.1, 0.04, 0.02, 0.01, 0.005, and 0.002 for 476 streamgaging stations. Stream basin characteristics computed using spatial data and a geographic information system are used as explanatory variables in regional regression model equations for six physiographic regions to estimate regional annual exceedance probabilities at gaged and ungaged sites. Weighted peak-flow values that combine annual exceedance probabilities computed from gaging station data and from regional regression equations provide improved peak-flow estimates. Text, figures, and lists are provided summarizing selected peak-flow sites, delineated physiographic regions, peak-flow estimates, basin characteristics, regional regression model equations, error estimates, definitions, data sources, and candidate regression model equations. This study supersedes previous studies of peak flows in Virginia.

Magnitude and Frequency of Floods for Urban and Small Rural Streams in Georgia, 2008

USGS Publications Warehouse

Gotvald, Anthony J.; Knaak, Andrew E.

2011-01-01

A study was conducted that updated methods for estimating the magnitude and frequency of floods in ungaged urban basins in Georgia that are not substantially affected by regulation or tidal fluctuations. Annual peak-flow data for urban streams from September 2008 were analyzed for 50 streamgaging stations (streamgages) in Georgia and 6 streamgages on adjacent urban streams in Florida and South Carolina having 10 or more years of data. Flood-frequency estimates were computed for the 56 urban streamgages by fitting logarithms of annual peak flows for each streamgage to a Pearson Type III distribution. Additionally, basin characteristics for the streamgages were computed by using a geographical information system and computer algorithms. Regional regression analysis, using generalized least-squares regression, was used to develop a set of equations for estimating flows with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities for ungaged urban basins in Georgia. In addition to the 56 urban streamgages, 171 rural streamgages were included in the regression analysis to maintain continuity between flood estimates for urban and rural basins as the basin characteristics pertaining to urbanization approach zero. Because 21 of the rural streamgages have drainage areas less than 1 square mile, the set of equations developed for this study can also be used for estimating small ungaged rural streams in Georgia. Flood-frequency estimates and basin characteristics for 227 streamgages were combined to form the final database used in the regional regression analysis. Four hydrologic regions were developed for Georgia. The final equations are functions of drainage area and percentage of impervious area for three of the regions and drainage area, percentage of developed land, and mean basin slope for the fourth region. Average standard errors of prediction for these regression equations range from 20.0 to 74.5 percent.

Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections.

PubMed

Dong, Chunjiao; Clarke, David B; Yan, Xuedong; Khattak, Asad; Huang, Baoshan

2014-09-01

Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types. Copyright © 2014 Elsevier Ltd. All rights reserved.

A comparison of long-term parallel measurements of sunshine duration obtained with a Campbell-Stokes sunshine recorder and two automated sunshine sensors

NASA Astrophysics Data System (ADS)

Baumgartner, D. J.; Pötzi, W.; Freislich, H.; Strutzmann, H.; Veronig, A. M.; Foelsche, U.; Rieder, H. E.

2017-06-01

In recent decades, automated sensors for sunshine duration (SD) measurements have been introduced in meteorological networks, thereby replacing traditional instruments, most prominently the Campbell-Stokes (CS) sunshine recorder. Parallel records of automated and traditional SD recording systems are rare. Nevertheless, such records are important to understand the differences/similarities in SD totals obtained with different instruments and how changes in monitoring device type affect the homogeneity of SD records. This study investigates the differences/similarities in parallel SD records obtained with a CS and two automated SD sensors between 2007 and 2016 at the Kanzelhöhe Observatory, Austria. Comparing individual records of daily SD totals, we find differences of both positive and negative sign, with smallest differences between the automated sensors. The larger differences between CS-derived SD totals and those from automated sensors can be attributed (largely) to the higher sensitivity threshold of the CS instrument. Correspondingly, the closest agreement among all sensors is found during summer, the time of year when sensitivity thresholds are least critical. Furthermore, we investigate the performance of various models to create the so-called sensor-type-equivalent (STE) SD records. Our analysis shows that regression models including all available data on daily (or monthly) time scale perform better than simple three- (or four-) point regression models. Despite general good performance, none of the considered regression models (of linear or quadratic form) emerges as the "optimal" model. Although STEs prove useful for relating SD records of individual sensors on daily/monthly time scales, this does not ensure that STE (or joint) records can be used for trend analysis.

Social network types and well-being among South Korean older adults.

PubMed

Park, Sojung; Smith, Jacqui; Dunkle, Ruth E

2014-01-01

The social networks of older individuals reflect personal life history and cultural factors. Despite these two sources of variation, four similar network types have been identified in Europe, North America, Japan, and China: namely 'restricted', 'family', 'friend', and 'diverse'. This study identified the social network types of Korean older adults and examined differential associations of the network types with well-being. The analysis used data from the 2008 wave of the Korean Longitudinal Study of Aging (KLoSA: N = 4251, age range 65-108). We used a two-step cluster analytical approach to identify network types from seven indicators of network structure and function. Regression models determined associations between network types and well-being outcomes, including life satisfaction and depressive symptomatology. Cluster analysis of indicators of network structure and function revealed four types, including the restricted, friend, and diverse types. Instead of a family type, we found a couple-focused type. The young-old (age 65-74) were more likely to be in the couple-focused type and more of the oldest old (age 85+) belonged to the restricted type. Compared with the restricted network, older adults in all other networks were more likely to report higher life satisfaction and lower depressive symptomatology. Life course and cohort-related factors contribute to similarities across societies in network types and their associations with well-being. Korean-specific life course and socio-historical factors, however, may contribute to our unique findings about network types.

Association of ADIPOQ variants with type 2 diabetes mellitus susceptibility in ethnic Han Chinese from northeast China.

PubMed

Yao, Meidong; Wu, Yanhui; Fang, Qingxiao; Sun, Lulu; Li, Tingting; Qiao, Hong

2016-11-01

To investigate the association between two single nucleotide polymorphisms (SNPs; rs3774261 and rs822393) in the ADIPOQ gene and type 2 diabetes mellitus in Han Chinese from northeast China. The present study comprised 993 type 2 diabetes mellitus patients and 966 unrelated controls from northeastern China. Two SNPs were sequenced using SNPscan. The distribution of genotype frequencies of the two SNPs in ADIPOQ between cases and controls, and in subgroups stratified based on body mass index, were compared using logistic regression analysis. Linear regression was used to analyze the association between each SNP and clinical indicators. The GG genotype of rs3774261 increased the risk of type 2 diabetes mellitus compared with the AA genotype in participants with a body mass index <24 (P = 0.021; odds ratio 1.636, 95% CI 1.708-2.484). Rs822393 was correlated with glycosylated hemoglobin (P = 0.043) in controls. Rs3774261 had an association with diastolic blood pressure (P = 0.017) in controls, and in controls with a body mass index <24; rs3774261 also had an association with both systolic blood pressure (P = 0.025) and diastolic blood pressure (P = 0.043). The present results confirm the association between ADIPOQ variants and type 2 diabetes mellitus in northeastern China. However, additional larger replication studies are required to validate these findings. © 2016 The Authors. Journal of Diabetes Investigation published by Asian Association for the Study of Diabetes (AASD) and John Wiley & Sons Australia, Ltd.

Estimates of the atmospheric parameters of M-type stars: a machine-learning perspective

NASA Astrophysics Data System (ADS)

Sarro, L. M.; Ordieres-Meré, J.; Bello-García, A.; González-Marcos, A.; Solano, E.

2018-05-01

Estimating the atmospheric parameters of M-type stars has been a difficult task due to the lack of simple diagnostics in the stellar spectra. We aim at uncovering good sets of predictive features of stellar atmospheric parameters (Teff, log (g), [M/H]) in spectra of M-type stars. We define two types of potential features (equivalent widths and integrated flux ratios) able to explain the atmospheric physical parameters. We search the space of feature sets using a genetic algorithm that evaluates solutions by their prediction performance in the framework of the BT-Settl library of stellar spectra. Thereafter, we construct eight regression models using different machine-learning techniques and compare their performances with those obtained using the classical χ2 approach and independent component analysis (ICA) coefficients. Finally, we validate the various alternatives using two sets of real spectra from the NASA Infrared Telescope Facility (IRTF) and Dwarf Archives collections. We find that the cross-validation errors are poor measures of the performance of regression models in the context of physical parameter prediction in M-type stars. For R ˜ 2000 spectra with signal-to-noise ratios typical of the IRTF and Dwarf Archives, feature selection with genetic algorithms or alternative techniques produces only marginal advantages with respect to representation spaces that are unconstrained in wavelength (full spectrum or ICA). We make available the atmospheric parameters for the two collections of observed spectra as online material.

Biomass Stoves and Lens Opacity and Cataract in Nepalese Women

PubMed Central

Pokhrel, Amod K.; Bates, Michael N.; Shrestha, Sachet P.; Bailey, Ian L.; DiMartino, Robert B.; Smith, Kirk R.; Joshi, N. D.

2014-01-01

Purpose Cataract is the most prevalent cause of blindness in Nepal. Several epidemiologic studies have associated cataracts with use of biomass cookstoves. These studies, however, have had limitations, including potential control selection bias and limited adjustment for possible confounding. This study, in Pokhara city, in an area of Nepal where biomass cookstoves are widely used without direct venting of the smoke to the outdoors, focuses on pre-clinical measures of opacity, while avoiding selection bias and taking into account comprehensive data on potential confounding factors Methods Using a cross-sectional study design, severity of lenticular damage, judged on the LOCS III scales, was investigated in females (n=143), aged 20-65 years, without previously diagnosed cataract. Linear and logistic regression analyses were used to examine the relationships with stove type and length of use. Clinically significant cataract, used in the logistic regression models, was defined as a LOCS III score > 2. Results Using gas cookstoves as the reference group, logistic regression analysis for nuclear cataract showed the evidence of relationships with stove type: for biomass stoves, the odds ratio (OR) was 2.58 (95% confidence interval [CI]: 1.22-5.46) and, for kerosene stoves, the OR was 5.18 (95% CI: 0.88-30.38). Similar results were found for nuclear color (LOCS III score > 2), but no association was found with cortical cataracts. Supporting a relationship between biomass stoves and nuclear cataract was a trend with years of exposure to biomass cookstoves (p=0.01). Linear regression analyses did not show clear evidence of an association between lenticular damage and stove types. Biomass fuel used for heating was not associated with any form of opacity. Conclusions This study provides support for associations of biomass and kerosene cookstoves with nuclear opacity and change in nuclear color. The novel associations with kerosene cookstove use deserve further investigation. PMID:23400024

Evaluating risk factors for endemic human Salmonella Enteritidis infections with different phage types in Ontario, Canada using multinomial logistic regression and a case-case study approach

PubMed Central

2012-01-01

Background Identifying risk factors for Salmonella Enteritidis (SE) infections in Ontario will assist public health authorities to design effective control and prevention programs to reduce the burden of SE infections. Our research objective was to identify risk factors for acquiring SE infections with various phage types (PT) in Ontario, Canada. We hypothesized that certain PTs (e.g., PT8 and PT13a) have specific risk factors for infection. Methods Our study included endemic SE cases with various PTs whose isolates were submitted to the Public Health Laboratory-Toronto from January 20th to August 12th, 2011. Cases were interviewed using a standardized questionnaire that included questions pertaining to demographics, travel history, clinical symptoms, contact with animals, and food exposures. A multinomial logistic regression method using the Generalized Linear Latent and Mixed Model procedure and a case-case study design were used to identify risk factors for acquiring SE infections with various PTs in Ontario, Canada. In the multinomial logistic regression model, the outcome variable had three categories representing human infections caused by SE PT8, PT13a, and all other SE PTs (i.e., non-PT8/non-PT13a) as a referent category to which the other two categories were compared. Results In the multivariable model, SE PT8 was positively associated with contact with dogs (OR=2.17, 95% CI 1.01-4.68) and negatively associated with pepper consumption (OR=0.35, 95% CI 0.13-0.94), after adjusting for age categories and gender, and using exposure periods and health regions as random effects to account for clustering. Conclusions Our study findings offer interesting hypotheses about the role of phage type-specific risk factors. Multinomial logistic regression analysis and the case-case study approach are novel methodologies to evaluate associations among SE infections with different PTs and various risk factors. PMID:23057531

Gene set analysis using variance component tests.

PubMed

Huang, Yen-Tsung; Lin, Xihong

2013-06-28

Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses. We propose to model the effects of an independent variable, e.g., exposure/biological status (yes/no), on multiple gene expression values in a gene set using a multivariate linear regression model, where the correlation among the genes is explicitly modeled using a working covariance matrix. We develop TEGS (Test for the Effect of a Gene Set), a variance component test for the gene set effects by assuming a common distribution for regression coefficients in multivariate linear regression models, and calculate the p-values using permutation and a scaled chi-square approximation. We show using simulations that type I error is protected under different choices of working covariance matrices and power is improved as the working covariance approaches the true covariance. The global test is a special case of TEGS when correlation among genes in a gene set is ignored. Using both simulation data and a published diabetes dataset, we show that our test outperforms the commonly used approaches, the global test and gene set enrichment analysis (GSEA). We develop a gene set analyses method (TEGS) under the multivariate regression framework, which directly models the interdependence of the expression values in a gene set using a working covariance. TEGS outperforms two widely used methods, GSEA and global test in both simulation and a diabetes microarray data.

Survival analysis of postoperative nausea and vomiting in patients receiving patient-controlled epidural analgesia.

PubMed

Lee, Shang-Yi; Hung, Chih-Jen; Chen, Chih-Chieh; Wu, Chih-Cheng

2014-11-01

Postoperative nausea and vomiting as well as postoperative pain are two major concerns when patients undergo surgery and receive anesthetics. Various models and predictive methods have been developed to investigate the risk factors of postoperative nausea and vomiting, and different types of preventive managements have subsequently been developed. However, there continues to be a wide variation in the previously reported incidence rates of postoperative nausea and vomiting. This may have occurred because patients were assessed at different time points, coupled with the overall limitation of the statistical methods used. However, using survival analysis with Cox regression, and thus factoring in these time effects, may solve this statistical limitation and reveal risk factors related to the occurrence of postoperative nausea and vomiting in the following period. In this retrospective, observational, uni-institutional study, we analyzed the results of 229 patients who received patient-controlled epidural analgesia following surgery from June 2007 to December 2007. We investigated the risk factors for the occurrence of postoperative nausea and vomiting, and also assessed the effect of evaluating patients at different time points using the Cox proportional hazards model. Furthermore, the results of this inquiry were compared with those results using logistic regression. The overall incidence of postoperative nausea and vomiting in our study was 35.4%. Using logistic regression, we found that only sex, but not the total doses and the average dose of opioids, had significant effects on the occurrence of postoperative nausea and vomiting at some time points. Cox regression showed that, when patients consumed a higher average dose of opioids, this correlated with a higher incidence of postoperative nausea and vomiting with a hazard ratio of 1.286. Survival analysis using Cox regression showed that the average consumption of opioids played an important role in postoperative nausea and vomiting, a result not found by logistic regression. Therefore, the incidence of postoperative nausea and vomiting in patients cannot be reliably determined on the basis of a single visit at one point in time. Copyright © 2014. Published by Elsevier Taiwan.

Demographic and Motivation Differences Among Online Sex Offenders by Type of Offense: An Exploration of Routine Activities Theories.

PubMed

Navarro, Jordana N; Jasinski, Jana L

2015-01-01

This article presents an analysis of the relationship between online sexual offenders' demographic background and characteristics indicative of motivation and offense type. Specifically, we investigate whether these characteristics can distinguish different online sexual offender groups from one another as well as inform routine activity theorists on what potentially motivates perpetrators. Using multinomial logistic regression, this study found that online sexual offenders' demographic backgrounds and characteristics indicative of motivation do vary by offense types. Two important implications of this study are that the term "online sexual offender" encompasses different types of offenders, including some who do not align with mainstream media's characterization of "predators," and that the potential offender within routine activity theory can be the focus of empirical investigation rather than taken as a given in research.

Disparities in Health Care Access and Receipt of Preventive Services by Disability Type: Analysis of the Medical Expenditure Panel Survey

PubMed Central

Horner-Johnson, Willi; Dobbertin, Konrad; Lee, Jae Chul; Andresen, Elena M

2014-01-01

Objective To examine differences in access to health care and receipt of clinical preventive services by type of disability among working-age adults with disabilities. Data Source Secondary analysis of Medical Expenditure Panel Survey (MEPS) data from 2002 to 2008. Study Design We conducted cross-sectional logistic regression analyses comparing people with different types of disabilities on health insurance status and type; presence of a usual source of health care; delayed or forgone care; and receipt of dental checkups and cancer screening. Data Collection We pooled annualized MEPS data files across years. Our analytic sample consisted of adults (18–64 years) with physical, sensory, or cognitive disabilities and nonmissing data for all variables of interest. Principal Findings Individuals with hearing impairment had better health care access and receipt than people with other disability types. People with multiple types of limitations were especially likely to have health care access problems and unmet health care needs. Conclusions There are differences in health care access and receipt of preventive care depending on what type of disability people have. More in-depth research is needed to identify specific causes of these disparities and assess interventions to address health care barriers for particular disability groups. PMID:24962662

New method to incorporate Type B uncertainty into least-squares procedures in radionuclide metrology.

PubMed

Han, Jubong; Lee, K B; Lee, Jong-Man; Park, Tae Soon; Oh, J S; Oh, Pil-Jei

2016-03-01

We discuss a new method to incorporate Type B uncertainty into least-squares procedures. The new method is based on an extension of the likelihood function from which a conventional least-squares function is derived. The extended likelihood function is the product of the original likelihood function with additional PDFs (Probability Density Functions) that characterize the Type B uncertainties. The PDFs are considered to describe one's incomplete knowledge on correction factors being called nuisance parameters. We use the extended likelihood function to make point and interval estimations of parameters in the basically same way as the least-squares function used in the conventional least-squares method is derived. Since the nuisance parameters are not of interest and should be prevented from appearing in the final result, we eliminate such nuisance parameters by using the profile likelihood. As an example, we present a case study for a linear regression analysis with a common component of Type B uncertainty. In this example we compare the analysis results obtained from using our procedure with those from conventional methods. Copyright © 2015. Published by Elsevier Ltd.

Microaggressions, Discrimination, and Phenotype among African Americans: A Latent Class Analysis of the Impact of Skin Tone and BMI.

PubMed

Keith, Verna M; Nguyen, Ann W; Taylor, Robert Joseph; Mouzon, Dawne M; Chatters, Linda M

2017-05-01

Data from the 2001-2003National Survey of American Life are used to investigate the effects of phenotype on everyday experiences with discrimination among African Americans (N=3343). Latent class analysis is used to identify four classes of discriminatory treatment: 1) low levels of discrimination, 2) disrespect and condescension, 3) character-based discrimination, and 4) high levels of discrimination. We then employ latent class multinomial logistic regression to evaluate the association between skin tone and body weight and these four classes of discrimination. Designating the low level discrimination class as the reference group, findings revealed that respondents with darker skin were more likely to be classified into the disrespect/condescension and the high level microaggression types. BMI was unrelated to the discrimination type, although there was a significant interaction effect between gender and BMI. BMI was strongly and positively associated with membership in the disrespect and condescension type among men but not among women. These findings indicate that skin tone and body weight are two phenotypic characteristics that influence the type and frequency of discrimination experienced by African Americans.

A Modified Double Multiple Nonlinear Regression Constitutive Equation for Modeling and Prediction of High Temperature Flow Behavior of BFe10-1-2 Alloy

NASA Astrophysics Data System (ADS)

Cai, Jun; Wang, Kuaishe; Shi, Jiamin; Wang, Wen; Liu, Yingying

2018-01-01

Constitutive analysis for hot working of BFe10-1-2 alloy was carried out by using experimental stress-strain data from isothermal hot compression tests, in a wide range of temperature of 1,023 1,273 K, and strain rate range of 0.001 10 s-1. A constitutive equation based on modified double multiple nonlinear regression was proposed considering the independent effects of strain, strain rate, temperature and their interrelation. The predicted flow stress data calculated from the developed equation was compared with the experimental data. Correlation coefficient (R), average absolute relative error (AARE) and relative errors were introduced to verify the validity of the developed constitutive equation. Subsequently, a comparative study was made on the capability of strain-compensated Arrhenius-type constitutive model. The results showed that the developed constitutive equation based on modified double multiple nonlinear regression could predict flow stress of BFe10-1-2 alloy with good correlation and generalization.

Power and type I error results for a bias-correction approach recently shown to provide accurate odds ratios of genetic variants for the secondary phenotypes associated with primary diseases.

PubMed

Wang, Jian; Shete, Sanjay

2011-11-01

We recently proposed a bias correction approach to evaluate accurate estimation of the odds ratio (OR) of genetic variants associated with a secondary phenotype, in which the secondary phenotype is associated with the primary disease, based on the original case-control data collected for the purpose of studying the primary disease. As reported in this communication, we further investigated the type I error probabilities and powers of the proposed approach, and compared the results to those obtained from logistic regression analysis (with or without adjustment for the primary disease status). We performed a simulation study based on a frequency-matching case-control study with respect to the secondary phenotype of interest. We examined the empirical distribution of the natural logarithm of the corrected OR obtained from the bias correction approach and found it to be normally distributed under the null hypothesis. On the basis of the simulation study results, we found that the logistic regression approaches that adjust or do not adjust for the primary disease status had low power for detecting secondary phenotype associated variants and highly inflated type I error probabilities, whereas our approach was more powerful for identifying the SNP-secondary phenotype associations and had better-controlled type I error probabilities. © 2011 Wiley Periodicals, Inc.

Factors influencing the hospitalization costs of patients with type 2 diabetes.

PubMed

Cao, Ping; Wang, Kaixiu; Zhang, Hua; Zhao, Rongzhi; Li, Chenglong

2015-03-01

This study aims to research the factors influencing the hospitalization costs of patients with type 2 diabetes, so as to provide some references for reducing their economic burden. Based on the Hospital Information System of a 3A grade hospital in China, we analyzed 2970 cases with type 2 diabetes during 2005-2012. Both the number of inpatients and the hospitalization costs had increased in the study period. Using multiple linear regression analysis, we found that patients in Urban Employee Basic Medical Insurance had higher costs than those in New Rural Cooperative Medical Scheme. We also found hospitalization costs to be higher in male patients and older patients, patients who stayed more days at hospital and who had surgeries, patients who had at least 1 complication, and patients whose admission status was emergency. After standardizing the regression coefficients, we found that the hospital stay, the forms of payment, and presence of complications were the first 3 factors influencing hospitalization costs in our study. In conclusion, the hospitalization costs of patients with type 2 diabetes could be influenced by age, gender, forms of payment, hospital stay, admission status, complications, and surgery. Medical workers in the studied region should take actions to reduce the duration of hospital stay for diabetic patients and prevent relevant complications. What is more, medical insurance needs further improvement. © 2015 APJPH.

Is there an improvement of antibiotic use in China? Evidence from the usage analysis of combination antibiotic therapy for type I incisions in 244 hospitals.

PubMed

Zhou, Wen-Juan; Luo, Zhen-Ni; Tang, Chang-Min; Zou, Xiao-Xu; Zhao, Lu; Fang, Peng-Qian

2016-10-01

The improvement of antibiotic rational use in China was studied by usage analysis of combination antibiotic therapy for type I incisions in 244 hospitals. Five kinds of hospitals, including general hospital, maternity hospital, children's hospital, stomatological hospital and cancer hospital, from 30 provinces were surveyed. A systematic random sampling strategy was employed to select outpatient prescriptions and inpatient cases in 2011 and 2012. A total of 29 280 outpatient prescriptions and 73 200 inpatient cases from 244 hospitals in each year were analyzed. Data were collected with regards to the implementation of the national antibiotic stewardship program (NASP), the overall usage and the prophylactic use of antibiotic for type I incisions. Univariate analysis was used for microbiological diagnosis rate before antimicrobial therapy, prophylactic use of antibiotics for type I incision operation, and so on. For multivariate analysis, the use of antibiotics was dichotomized according to the guidelines, and entered as binary values into logistic regression analysis. The results were compared with the corresponding criteria given by the guidelines of this campaign. The antibiotic stewardship in China was effective in that more than 80% of each kind of hospitals achieved the criteria of recommended antibiotics varieties. Hospital type appeared to be a factor statistically associated with stewardship outcome. The prophylactic use of antibiotics on type I incision operations decreased by 16.22% (P<0.05). The usage of combination antibiotic therapy for type I incisions was also decreased. Region and bed size were the main determinants on surgical prophylaxis for type I incision. This national analysis of hospitals on antibiotic use and stewardship allows relevant comparisons for bench marking. More efforts addressing the root cause of antibiotics abuse would continue to improve the rational use of antibiotics in China.

Commensurate Priors for Incorporating Historical Information in Clinical Trials Using General and Generalized Linear Models

PubMed Central

Hobbs, Brian P.; Sargent, Daniel J.; Carlin, Bradley P.

2014-01-01

Assessing between-study variability in the context of conventional random-effects meta-analysis is notoriously difficult when incorporating data from only a small number of historical studies. In order to borrow strength, historical and current data are often assumed to be fully homogeneous, but this can have drastic consequences for power and Type I error if the historical information is biased. In this paper, we propose empirical and fully Bayesian modifications of the commensurate prior model (Hobbs et al., 2011) extending Pocock (1976), and evaluate their frequentist and Bayesian properties for incorporating patient-level historical data using general and generalized linear mixed regression models. Our proposed commensurate prior models lead to preposterior admissible estimators that facilitate alternative bias-variance trade-offs than those offered by pre-existing methodologies for incorporating historical data from a small number of historical studies. We also provide a sample analysis of a colon cancer trial comparing time-to-disease progression using a Weibull regression model. PMID:24795786

Relationship of physical activity to fundamental movement skills among adolescents.

PubMed

Okely, A D; Booth, M L; Patterson, J W

2001-11-01

To determine the relationship of participation in organized and nonorganized physical activity with fundamental movement skills among adolescents. Male and female children in Grade 8 (mean age, 13.3 yr) and Grade 10 (mean age, 15.3 yr) were assessed on six fundamental movement skills (run, vertical jump, catch, overhand throw, forehand strike, and kick). Physical activity was assessed using a self-report recall measure where students reported the type, duration, and frequency of participation in organized physical activity and nonorganized physical activity during a usual week. Multiple regression analysis indicated that fundamental movement skills significantly predicted time in organized physical activity, although the percentage of variance it could explain was small. This prediction was stronger for girls than for boys. Multiple regression analysis showed no relationship between time in nonorganized physical activity and fundamental movement skills. Fundamental movement skills are significantly associated with adolescents' participation in organized physical activity, but predict only a small portion of it.

Gastroduodenal Ulcers and ABO Blood Group: the Japan Nurses’ Health Study (JNHS)

PubMed Central

Ideno, Yuki; Lee, Jung-Su; Suzuki, Shosuke; Nakajima-Shimada, Junko; Ohnishi, Hiroshi; Sato, Yasunori; Hayashi, Kunihiko

2018-01-01

Background Although several studies have shown that blood type O is associated with increased risk of peptic ulcer, few studies have investigated these associations in Japan. We sought to investigate the association between the ABO blood group and risk of gastroduodenal ulcers (GDU) using combined analysis of both retrospective and prospective data from a large cohort study of Japanese women, the Japan Nurses’ Health Study (JNHS; n = 15,019). Methods The impact of the ABO blood group on GDU risk was examined using Cox regression analysis to estimate hazard ratios (HRs) and 95% confidence intervals (CI), with adjustment for potential confounders. Results Compared with women with non-O blood types (A, B, and AB), women with blood type O had a significantly increased risk of GDU from birth (multivariable-adjusted HR 1.18; 95% CI, 1.04–1.34). Moreover, the highest cumulative incidence of GDU was observed in women born pre-1956 with blood type O. In a subgroup analysis stratified by birth year (pre-1956 or post-1955), the multivariable-adjusted HR of women with blood type O was 1.22 (95% CI, 1.00–1.49) and 1.15 (95% CI, 0.98–1.35) in the pre-1956 and post-1955 groups, respectively. Conclusion In this large, combined, ambispective cohort study of Japanese women, older women with blood type O had a higher risk of developing GDU than those with other blood types. PMID:29093357

A Study on the Relationship between Design Elements of Outdoor Leisure Spaces and Types of Leisure Activities in Sustainable Community Development - A Case Study on Tainan, Taiwan

NASA Astrophysics Data System (ADS)

Chang, J. H.; Zhang, H.

2018-01-01

Sustainable community development encompasses three aspects: “Lifestyle”, “Production” and “Ecology”. Among them, “Lifestyle” is closest to people and reflects basic human needs. Creating outdoor spaces that encourage residents to engage in leisure activities will not only provide them with spiritual sustenance but also fulfil one of the key criteria in sustainable community development. This study explores the relationship between design elements of outdoor leisure spaces and types of leisure activities from residents' perspective with the goal to inform future spatial planning. The study collected 365 valid questionnaires from Tainan residents. Factor analysis was used to extract factors from design elements of outdoor leisure spaces, and regression analysis was applied to understand the effect level. The result shows design elements have positive effect on the types of leisure activities. In addition, different elements exert different influences on the choice of activities.

Measuring quality of life in low-income, Spanish-speaking Puerto Ricans with type 2 diabetes residing in the mainland U.S.

PubMed

Lemon, Stephenie C; Rosal, Milagros C; Welch, Garry

2011-11-01

This study assessed the psychometric properties of the Audit of Diabetes-Dependent Quality of Life (ADDQoL) modified for low-income, low-education, Spanish-speaking Puerto Ricans with type 2 diabetes residing in the northeastern United States. Cross-sectional data from 226 patients were analyzed. Scale modifications included simplification of instructions, question wording and response format, and oral administration. Reliability was assessed with Cronbach's alpha coefficient and internal structure by exploratory factor analysis. Criterion validity was assessed using correlation analysis and linear and logistic regression models assessing the association of the ADDQoL with standardized physical health status, mental health status, depression, and comorbidity indices. Two ADDQoL items were dropped. The modified scale had excellent internal consistency and supported the original scale factor structure. Criterion validity results supported the validity of this measure. The modified ADDQoL showed psychometric properties that support its use in low-income, Spanish-speaking Puerto Ricans with type 2 diabetes who reside in mainland U.S.

Types of provincial structure and population health.

PubMed

Young, Frank W; Rodriguez, Eunice

2005-01-01

This paper explores the potential of using large administrative units for studies of population health within a country. The objective is to illustrate a new way of defining structural dimensions and to use them in examining variation in life expectancy rates. We use data from the 50 provinces of Spain as a case study. A factor analysis of organizational items such as schools, hotels and medical personnel is employed to define and generate "collective" measures for well-known provincial types, in this case: urban, commercial, industrial and tourist provinces. The scores derived from the factor analysis are then used in a regression model to predict life expectancy. The City-centered and Commercial provinces showed positive correlations with life expectancy while those for the Tourist provinces were negative. The industrial type was nonsignificant. Explanations of these correlations are proposed and the advantages and disadvantages of this exploratory technique are reviewed. The use of this technique for generating an overview of social organization and population health is discussed.

Medication Adherence Mediates the Association between Type D Personality and High HbA1c Level in Chinese Patients with Type 2 Diabetes Mellitus: A Six-Month Follow-Up Study

PubMed Central

Li, Xuemei; Gao, Min; Zhang, Shengfa; Xu, Huiwen; Zhou, Huixuan; Wang, Xiaohua; Qu, Zhiyong; Guo, Jing

2017-01-01

Aims. To examine the association between Type D personality and HbA1c level and to explore the mediating role of medication adherence between them in patients with type 2 diabetes mellitus (T2DM). Methods. 330 patients went on to complete a self-report measure of medication adherence and the HbA1c tests. Chi-square test, T test, Ordinary Least Square Regression (OLS), and Recentered Influence Function Regression (RIF) were employed. Results. Patients with Type D personality had significantly higher HbA1c value (P < 0.01). When Type D personality was operationalized as a categorical variable, SI was associated with HbA1c (P < 0.01). When NA, SI, and their interaction term were entered into regression, all of them were no longer associated with HbA1c level (P > 0.1). On the other hand, when Type D personality was operationalized as a continuous variable, only SI trait was associated with HbA1c level (P < 0.01). When NA, SI, and NA × SI term together were entered into regression, only SI was not related to HbA1c level. Furthermore, medication adherence had a significant mediation effect between Type D personality and HbA1c, accounting for 54.43% of the total effect. Conclusion. Type D personality was associated with HbA1c in direct and indirect ways, and medication adherence acted as a mediator role. PMID:28280745

Medication Adherence Mediates the Association between Type D Personality and High HbA1c Level in Chinese Patients with Type 2 Diabetes Mellitus: A Six-Month Follow-Up Study.

PubMed

Li, Xuemei; Gao, Min; Zhang, Shengfa; Xu, Huiwen; Zhou, Huixuan; Wang, Xiaohua; Qu, Zhiyong; Guo, Jing; Zhang, Weijun; Tian, Donghua

2017-01-01

Aims . To examine the association between Type D personality and HbA1c level and to explore the mediating role of medication adherence between them in patients with type 2 diabetes mellitus (T2DM). Methods . 330 patients went on to complete a self-report measure of medication adherence and the HbA1c tests. Chi-square test, T test, Ordinary Least Square Regression (OLS), and Recentered Influence Function Regression (RIF) were employed. Results . Patients with Type D personality had significantly higher HbA1c value ( P < 0.01). When Type D personality was operationalized as a categorical variable, SI was associated with HbA1c ( P < 0.01). When NA, SI, and their interaction term were entered into regression, all of them were no longer associated with HbA1c level ( P > 0.1). On the other hand, when Type D personality was operationalized as a continuous variable, only SI trait was associated with HbA1c level ( P < 0.01). When NA, SI, and NA × SI term together were entered into regression, only SI was not related to HbA1c level. Furthermore, medication adherence had a significant mediation effect between Type D personality and HbA1c, accounting for 54.43% of the total effect. Conclusion . Type D personality was associated with HbA1c in direct and indirect ways, and medication adherence acted as a mediator role.

Evaluation of the Williams-type model for barley yields in North Dakota and Minnesota

NASA Technical Reports Server (NTRS)

Barnett, T. L. (Principal Investigator)

1981-01-01

The Williams-type yield model is based on multiple regression analysis of historial time series data at CRD level pooled to regional level (groups of similar CRDs). Basic variables considered in the analysis include USDA yield, monthly mean temperature, monthly precipitation, soil texture and topographic information, and variables derived from these. Technologic trend is represented by piecewise linear and/or quadratic functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test (1970-1979) demonstrate that biases are small and performance based on root mean square appears to be acceptable for the intended AgRISTARS large area applications. The model is objective, adequate, timely, simple, and not costly. It consideres scientific knowledge on a broad scale but not in detail, and does not provide a good current measure of modeled yield reliability.

Associations of body mass index with cancer incidence among populations, genders, and menopausal status: A systematic review and meta-analysis.

PubMed

Wang, Jun; Yang, Dong-Lin; Chen, Zhong-Zhu; Gou, Ben-Fu

2016-06-01

In order to further reveal the differences of association between body mass index (BMI) and cancer incidence across populations, genders, and menopausal status, we performed comprehensive meta-analysis with eligible citations. The risk ratio (RR) of incidence at 10 different cancer sites (per 5kg/m(2) increase in BMI) were quantified separately by employing generalized least-squares to estimate trends, and combined by meta-analyses. We observed significantly stronger association between increased BMI and breast cancer incidence in the Asia-Pacific group (RR 1.18:1.11-1.26) than in European-Australian (1.05:1.00-1.09) and North-American group (1.06:1.03-1.08) (meta-regression p<0.05). No association between increased BMI and pancreatic cancer incidence (0.94:0.71-1.24) was shown in the Asia-Pacific group (meta-regression p<0.05), whereas positive associations were found in other two groups. A significantly higher RR in men was found for colorectal cancer in comparison with women (meta-regression p<0.05). Compared with postmenopausal women, premenopausal women displayed significantly higher RR for ovarian cancer (pre- vs. post-=1.10 vs. 1.01, meta-regression p<0.05), but lower RR for breast cancer (pre- vs. post-=0.99 vs. 1.11, meta-regression p<0.0001). Our results indicate that overweight or obesity is a strong risk factor of cancer incidence at several cancer sites. Genders, populations, and menopausal status are important factors effecting the association between obesity and cancer incidence for certain cancer types. Copyright © 2016 Elsevier Ltd. All rights reserved.

Matched cohort comparison of endovascular abdominal aortic aneurysm repair with and without EndoAnchors.

PubMed

Muhs, Bart E; Jordan, William; Ouriel, Kenneth; Rajaee, Sareh; de Vries, Jean-Paul

2018-06-01

The objective of this study was to examine whether prophylactic use of EndoAnchors (Medtronic, Santa Rosa, Calif) contributes to improved outcomes after endovascular aneurysm repair (EVAR) of abdominal aortic aneurysms through 2 years. The Aneurysm Treatment Using the Heli-FX Aortic Securement System Global Registry (ANCHOR) subjects who received prophylactic EndoAnchors during EVAR were considered for this analysis. Imaging data of retrospective subjects who underwent EVAR at ANCHOR enrolling institutions were obtained to create a control sample. Nineteen baseline anatomic measurements were used to perform propensity score matching, yielding 99 matched pairs. Follow-up imaging of the ANCHOR and control cohorts was then compared to examine outcomes through 2 years, using Kaplan-Meier survival analysis. Freedom from type Ia endoleak was 97.0% ± 2.1% in the ANCHOR cohort and 94.1% ± 2.5% in the control cohort through 2 years (P = .34). The 2-year freedom from neck dilation in the ANCHOR and control cohorts was 90.4% ± 5.6% and 87.3% ± 4.3%, respectively (P = .46); 2-year freedom from sac enlargement was 97.0% ± 2.1% and 94.0% ± 3.0%, respectively (P = .67). No device migration was observed. Aneurysm sac regression was observed in 81.1% ± 9.5% of ANCHOR subjects through 2 years compared with 48.7% ± 5.9% of control subjects (P = .01). Cox regression analysis found an inverse correlation between number of hostile neck criteria met and later sac regression (P = .05). Preoperative neck thrombus circumference and infrarenal diameter were also variables associated with later sac regression, although not to a significant degree (P = .10 and P = .06, respectively). Control subjects with thrombus were significantly less likely to experience later sac regression than those without thrombus (6% and 43%, respectively; P = .001). In ANCHOR subjects, rate of regression was not significantly different in subjects with or without thrombus (33% and 36%, respectively; P = .82). Control subjects with wide aortic necks (>28 mm) were observed to experience sac regression at a lower rate than subjects with smaller diameter necks (10% and 44%, respectively; P = .004). Wide neck and normal neck subjects implanted with EndoAnchors experienced later sac regression at roughly equivalent rates (44% and 33%, respectively; P = .50). In propensity-matched cohorts of subjects undergoing EVAR, the rate of sac regression in subjects treated with EndoAnchors was significantly higher. EndoAnchors may mitigate the adverse effect of wide infrarenal necks and neck thrombus on sac regression, although further studies are needed to evaluate the long-term effect of EndoAnchors. Copyright © 2017 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Flash Flood Type Identification within Catchments in Beijing Mountainous Area

NASA Astrophysics Data System (ADS)

Nan, W.

2017-12-01

Flash flood is a common type of disaster in mountainous area, Flash flood with the feature of large flow rate, strong flushing force, destructive power, has periodically caused loss to life and destruction to infrastructure in mountainous area. Beijing as China's political, economic and cultural center, the disaster prevention and control work in Beijing mountainous area has always been concerned widely. According to the transport mechanism, sediment concentration and density, the flash flood type identification within catchment can provide basis for making the hazards prevention and mitigation policy. Taking Beijing as the study area, this paper extracted parameters related to catchment morphological and topography features respectively. By using Bayes discriminant, Logistic regression and Random forest, the catchments in Beijing mountainous area were divided into water floods process, fluvial sediment transport process and debris flows process. The results found that Logistic regression analysis showed the highest accuracy, with the overall accuracy of 88.2%. Bayes discriminant and Random forest had poor prediction effects. This study confirmed the ability of morphological and topography features to identify flash flood process. The circularity ratio, elongation ratio and roughness index can be used to explain the flash flood types effectively, and the Melton ratio and elevation relief ratio also did a good job during the identification, whereas the drainage density seemed not to be an issue at this level of detail. Based on the analysis of spatial patterns of flash flood types, fluvial sediment transport process and debris flow process were the dominant hazards, while the pure water flood process was much less. The catchments dominated by fluvial sediment transport process were mainly distributed in the Yan Mountain region, where the fault belts were relatively dense. The debris flow process prone to occur in the Taihang Mountain region thanks to the abundant coal gangues. The pure water flood process catchments were mainly distributed in the transitional mountain front.

Effect of depression on mortality and cardiovascular morbidity in type 2 diabetes mellitus after 3 years follow up. The DIADEMA study protocol.

PubMed

de Burgos-Lunar, Carmen; Gómez-Campelo, Paloma; Cárdenas-Valladolid, Juan; Fuentes-Rodríguez, Carmen Y; Granados-Menéndez, María I; López-López, Francisco; Salinero-Fort, Miguel A

2012-07-30

Type 2 diabetes mellitus and depression are highly prevalent diseases that are associated with an increased risk of cardiovascular disease and mortality. There is evidence about a bidirectional association between depressive symptoms and type 2 diabetes mellitus. However, prognostic implications of the joint effects of these two diseases on cardiovascular morbidity and mortality are not well-known. A three-year, observational, prospective, cohort study, carried out in Primary Health Care Centres in Madrid (Spain). The project aims to analyze the effect of depression on cardiovascular events, all-cause and cardiovascular mortality in patients with type 2 diabetes mellitus, and to estimate a clinical predictive model of depression in these patients.The number of patients required is 3255, all them with type 2 diabetes mellitus, older than 18 years, who regularly visit their Primary Health Care Centres and agree to participate. They are chosen by simple random sampling from the list of patients with type 2 diabetes mellitus of each general practitioner.The main outcome measures are all-cause and cardiovascular mortality and cardiovascular morbidity; and exposure variable is the major depressive disorder.There will be a comparison between depressed and not depressed patients in all-cause mortality, cardiovascular mortality, coronary artery disease and stroke using the Chi-squared test. Logistic regression with random effects will be used to adjust for prognostic factors. Confounding factors that might alter the effect recorded will be taken into account in this analysis. To assess the effect of depression on the mortality, a survival analysis will be used comparing the two groups using the log-rank test. The control of potential confounding variables will be performed by the construction of a Cox regression model. Our study's main contribution is to evaluate the increase in the risk of cardiovascular morbidity and mortality, in depressed Spanish adults with type 2 diabetes mellitus attended in Primary Health Care Setting. It would also be useful to identify subgroups of patients for which the interventions could be more beneficial.

Effect of depression on mortality and cardiovascular morbidity in type 2 diabetes mellitus after 3 years follow up. The DIADEMA study protocol

PubMed Central

2012-01-01

Background Type 2 diabetes mellitus and depression are highly prevalent diseases that are associated with an increased risk of cardiovascular disease and mortality. There is evidence about a bidirectional association between depressive symptoms and type 2 diabetes mellitus. However, prognostic implications of the joint effects of these two diseases on cardiovascular morbidity and mortality are not well-known. Method/design A three-year, observational, prospective, cohort study, carried out in Primary Health Care Centres in Madrid (Spain). The project aims to analyze the effect of depression on cardiovascular events, all-cause and cardiovascular mortality in patients with type 2 diabetes mellitus, and to estimate a clinical predictive model of depression in these patients. The number of patients required is 3255, all them with type 2 diabetes mellitus, older than 18 years, who regularly visit their Primary Health Care Centres and agree to participate. They are chosen by simple random sampling from the list of patients with type 2 diabetes mellitus of each general practitioner. The main outcome measures are all-cause and cardiovascular mortality and cardiovascular morbidity; and exposure variable is the major depressive disorder. There will be a comparison between depressed and not depressed patients in all-cause mortality, cardiovascular mortality, coronary artery disease and stroke using the Chi-squared test. Logistic regression with random effects will be used to adjust for prognostic factors. Confounding factors that might alter the effect recorded will be taken into account in this analysis. To assess the effect of depression on the mortality, a survival analysis will be used comparing the two groups using the log-rank test. The control of potential confounding variables will be performed by the construction of a Cox regression model. Discussion Our study’s main contribution is to evaluate the increase in the risk of cardiovascular morbidity and mortality, in depressed Spanish adults with type 2 diabetes mellitus attended in Primary Health Care Setting. It would also be useful to identify subgroups of patients for which the interventions could be more beneficial. PMID:22846516

Applied Multiple Linear Regression: A General Research Strategy

ERIC Educational Resources Information Center

Smith, Brandon B.

1969-01-01

Illustrates some of the basic concepts and procedures for using regression analysis in experimental design, analysis of variance, analysis of covariance, and curvilinear regression. Applications to evaluation of instruction and vocational education programs are illustrated. (GR)

Effect of socioeconomic deprivation on waiting time for cardiac surgery: retrospective cohort study

PubMed Central

Pell, Jill P; Pell, Alastair C H; Norrie, John; Ford, Ian; Cobbe, Stuart M

2000-01-01

Objective To determine whether the priority given to patients referred for cardiac surgery is associated with socioeconomic status. Design Retrospective study with multivariate logistic regression analysis of the association between deprivation and classification of urgency with allowance for age, sex, and type of operation. Multivariate linear regression analysis was used to determine association between deprivation and waiting time within each category of urgency, with allowance for age, sex, and type of operation. Setting NHS waiting lists in Scotland. Participants 26 642 patients waiting for cardiac surgery, 1 January 1986 to 31 December 1997. Main outcome measures Deprivation as measured by Carstairs deprivation category. Time spent on NHS waiting list. Results Patients who were most deprived tended to be younger and were more likely to be female. Patients in deprivation categories 6 and 7 (most deprived) waited about three weeks longer for surgery than those in category 1 (mean difference 24 days, 95% confidence interval 15 to 32). Deprived patients had an odds ratio of 0.5 (0.46 to 0.61) for having their operations classified as urgent compared with the least deprived, after allowance for age, sex, and type of operation. When urgent and routine cases were considered separately, there was no significant difference in waiting times between the most and least deprived categories. Conclusions Socioeconomically deprived patients are thought to be more likely to develop coronary heart disease but are less likely to be investigated and offered surgery once it has developed. Such patients may be further disadvantaged by having to wait longer for surgery because of being given lower priority. PMID:10617517

Anthropometric Indices in Children With Refractory Epilepsy.

PubMed

Aminzadeh, Vahid; Dalili, Setila; Ashoorian, Yalda; Kohmanaee, Shahin; Hassanzadeh Rad, Afagh

2016-01-01

We aimed to assess the effect of body mass index (BMI) on reducing the risk of refractory seizure due to lipoid tissue factors. This matched case-control study, consisted of cases (Patients with refractory epilepsy) and controls (Healthy children) referred to 17 Shahrivar Hospital, Guilan University of Medical Sciences, Guilan, Iran during 2013-2014. Data were gathered by a form including demographic characteristics, type of epilepsy, predominant time of epilepsy, therapeutic approach, frequency of epilepsy, time of disease onset and anthropometric indices. We measured anthropometric indices and transformed them into Z-scores. Data were reported by descriptive statistics (mean and standard deviation) and analyzed by Pearson correlation coefficient, paired t test and multinomial regression analysis test using SPSS 19. There was no significant difference between sex groups regarding anthropometric indices. Generalized and focal types of epilepsies were noted on 57.5% and 38.75% of patients, respectively. Daytime epilepsies happened in 46.25% of patients and 33.75% noted no predominant time for epilepsies. Clinicians indicated poly-therapy for the majority of patients (92.5%). The most common onset times for epilepsies were 36-72 months for 32.5% of patients. Lower onset time indicated lower frequency of refractory epilepsies. Although, there was significant difference between Zheight and predominant time of epilepsies but no significant relation was found between types of epilepsies and frequency of epilepsies with anthropometric indices. Using multivariate regression analysis by backward LR, Zweight and birth weight were noted as the predicting factors of refractory epilepsies. This effect may be because of leptin. Therefore, researchers recommend further investigations regarding this issue in children with epilepsy.

Uncontrolled diabetes mellitus: prevalence and risk factors among people with type 2 diabetes mellitus in an Urban District of Karachi, Pakistan.

PubMed

Siddiqui, Fahad Javaid; Avan, Bilal Iqbal; Mahmud, Sadia; Nanan, Debra J; Jabbar, Abdul; Assam, Pryseley Nkouibert

2015-01-01

This study aimed to explore the prevalence of, and factors associated with, uncontrolled diabetes mellitus (UDM) in a community setting in Pakistan. A single-center, cross-sectional study, conducted in a community-based specialized care center (SCC) for diabetes in District Central Karachi, in 2003, registered 452 type 2 DM participants, tested for HbA1c and interviewed face-to-face for other information. Logistic regression analysis was conducted to identify factors associated with UDM. Prevalence of UDM among diabetes patients was found to be 38.9% (95% CI: 34.4-43.4%). Multivariable logistic regression model analysis indicated that age <50 years (OR: 1.9; 95% CI: 1.2-2.9), being diagnosed in a hospital (vs. a clinic) (OR: 1.8; 95% CI: 1.1-2.8), diabetes information from a doctor or nurse only (vs. multiple sources) (OR: 1.8; 95% CI: 1.2-2.9), higher monthly treatment cost (OR: 1.3; 95% CI: 1.1-1.6; for every extra 500 PKR), and higher consumption of tea (OR: 1.5; 95% CI: 1.0-2.2; for every 2 extra cups) were independently associated with UDM. The prevalence of UDM was approximately 39% among persons with type 2 diabetes visiting a community based SCC for diabetes. Modifiable risk factors such as sources of diabetes information and black tea consumption can be considered as potential targets of interventions in Karachi. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Water and solute absorption from carbohydrate-electrolyte solutions in the human proximal small intestine: a review and statistical analysis.

PubMed

Shi, Xiaocai; Passe, Dennis H

2010-10-01

The purpose of this study is to summarize water, carbohydrate (CHO), and electrolyte absorption from carbohydrate-electrolyte (CHO-E) solutions based on all of the triple-lumen-perfusion studies in humans since the early 1960s. The current statistical analysis included 30 reports from which were obtained information on water absorption, CHO absorption, total solute absorption, CHO concentration, CHO type, osmolality, sodium concentration, and sodium absorption in the different gut segments during exercise and at rest. Mean differences were assessed using independent-samples t tests. Exploratory multiple-regression analyses were conducted to create prediction models for intestinal water absorption. The factors influencing water and solute absorption are carefully evaluated and extensively discussed. The authors suggest that in the human proximal small intestine, water absorption is related to both total solute and CHO absorption; osmolality exerts various impacts on water absorption in the different segments; the multiple types of CHO in the ingested CHO-E solutions play a critical role in stimulating CHO, sodium, total solute, and water absorption; CHO concentration is negatively related to water absorption; and exercise may result in greater water absorption than rest. A potential regression model for predicting water absorption is also proposed for future research and practical application. In conclusion, water absorption in the human small intestine is influenced by osmolality, solute absorption, and the anatomical structures of gut segments. Multiple types of CHO in a CHO-E solution facilitate water absorption by stimulating CHO and solute absorption and lowering osmolality in the intestinal lumen.

Factors associated with past research participation among low-income persons living with HIV.

PubMed

Slomka, Jacquelyn; Kypriotakis, Georgios; Atkinson, John; Diamond, Pamela M; Williams, Mark L; Vidrine, Damon J; Andrade, Roberto; Arduino, Roberto

2012-08-01

We described influences on past research participation among low-income persons living with HIV (PLWH) and examined whether such influences differed by study type. We analyzed a convenience sample of individuals from a large, urban clinic specializing in treating low-income PLWH. Using a computer-assisted survey, we elicited perceptions of research and participating in research, barriers, benefits, "trigger" influences, and self-efficacy in participating in research. Of 193 participants, we excluded 14 who did not identify any type of study participation, and 17 who identified "other" as study type, resulting in 162 cases for analysis. We compared results among four groups (i.e., 6 comparisons): past medical participants (n=36, 22%), past behavioral participants (n=49, 30%), individuals with no past research participation (n=52, 32%), and persons who had participated in both medical and behavioral studies (n=25, 15%). Data were analyzed using chi-square tests for categorical variables and ANOVA for continuous variables. We employed a multinomial probit (MNP) model to examine the association of multiple factors with the outcome. Confidence in ability to keep appointments, and worry about being a 'guinea pig' showed statistical differences in bivariate analyses. The MNP regression analysis showed differences between and across all 6 comparison groups. Fewer differences were seen across groupings of medical participants, behavioral participants, and those with no past research experience, than in comparisons with the medical-behavioral group. In the MNP regression model 'age' and level of certainty regarding 'keeping yourself from being a guinea pig' showed significant differences between past medical participants and past behavioral participants.

Adjusting for multiple prognostic factors in the analysis of randomised trials

PubMed Central

2013-01-01

Background When multiple prognostic factors are adjusted for in the analysis of a randomised trial, it is unclear (1) whether it is necessary to account for each of the strata, formed by all combinations of the prognostic factors (stratified analysis), when randomisation has been balanced within each stratum (stratified randomisation), or whether adjusting for the main effects alone will suffice, and (2) the best method of adjustment in terms of type I error rate and power, irrespective of the randomisation method. Methods We used simulation to (1) determine if a stratified analysis is necessary after stratified randomisation, and (2) to compare different methods of adjustment in terms of power and type I error rate. We considered the following methods of analysis: adjusting for covariates in a regression model, adjusting for each stratum using either fixed or random effects, and Mantel-Haenszel or a stratified Cox model depending on outcome. Results Stratified analysis is required after stratified randomisation to maintain correct type I error rates when (a) there are strong interactions between prognostic factors, and (b) there are approximately equal number of patients in each stratum. However, simulations based on real trial data found that type I error rates were unaffected by the method of analysis (stratified vs unstratified), indicating these conditions were not met in real datasets. Comparison of different analysis methods found that with small sample sizes and a binary or time-to-event outcome, most analysis methods lead to either inflated type I error rates or a reduction in power; the lone exception was a stratified analysis using random effects for strata, which gave nominal type I error rates and adequate power. Conclusions It is unlikely that a stratified analysis is necessary after stratified randomisation except in extreme scenarios. Therefore, the method of analysis (accounting for the strata, or adjusting only for the covariates) will not generally need to depend on the method of randomisation used. Most methods of analysis work well with large sample sizes, however treating strata as random effects should be the analysis method of choice with binary or time-to-event outcomes and a small sample size. PMID:23898993

Methods of estimating or accounting for neighborhood associations with health using complex survey data.

PubMed

Brumback, Babette A; Cai, Zhuangyu; Dailey, Amy B

2014-05-15

Reasons for health disparities may include neighborhood-level factors, such as availability of health services, social norms, and environmental determinants, as well as individual-level factors. Investigating health inequalities using nationally or locally representative data often requires an approach that can accommodate a complex sampling design, in which individuals have unequal probabilities of selection into the study. The goal of the present article is to review and compare methods of estimating or accounting for neighborhood influences with complex survey data. We considered 3 types of methods, each generalized for use with complex survey data: ordinary regression, conditional likelihood regression, and generalized linear mixed-model regression. The relative strengths and weaknesses of each method differ from one study to another; we provide an overview of the advantages and disadvantages of each method theoretically, in terms of the nature of the estimable associations and the plausibility of the assumptions required for validity, and also practically, via a simulation study and 2 epidemiologic data analyses. The first analysis addresses determinants of repeat mammography screening use using data from the 2005 National Health Interview Survey. The second analysis addresses disparities in preventive oral health care using data from the 2008 Florida Behavioral Risk Factor Surveillance System Survey.

Psychological trauma symptoms and Type 2 diabetes prevalence, glucose control, and treatment modality among American Indians in the Strong Heart Family Study

PubMed Central

Jacob, Michelle M.; Gonzales, Kelly L.; Calhoun, Darren; Beals, Janette; Muller, Clemma Jacobsen; Goldberg, Jack; Nelson, Lonnie; Welty, Thomas K.; Howard, Barbara V.

2013-01-01

Aims The aims of this paper are to examine the relationship between psychological trauma symptoms and Type 2 diabetes prevalence, glucose control, and treatment modality among 3,776 American Indians in Phase V of the Strong Heart Family Study. Methods This cross-sectional analysis measured psychological trauma symptoms using the National Anxiety Disorder Screening Day instrument, diabetes by American Diabetes Association criteria, and treatment modality by four categories: no medication, oral medication only, insulin only, or both oral medication and insulin. We used binary logistic regression to evaluate the association between psychological trauma symptoms and diabetes prevalence. We used ordinary least squares regression to evaluate the association between psychological trauma symptoms and glucose control. We used binary logistic regression to model the association of psychological trauma symptoms with treatment modality. Results Neither diabetes prevalence (22-31%; p = 0.19) nor control (8.0-8.6; p = 0.25) varied significantly by psychological trauma symptoms categories. However, diabetes treatment modality was associated with psychological trauma symptoms categories, as people with greater burden used either no medication, or both oral and insulin medications (odds ratio = 3.1, p < 0.001). Conclusions The positive relationship between treatment modality and psychological trauma symptoms suggests future research investigate patient and provider treatment decision making. PMID:24051029

Weighted functional linear regression models for gene-based association analysis.

PubMed

Belonogova, Nadezhda M; Svishcheva, Gulnara R; Wilson, James F; Campbell, Harry; Axenovich, Tatiana I

2018-01-01

Functional linear regression models are effectively used in gene-based association analysis of complex traits. These models combine information about individual genetic variants, taking into account their positions and reducing the influence of noise and/or observation errors. To increase the power of methods, where several differently informative components are combined, weights are introduced to give the advantage to more informative components. Allele-specific weights have been introduced to collapsing and kernel-based approaches to gene-based association analysis. Here we have for the first time introduced weights to functional linear regression models adapted for both independent and family samples. Using data simulated on the basis of GAW17 genotypes and weights defined by allele frequencies via the beta distribution, we demonstrated that type I errors correspond to declared values and that increasing the weights of causal variants allows the power of functional linear models to be increased. We applied the new method to real data on blood pressure from the ORCADES sample. Five of the six known genes with P < 0.1 in at least one analysis had lower P values with weighted models. Moreover, we found an association between diastolic blood pressure and the VMP1 gene (P = 8.18×10-6), when we used a weighted functional model. For this gene, the unweighted functional and weighted kernel-based models had P = 0.004 and 0.006, respectively. The new method has been implemented in the program package FREGAT, which is freely available at https://cran.r-project.org/web/packages/FREGAT/index.html.

Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression

PubMed Central

Chen, Yanguang

2016-01-01

In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson’s statistic depends on the sequence of data points. This paper develops two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran’s index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then by analogy with the Durbin-Watson statistic, two types of new serial correlation indices are constructed. As a case study, the two newly presented statistics are applied to a spatial sample of 29 China’s regions. These results show that the new spatial autocorrelation models can be used to test the serial correlation of residuals from regression analysis. In practice, the new statistics can make up for the deficiencies of the Durbin-Watson test. PMID:26800271

Broadband external cavity quantum cascade laser based sensor for gasoline detection

NASA Astrophysics Data System (ADS)

Ding, Junya; He, Tianbo; Zhou, Sheng; Li, Jinsong

2018-02-01

A new type of tunable diode spectroscopy sensor based on an external cavity quantum cascade laser (ECQCL) and a quartz crystal tuning fork (QCTF) were used for quantitative analysis of volatile organic compounds. In this work, the sensor system had been tested on different gasoline sample analysis. For signal processing, the self-established interpolation algorithm and multiple linear regression algorithm model were used for quantitative analysis of major volatile organic compounds in gasoline samples. The results were very consistent with that of the standard spectra taken from the Pacific Northwest National Laboratory (PNNL) database. In future, The ECQCL sensor will be used for trace explosive, chemical warfare agent, and toxic industrial chemical detection and spectroscopic analysis, etc.

The impact of moderate wine consumption on the risk of developing prostate cancer.

PubMed

Vartolomei, Mihai Dorin; Kimura, Shoji; Ferro, Matteo; Foerster, Beat; Abufaraj, Mohammad; Briganti, Alberto; Karakiewicz, Pierre I; Shariat, Shahrokh F

2018-01-01

To investigate the impact of moderate wine consumption on the risk of prostate cancer (PCa). We focused on the differential effect of moderate consumption of red versus white wine. This study was a meta-analysis that includes data from case-control and cohort studies. A systematic search of Web of Science, Medline/PubMed, and Cochrane library was performed on December 1, 2017. Studies were deemed eligible if they assessed the risk of PCa due to red, white, or any wine using multivariable logistic regression analysis. We performed a formal meta-analysis for the risk of PCa according to moderate wine and wine type consumption (white or red). Heterogeneity between studies was assessed using Cochrane's Q test and I 2 statistics. Publication bias was assessed using Egger's regression test. A total of 930 abstracts and titles were initially identified. After removal of duplicates, reviews, and conference abstracts, 83 full-text original articles were screened. Seventeen studies (611,169 subjects) were included for final evaluation and fulfilled the inclusion criteria. In the case of moderate wine consumption: the pooled risk ratio (RR) for the risk of PCa was 0.98 (95% CI 0.92-1.05, p =0.57) in the multivariable analysis. Moderate white wine consumption increased the risk of PCa with a pooled RR of 1.26 (95% CI 1.10-1.43, p =0.001) in the multi-variable analysis. Meanwhile, moderate red wine consumption had a protective role reducing the risk by 12% (RR 0.88, 95% CI 0.78-0.999, p =0.047) in the multivariable analysis that comprised 222,447 subjects. In this meta-analysis, moderate wine consumption did not impact the risk of PCa. Interestingly, regarding the type of wine, moderate consumption of white wine increased the risk of PCa, whereas moderate consumption of red wine had a protective effect. Further analyses are needed to assess the differential molecular effect of white and red wine conferring their impact on PCa risk.

From concepts, theory, and evidence of heterogeneity of treatment effects to methodological approaches: a primer

PubMed Central

2012-01-01

Implicit in the growing interest in patient-centered outcomes research is a growing need for better evidence regarding how responses to a given intervention or treatment may vary across patients, referred to as heterogeneity of treatment effect (HTE). A variety of methods are available for exploring HTE, each associated with unique strengths and limitations. This paper reviews a selected set of methodological approaches to understanding HTE, focusing largely but not exclusively on their uses with randomized trial data. It is oriented for the “intermediate” outcomes researcher, who may already be familiar with some methods, but would value a systematic overview of both more and less familiar methods with attention to when and why they may be used. Drawing from the biomedical, statistical, epidemiological and econometrics literature, we describe the steps involved in choosing an HTE approach, focusing on whether the intent of the analysis is for exploratory, initial testing, or confirmatory testing purposes. We also map HTE methodological approaches to data considerations as well as the strengths and limitations of each approach. Methods reviewed include formal subgroup analysis, meta-analysis and meta-regression, various types of predictive risk modeling including classification and regression tree analysis, series of n-of-1 trials, latent growth and growth mixture models, quantile regression, and selected non-parametric methods. In addition to an overview of each HTE method, examples and references are provided for further reading. By guiding the selection of the methods and analysis, this review is meant to better enable outcomes researchers to understand and explore aspects of HTE in the context of patient-centered outcomes research. PMID:23234603

Is physician adherence to prescription guidelines a general trait of health care practices or dependent on drug type?--a multilevel logistic regression analysis in South Sweden.

PubMed

Ohlsson, Henrik; Merlo, Juan

2009-08-01

Therapeutic traditions at health care practices (HCPs) influence physicians' adherence to prescription guidelines for specific drugs, however, it is not known if such traditions affect all kinds of prescriptions or only specific types of drug. Our goal was to determine whether adherence to prescription guidelines is a common trait of HCPs or dependent on drug type. We fitted separate multi-level logistic regression models to all patients in the Skåne region who received a prescription for a statin drug (ATC: C10AA, n = 6232), an agent acting on the renin-angiotensin system (ATC: C09, n = 7222) or a proton pump inhibitor (ATC: A02BC, n = 11 563) at 198 HCPs from July 2006 to December 2006. There was a high clustering of adherence to prescription guidelines at HCPs for the different drug types (MOR(agents acting on the renin-angiotensin system) = 4.72 [95% CI: 3.90-5.92], MOR(Statins) = 2.71 [95% CI: 2.23-3.39] and MOR(Proton pump inhibitors) = 2.16 [95% CI: 1.95-2.45]). Compared with HCPs with low adherence to guidelines in two drug types, those HCPs with the highest level of adherence for these two drug types also showed a higher probability of adherence for the third drug type. Physicians' decisions to follow prescription guidelines seem to be influenced by therapeutic traditions at the HCP. Moreover, these therapeutic traditions seem to affect all kinds of prescriptions. This information can be used as basis for interventions to support rational and cost-effective medication use. Copyright 2009 John Wiley & Sons, Ltd.

External Tank Liquid Hydrogen (LH2) Prepress Regression Analysis Independent Review Technical Consultation Report

NASA Technical Reports Server (NTRS)

Parsons, Vickie s.

2009-01-01

The request to conduct an independent review of regression models, developed for determining the expected Launch Commit Criteria (LCC) External Tank (ET)-04 cycle count for the Space Shuttle ET tanking process, was submitted to the NASA Engineering and Safety Center NESC on September 20, 2005. The NESC team performed an independent review of regression models documented in Prepress Regression Analysis, Tom Clark and Angela Krenn, 10/27/05. This consultation consisted of a peer review by statistical experts of the proposed regression models provided in the Prepress Regression Analysis. This document is the consultation's final report.

Association of Alimentary Factors and Nutritional Status with Caries in Children of Leon, Mexico.

PubMed

Guizar, Juan Manuel; Muñoz, Nathalie; Amador, Norma; Garcia, Gabriela

To determine the association between types of food consumed, nutritional status (BMI) and caries in schoolchildren. A cross-sectional study was performed with 224 schoolchildren 6 to 12 years of age. DMFT/ dmft indices, level of oral hygiene, nutritional status as quantified by BMI and types of food consumed were determined in all participants. Data were analysed using multiple linear regression with significance set at p < 0.05. Caries prevalence was 36%. In the multiple linear regression analysis adjusted for BMI, variables related to a higher number of caries were younger age and lower intake of vitamin D, calcium and fiber, with higher consumption of phosphorous and carbohydrates (R2 = 0.30; p < 0.0001 for the model). Sweetened softdrinks and chewy candy were risk factors for higher caries prevalence, while consuming milk and carrots were protectors. Caries in schoolchildren is highly prevalent in this community and is related to younger age and lower intake of vitamin D, calcium and fiber, but a higher consumption of phosphorous and carbohydrates. No relationship was found between caries and nutritional status.

Blood Glucose Reduction by Diabetic Drugs with Minimal Hypoglycemia Risk for Cardiovascular Outcomes: Evidence from Meta-regression Analysis of Randomized Controlled Trials.

PubMed

Huang, Chi-Jung; Wang, Wei-Ting; Sung, Shih-Hsien; Chen, Chen-Huan; Lip, Gregory Yh; Cheng, Hao-Min; Chiang, Chern-En

2018-05-02

To investigate the effects of blood glucose control with antihyperglycemic agents with minimal hypoglycemia risk on cardiovascular outcomes in patients with type 2 diabetes (T2D). Randomized controlled trials (RCTs) comparing the relative efficacy and safety of antidiabetic drugs with less hypoglycemia risk were comprehensively searched in MEDLINE, Embase, and the Cochrane Library up to January 27, 2018. Mixed-effects meta-regression analysis was conducted to explore the relationship between haemoglobin A1c (HbA1c) reduction and the risk of major adverse cardiovascular events (MACE), myocardial infarction, stroke, cardiovascular death, all-cause death, and hospitalization for heart failure. Ten RCTs comprising 92400 participants with T2D were included and provided information on 9773 MACE during a median follow-up of 2.6 years. The mean HbA1c concentration was 0.42% lower (median, 0.27-0.86%) for participants given antihyperglycemic agents than those given placebo. The meta-regression analysis demonstrated that HbA1c reduction was significantly associated with a decreased risk of MACE (β value, -0.39 to -0.55; P<0.02) even after adjusting for each of the following possible confounding factors including age, sex, baseline HbA1c, duration of follow-up, difference in achieved systolic blood pressure, difference in achieved body weight, or risk difference in hypoglycemia. Lowering HbA1c by 1% conferred a significant risk reduction of 30% (95% CI, 17-40%) for MACE. By contrast, the meta-regression analysis for trials using conventional agents failed to demonstrate a significant relationship between achieved HbA1c difference and MACE risk (P>0.74). Compared with placebo, newer T2D agents with less hypoglycemic hazard significantly reduced the risk of MACE. The MACE reduction seems to be associated with HbA1c reduction in a linear relationship. This article is protected by copyright. All rights reserved.

Classical Statistics and Statistical Learning in Imaging Neuroscience

PubMed Central

Bzdok, Danilo

2017-01-01

Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques. PMID:29056896

[Relationship between Life Habits and Constitution Types of Chinese Medicine in the Senile Living at Home in Beijing Downtown].

PubMed

Shi, Mai; Liu, Zhao-lan; Xu, Mei-yan; Chen, Jie; Lin, Bing; Yu, Yong-chao; Ma, Xiao-tao

2016-05-01

To investigate the distribution of constitution types of Chinese medicine (CM) in the elderly living at home in Beijing downtown, and to explore its relationship with life habits. A total of 3894 senile more than 60 years old were enrolled in this study. Their constitution types of CM were typed using CM constitution questionnaire. Meanwhile, their demographic features, disease condition, diet habits, exercise habits, sleep habits, and so on were investigated. Multivariate Logistic regression analysis was used to evaluate the relationship between life habits and constitution types of CM. The number of mild type constitution senile was 1111 (28.53%) and the number of biased constitutions 2783 (71.47%). Biased constitutions of the top three were qi deficiency constitution (662, 17.00%), yang deficiency constitution (445, 11.43%), and blood stasis constitution (363, 9.32%). Univariate analysis showed that different habits of diet, exercise, and sleep exist among the senile of different constitutions (P < 0.05). By taking mild type constitution, multivariate Logistic regression analysis (except demographic indices and chronic history) showed that significantly positive correlation existed between qi deficiency constitution and favor for hot food (OR = 1.349, P = 0.015), yang deficiency constitution and favor for hot food (OR = 2.448, P < 0.01), phlegm-wetness constitution and favor for barbecue food (OR = 2.144, P = 0.003), wet-heat constitution and favor for sweet food (OR = 1.355, P = 0.032), wet-heat constitution and favor for tea (OR = 1.359, P = 0.047), blood stasis constitution and favor for hot food (OR = 1.422, P = 0.017), and qi depression constitution and favor for hot food (OR = 1.446, P = 0.031). Regular exercise had negative correlation with qi deficiency constitution (OR = 0.397, P < 0.01), yang deficiency constitution (OR = 0.522, P < 0.01) , phlegm-wetness constitution (OR = 0.475, P < 0.01), wet-heat constitution (OR = 0.647, P = 0.015), blood stasis constitution (OR = 0.608, P = 0.001), qi depression constitution (OR = 0.541, P = 0.001), and special diathesis constitution (OR = 0.466, P < 0.01). Early sleep and rise habit had negative with phlegm-wetness constitution (OR = 0.414, P < 0.01), wet-heat constitution (OR = 0.536, P = 0.015), blood stasis constitution (OR = 0.515, P = 0.004), and special diathesis constitution (OR = 0.526, P = 0.039). Different constitution types of CM might be highly related to specific life habits. Cultivating better life habits can improve biased constitutions of CM.

The impact of blood type O on mortality of severe trauma patients: a retrospective observational study.

PubMed

Takayama, Wataru; Endo, Akira; Koguchi, Hazuki; Sugimoto, Momoko; Murata, Kiyoshi; Otomo, Yasuhiro

2018-05-02

Recent studies have implicated the differences in the ABO blood system as a potential risk for various diseases, including hemostatic disorders and hemorrhage. In this study, we evaluated the impact of the difference in the ABO blood type on mortality in patients with severe trauma. A retrospective observational study was conducted in two tertiary emergency critical care medical centers in Japan. Patients with trauma with an Injury Severity Score (ISS) > 15 were included. The association between the different blood types (type O versus other blood types) and the outcomes of all-cause mortality, cause-specific mortalities (exsanguination, traumatic brain injury, and others), ventilator-free days (VFD), and total transfusion volume were evaluated using univariate and multivariate competing-risk regression models. Moreover, the impact of blood type O on the outcomes was assessed using regression coefficients in the multivariate analysis adjusted for age, ISS, and the Revised Trauma Score (RTS). A total of 901 patients were included in this study. The study population was divided based on the ABO blood type: type O, 284 (32%); type A, 285 (32%); type B, 209 (23%); and type AB, 123 (13%). Blood type O was associated with high mortality (28% in patients with blood type O versus 11% in patients with other blood types; p <  0.001). Moreover, this association was observed in a multivariate model (adjusted odds ratio = 2.86, 95% confidence interval 1.84-4.46; p <  0.001). The impact of blood type O on all-cause in-hospital mortality was comparable to 12 increases in the ISS, 1.5 decreases in the RTS, and 26 increases in age. Furthermore, blood type O was significantly associated with higher cause-specific mortalities and shorter VFD compared with the other blood types; however, a significant difference was not observed in the transfusion volume between the two groups. Blood type O was significantly associated with high mortality in severe trauma patients and might have a great impact on outcomes. Further studies elucidating the mechanism underlying this association are warranted to develop the appropriate intervention.

Determination of riverbank erosion probability using Locally Weighted Logistic Regression

NASA Astrophysics Data System (ADS)

Ioannidou, Elena; Flori, Aikaterini; Varouchakis, Emmanouil A.; Giannakis, Georgios; Vozinaki, Anthi Eirini K.; Karatzas, George P.; Nikolaidis, Nikolaos

2015-04-01

Riverbank erosion is a natural geomorphologic process that affects the fluvial environment. The most important issue concerning riverbank erosion is the identification of the vulnerable locations. An alternative to the usual hydrodynamic models to predict vulnerable locations is to quantify the probability of erosion occurrence. This can be achieved by identifying the underlying relations between riverbank erosion and the geomorphological or hydrological variables that prevent or stimulate erosion. Thus, riverbank erosion can be determined by a regression model using independent variables that are considered to affect the erosion process. The impact of such variables may vary spatially, therefore, a non-stationary regression model is preferred instead of a stationary equivalent. Locally Weighted Regression (LWR) is proposed as a suitable choice. This method can be extended to predict the binary presence or absence of erosion based on a series of independent local variables by using the logistic regression model. It is referred to as Locally Weighted Logistic Regression (LWLR). Logistic regression is a type of regression analysis used for predicting the outcome of a categorical dependent variable (e.g. binary response) based on one or more predictor variables. The method can be combined with LWR to assign weights to local independent variables of the dependent one. LWR allows model parameters to vary over space in order to reflect spatial heterogeneity. The probabilities of the possible outcomes are modelled as a function of the independent variables using a logistic function. Logistic regression measures the relationship between a categorical dependent variable and, usually, one or several continuous independent variables by converting the dependent variable to probability scores. Then, a logistic regression is formed, which predicts success or failure of a given binary variable (e.g. erosion presence or absence) for any value of the independent variables. The erosion occurrence probability can be calculated in conjunction with the model deviance regarding the independent variables tested. The most straightforward measure for goodness of fit is the G statistic. It is a simple and effective way to study and evaluate the Logistic Regression model efficiency and the reliability of each independent variable. The developed statistical model is applied to the Koiliaris River Basin on the island of Crete, Greece. Two datasets of river bank slope, river cross-section width and indications of erosion were available for the analysis (12 and 8 locations). Two different types of spatial dependence functions, exponential and tricubic, were examined to determine the local spatial dependence of the independent variables at the measurement locations. The results show a significant improvement when the tricubic function is applied as the erosion probability is accurately predicted at all eight validation locations. Results for the model deviance show that cross-section width is more important than bank slope in the estimation of erosion probability along the Koiliaris riverbanks. The proposed statistical model is a useful tool that quantifies the erosion probability along the riverbanks and can be used to assist managing erosion and flooding events. Acknowledgements This work is part of an on-going THALES project (CYBERSENSORS - High Frequency Monitoring System for Integrated Water Resources Management of Rivers). The project has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: THALES. Investing in knowledge society through the European Social Fund.

Techniques for estimating peak-streamflow frequency for unregulated streams and streams regulated by small floodwater retarding structures in Oklahoma

USGS Publications Warehouse

Tortorelli, Robert L.

1997-01-01

Statewide regression equations for Oklahoma were determined for estimating peak discharge and flood frequency for selected recurrence intervals from 2 to 500 years for ungaged sites on natural unregulated streams. The most significant independent variables required to estimate peak-streamflow frequency for natural unregulated streams in Oklahoma are contributing drainage area, main-channel slope, and mean-annual precipitation. The regression equations are applicable for watersheds with drainage areas less than 2,510 square miles that are not affected by regulation from manmade works. Limitations on the use of the regression relations and the reliability of regression estimates for natural unregulated streams are discussed. Log-Pearson Type III analysis information, basin and climatic characteristics, and the peak-stream-flow frequency estimates for 251 gaging stations in Oklahoma and adjacent states are listed. Techniques are presented to make a peak-streamflow frequency estimate for gaged sites on natural unregulated streams and to use this result to estimate a nearby ungaged site on the same stream. For ungaged sites on urban streams, an adjustment of the statewide regression equations for natural unregulated streams can be used to estimate peak-streamflow frequency. For ungaged sites on streams regulated by small floodwater retarding structures, an adjustment of the statewide regression equations for natural unregulated streams can be used to estimate peak-streamflow frequency. The statewide regression equations are adjusted by substituting the drainage area below the floodwater retarding structures, or drainage area that represents the percentage of the unregulated basin, in the contributing drainage area parameter to obtain peak-streamflow frequency estimates.

Hierarchical Bayesian Logistic Regression to forecast metabolic control in type 2 DM patients.

PubMed

Dagliati, Arianna; Malovini, Alberto; Decata, Pasquale; Cogni, Giulia; Teliti, Marsida; Sacchi, Lucia; Cerra, Carlo; Chiovato, Luca; Bellazzi, Riccardo

2016-01-01

In this work we present our efforts in building a model able to forecast patients' changes in clinical conditions when repeated measurements are available. In this case the available risk calculators are typically not applicable. We propose a Hierarchical Bayesian Logistic Regression model, which allows taking into account individual and population variability in model parameters estimate. The model is used to predict metabolic control and its variation in type 2 diabetes mellitus. In particular we have analyzed a population of more than 1000 Italian type 2 diabetic patients, collected within the European project Mosaic. The results obtained in terms of Matthews Correlation Coefficient are significantly better than the ones gathered with standard logistic regression model, based on data pooling.

Guidelines and Procedures for Computing Time-Series Suspended-Sediment Concentrations and Loads from In-Stream Turbidity-Sensor and Streamflow Data

USGS Publications Warehouse

Rasmussen, Patrick P.; Gray, John R.; Glysson, G. Douglas; Ziegler, Andrew C.

2009-01-01

In-stream continuous turbidity and streamflow data, calibrated with measured suspended-sediment concentration data, can be used to compute a time series of suspended-sediment concentration and load at a stream site. Development of a simple linear (ordinary least squares) regression model for computing suspended-sediment concentrations from instantaneous turbidity data is the first step in the computation process. If the model standard percentage error (MSPE) of the simple linear regression model meets a minimum criterion, this model should be used to compute a time series of suspended-sediment concentrations. Otherwise, a multiple linear regression model using paired instantaneous turbidity and streamflow data is developed and compared to the simple regression model. If the inclusion of the streamflow variable proves to be statistically significant and the uncertainty associated with the multiple regression model results in an improvement over that for the simple linear model, the turbidity-streamflow multiple linear regression model should be used to compute a suspended-sediment concentration time series. The computed concentration time series is subsequently used with its paired streamflow time series to compute suspended-sediment loads by standard U.S. Geological Survey techniques. Once an acceptable regression model is developed, it can be used to compute suspended-sediment concentration beyond the period of record used in model development with proper ongoing collection and analysis of calibration samples. Regression models to compute suspended-sediment concentrations are generally site specific and should never be considered static, but they represent a set period in a continually dynamic system in which additional data will help verify any change in sediment load, type, and source.

Multidimensional analysis of the effect of occupational exposure to organic solvents on lung cancer risk: the ICARE study

PubMed Central

Mattei, Francesca; Liverani, Silvia; Guida, Florence; Matrat, Mireille; Cenée, Sylvie; Azizi, Lamiae; Menvielle, Gwenn; Sanchez, Marie; Pilorget, Corinne; Lapôtre-Ledoux, Bénédicte; Luce, Danièle; Richardson, Sylvia; Stücker, Isabelle

2016-01-01

Background The association between lung cancer and occupational exposure to organic solvents is discussed. Since different solvents are often used simultaneously, it is difficult to assess the role of individual substances. Objectives The present study is focused on an in-depth investigation of the potential association between lung cancer risk and occupational exposure to a large group of organic solvents, taking into account the well-known risk factors for lung cancer, tobacco smoking and occupational exposure to asbestos. Methods We analysed data from the Investigation of occupational and environmental causes of respiratory cancers (ICARE) study, a large French population-based case–control study, set up between 2001 and 2007. A total of 2276 male cases and 2780 male controls were interviewed, and long-life occupational history was collected. In order to overcome the analytical difficulties created by multiple correlated exposures, we carried out a novel type of analysis based on Bayesian profile regression. Results After analysis with conventional logistic regression methods, none of the 11 solvents examined were associated with lung cancer risk. Through a profile regression approach, we did not observe any significant association between solvent exposure and lung cancer. However, we identified clusters at high risk that are related to occupations known to be at risk of developing lung cancer, such as painters. Conclusions Organic solvents do not appear to be substantial contributors to the occupational risk of lung cancer for the occupations known to be at risk. PMID:26911986

A cross-sectional study for predicting tail biting risk in pig farms using classification and regression tree analysis.

PubMed

Scollo, Annalisa; Gottardo, Flaviana; Contiero, Barbara; Edwards, Sandra A

2017-10-01

Tail biting in pigs has been an identified behavioural, welfare and economic problem for decades, and requires appropriate but sometimes difficult on-farm interventions. The aim of the paper is to introduce the Classification and Regression Tree (CRT) methodologies to develop a tool for prevention of acute tail biting lesions in pigs on-farm. A sample of 60 commercial farms rearing heavy pigs were involved; an on-farm visit and an interview with the farmer collected data on general management, herd health, disease prevention, climate control, feeding and production traits. Results suggest a value for the CRT analysis in managing the risk factors behind tail biting on a farm-specific level, showing 86.7% sensitivity for the Classification Tree and a correlation of 0.7 between observed and predicted prevalence of tail biting obtained with the Regression Tree. CRT analysis showed five main variables (stocking density, ammonia levels, number of pigs per stockman, type of floor and timeliness in feed supply) as critical predictors of acute tail biting lesions, which demonstrate different importance in different farms subgroups. The model might have reliable and practical applications for the support and implementation of tail biting prevention interventions, especially in case of subgroups of pigs with higher risk, helping farmers and veterinarians to assess the risk in their own farm and to manage their predisposing variables in order to reduce acute tail biting lesions. Copyright © 2017 Elsevier B.V. All rights reserved.

Solar cycle in current reanalyses: (non)linear attribution study

NASA Astrophysics Data System (ADS)

Kuchar, A.; Sacha, P.; Miksovsky, J.; Pisoft, P.

2014-12-01

This study focusses on the variability of temperature, ozone and circulation characteristics in the stratosphere and lower mesosphere with regard to the influence of the 11 year solar cycle. It is based on attribution analysis using multiple nonlinear techniques (Support Vector Regression, Neural Networks) besides the traditional linear approach. The analysis was applied to several current reanalysis datasets for the 1979-2013 period, including MERRA, ERA-Interim and JRA-55, with the aim to compare how this type of data resolves especially the double-peaked solar response in temperature and ozone variables and the consequent changes induced by these anomalies. Equatorial temperature signals in the lower and upper stratosphere were found to be sufficiently robust and in qualitative agreement with previous observational studies. The analysis also pointed to the solar signal in the ozone datasets (i.e. MERRA and ERA-Interim) not being consistent with the observed double-peaked ozone anomaly extracted from satellite measurements. Consequently the results obtained by linear regression were confirmed by the nonlinear approach through all datasets, suggesting that linear regression is a relevant tool to sufficiently resolve the solar signal in the middle atmosphere. Furthermore, the seasonal dependence of the solar response was also discussed, mainly as a source of dynamical causalities in the wave propagation characteristics in the zonal wind and the induced meridional circulation in the winter hemispheres. The hypothetical mechanism of a weaker Brewer Dobson circulation was reviewed together with discussion of polar vortex stability.

Central Plant Optimization for Waste Energy Reduction (CPOWER). ESTCP Cost and Performance Report

DTIC Science & Technology

2016-12-01

in the regression models. The solar radiation data did not appear reliable in the weather dataset for the location, and hence it was not used. The...and additional factors (e.g., solar insolation) may be needed to obtain a better model. 2. Inputs to optimizer: During several periods of...Location: North Carolina Energy Consumption Cost Savings $ 443,698.00 Analysis Type: FEMP PV of total savings 215,698.00$ Base Date: April 1

On the calibration process of film dosimetry: OLS inverse regression versus WLS inverse prediction.

PubMed

Crop, F; Van Rompaye, B; Paelinck, L; Vakaet, L; Thierens, H; De Wagter, C

2008-07-21

The purpose of this study was both putting forward a statistically correct model for film calibration and the optimization of this process. A reliable calibration is needed in order to perform accurate reference dosimetry with radiographic (Gafchromic) film. Sometimes, an ordinary least squares simple linear (in the parameters) regression is applied to the dose-optical-density (OD) curve with the dose as a function of OD (inverse regression) or sometimes OD as a function of dose (inverse prediction). The application of a simple linear regression fit is an invalid method because heteroscedasticity of the data is not taken into account. This could lead to erroneous results originating from the calibration process itself and thus to a lower accuracy. In this work, we compare the ordinary least squares (OLS) inverse regression method with the correct weighted least squares (WLS) inverse prediction method to create calibration curves. We found that the OLS inverse regression method could lead to a prediction bias of up to 7.3 cGy at 300 cGy and total prediction errors of 3% or more for Gafchromic EBT film. Application of the WLS inverse prediction method resulted in a maximum prediction bias of 1.4 cGy and total prediction errors below 2% in a 0-400 cGy range. We developed a Monte-Carlo-based process to optimize calibrations, depending on the needs of the experiment. This type of thorough analysis can lead to a higher accuracy for film dosimetry.

Maintenance Operations in Mission Oriented Protective Posture Level IV (MOPPIV)

DTIC Science & Technology

1987-10-01

Repair FADAC Printed Circuit Board ............. 6 3. Data Analysis Techniques ............................. 6 a. Multiple Linear Regression... ANALYSIS /DISCUSSION ............................... 12 1. Exa-ple of Regression Analysis ..................... 12 S2. Regression results for all tasks...6 * TABLE 9. Task Grouping for Analysis ........................ 7 "TABXLE 10. Remove/Replace H60A3 Power Pack................. 8 TABLE

[Survival analysis of patients with pneumoconiosis from 1956 to 2010 in Changsha].

PubMed

Xue, Jing; Chen, Lizhang

2012-01-01

To investigate the survival rate and life expectancy of patients with pneumoconiosis and influence factors in Changsha from 1956 to 2010. A total of 3685 patients with pneumoconiosis were diagnosed and reported from 1956 to 2010 in Changsha. The fatality rate and life expectancy were analyzed by life table and the cause of death was analyzed by Kaplan-Meier method and Cox regression model. The death rate increased obviously with age. Age and accumulation death probability showed linearity (Ŷ=1.271+0.041X, r=0.989). The life expectancy was 60.12 years. The first cause of death was pulmonary tuberculosis in patients with pneumoconiosis. Ruling out the influence of pulmonary tuberculosis, pneumoconiosis, and lung source heart disease, the life expectancy of patients with pneumoconiosis averagely extended 0.83, 0.99, and 0.02 years. The death rate of pneumoconiosis-tuberculosis had significant difference with that of the pneumoconiosisnontuberculosis (P<0.01). Cox regression analysis revealed that the main risk factors for the survival of patients with pneumoconiosis included type of work (smashing worker), complication with tuberculosis, type of pneumoconiosis (silicosis). The death hazard ratio or relative risk caused by them was 1.927, 1.749, and 1.609, respectively. Prevention of pneumoconiosis should focus on smashing workers in Changsha, while its the treatment primarily attaches importance to complication of tuberculosis and lung infection.

[Possibility and predictors of successful cigarette purchase attempts by 201 primary school students in Guangzhou, China].

PubMed

Wen, Xiao-zhong; Huang, Jian-hua; Chen, Wei-qing; Liang, Cai-hua; Han, Ke; Ling, Wen-hua

2007-01-01

To explore the access to tobacco and exam the predictors of successful tobacco purchase attempts among Chinese minors. A simulative trial of purchasing cigarettes was participated by 201 sixth grade students to assess the prevalence of illegal cigarette sales to minors in Guangzhou. Methods of Chi-square and unconditional logistic regression were used to identify the significant predictors,with the result of tobacco purchase as the dependent variable and the characteristics of stores, retailers and minors as the independent variables. A total of 165 students succeeded in purchasing cigarettes but 36 failed, and the percentage of successful purchase attempts was 82. 1% . Data from univariate analysis indicated that 9 factors were significantly associated with students' success in purchasing cigarettes. They were age and height of the purchasers, types of stores, seller's gender and age, posting cigarette advertisements,showing warning signs of 'no cigarette selling to minors' ,asking buyer's age,and asking whom you buy the cigarettes for. The results of multivariable analysis showed that only three variables entering the final logistic regression: the age of students, the type of stores, and showing warning signs of 'no cigarette selling to minors'. Chinese minors have easy access to purchasing cigarettes, especially in groceries and small markets. Selling cigarettes by sellers to minors should be monitored and managed in the future.

Differing manifestations of hepatitis C and tacrolimus on hospitalized diabetes mellitus occurring after kidney transplantation.

PubMed

Abbott, Kevin C; Bernet, Victor J; Agodoa, Lawrence Y; Yuan, Christina M

2005-09-01

Previous studies suggest the association of recipient hepatitis C seropositivity (HCV+) and use of tacrolimus (TAC) with post-transplant diabetes mellitus (PTDM) may differ by manifestations of type I or type II diabetes, but this has not been assessed in the era of current immunosuppression. We performed a retrospective cohort study of 10,342 Medicare primary renal transplantation recipients without evidence of diabetes at the time of listing in the United States Renal Data System between January 1, 1998 and July 31, 2000, followed until December 31, 2000. Outcomes were hospitalizations for a primary diagnosis of diabetic ketoacidosis (DKA) or hyperglycemic hyperosmolar syndrome (HHS). Cox regression analysis was used to calculate adjusted hazard ratios (AHR) for time to DKA or HHS, stratified by diabetes status at the time of transplant. In Cox regression analysis, use of TAC at discharge was independently associated with shorter time to DKA (AHR, 1.88; 95% CI, 1.05-3.37, p=0.034) but not HHS. In contrast, recipient HCV+ was independently associated with shorter time to HHS (AHR, 3.90; 1.59-9.60, p=.003), but not DKA. There was no interaction between TAC and HCV+ for either outcome. These results confirm earlier findings that TAC and HCV+ may mediate the risk of PTDM through different mechanisms, even in the modern era.

Testosterone is an independent determinant of bone mineral density in men with type 2 diabetes mellitus.

PubMed

Vasilkova, Olga; Mokhort, Tatiana; Sanec, Igor; Sharshakova, Tamara; Hayashida, Naomi; Takamura, Noboru

2011-01-01

Although many reports have elucidated pathophysiological characteristics of abnormal bone metabolism in patients with type 2 diabetes mellitus (DT2), determinants of bone mineral density (BMD) in patients with DT2 are still controversial. We examined 168 Belarussian men 45-60 years of age. Plasma total cholesterol (TC), high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, very low-density lipoprotein cholesterol, triglycerides, hemoglobin A(1c) (HbA(1c)), immunoreactive insulin, and C-reactive protein concentrations were assessed. BMD was measured using dual energy X-ray densitometry of the lumbar spine (L(1)-L(4)). Total testosterone (TT) and sex hormone-binding globulin were measured, and free testosterone (FT) was calculated. Using univariate linear regression analysis, BMD of the lumbar spine was significantly correlated with FT (r=0.32, p<0.01) and TT (r=0.36, p<0.01). Using multiple linear regression analysis adjusted for confounding factors, BMD was significantly correlated with TT (β=0.23, p<0.001) and TC (β=-0.029, p=0.005). Age (β=0.005, p=0.071), body mass index (β=0.005, p=0.053), HbA(1c) (β=-0.002, p=0.72) and duration of diabetes (β=0.001, p=0.62) were not significantly correlated with BMD. Our data indicate that androgens are independent determinants of BMD in male patients with DT2.

Creep-Rupture Data Analysis - Engineering Application of Regression Techniques. Ph.D. Thesis - North Carolina State Univ.

NASA Technical Reports Server (NTRS)

Rummler, D. R.

1976-01-01

The results are presented of investigations to apply regression techniques to the development of methodology for creep-rupture data analysis. Regression analysis techniques are applied to the explicit description of the creep behavior of materials for space shuttle thermal protection systems. A regression analysis technique is compared with five parametric methods for analyzing three simulated and twenty real data sets, and a computer program for the evaluation of creep-rupture data is presented.

Assessment of NHTSA’s Report “Relationships Between Fatality Risk, Mass, and Footprint in Model Year 2003-2010 Passenger Cars and LTVs”

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wenzel, Tom

NHTSA recently completed a logistic regression analysis updating its 2003, 2010, and 2012 studies of the relationship between vehicle mass and US fatality risk per vehicle mile traveled (VMT; Kahane 2010, Kahane 2012, Puckett 2016). The new study updates the 2012 analysis using FARS data from 2005 to 2011 for model year 2003 to 2010. Using the updated databases, NHTSA estimates that reducing vehicle mass by 100 pounds while holding footprint fixed would increase fatality risk per VMT by 1.49% for lighter-than-average cars and by 0.50% for heavierthan- average cars, but reduce risk by 0.10% for lighter-than-average light-duty trucks, bymore » 0.71% for heavier-than-average light-duty trucks, and by 0.99% for CUVs/minivans. Using a jack knife method to estimate the statistical uncertainty of these point estimates, NHTSA finds that none of these estimates are statistically significant at the 95% confidence level; however, the 1.49% increase in risk associated with mass reduction in lighter-than-average cars, and the 0.71% and 0.99% decreases in risk associated with mass reduction in heavier-than-average light trucks and CUVs/minivans, are statistically significant at the 90% confidence interval. The effect of mass reduction on risk that NHTSA estimated in 2016 is more beneficial than in its 2012 study, particularly for light trucks and CUVs/minivans. The 2016 NHTSA analysis estimates that reducing vehicle footprint by one square foot while holding mass constant would increase fatality risk per VMT by 0.28% in cars, by 0.38% in light trucks, and by 1.18% in CUVs and minivans.This report replicates the 2016 NHTSA analysis, and reproduces their main results. This report uses the confidence intervals output by the logistic regression models, which are smaller than the intervals NHTSA estimated using a jack-knife technique that accounts for the sampling error in the FARS fatality and state crash data. In addition to reproducing the NHTSA results, this report also examines the NHTSA data in slightly different ways to get a deeper understanding of the relationship between vehicle weight, footprint, and safety. The results of the NHTSA baseline results, and these alternative analyses, are summarized in Table ES.1; statistically significant estimates, based on the confidence intervals output by the logistic regression models, are shown in red in the tables. We found that NHTSA’s reasonable assumption that all vehicles will have ESC installed by 2017 in its baseline regression model slightly increases the estimated increase in risk from mass reduction in cars, but substantially decreases the estimated increase in risk from footprint reduction in all three vehicle types (Alternative 1 in Table ES.1; explained in more detail in Section 2.1 of this report). This is because NHTSA projects ESC to substantially reduce the number of fatalities in rollovers and crashes with stationary objects, and mass reduction appears to reduce risk, while footprint reduction appears to increase risk, in these types of crashes, particularly in cars and CUVs/minivans. A single regression model including all crash types results in slightly different estimates of the relationship between decreasing mass and risk, as shown in Alternative 2 in Table ES.1.« less

Resting-state functional magnetic resonance imaging: the impact of regression analysis.

PubMed

Yeh, Chia-Jung; Tseng, Yu-Sheng; Lin, Yi-Ru; Tsai, Shang-Yueh; Huang, Teng-Yi

2015-01-01

To investigate the impact of regression methods on resting-state functional magnetic resonance imaging (rsfMRI). During rsfMRI preprocessing, regression analysis is considered effective for reducing the interference of physiological noise on the signal time course. However, it is unclear whether the regression method benefits rsfMRI analysis. Twenty volunteers (10 men and 10 women; aged 23.4 ± 1.5 years) participated in the experiments. We used node analysis and functional connectivity mapping to assess the brain default mode network by using five combinations of regression methods. The results show that regressing the global mean plays a major role in the preprocessing steps. When a global regression method is applied, the values of functional connectivity are significantly lower (P ≤ .01) than those calculated without a global regression. This step increases inter-subject variation and produces anticorrelated brain areas. rsfMRI data processed using regression should be interpreted carefully. The significance of the anticorrelated brain areas produced by global signal removal is unclear. Copyright © 2014 by the American Society of Neuroimaging.

Dietary patterns and risk of ductal carcinoma of the breast: a factor analysis in Uruguay.

PubMed

Ronco, Alvaro L; De Stefani, Eduardo; Deneo-Pellegrini, Hugo; Boffetta, Paolo; Aune, Dagfinn; Silva, Cecilia; Landó, Gabriel; Luaces, María E; Acosta, Gisele; Mendilaharsu, María

2010-01-01

Breast cancer (BC) shows very high incidence rates in Uruguayan women. The present factor analysis of ductal carcinoma of the breast, the most frequent histological type of this malignancy both in Uruguay and in the World, was conducted at a prepaid hospital of Montevideo, Uruguay. We identified 111 cases with ductal BC and 222 controls with normal mammograms. A factor analysis was conducted using 39 food groups, allowing retention of six factors analyzed through logistic regression in order to obtain odds ratios (OR) associated with ductal BC. The low fat and non-alcoholic beverage patterns were inversely associated (OR=0.30 and OR=0.45, respectively) with risk. Conversely, the fatty cheese pattern was positively associated (OR=4.17) as well as the fried white meat (OR=2.28) and Western patterns (OR 2.13). Ductal BC shared similar dietary risk patterns as those identified by studies not discriminating between histologic type of breast cancer.

Short sleep duration is associated with B-type natriuretic peptide levels and predicts the death of Japanese patients with type 2 diabetes.

PubMed

Hamasaki, Hidetaka; Katsuyama, Hisayuki; Sako, Akahito; Yanai, Hidekatsu

2017-08-01

To investigate the associations of sleep duration with all-cause mortality, glycemic control, and other clinical parameters of patients with type 2 diabetes. From April 2013 to December 2015, we conducted a retrospective cohort study. Study participants were divided into three groups according to their sleep duration. Multiple regression analysis and Cox proportional hazards analysis were performed to assess the independent associations of sleep duration with clinical parameters and all-cause mortality. We enrolled 1233 patients who were then followed for 860 ± 264 days. During the follow-up period, 20 patients (1.6%) died. Sleep duration inversely associated with plasma B-type natriuretic peptide levels (β = -0.203, p = 0.012) in short (<7 h) sleepers, whereas it was positively associated with hemoglobin A1c levels (β = 0.156, p = 0.021) in long (≥9 h) sleepers. Moreover, Cox proportional hazard analysis revealed that short sleep duration was a significant predictor of all-cause mortality (hazard ratio = 0.473; confidence interval 0.248-0.905, p = 0.024). Short sleep duration may serve as a prognostic indicator of mortality in Japanese patients with type 2 diabetes and may increase cardiovascular stress. Adequate sleep is essential for the management of type 2 diabetes. Copyright © 2017 Elsevier B.V. All rights reserved.

Standards for Standardized Logistic Regression Coefficients

ERIC Educational Resources Information Center

Menard, Scott

2011-01-01

Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…

Predicting No-Shows in Radiology Using Regression Modeling of Data Available in the Electronic Medical Record.

PubMed

Harvey, H Benjamin; Liu, Catherine; Ai, Jing; Jaworsky, Cristina; Guerrier, Claude Emmanuel; Flores, Efren; Pianykh, Oleg

2017-10-01

To test whether data elements available in the electronic medical record (EMR) can be effectively leveraged to predict failure to attend a scheduled radiology examination. Using data from a large academic medical center, we identified all patients with a diagnostic imaging examination scheduled from January 1, 2016, to April 1, 2016, and determined whether the patient successfully attended the examination. Demographic, clinical, and health services utilization variables available in the EMR potentially relevant to examination attendance were recorded for each patient. We used descriptive statistics and logistic regression models to test whether these data elements could predict failure to attend a scheduled radiology examination. The predictive accuracy of the regression models were determined by calculating the area under the receiver operator curve. Among the 54,652 patient appointments with radiology examinations scheduled during the study period, 6.5% were no-shows. No-show rates were highest for the modalities of mammography and CT and lowest for PET and MRI. Logistic regression indicated that 16 of the 27 demographic, clinical, and health services utilization factors were significantly associated with failure to attend a scheduled radiology examination (P ≤ .05). Stepwise logistic regression analysis demonstrated that previous no-shows, days between scheduling and appointments, modality type, and insurance type were most strongly predictive of no-show. A model considering all 16 data elements had good ability to predict radiology no-shows (area under the receiver operator curve = 0.753). The predictive ability was similar or improved when these models were analyzed by modality. Patient and examination information readily available in the EMR can be successfully used to predict radiology no-shows. Moving forward, this information can be proactively leveraged to identify patients who might benefit from additional patient engagement through appointment reminders or other targeted interventions to avoid no-shows. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Comparison of Linear and Non-linear Regression Analysis to Determine Pulmonary Pressure in Hyperthyroidism.

PubMed

Scarneciu, Camelia C; Sangeorzan, Livia; Rus, Horatiu; Scarneciu, Vlad D; Varciu, Mihai S; Andreescu, Oana; Scarneciu, Ioan

2017-01-01

This study aimed at assessing the incidence of pulmonary hypertension (PH) at newly diagnosed hyperthyroid patients and at finding a simple model showing the complex functional relation between pulmonary hypertension in hyperthyroidism and the factors causing it. The 53 hyperthyroid patients (H-group) were evaluated mainly by using an echocardiographical method and compared with 35 euthyroid (E-group) and 25 healthy people (C-group). In order to identify the factors causing pulmonary hypertension the statistical method of comparing the values of arithmetical means is used. The functional relation between the two random variables (PAPs and each of the factors determining it within our research study) can be expressed by linear or non-linear function. By applying the linear regression method described by a first-degree equation the line of regression (linear model) has been determined; by applying the non-linear regression method described by a second degree equation, a parabola-type curve of regression (non-linear or polynomial model) has been determined. We made the comparison and the validation of these two models by calculating the determination coefficient (criterion 1), the comparison of residuals (criterion 2), application of AIC criterion (criterion 3) and use of F-test (criterion 4). From the H-group, 47% have pulmonary hypertension completely reversible when obtaining euthyroidism. The factors causing pulmonary hypertension were identified: previously known- level of free thyroxin, pulmonary vascular resistance, cardiac output; new factors identified in this study- pretreatment period, age, systolic blood pressure. According to the four criteria and to the clinical judgment, we consider that the polynomial model (graphically parabola- type) is better than the linear one. The better model showing the functional relation between the pulmonary hypertension in hyperthyroidism and the factors identified in this study is given by a polynomial equation of second degree where the parabola is its graphical representation.

Modeling animal-vehicle collisions using diagonal inflated bivariate Poisson regression.

PubMed

Lao, Yunteng; Wu, Yao-Jan; Corey, Jonathan; Wang, Yinhai

2011-01-01

Two types of animal-vehicle collision (AVC) data are commonly adopted for AVC-related risk analysis research: reported AVC data and carcass removal data. One issue with these two data sets is that they were found to have significant discrepancies by previous studies. In order to model these two types of data together and provide a better understanding of highway AVCs, this study adopts a diagonal inflated bivariate Poisson regression method, an inflated version of bivariate Poisson regression model, to fit the reported AVC and carcass removal data sets collected in Washington State during 2002-2006. The diagonal inflated bivariate Poisson model not only can model paired data with correlation, but also handle under- or over-dispersed data sets as well. Compared with three other types of models, double Poisson, bivariate Poisson, and zero-inflated double Poisson, the diagonal inflated bivariate Poisson model demonstrates its capability of fitting two data sets with remarkable overlapping portions resulting from the same stochastic process. Therefore, the diagonal inflated bivariate Poisson model provides researchers a new approach to investigating AVCs from a different perspective involving the three distribution parameters (λ(1), λ(2) and λ(3)). The modeling results show the impacts of traffic elements, geometric design and geographic characteristics on the occurrences of both reported AVC and carcass removal data. It is found that the increase of some associated factors, such as speed limit, annual average daily traffic, and shoulder width, will increase the numbers of reported AVCs and carcass removals. Conversely, the presence of some geometric factors, such as rolling and mountainous terrain, will decrease the number of reported AVCs. Published by Elsevier Ltd.

Correlates and predictors of missed nursing care in hospitals.

PubMed

Bragadóttir, Helga; Kalisch, Beatrice J; Tryggvadóttir, Gudný Bergthora

2017-06-01

To identify the contribution of hospital, unit, staff characteristics, staffing adequacy and teamwork to missed nursing care in Iceland hospitals. A recently identified quality indicator for nursing care and patient safety is missed nursing care defined as any standard, required nursing care omitted or significantly delayed, indicating an error of omission. Former studies point to contributing factors to missed nursing care regarding hospital, unit and staff characteristics, perceptions of staffing adequacy as well as nursing teamwork, displayed in the Missed Nursing Care Model. This was a quantitative cross-sectional survey study. The samples were all registered nurses and practical nurses (n = 864) working on 27 medical, surgical and intensive care inpatient units in eight hospitals throughout Iceland. Response rate was 69·3%. Data were collected in March-April 2012 using the combined MISSCARE Survey-Icelandic and the Nursing Teamwork Survey-Icelandic. Descriptive, correlational and regression statistics were used for data analysis. Missed nursing care was significantly related to hospital and unit type, participants' age and role and their perception of adequate staffing and level of teamwork. The multiple regression testing of Model 1 indicated unit type, role, age and staffing adequacy to predict 16% of the variance in missed nursing care. Controlling for unit type, role, age and perceptions of staffing adequacy, the multiple regression testing of Model 2 showed that nursing teamwork predicted an additional 14% of the variance in missed nursing care. The results shed light on the correlates and predictors of missed nursing care in hospitals. This study gives direction as to the development of strategies for decreasing missed nursing care, including ensuring appropriate staffing levels and enhanced teamwork. By identifying contributing factors to missed nursing care, appropriate interventions can be developed and tested. © 2016 John Wiley & Sons Ltd.

Associations between joint attention and language in autism spectrum disorder and typical development: A systematic review and meta-regression analysis.

PubMed

Bottema-Beutel, Kristen

2016-10-01

Using a structured literature search and meta-regression procedures, this study sought to determine whether associations between joint attention and language are moderated by group (autism spectrum disorder [ASD] vs. typical development [TD]), joint attention type (responding to joint attention [RJA] vs. other), and other study design features and participant characteristics. Studies were located using database searches, hand searches, and electronic requests for data from experts in the field. This resulted in 71 reports or datasets and 605 effect sizes, representing 1,859 participants with ASD and 1,835 TD participants. Meta-regression was used to answer research questions regarding potential moderators of the effect sizes of interest, which were Pearson's r values quantifying the association between joint attention and language variables. In the final models, conducted separately for each language variable, effect sizes were significantly higher for the ASD group as compared to the TD group, and for RJA as compared to non-RJA joint attention types. Approximate mental age trended toward significance for the expressive language model. Joint attention may be more tightly tied to language in children with ASD as compared to TD children because TD children exhibit joint attention at sufficient thresholds so that language development becomes untethered to variations in joint attention. Conversely, children with ASD who exhibit deficits in joint attention develop language contingent upon their joint attention abilities. Because RJA was more strongly related to language than other types of joint attention, future research should involve careful consideration of the operationalization and measurement of joint attention constructs. Autism Res 2016, 9: 1021-1035. © 2016 International Society for Autism Research, Wiley Periodicals, Inc. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

Linear regression analysis: part 14 of a series on evaluation of scientific publications.

PubMed

Schneider, Astrid; Hommel, Gerhard; Blettner, Maria

2010-11-01

Regression analysis is an important statistical method for the analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication. This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience. After a brief introduction of the uni- and multivariable regression models, illustrative examples are given to explain what the important considerations are before a regression analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately. The performance and interpretation of linear regression analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying linear regression analysis and its limitations are presented.

An improved multiple linear regression and data analysis computer program package

NASA Technical Reports Server (NTRS)

Sidik, S. M.

1972-01-01

NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.

Reconstruction of the Foot and Ankle Using Pedicled or Free Flaps: Perioperative Flap Survival Analysis

PubMed Central

Li, Xiucun; Cui, Jianli; Maharjan, Suraj; Lu, Laijin; Gong, Xu

2016-01-01

Objective The purpose of this study is to determine the correlation between non-technical risk factors and the perioperative flap survival rate and to evaluate the choice of skin flap for the reconstruction of foot and ankle. Methods This was a clinical retrospective study. Nine variables were identified. The Kaplan-Meier method coupled with a log-rank test and a Cox regression model was used to predict the risk factors that influence the perioperative flap survival rate. The relationship between postoperative wound infection and risk factors was also analyzed using a logistic regression model. Results The overall flap survival rate was 85.42%. The necrosis rates of free flaps and pedicled flaps were 5.26% and 20.69%, respectively. According to the Cox regression model, flap type (hazard ratio [HR] = 2.592; 95% confidence interval [CI] (1.606, 4.184); P < 0.001) and postoperative wound infection (HR = 0.266; 95% CI (0.134, 0.529); P < 0.001) were found to be statistically significant risk factors associated with flap necrosis. Based on the logistic regression model, preoperative wound bed inflammation (odds ratio [OR] = 11.371,95% CI (3.117, 41.478), P < 0.001) was a statistically significant risk factor for postoperative wound infection. Conclusion Flap type and postoperative wound infection were both independent risk factors influencing the flap survival rate in the foot and ankle. However, postoperative wound infection was a risk factor for the pedicled flap but not for the free flap. Microvascular anastomosis is a major cause of free flap necrosis. To reconstruct complex or wide soft tissue defects of the foot or ankle, free flaps are safer and more reliable than pedicled flaps and should thus be the primary choice. PMID:27930679

Evaluation of visual impairment in Usher syndrome 1b and Usher syndrome 2a.

PubMed

Pennings, Ronald J E; Huygen, Patrick L M; Orten, Dana J; Wagenaar, Mariette; van Aarem, Annelies; Kremer, Hannie; Kimberling, William J; Cremers, Cor W R J; Deutman, August F

2004-04-01

To evaluate visual impairment in Usher syndrome 1b (USH1b) and Usher syndrome 2a (USH2a). We carried out a retrospective study of 19 USH1b patients and 40 USH2a patients. Cross-sectional regression analyses of the functional acuity score (FAS), functional field score (FFS) and functional vision score (FVS) related to age were performed. Statistical tests relating to regression lines and Student's t-test were used to compare between (sub)groups of patients. Parts of the available individual longitudinal data were used to obtain individual estimates of progressive deterioration and compare these to those obtained with cross-sectional analysis. Results were compared between subgroups of USH2a patients pertaining to combinations of different types of mutations. Cross-sectional analyses revealed significant deterioration of the FAS (0.7% per year), FFS (1.0% per year) and FVS (1.5% per year) with advancing age in both patient groups, without a significant difference between the USH1b and USH2a patients. Individual estimates of the deterioration rates were substantially and significantly higher than the cross-sectional estimates in some USH2a cases, including values of about 5% per year (or even higher) for the FAS (age 35-50 years), 3-4% per year for the FFS and 4-5% per year for the FVS (age > 20 years). There was no difference in functional vision score behaviour detected between subgroups of patients pertaining to different biallelic combinations of specific types of mutations. The FAS, FFS and FVS deteriorated significantly by 0.7-1.5% per year according to cross-sectional linear regression analysis in both USH1b and USH2a patients. Higher deterioration rates (3-5% per year) in any of these scores were attained, according to longitudinal data collected from individual USH2a patients. Score behaviour was similar across the patient groups and across different biallelic combinations of various types of mutations. However, more elaborate studies, preferably covering longitudinal data, are needed to obtain conclusive evidence.

Pregnancy eHealth and mHealth: user proportions and characteristics of pregnant women using Web-based information sources-a cross-sectional study.

PubMed

Wallwiener, Stephanie; Müller, Mitho; Doster, Anne; Laserer, Wolfgang; Reck, Corinna; Pauluschke-Fröhlich, Jan; Brucker, Sara Y; Wallwiener, Christian W; Wallwiener, Markus

2016-11-01

To analyze the current proportions and characteristics of women using Internet (eHealth) and smartphone (mHealth) based sources of information during pregnancy and to investigate the influence, this information-seeking behavior has on decision-making. A cross-sectional study was conducted at two major German university hospitals. Questionnaires covering socio-demographic data, medical data and details of Internet, and smartphone application use were administered to 220 pregnant women. Data analysis utilized descriptive statistics and multiple regression analysis. 50.7 % of pregnant women were online information seekers. 22.4 % used an mHealth pregnancy application. Women using eHealth information showed no specific profile, while women using mHealth applications proved to be younger, were more likely to be in their first pregnancy, felt less healthy, and were more likely to be influenced by the retrieved information. Stepwise backward regression analysis explained 25.8 % of the variance of mHealth use. 80.5 % of cases were classified correctly by the identified predictors. All types of Web-based information correlated significantly with decision-making during pregnancy. Pregnant women frequently use the Internet and smartphone applications as a source of information. While Web usage was a common phenomenon, this study revealed specific characteristics of mHealth users during pregnancy. Improved, medically accurate smartphone applications might provide a way to specifically target the mHealth user group. As user influenceability was of major relevance to all types of information, all medical content should be carefully reviewed by a multidisciplinary board of medical specialists.

Evolving Epidemiology of Staphylococcus aureus Bacteremia.

PubMed

Rhee, Yoona; Aroutcheva, Alla; Hota, Bala; Weinstein, Robert A; Popovich, Kyle J

2015-12-01

Methicillin-resistant Staphylococcus aureus (MRSA) infections due to USA300 have become widespread in community and healthcare settings. It is unclear whether risk factors for bloodstream infections (BSIs) differ by strain type. To examine the epidemiology of S. aureus BSIs, including USA300 and non-USA300 MRSA strains. Retrospective observational study with molecular analysis. Large urban public hospital. Individuals with S. aureus BSIs from January 1, 2007 through December 31, 2013. We used electronic surveillance data to identify cases of S. aureus BSI. Available MRSA isolates were analyzed by pulsed-field gel electrophoresis. Poisson regression was used to evaluate changes in BSI incidence over time. Risk factor data were collected by medical chart review and logistic regression was used for multivariate analysis of risk factors. A total of 1,015 cases of S. aureus BSIs were identified during the study period; 36% were due to MRSA. The incidence of hospital-onset (HO) MRSA BSIs decreased while that of community-onset (CO) MRSA BSIs remained stable. The rate of CO- and HO- methicillin-susceptible S. aureus infections both decreased over time. More than half of HO-MRSA BSIs were due to the USA300 strain type and for 4 years, the proportion of HO-MRSA BSIs due to USA300 exceeded 60%. On multivariate analysis, current or former drug use was the only epidemiologic risk factor for CO- or HO-MRSA BSIs due to USA300 strains. USA300 MRSA is endemic in communities and hospitals and certain populations (eg, those who use illicit drugs) may benefit from enhanced prevention efforts in the community.

Housing Archetype Analysis for Home Energy-Efficient Retrofit in the Great Lakes Region

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, S. K.; Mrozowski, T.; Harrell-Seyburn, A.

This project report details activities and results of the 'Market Characterization' project undertaken by the Cost Effective Energy Retrofit (CEER) team targeted toward the DOE goal of achieving 30%-50% reduction in existing building energy use. CEER consists of members from the Dow Chemical Company, Michigan State University, Ferris State University and Habitat for Humanity Kent County. The purpose of this market characterization project was to identify housing archetypes which are dominant within Great Lakes region and therefore offer significant potential for energy-efficient retrofit research and implementation due to the substantial number of homes possessing similar characteristics. Understanding the characteristics ofmore » housing groups referred to as 'archetypes' by vintage, style, and construction characteristics can allow research teams to focus their retrofit research and develop prescriptive solutions for those structure types which are prevalent and offer high potential uptake within a region or market. Key research activities included; literature review, statistical analysis of national and regional data of the American Housing Survey (AHS) collected by the U.S. Census Bureau, analysis of Michigan specific data, development of a housing taxonomy of architectural styles, case studies of two local markets (i.e., Ann Arbor and Grand Rapids in Michigan) and development of a suggested framework (or process) for characterizing local markets. In order to gain a high level perspective, national and regional data from the U.S. Census Bureau was analyzed using cross tabulations, multiple regression models, and logistic regression to characterize the housing stock and determine dominant house types using 21 variables.« less

Inflammation is related to preoperative hypoxemia in patients with acute Stanford type A aortic dissection.

PubMed

Duan, Xu-Zhou; Xu, Zhi-Yun; Lu, Fang-Lin; Han, Lin; Tang, Yang-Feng; Tang, Hao; Liu, Yang

2018-03-01

Preoperative hypoxemia is a frequent complication of acute Stanford type A aortic dissection (ATAAD). The aim of the present study was to determine which factors were associated with hypoxemia. A series of data were collected in a statistical analysis to evaluate preoperative hypoxemia in patients with ATAAD. After retrospectively analyzing data for 172 patients, we identified the risk factors for preoperative hypoxemia. Hypoxemia was defined by an arterial partial pressure of oxygen to fraction of inspired oxygen (PaO 2 /FiO 2 ) ratio of 200 or lower. Subsequent to identifying the patient population, a prospective study was conducted using ulinastatin as a preoperative intervention. The ulinastatin group received ulinastatin at a total dose of 300,000 units prior to surgery. All the pertinent factors were investigated through univariate and multiple logistic regression analysis. The factors associated with preoperative hypoxemia in ATAAD comprised the following: body mass index (BMI) ≥25; white blood cell count (WBC) and neutrophil counts; levels of C-reactive protein (CRP), D-dimer, and interleukin-6 (IL-6); ATAAD involving the celiac trunk, renal artery, or mesenteric artery. Logistic regression analysis showed that CRP and IL-6 levels were independent predictive factors. We found that ulinastatin effectively could improve oxygenation, since compared to the control group the oxygenation in the ulinastatin group was significantly improved. Systemic inflammatory reactions played a vital role in preoperative hypoxemia after the onset of ATAAD. The oxygenation of the patient could be improved significantly by inhibiting the inflammatory response prior to surgery.

Fate of abstracts presented at the 2008 European Congress of Physical and Rehabilitation Medicine.

PubMed

Allart, E; Beaucamp, F; Tiffreau, V; Thevenon, A

2015-08-01

The subsequent full-text publication of abstracts presented at a scientific congress reflects the latter's scientific quality. The aim of this paper was to evaluate the publication rate for abstracts presented at the 2008 European Congress of Physical and Rehabilitation Medicine (ECPRM), characterize the publications and identify factors that were predictive of publication. It is a bibliography search. We used the PubMed database to search for subsequent publication of abstracts. We screened the abstracts' characteristics for features that were predictive of publication among abstracts features, such the status of the authors, the topic and the type of work. We performed univariate analyses and a logistic regression analysis. Of 779 abstracts presented at ECPRM 2008, 169 (21.2%) were subsequently published. The mean time to publication was 12±15.7 months and the mean impact factor of the publishing journals was 2.05±2.1. In a univariate analysis, university status (P<10-6), geographic origin (P=10-3), oral presentation (P<10-6), and original research (P<10-6) (and particularly multicentre trials [P<0.01] and randomized controlled trials [P=10-3]) were predictive of publication. In a logistic regression analysis, oral presentation (odds ratio [OR]=0.37) and university status (OR=0.36) were significant, independent predictors of publication. ECPRM 2008 publication rate and impact factor were relatively low, when compared with most other national and international conferences in this field. University status, the type of abstract and oral presentation were predictive of subsequent publication.

Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks.

PubMed

Duda, Piotr; Jaworski, Maciej; Rutkowski, Leszek

2018-03-01

One of the greatest challenges in data mining is related to processing and analysis of massive data streams. Contrary to traditional static data mining problems, data streams require that each element is processed only once, the amount of allocated memory is constant and the models incorporate changes of investigated streams. A vast majority of available methods have been developed for data stream classification and only a few of them attempted to solve regression problems, using various heuristic approaches. In this paper, we develop mathematically justified regression models working in a time-varying environment. More specifically, we study incremental versions of generalized regression neural networks, called IGRNNs, and we prove their tracking properties - weak (in probability) and strong (with probability one) convergence assuming various concept drift scenarios. First, we present the IGRNNs, based on the Parzen kernels, for modeling stationary systems under nonstationary noise. Next, we extend our approach to modeling time-varying systems under nonstationary noise. We present several types of concept drifts to be handled by our approach in such a way that weak and strong convergence holds under certain conditions. Finally, in the series of simulations, we compare our method with commonly used heuristic approaches, based on forgetting mechanism or sliding windows, to deal with concept drift. Finally, we apply our concept in a real life scenario solving the problem of currency exchange rates prediction.

[A SAS marco program for batch processing of univariate Cox regression analysis for great database].

PubMed

Yang, Rendong; Xiong, Jie; Peng, Yangqin; Peng, Xiaoning; Zeng, Xiaomin

2015-02-01

To realize batch processing of univariate Cox regression analysis for great database by SAS marco program. We wrote a SAS macro program, which can filter, integrate, and export P values to Excel by SAS9.2. The program was used for screening survival correlated RNA molecules of ovarian cancer. A SAS marco program could finish the batch processing of univariate Cox regression analysis, the selection and export of the results. The SAS macro program has potential applications in reducing the workload of statistical analysis and providing a basis for batch processing of univariate Cox regression analysis.

Estimation of Logistic Regression Models in Small Samples. A Simulation Study Using a Weakly Informative Default Prior Distribution

ERIC Educational Resources Information Center

Gordovil-Merino, Amalia; Guardia-Olmos, Joan; Pero-Cebollero, Maribel

2012-01-01

In this paper, we used simulations to compare the performance of classical and Bayesian estimations in logistic regression models using small samples. In the performed simulations, conditions were varied, including the type of relationship between independent and dependent variable values (i.e., unrelated and related values), the type of variable…

Exact Analysis of Squared Cross-Validity Coefficient in Predictive Regression Models

ERIC Educational Resources Information Center

Shieh, Gwowen

2009-01-01

In regression analysis, the notion of population validity is of theoretical interest for describing the usefulness of the underlying regression model, whereas the presumably more important concept of population cross-validity represents the predictive effectiveness for the regression equation in future research. It appears that the inference…

Selective principal component regression analysis of fluorescence hyperspectral image to assess aflatoxin contamination in corn

USDA-ARS?s Scientific Manuscript database

Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression co...

Genome size evolution in Ontario ferns (Polypodiidae): evolutionary correlations with cell size, spore size, and habitat type and an absence of genome downsizing.

PubMed

Henry, Thomas A; Bainard, Jillian D; Newmaster, Steven G

2014-10-01

Genome size is known to correlate with a number of traits in angiosperms, but less is known about the phenotypic correlates of genome size in ferns. We explored genome size variation in relation to a suite of morphological and ecological traits in ferns. Thirty-six fern taxa were collected from wild populations in Ontario, Canada. 2C DNA content was measured using flow cytometry. We tested for genome downsizing following polyploidy using a phylogenetic comparative analysis to explore the correlation between 1Cx DNA content and ploidy. There was no compelling evidence for the occurrence of widespread genome downsizing during the evolution of Ontario ferns. The relationship between genome size and 11 morphological and ecological traits was explored using a phylogenetic principal component regression analysis. Genome size was found to be significantly associated with cell size, spore size, spore type, and habitat type. These results are timely as past and recent studies have found conflicting support for the association between ploidy/genome size and spore size in fern polyploid complexes; this study represents the first comparative analysis of the trend across a broad taxonomic group of ferns.

Social network types among older Korean adults: Associations with subjective health.

PubMed

Sohn, Sung Yun; Joo, Won-Tak; Kim, Woo Jung; Kim, Se Joo; Youm, Yoosik; Kim, Hyeon Chang; Park, Yeong-Ran; Lee, Eun

2017-01-01

With population aging now a global phenomenon, the health of older adults is becoming an increasingly important issue. Because the Korean population is aging at an unprecedented rate, preparing for public health problems associated with old age is particularly salient in this country. As the physical and mental health of older adults is related to their social relationships, investigating the social networks of older adults and their relationship to health status is important for establishing public health policies. The aims of this study were to identify social network types among older adults in South Korea and to examine the relationship of these social network types with self-rated health and depression. Data from the Korean Social Life, Health, and Aging Project were analyzed. Model-based clustering using finite normal mixture modeling was conducted to identify the social network types based on ten criterion variables of social relationships and activities: marital status, number of children, number of close relatives, number of friends, frequency of attendance at religious services, attendance at organized group meetings, in-degree centrality, out-degree centrality, closeness centrality, and betweenness centrality. Multivariate regression analysis was conducted to examine associations between the identified social network types and self-rated health and depression. The model-based clustering analysis revealed that social networks clustered into five types: diverse, family, congregant, congregant-restricted, and restricted. Diverse or family social network types were significantly associated with more favorable subjective mental health, whereas the restricted network type was significantly associated with poorer ratings of mental and physical health. In addition, our analysis identified unique social network types related to religious activities. In summary, we developed a comprehensive social network typology for older Korean adults. Copyright © 2016 Elsevier Ltd. All rights reserved.

Associations of a social network typology with physical and mental health risks among older adults in South Korea.

PubMed

Park, N S; Jang, Y; Lee, B S; Chiriboga, D A; Chang, S; Kim, S Y

2018-05-01

The objectives of this study were to (1) develop an empirical typology of social networks in older Koreans; and (2) examine its effect on physical and mental health. A sample of 6900 community-dwelling older adults in South Korea was drawn from the 2014 Korean National Elderly Survey. Latent profile analysis (LPA) was conducted to derive social network types using eight common social network characteristics (marital status, living arrangement, the number and frequency of contact with close family/relatives, the number and frequency of contact with close friends, frequency of participation in social activities, and frequency of having visitors at home). The identified typologies were then regressed on self-rated health and depressive symptoms to explore the health risks posed by the group membership. The LPA identified a model with five types of social network as being most optimal (BIC = 153,848.34, entropy = .90). The groups were named diverse/family (enriched networks with more engagement with family), diverse/friend (enriched networks with more engagement with friends), friend-focused (high engagement with friends), distant (structurally disengaged), and restricted (structurally engaged but disengaged in family/friends networks). A series of regression analyses showed that membership in the restricted type was associated with more health and mental health risks than all types of social networks except the distant type. Findings demonstrate the importance of family and friends as a source of social network and call attention to not only structural but also non-structural aspects of social isolation. Findings and implications are discussed in cultural contexts.

Determine precipitation rates from visible and infrared satellite images of clouds by pattern recognition technique. Progress Report, 1 Jul. 1985 - 31 Mar. 1987 Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Weinman, James A.; Garan, Louis

1987-01-01

A more advanced cloud pattern analysis algorithm was subsequently developed to take the shape and brightness of the various clouds into account in a manner that is more consistent with the human analyst's perception of GOES cloud imagery. The results of that classification scheme were compared with precipitation probabilities observed from ships of opportunity off the U.S. east coast to derive empirical regressions between cloud types and precipitation probability. The cloud morphology was then quantitatively and objectively used to map precipitation probabilities during two winter months during which severe cold air outbreaks were observed over the northwest Atlantic. Precipitation probabilities associated with various cloud types are summarized. Maps of precipitation probability derived from the cloud morphology analysis program for two months and the precipitation probability derived from thirty years of ship observation were observed.

Financial Conflicts of Interest and Study Results in Environmental and Occupational Health Research.

PubMed

Friedman, Lee; Friedman, Michael

2016-03-01

To date, there is no comprehensive analysis of the relationship between financial conflict of interest (COI) and a potential publication bias in environmental and occupational health studies. We analyzed original research articles published in 2012 in 17 peer-reviewed journals. Multivariable ordinal logistic regression models were developed to evaluate the relationship between financial COI and the study outcome. Of the 373 studies included in the analysis, 17.2% had a financial COI associated with organizations involved with the processing, use, or disposal of industrial and commercial products, and studies with this type of COI were more likely to report negative results (Adjusted Odds Ratio = 4.31), as were studies with any COI associated with the military (employment or funding; Adjusted Odds Ratio = 9.15). Our findings show a clear relationship between direction of reported findings and specific types of financial COI.

The association of insulin medication possession ratio, use of insulin glargine, and health benefit costs in employees and spouses with type 2 diabetes.

PubMed

Kleinman, Nathan L; Schaneman, Justin L; Lynch, Wendy D

2008-12-01

Measure the impact of insulin utilization on health costs and absenteeism. Compare outcomes between users of insulin glargine and other insulin. Using a large retrospective database, this regression analysis examined annual health costs and absenteeism among employees and spouses with type 2 diabetes who used insulin. The analysis studied impacts of medication possession ratio (MPR) and glargine use, controlling for demographic factors, salary, and prior health care. Higher MPR was associated with significantly lower health costs for patients with high prior costs. Glargine users' MPR was higher than other insulin users' MPR (66% vs 54%, P < 0.0001). Among all insulin users, those using glargine had significantly lower total ($6771 vs $7969, P = 0.0046) and circulatory-specific ($312 vs $636, P < 0.0001) costs. Insulin MPR and the use of insulin glargine were associated with lower health care costs.

Prognostic value of inflammation-based scores in patients with osteosarcoma

PubMed Central

Liu, Bangjian; Huang, Yujing; Sun, Yuanjue; Zhang, Jianjun; Yao, Yang; Shen, Zan; Xiang, Dongxi; He, Aina

2016-01-01

Systemic inflammation responses have been associated with cancer development and progression. C-reactive protein (CRP), Glasgow prognostic score (GPS), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), and neutrophil-platelet score (NPS) have been shown to be independent risk factors in various types of malignant tumors. This retrospective analysis of 162 osteosarcoma cases was performed to estimate their predictive value of survival in osteosarcoma. All statistical analyses were performed by SPSS statistical software. Receiver operating characteristic (ROC) analysis was generated to set optimal thresholds; area under the curve (AUC) was used to show the discriminatory abilities of inflammation-based scores; Kaplan-Meier analysis was performed to plot the survival curve; cox regression models were employed to determine the independent prognostic factors. The optimal cut-off points of NLR, PLR, and LMR were 2.57, 123.5 and 4.73, respectively. GPS and NLR had a markedly larger AUC than CRP, PLR and LMR. High levels of CRP, GPS, NLR, PLR, and low level of LMR were significantly associated with adverse prognosis (P < 0.05). Multivariate Cox regression analyses revealed that GPS, NLR, and occurrence of metastasis were top risk factors associated with death of osteosarcoma patients. PMID:28008988

Factors Affecting the Clinical Success Rate of Miniscrew Implants for Orthodontic Treatment.

PubMed

Jing, Zheng; Wu, Yeke; Jiang, Wenlu; Zhao, Lixing; Jing, Dian; Zhang, Nian; Cao, Xiaoqing; Xu, Zhenrui; Zhao, Zhihe

2016-01-01

The purpose of this study was to evaluate the various factors that influence the success rate of miniscrew implants used as orthodontic anchorage. Potential confounding variables examined were sex, age, vertical (FMA) and sagittal (ANB) skeletal facial pattern, site of placement (labial and buccal, palatal, and retromandibular triangle), arch of placement (maxilla and mandible), placement soft tissue type, oral hygiene, diameter and length of miniscrew implants, insertion method (predrilled or drill-free), angle of placement, onset and strength of force application, and clinical purpose. The correlations between success rate and overall variables were investigated by logistic regression analysis, and the effect of each variable on the success rate was utilized by variance analysis. One hundred fourteen patients were included with a total of 253 miniscrew implants. The overall success rate was 88.54% with an average loading period of 9.5 months in successful cases. Age, oral hygiene, vertical skeletal facial pattern (FMA), and general placement sites (maxillary and mandibular) presented significant differences in success rates both by logistic regression analysis and variance analysis (P < .05). To minimize the failure of miniscrew implants, proper oral hygiene instruction and effective supervision should be given for patients, especially young (< 12 years) high-angle patients with miniscrew implants placed in the mandible.

Response of dissolved trace metals to land use/land cover and their source apportionment using a receptor model in a subtropic river, China.

PubMed

Li, Siyue; Zhang, Quanfa

2011-06-15

Water samples were collected for determination of dissolved trace metals in 56 sampling sites throughout the upper Han River, China. Multivariate statistical analyses including correlation analysis, stepwise multiple linear regression models, and principal component and factor analysis (PCA/FA) were employed to examine the land use influences on trace metals, and a receptor model of factor analysis-multiple linear regression (FA-MLR) was used for source identification/apportionment of anthropogenic heavy metals in the surface water of the River. Our results revealed that land use was an important factor in water metals in the snow melt flow period and land use in the riparian zone was not a better predictor of metals than land use away from the river. Urbanization in a watershed and vegetation along river networks could better explain metals, and agriculture, regardless of its relative location, however slightly explained metal variables in the upper Han River. FA-MLR analysis identified five source types of metals, and mining, fossil fuel combustion, and vehicle exhaust were the dominant pollutions in the surface waters. The results demonstrated great impacts of human activities on metal concentrations in the subtropical river of China. Copyright © 2011 Elsevier B.V. All rights reserved.