The contextual effects of social capital on health: a cross-national instrumental variable analysis

PubMed Central

Kim, Daniel; Baum, Christopher F; Ganz, Michael; Subramanian, S V; Kawachi, Ichiro

2011-01-01

Past observational studies of the associations of area-level/contextual social capital with health have revealed conflicting findings. However, interpreting this rapidly growing literature is difficult because estimates using conventional regression are prone to major sources of bias including residual confounding and reverse causation. Instrumental variable (IV) analysis can reduce such bias. Using data on up to 167 344 adults in 64 nations in the European and World Values Surveys and applying IV and ordinary least squares (OLS) regression, we estimated the contextual effects of country-level social trust on individual self-rated health. We further explored whether these associations varied by gender and individual levels of trust. Using OLS regression, we found higher average country-level trust to be associated with better self-rated health in both women and men. Instrumental variable analysis yielded qualitatively similar results, although the estimates were more than double in size in women and men using country population density and corruption as instruments. The estimated health effects of raising the percentage of a country's population that trusts others by 10 percentage points were at least as large as the estimated health effects of an individual developing trust in others. These findings were robust to alternative model specifications and instruments. Conventional regression and to a lesser extent IV analysis suggested that these associations are more salient in women and in women reporting social trust. In a large cross-national study, our findings, including those using instrumental variables, support the presence of beneficial effects of higher country-level trust on self-rated health. Past findings for contextual social capital using traditional regression may have underestimated the true associations. Given the close linkages between self-rated health and all-cause mortality, the public health gains from raising social capital within countries may be large. PMID:22078106

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.

ESTIMATE OF METHANE EMISSIONS FROM U.S. LANDFILLS

EPA Science Inventory

The report describes the development of a statistical regression model used for estimating methane (CH4) emissions, which relates landfill gas (LFG) flow rates to waste-in-place data from 105 landfills with LFG recovery projects. (NOTE: CH4 flow rates from landfills with LFG reco...

A Practical Guide to Regression Discontinuity

ERIC Educational Resources Information Center

Jacob, Robin; Zhu, Pei; Somers, Marie-Andrée; Bloom, Howard

2012-01-01

Regression discontinuity (RD) analysis is a rigorous nonexperimental approach that can be used to estimate program impacts in situations in which candidates are selected for treatment based on whether their value for a numeric rating exceeds a designated threshold or cut-point. Over the last two decades, the regression discontinuity approach has…

Estimating methane emissions from landfills based on rainfall, ambient temperature, and waste composition: The CLEEN model.

PubMed

Karanjekar, Richa V; Bhatt, Arpita; Altouqui, Said; Jangikhatoonabad, Neda; Durai, Vennila; Sattler, Melanie L; Hossain, M D Sahadat; Chen, Victoria

2015-12-01

Accurately estimating landfill methane emissions is important for quantifying a landfill's greenhouse gas emissions and power generation potential. Current models, including LandGEM and IPCC, often greatly simplify treatment of factors like rainfall and ambient temperature, which can substantially impact gas production. The newly developed Capturing Landfill Emissions for Energy Needs (CLEEN) model aims to improve landfill methane generation estimates, but still require inputs that are fairly easy to obtain: waste composition, annual rainfall, and ambient temperature. To develop the model, methane generation was measured from 27 laboratory scale landfill reactors, with varying waste compositions (ranging from 0% to 100%); average rainfall rates of 2, 6, and 12 mm/day; and temperatures of 20, 30, and 37°C, according to a statistical experimental design. Refuse components considered were the major biodegradable wastes, food, paper, yard/wood, and textile, as well as inert inorganic waste. Based on the data collected, a multiple linear regression equation (R(2)=0.75) was developed to predict first-order methane generation rate constant values k as functions of waste composition, annual rainfall, and temperature. Because, laboratory methane generation rates exceed field rates, a second scale-up regression equation for k was developed using actual gas-recovery data from 11 landfills in high-income countries with conventional operation. The Capturing Landfill Emissions for Energy Needs (CLEEN) model was developed by incorporating both regression equations into the first-order decay based model for estimating methane generation rates from landfills. CLEEN model values were compared to actual field data from 6 US landfills, and to estimates from LandGEM and IPCC. For 4 of the 6 cases, CLEEN model estimates were the closest to actual. Copyright © 2015 Elsevier Ltd. All rights reserved.

Estimation of Circadian Body Temperature Rhythm Based on Heart Rate in Healthy, Ambulatory Subjects.

PubMed

Sim, Soo Young; Joo, Kwang Min; Kim, Han Byul; Jang, Seungjin; Kim, Beomoh; Hong, Seungbum; Kim, Sungwan; Park, Kwang Suk

2017-03-01

Core body temperature is a reliable marker for circadian rhythm. As characteristics of the circadian body temperature rhythm change during diverse health problems, such as sleep disorder and depression, body temperature monitoring is often used in clinical diagnosis and treatment. However, the use of current thermometers in circadian rhythm monitoring is impractical in daily life. As heart rate is a physiological signal relevant to thermoregulation, we investigated the feasibility of heart rate monitoring in estimating circadian body temperature rhythm. Various heart rate parameters and core body temperature were simultaneously acquired in 21 healthy, ambulatory subjects during their routine life. The performance of regression analysis and the extended Kalman filter on daily body temperature and circadian indicator (mesor, amplitude, and acrophase) estimation were evaluated. For daily body temperature estimation, mean R-R interval (RRI), mean heart rate (MHR), or normalized MHR provided a mean root mean square error of approximately 0.40 °C in both techniques. The mesor estimation regression analysis showed better performance than the extended Kalman filter. However, the extended Kalman filter, combined with RRI or MHR, provided better accuracy in terms of amplitude and acrophase estimation. We suggest that this noninvasive and convenient method for estimating the circadian body temperature rhythm could reduce discomfort during body temperature monitoring in daily life. This, in turn, could facilitate more clinical studies based on circadian body temperature rhythm.

Matched samples logistic regression in case-control studies with missing values: when to break the matches.

PubMed

Hansson, Lisbeth; Khamis, Harry J

2008-12-01

Simulated data sets are used to evaluate conditional and unconditional maximum likelihood estimation in an individual case-control design with continuous covariates when there are different rates of excluded cases and different levels of other design parameters. The effectiveness of the estimation procedures is measured by method bias, variance of the estimators, root mean square error (RMSE) for logistic regression and the percentage of explained variation. Conditional estimation leads to higher RMSE than unconditional estimation in the presence of missing observations, especially for 1:1 matching. The RMSE is higher for the smaller stratum size, especially for the 1:1 matching. The percentage of explained variation appears to be insensitive to missing data, but is generally higher for the conditional estimation than for the unconditional estimation. It is particularly good for the 1:2 matching design. For minimizing RMSE, a high matching ratio is recommended; in this case, conditional and unconditional logistic regression models yield comparable levels of effectiveness. For maximizing the percentage of explained variation, the 1:2 matching design with the conditional logistic regression model is recommended.

Estimation of diffusion coefficients from voltammetric signals by support vector and gaussian process regression

PubMed Central

2014-01-01

Background Support vector regression (SVR) and Gaussian process regression (GPR) were used for the analysis of electroanalytical experimental data to estimate diffusion coefficients. Results For simulated cyclic voltammograms based on the EC, Eqr, and EqrC mechanisms these regression algorithms in combination with nonlinear kernel/covariance functions yielded diffusion coefficients with higher accuracy as compared to the standard approach of calculating diffusion coefficients relying on the Nicholson-Shain equation. The level of accuracy achieved by SVR and GPR is virtually independent of the rate constants governing the respective reaction steps. Further, the reduction of high-dimensional voltammetric signals by manual selection of typical voltammetric peak features decreased the performance of both regression algorithms compared to a reduction by downsampling or principal component analysis. After training on simulated data sets, diffusion coefficients were estimated by the regression algorithms for experimental data comprising voltammetric signals for three organometallic complexes. Conclusions Estimated diffusion coefficients closely matched the values determined by the parameter fitting method, but reduced the required computational time considerably for one of the reaction mechanisms. The automated processing of voltammograms according to the regression algorithms yields better results than the conventional analysis of peak-related data. PMID:24987463

Assessing the prediction accuracy of cure in the Cox proportional hazards cure model: an application to breast cancer data.

PubMed

Asano, Junichi; Hirakawa, Akihiro; Hamada, Chikuma

2014-01-01

A cure rate model is a survival model incorporating the cure rate with the assumption that the population contains both uncured and cured individuals. It is a powerful statistical tool for prognostic studies, especially in cancer. The cure rate is important for making treatment decisions in clinical practice. The proportional hazards (PH) cure model can predict the cure rate for each patient. This contains a logistic regression component for the cure rate and a Cox regression component to estimate the hazard for uncured patients. A measure for quantifying the predictive accuracy of the cure rate estimated by the Cox PH cure model is required, as there has been a lack of previous research in this area. We used the Cox PH cure model for the breast cancer data; however, the area under the receiver operating characteristic curve (AUC) could not be estimated because many patients were censored. In this study, we used imputation-based AUCs to assess the predictive accuracy of the cure rate from the PH cure model. We examined the precision of these AUCs using simulation studies. The results demonstrated that the imputation-based AUCs were estimable and their biases were negligibly small in many cases, although ordinary AUC could not be estimated. Additionally, we introduced the bias-correction method of imputation-based AUCs and found that the bias-corrected estimate successfully compensated the overestimation in the simulation studies. We also illustrated the estimation of the imputation-based AUCs using breast cancer data. Copyright © 2014 John Wiley & Sons, Ltd.

Estimation of particulate nutrient load using turbidity meter.

PubMed

Yamamoto, K; Suetsugi, T

2006-01-01

The "Nutrient Load Hysteresis Coefficient" was proposed to evaluate the hysteresis of the nutrient loads to flow rate quantitatively. This could classify the runoff patterns of nutrient load into 15 patterns. Linear relationships between the turbidity and the concentrations of particulate nutrients were observed. It was clarified that the linearity was caused by the influence of the particle size on turbidity output and accumulation of nutrients on smaller particles (diameter < 23 microm). The L-Q-Turb method, which is a new method for the estimation of runoff loads of nutrients using a regression curve between the turbidity and the concentrations of particulate nutrients, was developed. This method could raise the precision of the estimation of nutrient loads even if they had strong hysteresis to flow rate. For example, as for the runoff load of total phosphorus load on flood events in a total of eight cases, the averaged error of estimation of total phosphorus load by the L-Q-Turb method was 11%, whereas the averaged estimation error by the regression curve between flow rate and nutrient load was 28%.

Improving precision of glomerular filtration rate estimating model by ensemble learning.

PubMed

Liu, Xun; Li, Ningshan; Lv, Linsheng; Fu, Yongmei; Cheng, Cailian; Wang, Caixia; Ye, Yuqiu; Li, Shaomin; Lou, Tanqi

2017-11-09

Accurate assessment of kidney function is clinically important, but estimates of glomerular filtration rate (GFR) by regression are imprecise. We hypothesized that ensemble learning could improve precision. A total of 1419 participants were enrolled, with 1002 in the development dataset and 417 in the external validation dataset. GFR was independently estimated from age, sex and serum creatinine using an artificial neural network (ANN), support vector machine (SVM), regression, and ensemble learning. GFR was measured by 99mTc-DTPA renal dynamic imaging calibrated with dual plasma sample 99mTc-DTPA GFR. Mean measured GFRs were 70.0 ml/min/1.73 m 2 in the developmental and 53.4 ml/min/1.73 m 2 in the external validation cohorts. In the external validation cohort, precision was better in the ensemble model of the ANN, SVM and regression equation (IQR = 13.5 ml/min/1.73 m 2 ) than in the new regression model (IQR = 14.0 ml/min/1.73 m 2 , P < 0.001). The precision of ensemble learning was the best of the three models, but the models had similar bias and accuracy. The median difference ranged from 2.3 to 3.7 ml/min/1.73 m 2 , 30% accuracy ranged from 73.1 to 76.0%, and P was > 0.05 for all comparisons of the new regression equation and the other new models. An ensemble learning model including three variables, the average ANN, SVM, and regression equation values, was more precise than the new regression model. A more complex ensemble learning strategy may further improve GFR estimates.

Detecting influential observations in nonlinear regression modeling of groundwater flow

USGS Publications Warehouse

Yager, Richard M.

1998-01-01

Nonlinear regression is used to estimate optimal parameter values in models of groundwater flow to ensure that differences between predicted and observed heads and flows do not result from nonoptimal parameter values. Parameter estimates can be affected, however, by observations that disproportionately influence the regression, such as outliers that exert undue leverage on the objective function. Certain statistics developed for linear regression can be used to detect influential observations in nonlinear regression if the models are approximately linear. This paper discusses the application of Cook's D, which measures the effect of omitting a single observation on a set of estimated parameter values, and the statistical parameter DFBETAS, which quantifies the influence of an observation on each parameter. The influence statistics were used to (1) identify the influential observations in the calibration of a three-dimensional, groundwater flow model of a fractured-rock aquifer through nonlinear regression, and (2) quantify the effect of omitting influential observations on the set of estimated parameter values. Comparison of the spatial distribution of Cook's D with plots of model sensitivity shows that influential observations correspond to areas where the model heads are most sensitive to certain parameters, and where predicted groundwater flow rates are largest. Five of the six discharge observations were identified as influential, indicating that reliable measurements of groundwater flow rates are valuable data in model calibration. DFBETAS are computed and examined for an alternative model of the aquifer system to identify a parameterization error in the model design that resulted in overestimation of the effect of anisotropy on horizontal hydraulic conductivity.

Automatic energy expenditure measurement for health science.

PubMed

Catal, Cagatay; Akbulut, Akhan

2018-04-01

It is crucial to predict the human energy expenditure in any sports activity and health science application accurately to investigate the impact of the activity. However, measurement of the real energy expenditure is not a trivial task and involves complex steps. The objective of this work is to improve the performance of existing estimation models of energy expenditure by using machine learning algorithms and several data from different sensors and provide this estimation service in a cloud-based platform. In this study, we used input data such as breathe rate, and hearth rate from three sensors. Inputs are received from a web form and sent to the web service which applies a regression model on Azure cloud platform. During the experiments, we assessed several machine learning models based on regression methods. Our experimental results showed that our novel model which applies Boosted Decision Tree Regression in conjunction with the median aggregation technique provides the best result among other five regression algorithms. This cloud-based energy expenditure system which uses a web service showed that cloud computing technology is a great opportunity to develop estimation systems and the new model which applies Boosted Decision Tree Regression with the median aggregation provides remarkable results. Copyright © 2018 Elsevier B.V. All rights reserved.

Exposure reconstruction for the TCDD-exposed NIOSH cohort using a concentration- and age-dependent model of elimination.

PubMed

Aylward, Lesa L; Brunet, Robert C; Starr, Thomas B; Carrier, Gaétan; Delzell, Elizabeth; Cheng, Hong; Beall, Colleen

2005-08-01

Recent studies demonstrating a concentration dependence of elimination of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) suggest that previous estimates of exposure for occupationally exposed cohorts may have underestimated actual exposure, resulting in a potential overestimate of the carcinogenic potency of TCDD in humans based on the mortality data for these cohorts. Using a database on U.S. chemical manufacturing workers potentially exposed to TCDD compiled by the National Institute for Occupational Safety and Health (NIOSH), we evaluated the impact of using a concentration- and age-dependent elimination model (CADM) (Aylward et al., 2005) on estimates of serum lipid area under the curve (AUC) for the NIOSH cohort. These data were used previously by Steenland et al. (2001) in combination with a first-order elimination model with an 8.7-year half-life to estimate cumulative serum lipid concentration (equivalent to AUC) for these workers for use in cancer dose-response assessment. Serum lipid TCDD measurements taken in 1988 for a subset of the cohort were combined with the NIOSH job exposure matrix and work histories to estimate dose rates per unit of exposure score. We evaluated the effect of choices in regression model (regression on untransformed vs. ln-transformed data and inclusion of a nonzero regression intercept) as well as the impact of choices of elimination models and parameters on estimated AUCs for the cohort. Central estimates for dose rate parameters derived from the serum-sampled subcohort were applied with the elimination models to time-specific exposure scores for the entire cohort to generate AUC estimates for all cohort members. Use of the CADM resulted in improved model fits to the serum sampling data compared to the first-order models. Dose rates varied by a factor of 50 among different combinations of elimination model, parameter sets, and regression models. Use of a CADM results in increases of up to five-fold in AUC estimates for the more highly exposed members of the cohort compared to estimates obtained using the first-order model with 8.7-year half-life. This degree of variation in the AUC estimates for this cohort would affect substantially the cancer potency estimates derived from the mortality data from this cohort. Such variability and uncertainty in the reconstructed serum lipid AUC estimates for this cohort, depending on elimination model, parameter set, and regression model, have not been described previously and are critical components in evaluating the dose-response data from the occupationally exposed populations.

Estimation of the rate of oxygen consumption of the common eider duck (Somateria mollissima), with some measurements of heart rate during voluntary dives.

PubMed

Hawkins, P A; Butler, P J; Woakes, A J; Speakman, J R

2000-09-01

The relationship between heart rate (f(H)) and rate of oxygen consumption (V(O2)) was established for a marine diving bird, the common eider duck (Somateria mollissima), during steady-state swimming and running exercise. Both variables increased exponentially with speed during swimming and in a linear fashion during running. Eleven linear regressions of V(O2) (ml kg(-1 )min(-1)) on f(H) (beats min(-1)) were obtained: five by swimming and six by running the birds. The common regression was described by V(O2)=10.1 + 0.15f(H) (r(2)=0.46, N=272, P<0.0001). The accuracy of this relationship for predicting mean V(O2) was determined for a group of six birds by recording f(H) continuously over a 2-day period and comparing estimated V(O2) obtained using the common regression with (i) V(O2) estimated using the doubly labelled water technique (DLW) and (ii) V(O2) measured using respirometry. A two-pool model produced the most accurate estimated V(O2) using DLW. Because of individual variability within mean values of V(O2) estimated using both techniques, there was no significant difference between mean V(O2) estimated using f(H) or DLW and measured V(O2) values (P>0.2), although individual errors were substantially less when f(H) was used rather than DLW to estimate V(O2). Both techniques are, however, only suitable for estimating mean V(O2) for a group of animals, not for individuals. Heart rate and behaviour were monitored during a bout of 63 voluntary dives by one female bird in an indoor tank 1.7 m deep. Tachycardia occurred both in anticipation of and following each dive. Heart rate decreased before submersion but was above resting values for the whole of the dive cycle. Mean f(H) at mean dive duration was significantly greater than f(H) while swimming at maximum sustainable surface speeds. Heart rate was used to estimate mean V(O2) during the dive cycle and to predict aerobic dive limit (ADL) for shallow dives.

Practical aspects of estimating energy components in rodents

PubMed Central

van Klinken, Jan B.; van den Berg, Sjoerd A. A.; van Dijk, Ko Willems

2013-01-01

Recently there has been an increasing interest in exploiting computational and statistical techniques for the purpose of component analysis of indirect calorimetry data. Using these methods it becomes possible to dissect daily energy expenditure into its components and to assess the dynamic response of the resting metabolic rate (RMR) to nutritional and pharmacological manipulations. To perform robust component analysis, however, is not straightforward and typically requires the tuning of parameters and the preprocessing of data. Moreover the degree of accuracy that can be attained by these methods depends on the configuration of the system, which must be properly taken into account when setting up experimental studies. Here, we review the methods of Kalman filtering, linear, and penalized spline regression, and minimal energy expenditure estimation in the context of component analysis and discuss their results on high resolution datasets from mice and rats. In addition, we investigate the effect of the sample time, the accuracy of the activity sensor, and the washout time of the chamber on the estimation accuracy. We found that on the high resolution data there was a strong correlation between the results of Kalman filtering and penalized spline (P-spline) regression, except for the activity respiratory quotient (RQ). For low resolution data the basal metabolic rate (BMR) and resting RQ could still be estimated accurately with P-spline regression, having a strong correlation with the high resolution estimate (R2 > 0.997; sample time of 9 min). In contrast, the thermic effect of food (TEF) and activity related energy expenditure (AEE) were more sensitive to a reduction in the sample rate (R2 > 0.97). In conclusion, for component analysis on data generated by single channel systems with continuous data acquisition both Kalman filtering and P-spline regression can be used, while for low resolution data from multichannel systems P-spline regression gives more robust results. PMID:23641217

A novel method to estimate changes in stress-induced salivary α-amylase using heart rate variability and respiratory rate, as measured in a non-contact manner using a single radar attached to the back of a chair.

PubMed

Matsui, Takemi; Katayose, Satoshi

2014-08-01

The authors have developed a non-contact system which estimates changes in salivary α-amylase (sAA ratio) induced by stress. Before and after stressful sound exposure, a single 24 GHz compact radar which is attached to the back of a chair measures the low frequency (LF) component of heart rate variability and respiratory rate, α-amylase in the subjects' buccal secretions was measured by using an α-amylase assay kit. Using multiple regression analysis, sAA ratio was estimated using stress-induced LF change (LF ratio) and stress-induced respiratory rate change (respiratory rate ratio). Twelve healthy subjects were tested (12 males, 22 ± 2 years), who were exposed to audio stimuli with a composite tone of 2120 Hz and 2130 Hz sine waves at a sound pressure level of 95 dB after a silent period through a headphone. The result showed that sAA ratio estimated using multiple regression analysis significantly correlated with measured sAA ratio (R = 0.76, p < 0.01). This indicates that the system may serve for a stress management in the future.

Factors associated with automobile accidents and survival.

PubMed

Kim, Hong Sok; Kim, Hyung Jin; Son, Bongsoo

2006-09-01

This paper develops an econometric model for vehicles' inherent mortality rate and estimates the probability of accidents and survival in the United States. Logistic regression model is used to estimate probability of survival, and censored regression model is used to estimate probability of accidents. The estimation results indicated that the probability of accident and survival are influenced by the physical characteristics of the vehicles involved in the accident, and by the characteristics of the driver and the occupants. Using restrain system and riding in heavy vehicle increased the survival rate. Middle-aged drivers are less susceptible to involve in an accident, and surprisingly, female drivers are more likely to have an accident than male drivers. Riding in powerful vehicles (high horsepower) and driving late night increase the probability of accident. Overall, the driving behavior and characteristics of vehicle does matter and affects the probabilities of having a fatal accident for different types of vehicles.

Height and Weight Estimation From Anthropometric Measurements Using Machine Learning Regressions

PubMed Central

Fernandes, Bruno J. T.; Roque, Alexandre

2018-01-01

Height and weight are measurements explored to tracking nutritional diseases, energy expenditure, clinical conditions, drug dosages, and infusion rates. Many patients are not ambulant or may be unable to communicate, and a sequence of these factors may not allow accurate estimation or measurements; in those cases, it can be estimated approximately by anthropometric means. Different groups have proposed different linear or non-linear equations which coefficients are obtained by using single or multiple linear regressions. In this paper, we present a complete study of the application of different learning models to estimate height and weight from anthropometric measurements: support vector regression, Gaussian process, and artificial neural networks. The predicted values are significantly more accurate than that obtained with conventional linear regressions. In all the cases, the predictions are non-sensitive to ethnicity, and to gender, if more than two anthropometric parameters are analyzed. The learning model analysis creates new opportunities for anthropometric applications in industry, textile technology, security, and health care. PMID:29651366

Local Composite Quantile Regression Smoothing for Harris Recurrent Markov Processes

PubMed Central

Li, Degui; Li, Runze

2016-01-01

In this paper, we study the local polynomial composite quantile regression (CQR) smoothing method for the nonlinear and nonparametric models under the Harris recurrent Markov chain framework. The local polynomial CQR regression method is a robust alternative to the widely-used local polynomial method, and has been well studied in stationary time series. In this paper, we relax the stationarity restriction on the model, and allow that the regressors are generated by a general Harris recurrent Markov process which includes both the stationary (positive recurrent) and nonstationary (null recurrent) cases. Under some mild conditions, we establish the asymptotic theory for the proposed local polynomial CQR estimator of the mean regression function, and show that the convergence rate for the estimator in nonstationary case is slower than that in stationary case. Furthermore, a weighted type local polynomial CQR estimator is provided to improve the estimation efficiency, and a data-driven bandwidth selection is introduced to choose the optimal bandwidth involved in the nonparametric estimators. Finally, we give some numerical studies to examine the finite sample performance of the developed methodology and theory. PMID:27667894

Minute ventilation of cyclists, car and bus passengers: an experimental study.

PubMed

Zuurbier, Moniek; Hoek, Gerard; van den Hazel, Peter; Brunekreef, Bert

2009-10-27

Differences in minute ventilation between cyclists, pedestrians and other commuters influence inhaled doses of air pollution. This study estimates minute ventilation of cyclists, car and bus passengers, as part of a study on health effects of commuters' exposure to air pollutants. Thirty-four participants performed a submaximal test on a bicycle ergometer, during which heart rate and minute ventilation were measured simultaneously at increasing cycling intensity. Individual regression equations were calculated between heart rate and the natural log of minute ventilation. Heart rates were recorded during 280 two hour trips by bicycle, bus and car and were calculated into minute ventilation levels using the individual regression coefficients. Minute ventilation during bicycle rides were on average 2.1 times higher than in the car (individual range from 1.3 to 5.3) and 2.0 times higher than in the bus (individual range from 1.3 to 5.1). The ratio of minute ventilation of cycling compared to travelling by bus or car was higher in women than in men. Substantial differences in regression equations were found between individuals. The use of individual regression equations instead of average regression equations resulted in substantially better predictions of individual minute ventilations. The comparability of the gender-specific overall regression equations linking heart rate and minute ventilation with one previous American study, supports that for studies on the group level overall equations can be used. For estimating individual doses, the use of individual regression coefficients provides more precise data. Minute ventilation levels of cyclists are on average two times higher than of bus and car passengers, consistent with the ratio found in one small previous study of young adults. The study illustrates the importance of inclusion of minute ventilation data in comparing air pollution doses between different modes of transport.

Comparison of regression coefficient and GIS-based methodologies for regional estimates of forest soil carbon stocks.

PubMed

Campbell, J Elliott; Moen, Jeremie C; Ney, Richard A; Schnoor, Jerald L

2008-03-01

Estimates of forest soil organic carbon (SOC) have applications in carbon science, soil quality studies, carbon sequestration technologies, and carbon trading. Forest SOC has been modeled using a regression coefficient methodology that applies mean SOC densities (mass/area) to broad forest regions. A higher resolution model is based on an approach that employs a geographic information system (GIS) with soil databases and satellite-derived landcover images. Despite this advancement, the regression approach remains the basis of current state and federal level greenhouse gas inventories. Both approaches are analyzed in detail for Wisconsin forest soils from 1983 to 2001, applying rigorous error-fixing algorithms to soil databases. Resulting SOC stock estimates are 20% larger when determined using the GIS method rather than the regression approach. Average annual rates of increase in SOC stocks are 3.6 and 1.0 million metric tons of carbon per year for the GIS and regression approaches respectively.

Background stratified Poisson regression analysis of cohort data.

PubMed

Richardson, David B; Langholz, Bryan

2012-03-01

Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.

Rates of Femicide in Women of Different Races, Ethnicities, and Places of Birth: Massachusetts, 1993-2007

ERIC Educational Resources Information Center

Azziz-Baumgartner, Eduardo; McKeown, Loreta; Melvin, Patrice; Dang, Quynh; Reed, Joan

2011-01-01

To describe the epidemiology of intimate partner violence (IPV) homicide in Massachusetts, an IPV mortality data set developed by the Massachusetts Department of Public Health was analyzed. The rates of death were estimated by dividing the number of decedents over the aged-matched population and Poisson regression was used to estimate the…

Robust Variable Selection with Exponential Squared Loss.

PubMed

Wang, Xueqin; Jiang, Yunlu; Huang, Mian; Zhang, Heping

2013-04-01

Robust variable selection procedures through penalized regression have been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. However, to the best of our knowledge, the robustness of those penalized regression procedures has not been well characterized. In this paper, we propose a class of penalized robust regression estimators based on exponential squared loss. The motivation for this new procedure is that it enables us to characterize its robustness that has not been done for the existing procedures, while its performance is near optimal and superior to some recently developed methods. Specifically, under defined regularity conditions, our estimators are [Formula: see text] and possess the oracle property. Importantly, we show that our estimators can achieve the highest asymptotic breakdown point of 1/2 and that their influence functions are bounded with respect to the outliers in either the response or the covariate domain. We performed simulation studies to compare our proposed method with some recent methods, using the oracle method as the benchmark. We consider common sources of influential points. Our simulation studies reveal that our proposed method performs similarly to the oracle method in terms of the model error and the positive selection rate even in the presence of influential points. In contrast, other existing procedures have a much lower non-causal selection rate. Furthermore, we re-analyze the Boston Housing Price Dataset and the Plasma Beta-Carotene Level Dataset that are commonly used examples for regression diagnostics of influential points. Our analysis unravels the discrepancies of using our robust method versus the other penalized regression method, underscoring the importance of developing and applying robust penalized regression methods.

Robust Variable Selection with Exponential Squared Loss

PubMed Central

Wang, Xueqin; Jiang, Yunlu; Huang, Mian; Zhang, Heping

2013-01-01

Robust variable selection procedures through penalized regression have been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. However, to the best of our knowledge, the robustness of those penalized regression procedures has not been well characterized. In this paper, we propose a class of penalized robust regression estimators based on exponential squared loss. The motivation for this new procedure is that it enables us to characterize its robustness that has not been done for the existing procedures, while its performance is near optimal and superior to some recently developed methods. Specifically, under defined regularity conditions, our estimators are n-consistent and possess the oracle property. Importantly, we show that our estimators can achieve the highest asymptotic breakdown point of 1/2 and that their influence functions are bounded with respect to the outliers in either the response or the covariate domain. We performed simulation studies to compare our proposed method with some recent methods, using the oracle method as the benchmark. We consider common sources of influential points. Our simulation studies reveal that our proposed method performs similarly to the oracle method in terms of the model error and the positive selection rate even in the presence of influential points. In contrast, other existing procedures have a much lower non-causal selection rate. Furthermore, we re-analyze the Boston Housing Price Dataset and the Plasma Beta-Carotene Level Dataset that are commonly used examples for regression diagnostics of influential points. Our analysis unravels the discrepancies of using our robust method versus the other penalized regression method, underscoring the importance of developing and applying robust penalized regression methods. PMID:23913996

Marginal regression approach for additive hazards models with clustered current status data.

PubMed

Su, Pei-Fang; Chi, Yunchan

2014-01-15

Current status data arise naturally from tumorigenicity experiments, epidemiology studies, biomedicine, econometrics and demographic and sociology studies. Moreover, clustered current status data may occur with animals from the same litter in tumorigenicity experiments or with subjects from the same family in epidemiology studies. Because the only information extracted from current status data is whether the survival times are before or after the monitoring or censoring times, the nonparametric maximum likelihood estimator of survival function converges at a rate of n(1/3) to a complicated limiting distribution. Hence, semiparametric regression models such as the additive hazards model have been extended for independent current status data to derive the test statistics, whose distributions converge at a rate of n(1/2) , for testing the regression parameters. However, a straightforward application of these statistical methods to clustered current status data is not appropriate because intracluster correlation needs to be taken into account. Therefore, this paper proposes two estimating functions for estimating the parameters in the additive hazards model for clustered current status data. The comparative results from simulation studies are presented, and the application of the proposed estimating functions to one real data set is illustrated. Copyright © 2013 John Wiley & Sons, Ltd.

A New Approach for Mobile Advertising Click-Through Rate Estimation Based on Deep Belief Nets.

PubMed

Chen, Jie-Hao; Zhao, Zi-Qian; Shi, Ji-Yun; Zhao, Chong

2017-01-01

In recent years, with the rapid development of mobile Internet and its business applications, mobile advertising Click-Through Rate (CTR) estimation has become a hot research direction in the field of computational advertising, which is used to achieve accurate advertisement delivery for the best benefits in the three-side game between media, advertisers, and audiences. Current research on the estimation of CTR mainly uses the methods and models of machine learning, such as linear model or recommendation algorithms. However, most of these methods are insufficient to extract the data features and cannot reflect the nonlinear relationship between different features. In order to solve these problems, we propose a new model based on Deep Belief Nets to predict the CTR of mobile advertising, which combines together the powerful data representation and feature extraction capability of Deep Belief Nets, with the advantage of simplicity of traditional Logistic Regression models. Based on the training dataset with the information of over 40 million mobile advertisements during a period of 10 days, our experiments show that our new model has better estimation accuracy than the classic Logistic Regression (LR) model by 5.57% and Support Vector Regression (SVR) model by 5.80%.

A New Approach for Mobile Advertising Click-Through Rate Estimation Based on Deep Belief Nets

PubMed Central

Zhao, Zi-Qian; Shi, Ji-Yun; Zhao, Chong

2017-01-01

In recent years, with the rapid development of mobile Internet and its business applications, mobile advertising Click-Through Rate (CTR) estimation has become a hot research direction in the field of computational advertising, which is used to achieve accurate advertisement delivery for the best benefits in the three-side game between media, advertisers, and audiences. Current research on the estimation of CTR mainly uses the methods and models of machine learning, such as linear model or recommendation algorithms. However, most of these methods are insufficient to extract the data features and cannot reflect the nonlinear relationship between different features. In order to solve these problems, we propose a new model based on Deep Belief Nets to predict the CTR of mobile advertising, which combines together the powerful data representation and feature extraction capability of Deep Belief Nets, with the advantage of simplicity of traditional Logistic Regression models. Based on the training dataset with the information of over 40 million mobile advertisements during a period of 10 days, our experiments show that our new model has better estimation accuracy than the classic Logistic Regression (LR) model by 5.57% and Support Vector Regression (SVR) model by 5.80%. PMID:29209363

Toxocara infection in the United States: the relevance of poverty, geography and demography as risk factors, and implications for estimating county prevalence.

PubMed

Congdon, Peter; Lloyd, Patsy

2011-02-01

To estimate Toxocara infection rates by age, gender and ethnicity for US counties using data from the National Health and Nutrition Examination Survey (NHANES). After initial analysis to account for missing data, a binary regression model is applied to obtain relative risks of Toxocara infection for 20,396 survey subjects. The regression incorporates interplay between demographic attributes (age, ethnicity and gender), family poverty and geographic context (region, metropolitan status). Prevalence estimates for counties are then made, distinguishing between subpopulations in poverty and not in poverty. Even after allowing for elevated infection risk associated with poverty, seropositivity is elevated among Black non-Hispanics and other ethnic groups. There are also distinct effects of region. When regression results are translated into county prevalence estimates, the main influences on variation in county rates are percentages of non-Hispanic Blacks and county poverty. For targeting prevention it is important to assess implications of national survey data for small area prevalence. Using data from NHANES, the study confirms that both individual level risk factors and geographic contextual factors affect chances of Toxocara infection.

Estimating Contraceptive Prevalence Using Logistics Data for Short-Acting Methods: Analysis Across 30 Countries.

PubMed

Cunningham, Marc; Bock, Ariella; Brown, Niquelle; Sacher, Suzy; Hatch, Benjamin; Inglis, Andrew; Aronovich, Dana

2015-09-01

Contraceptive prevalence rate (CPR) is a vital indicator used by country governments, international donors, and other stakeholders for measuring progress in family planning programs against country targets and global initiatives as well as for estimating health outcomes. Because of the need for more frequent CPR estimates than population-based surveys currently provide, alternative approaches for estimating CPRs are being explored, including using contraceptive logistics data. Using data from the Demographic and Health Surveys (DHS) in 30 countries, population data from the United States Census Bureau International Database, and logistics data from the Procurement Planning and Monitoring Report (PPMR) and the Pipeline Monitoring and Procurement Planning System (PipeLine), we developed and evaluated 3 models to generate country-level, public-sector contraceptive prevalence estimates for injectable contraceptives, oral contraceptives, and male condoms. Models included: direct estimation through existing couple-years of protection (CYP) conversion factors, bivariate linear regression, and multivariate linear regression. Model evaluation consisted of comparing the referent DHS prevalence rates for each short-acting method with the model-generated prevalence rate using multiple metrics, including mean absolute error and proportion of countries where the modeled prevalence rate for each method was within 1, 2, or 5 percentage points of the DHS referent value. For the methods studied, family planning use estimates from public-sector logistics data were correlated with those from the DHS, validating the quality and accuracy of current public-sector logistics data. Logistics data for oral and injectable contraceptives were significantly associated (P<.05) with the referent DHS values for both bivariate and multivariate models. For condoms, however, that association was only significant for the bivariate model. With the exception of the CYP-based model for condoms, models were able to estimate public-sector prevalence rates for each short-acting method to within 2 percentage points in at least 85% of countries. Public-sector contraceptive logistics data are strongly correlated with public-sector prevalence rates for short-acting methods, demonstrating the quality of current logistics data and their ability to provide relatively accurate prevalence estimates. The models provide a starting point for generating interim estimates of contraceptive use when timely survey data are unavailable. All models except the condoms CYP model performed well; the regression models were most accurate but the CYP model offers the simplest calculation method. Future work extending the research to other modern methods, relating subnational logistics data with prevalence rates, and tracking that relationship over time is needed. © Cunningham et al.

Estimating Contraceptive Prevalence Using Logistics Data for Short-Acting Methods: Analysis Across 30 Countries

PubMed Central

Cunningham, Marc; Brown, Niquelle; Sacher, Suzy; Hatch, Benjamin; Inglis, Andrew; Aronovich, Dana

2015-01-01

Background: Contraceptive prevalence rate (CPR) is a vital indicator used by country governments, international donors, and other stakeholders for measuring progress in family planning programs against country targets and global initiatives as well as for estimating health outcomes. Because of the need for more frequent CPR estimates than population-based surveys currently provide, alternative approaches for estimating CPRs are being explored, including using contraceptive logistics data. Methods: Using data from the Demographic and Health Surveys (DHS) in 30 countries, population data from the United States Census Bureau International Database, and logistics data from the Procurement Planning and Monitoring Report (PPMR) and the Pipeline Monitoring and Procurement Planning System (PipeLine), we developed and evaluated 3 models to generate country-level, public-sector contraceptive prevalence estimates for injectable contraceptives, oral contraceptives, and male condoms. Models included: direct estimation through existing couple-years of protection (CYP) conversion factors, bivariate linear regression, and multivariate linear regression. Model evaluation consisted of comparing the referent DHS prevalence rates for each short-acting method with the model-generated prevalence rate using multiple metrics, including mean absolute error and proportion of countries where the modeled prevalence rate for each method was within 1, 2, or 5 percentage points of the DHS referent value. Results: For the methods studied, family planning use estimates from public-sector logistics data were correlated with those from the DHS, validating the quality and accuracy of current public-sector logistics data. Logistics data for oral and injectable contraceptives were significantly associated (P<.05) with the referent DHS values for both bivariate and multivariate models. For condoms, however, that association was only significant for the bivariate model. With the exception of the CYP-based model for condoms, models were able to estimate public-sector prevalence rates for each short-acting method to within 2 percentage points in at least 85% of countries. Conclusions: Public-sector contraceptive logistics data are strongly correlated with public-sector prevalence rates for short-acting methods, demonstrating the quality of current logistics data and their ability to provide relatively accurate prevalence estimates. The models provide a starting point for generating interim estimates of contraceptive use when timely survey data are unavailable. All models except the condoms CYP model performed well; the regression models were most accurate but the CYP model offers the simplest calculation method. Future work extending the research to other modern methods, relating subnational logistics data with prevalence rates, and tracking that relationship over time is needed. PMID:26374805

Fuel Regression Rate Behavior of CAMUI Hybrid Rocket

NASA Astrophysics Data System (ADS)

Kaneko, Yudai; Itoh, Mitsunori; Kakikura, Akihito; Mori, Kazuhiro; Uejima, Kenta; Nakashima, Takuji; Wakita, Masashi; Totani, Tsuyoshi; Oshima, Nobuyuki; Nagata, Harunori

A series of static firing tests was conducted to investigate the fuel regression characteristics of a Cascaded Multistage Impinging-jet (CAMUI) type hybrid rocket motor. A CAMUI type hybrid rocket uses the combination of liquid oxygen and a fuel grain made of polyethylene as a propellant. The collision distance divided by the port diameter, H/D, was varied to investigate the effect of the grain geometry on the fuel regression rate. As a result, the H/D geometry has little effect on the regression rate near the stagnation point, where the heat transfer coefficient is high. On the contrary, the fuel regression rate decreases near the circumference of the forward-end face and the backward-end face of fuel blocks. Besides the experimental approaches, a method of computational fluid dynamics clarified the heat transfer distribution on the grain surface with various H/D geometries. The calculation shows the decrease of the flow velocity due to the increase of H/D on the area where the fuel regression rate decreases with the increase of H/D. To estimate the exact fuel consumption, which is necessary to design a fuel grain, real-time measurement by an ultrasonic pulse-echo method was performed.

Estimation of Recurrence of Colorectal Adenomas with Dependent Censoring Using Weighted Logistic Regression

PubMed Central

Hsu, Chiu-Hsieh; Li, Yisheng; Long, Qi; Zhao, Qiuhong; Lance, Peter

2011-01-01

In colorectal polyp prevention trials, estimation of the rate of recurrence of adenomas at the end of the trial may be complicated by dependent censoring, that is, time to follow-up colonoscopy and dropout may be dependent on time to recurrence. Assuming that the auxiliary variables capture the dependence between recurrence and censoring times, we propose to fit two working models with the auxiliary variables as covariates to define risk groups and then extend an existing weighted logistic regression method for independent censoring to each risk group to accommodate potential dependent censoring. In a simulation study, we show that the proposed method results in both a gain in efficiency and reduction in bias for estimating the recurrence rate. We illustrate the methodology by analyzing a recurrent adenoma dataset from a colorectal polyp prevention trial. PMID:22065985

A study on industrial accident rate forecasting and program development of estimated zero accident time in Korea.

PubMed

Kim, Tae-gu; Kang, Young-sig; Lee, Hyung-won

2011-01-01

To begin a zero accident campaign for industry, the first thing is to estimate the industrial accident rate and the zero accident time systematically. This paper considers the social and technical change of the business environment after beginning the zero accident campaign through quantitative time series analysis methods. These methods include sum of squared errors (SSE), regression analysis method (RAM), exponential smoothing method (ESM), double exponential smoothing method (DESM), auto-regressive integrated moving average (ARIMA) model, and the proposed analytic function method (AFM). The program is developed to estimate the accident rate, zero accident time and achievement probability of an efficient industrial environment. In this paper, MFC (Microsoft Foundation Class) software of Visual Studio 2008 was used to develop a zero accident program. The results of this paper will provide major information for industrial accident prevention and be an important part of stimulating the zero accident campaign within all industrial environments.

Estimating evolutionary rates using time-structured data: a general comparison of phylogenetic methods.

PubMed

Duchêne, Sebastián; Geoghegan, Jemma L; Holmes, Edward C; Ho, Simon Y W

2016-11-15

In rapidly evolving pathogens, including viruses and some bacteria, genetic change can accumulate over short time-frames. Accordingly, their sampling times can be used to calibrate molecular clocks, allowing estimation of evolutionary rates. Methods for estimating rates from time-structured data vary in how they treat phylogenetic uncertainty and rate variation among lineages. We compiled 81 virus data sets and estimated nucleotide substitution rates using root-to-tip regression, least-squares dating and Bayesian inference. Although estimates from these three methods were often congruent, this largely relied on the choice of clock model. In particular, relaxed-clock models tended to produce higher rate estimates than methods that assume constant rates. Discrepancies in rate estimates were also associated with high among-lineage rate variation, and phylogenetic and temporal clustering. These results provide insights into the factors that affect the reliability of rate estimates from time-structured sequence data, emphasizing the importance of clock-model testing. sduchene@unimelb.edu.au or garzonsebastian@hotmail.comSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Improving Lidar-based Aboveground Biomass Estimation with Site Productivity for Central Hardwood Forests, USA

NASA Astrophysics Data System (ADS)

Shao, G.; Gallion, J.; Fei, S.

2016-12-01

Sound forest aboveground biomass estimation is required to monitor diverse forest ecosystems and their impacts on the changing climate. Lidar-based regression models provided promised biomass estimations in most forest ecosystems. However, considerable uncertainties of biomass estimations have been reported in the temperate hardwood and hardwood-dominated mixed forests. Varied site productivities in temperate hardwood forests largely diversified height and diameter growth rates, which significantly reduced the correlation between tree height and diameter at breast height (DBH) in mature and complex forests. It is, therefore, difficult to utilize height-based lidar metrics to predict DBH-based field-measured biomass through a simple regression model regardless the variation of site productivity. In this study, we established a multi-dimension nonlinear regression model incorporating lidar metrics and site productivity classes derived from soil features. In the regression model, lidar metrics provided horizontal and vertical structural information and productivity classes differentiated good and poor forest sites. The selection and combination of lidar metrics were discussed. Multiple regression models were employed and compared. Uncertainty analysis was applied to the best fit model. The effects of site productivity on the lidar-based biomass model were addressed.

Potential barge transportation for inbound corn and grain

DOT National Transportation Integrated Search

1997-12-31

This research develops a model for estimating future barge and rail rates for decision making. The Box-Jenkins and the Regression Analysis with ARIMA errors forecasting methods were used to develop appropriate models for determining future rates. A s...

The Highly Adaptive Lasso Estimator

PubMed Central

Benkeser, David; van der Laan, Mark

2017-01-01

Estimation of a regression functions is a common goal of statistical learning. We propose a novel nonparametric regression estimator that, in contrast to many existing methods, does not rely on local smoothness assumptions nor is it constructed using local smoothing techniques. Instead, our estimator respects global smoothness constraints by virtue of falling in a class of right-hand continuous functions with left-hand limits that have variation norm bounded by a constant. Using empirical process theory, we establish a fast minimal rate of convergence of our proposed estimator and illustrate how such an estimator can be constructed using standard software. In simulations, we show that the finite-sample performance of our estimator is competitive with other popular machine learning techniques across a variety of data generating mechanisms. We also illustrate competitive performance in real data examples using several publicly available data sets. PMID:29094111

Validation of Statistical Models for Estimating Hospitalization Associated with Influenza and Other Respiratory Viruses

PubMed Central

Chan, King-Pan; Chan, Kwok-Hung; Wong, Wilfred Hing-Sang; Peiris, J. S. Malik; Wong, Chit-Ming

2011-01-01

Background Reliable estimates of disease burden associated with respiratory viruses are keys to deployment of preventive strategies such as vaccination and resource allocation. Such estimates are particularly needed in tropical and subtropical regions where some methods commonly used in temperate regions are not applicable. While a number of alternative approaches to assess the influenza associated disease burden have been recently reported, none of these models have been validated with virologically confirmed data. Even fewer methods have been developed for other common respiratory viruses such as respiratory syncytial virus (RSV), parainfluenza and adenovirus. Methods and Findings We had recently conducted a prospective population-based study of virologically confirmed hospitalization for acute respiratory illnesses in persons <18 years residing in Hong Kong Island. Here we used this dataset to validate two commonly used models for estimation of influenza disease burden, namely the rate difference model and Poisson regression model, and also explored the applicability of these models to estimate the disease burden of other respiratory viruses. The Poisson regression models with different link functions all yielded estimates well correlated with the virologically confirmed influenza associated hospitalization, especially in children older than two years. The disease burden estimates for RSV, parainfluenza and adenovirus were less reliable with wide confidence intervals. The rate difference model was not applicable to RSV, parainfluenza and adenovirus and grossly underestimated the true burden of influenza associated hospitalization. Conclusion The Poisson regression model generally produced satisfactory estimates in calculating the disease burden of respiratory viruses in a subtropical region such as Hong Kong. PMID:21412433

Spatially Explicit Estimates of Suspended Sediment and Bedload Transport Rates for Western Oregon and Northwestern California

NASA Astrophysics Data System (ADS)

O'Connor, J. E.; Wise, D. R.; Mangano, J.; Jones, K.

2015-12-01

Empirical analyses of suspended sediment and bedload transport gives estimates of sediment flux for western Oregon and northwestern California. The estimates of both bedload and suspended load are from regression models relating measured annual sediment yield to geologic, physiographic, and climatic properties of contributing basins. The best models include generalized geology and either slope or precipitation. The best-fit suspended-sediment model is based on basin geology, precipitation, and area of recent wildfire. It explains 65% of the variance for 68 suspended sediment measurement sites within the model area. Predicted suspended sediment yields range from no yield from the High Cascades geologic province to 200 tonnes/ km2-yr in the northern Oregon Coast Range and 1000 tonnes/km2-yr in recently burned areas of the northern Klamath terrain. Bed-material yield is similarly estimated from a regression model based on 22 sites of measured bed-material transport, mostly from reservoir accumulation analyses but also from several bedload measurement programs. The resulting best-fit regression is based on basin slope and the presence/absence of the Klamath geologic terrane. For the Klamath terrane, bed-material yield is twice that of the other geologic provinces. This model explains more than 80% of the variance of the better-quality measurements. Predicted bed-material yields range up to 350 tonnes/ km2-yr in steep areas of the Klamath terrane. Applying these regressions to small individual watersheds (mean size; 66 km2 for bed-material; 3 km2 for suspended sediment) and cumulating totals down the hydrologic network (but also decreasing the bed-material flux by experimentally determined attrition rates) gives spatially explicit estimates of both bed-material and suspended sediment flux. This enables assessment of several management issues, including the effects of dams on bedload transport, instream gravel mining, habitat formation processes, and water-quality. The combined fluxes can also be compared to long-term rock uplift and cosmogenically determined landscape erosion rates.

Estimation of physical work load by statistical analysis of the heart rate in a conveyor-belt worker.

PubMed

Kontosic, I; Vukelić, M; Pancić, M; Kunisek, J

1994-12-01

Physical work load was estimated in a female conveyor-belt worker in a bottling plant. Estimation was based on continuous measurement and on calculation of average heart rate values in three-minute and one-hour periods and during the total measuring period. The thermal component of the heart rate was calculated by means of the corrected effective temperature, for the one-hour periods. The average heart rate at rest was also determined. The work component of the heart rate was calculated by subtraction of the resting heart rate and the heart rate measured at 50 W, using a regression equation. The average estimated gross energy expenditure during the work was 9.6 +/- 1.3 kJ/min corresponding to the category of light industrial work. The average estimated oxygen uptake was 0.42 +/- 0.06 L/min. The average performed mechanical work was 12.2 +/- 4.2 W, i.e. the energy expenditure was 8.3 +/- 1.5%.

Self-rated health: small area large area comparisons amongst older adults at the state, district and sub-district level in India.

PubMed

Hirve, Siddhivinayak; Vounatsou, Penelope; Juvekar, Sanjay; Blomstedt, Yulia; Wall, Stig; Chatterji, Somnath; Ng, Nawi

2014-03-01

We compared prevalence estimates of self-rated health (SRH) derived indirectly using four different small area estimation methods for the Vadu (small) area from the national Study on Global AGEing (SAGE) survey with estimates derived directly from the Vadu SAGE survey. The indirect synthetic estimate for Vadu was 24% whereas the model based estimates were 45.6% and 45.7% with smaller prediction errors and comparable to the direct survey estimate of 50%. The model based techniques were better suited to estimate the prevalence of SRH than the indirect synthetic method. We conclude that a simplified mixed effects regression model can produce valid small area estimates of SRH. © 2013 Published by Elsevier Ltd.

NaCl nucleation from brine in seeded simulations: Sources of uncertainty in rate estimates.

PubMed

Zimmermann, Nils E R; Vorselaars, Bart; Espinosa, Jorge R; Quigley, David; Smith, William R; Sanz, Eduardo; Vega, Carlos; Peters, Baron

2018-06-14

This work reexamines seeded simulation results for NaCl nucleation from a supersaturated aqueous solution at 298.15 K and 1 bar pressure. We present a linear regression approach for analyzing seeded simulation data that provides both nucleation rates and uncertainty estimates. Our results show that rates obtained from seeded simulations rely critically on a precise driving force for the model system. The driving force vs. solute concentration curve need not exactly reproduce that of the real system, but it should accurately describe the thermodynamic properties of the model system. We also show that rate estimates depend strongly on the nucleus size metric. We show that the rate estimates systematically increase as more stringent local order parameters are used to count members of a cluster and provide tentative suggestions for appropriate clustering criteria.

Trends in Mortality of Tuberculosis Patients in the United States: The Long-term Perspective

PubMed Central

Barnes, Richard F.W.; Moore, Maria Luisa; Garfein, Richard S.; Brodine, Stephanie; Strathdee, Steffanie A.; Rodwell, Timothy C.

2011-01-01

PURPOSE To describe long-term trends in TB mortality and to compare trends estimated from two different sources of public health surveillance data. METHODS Trends and changes in trend were estimated by joinpoint regression. Comparisons between datasets were made by fitting a Poisson regression model. RESULTS Since 1900, TB mortality rates estimated from death certificates have declined steeply, except for a period of no change in the 1980s. This decade had long-term consequences resulting in more TB deaths in later years than would have occurred had there been no flattening of the trend. Recent trends in TB mortality estimated from National Tuberculosis Surveillance System (NTSS) data, which record all-cause mortality, differed from trends based on death certificates. In particular, NTSS data showed TB mortality rates flattening since 2002. CONCLUSIONS Estimates of trends in TB mortality vary by data source, and therefore interpretation of the success of control efforts will depend upon the surveillance dataset used. The datasets may be subject to different biases that vary with time. One dataset showed a sustained improvement in the control of TB since the early 1990s while the other indicated that the rate of TB mortality was no longer declining. PMID:21820320

Estimation of the impact of warfarin's time-in-therapeutic range on stroke and major bleeding rates and its influence on the medical cost avoidance associated with novel oral anticoagulant use-learnings from ARISTOTLE, ROCKET-AF, and RE-LY trials.

PubMed

Amin, Alpesh; Deitelzweig, Steve; Jing, Yonghua; Makenbaeva, Dinara; Wiederkehr, Daniel; Lin, Jay; Graham, John

2014-01-01

Warfarin's time-in-therapeutic range (TTR) is highly variable among patients with nonvalvular atrial fibrillation (NVAF). The objective of this study was to estimate the impact of variations in wafarin's TTR on rates of stroke/systemic embolism (SSE) and major bleedings among NVAF patients in the ARISTOTLE, ROCKET-AF, and RE-LY trials. Additionally, differences in medical costs for clinical endpoints when novel oral anticoagulants (NOACs) were used instead of warfarin at different TTR values were estimated. Quartile ranges of TTR values and corresponding event rates (%/patient - year = %/py) of SSE and major bleedings among NVAF patients treated with warfarin were estimated from published literature and FDA documents. The associations of SSE and major bleeding rates with TTR values were evaluated by regression analysis and then the calculated regression coefficients were used in analysis of medical cost differences associated with use of each NOAC versus warfarin (2010 costs; US payer perspective) at different TTRs. Each 10 % increase in warfarin's TTR correlated with a -0.32%/py decrease in SSE rate (R(2) = 0.61; p < 0.001). Although, the rate of major bleedings decreased as TTR increased, it was not significant (-0.035%/py, p = 0.63). As warfarin's TTR increased from 30 to 90% the estimated medical cost decreased from -$902 to -$83 for apixaban, from -$506 to +$314 for rivaroxaban, and from -$596 to +$223 for dabigatran. Among NVAF patients there is a significant negative correlation between warfarin's TTR and SSE rate, but not major bleedings. The variations in warfarin's TTR impacted the economic comparison of use of individual NOACs versus warfarin.

Analysis of cerebrovascular disease mortality trends in Andalusia (1980-2014).

PubMed

Cayuela, A; Cayuela, L; Rodríguez-Domínguez, S; González, A; Moniche, F

2017-03-15

In recent decades, mortality rates for cerebrovascular diseases (CVD) have decreased significantly in many countries. This study analyses recent tendencies in CVD mortality rates in Andalusia (1980-2014) to identify any changes in previously observed sex and age trends. CVD mortality and population data were obtained from Spain's National Statistics Institute database. We calculated age-specific and age-standardised mortality rates using the direct method (European standard population). Joinpoint regression analysis was used to estimate the annual percentage change in rates and identify significant changes in mortality trends. We also estimated rate ratios between Andalusia and Spain. Standardised rates for both males and females showed 3 periods in joinpoint regression analysis: an initial period of significant decline (1980-1997), a period of rate stabilisation (1997-2003), and another period of significant decline (2003-2014). Between 1997 and 2003, age-standardised rates stabilised in Andalusia but continued to decrease in Spain as a whole. This increased in the gap between CVD mortality rates in Andalusia and Spain for both sexes and most age groups. Copyright © 2017 The Author(s). Publicado por Elsevier España, S.L.U. All rights reserved.

Spatiotemporal Bayesian analysis of Lyme disease in New York state, 1990-2000.

PubMed

Chen, Haiyan; Stratton, Howard H; Caraco, Thomas B; White, Dennis J

2006-07-01

Mapping ordinarily increases our understanding of nontrivial spatial and temporal heterogeneities in disease rates. However, the large number of parameters required by the corresponding statistical models often complicates detailed analysis. This study investigates the feasibility of a fully Bayesian hierarchical regression approach to the problem and identifies how it outperforms two more popular methods: crude rate estimates (CRE) and empirical Bayes standardization (EBS). In particular, we apply a fully Bayesian approach to the spatiotemporal analysis of Lyme disease incidence in New York state for the period 1990-2000. These results are compared with those obtained by CRE and EBS in Chen et al. (2005). We show that the fully Bayesian regression model not only gives more reliable estimates of disease rates than the other two approaches but also allows for tractable models that can accommodate more numerous sources of variation and unknown parameters.

Sex differences in estimating multiple intelligences in self and others: a replication in Russia.

PubMed

Furnham, Adrian; Shagabutdinova, Ksenia

2012-01-01

This was a crosscultural study that focused on sex differences in self- and other-estimates of multiple intelligences (including 10 that were specified by Gardner, 1999 and three by Sternberg, 1988) as well as in an overall general intelligence estimate. It was one of a programmatic series of studies done in over 30 countries that has demonstrated the female "humility" and male "hubris" effect in self-estimated and other-estimated intelligence. Two hundred and thirty Russian university students estimated their own and their parents' overall intelligence and "multiple intelligences." Results revealed no sex difference in estimates of overall intelligence for both self and parents, but men rated themselves higher on spatial intelligence. This contradicted many previous findings in the area which have shown that men rate their own overall intelligence and mathematical intelligence significantly higher than do women. Regressions indicated that estimates of verbal, logical, and spatial intelligences were the best predictors of estimates of overall intelligence, which is a consistent finding over many studies. Regressions also showed that participants' openness to experience and self-respect were good predictors of intelligence estimates. A comparison with a British sample showed that Russians gave higher mother estimates, and were less likely to believe that IQ tests measure intelligence. Results were discussed in relation to the influence of gender role stereotypes on lay conception of intelligence across cultures.

Estimated prevalence of erosive tooth wear in permanent teeth of children and adolescents: an epidemiological systematic review and meta-regression analysis.

PubMed

Salas, M M S; Nascimento, G G; Huysmans, M C; Demarco, F F

2015-01-01

The main purpose of this systematic review was to estimate the prevalence of dental erosion in permanent teeth of children and adolescents. An electronic search was performed up to and including March 2014. Eligibility criteria included population-based studies in permanent teeth of children and adolescents aged 8-19-year-old reporting the prevalence or data that allowed the calculation of prevalence rates of tooth erosion. Data collection assessed information regarding geographic location, type of index used for clinical examination, sample size, year of publication, age, examined teeth and tissue exposure. The estimated prevalence of erosive wear was determined, followed by a meta-regression analysis. Twenty-two papers were included in the systematic review. The overall estimated prevalence of tooth erosion was 30.4% (95%IC 23.8-37.0). In the multivariate meta-regression model use of the Tooth Wear Index for clinical examination, studies with sample smaller than 1000 subjects and those conducted in the Middle East and Africa remained associated with higher dental erosion prevalence rates. Our results demonstrated that the estimated prevalence of erosive wear in permanent teeth of children and adolescents is 30.4% with high heterogeneity between studies. Additionally, the correct choice of a clinical index for dental erosion detection and the geographic location play an important role for the large variability of erosive tooth wear in permanent teeth of children and adolescents. The prevalence of tooth erosion observed in permanent teeth of children and adolescents was considerable high. Our results demonstrated that prevalence rate of erosive wear was influenced by methodological and diagnosis factors. When tooth erosion is assessed, the clinical index should be considered. Copyright © 2014 Elsevier Ltd. All rights reserved.

Rethinking Headache Chronification

PubMed Central

Turner, Dana P.; Smitherman, Todd A.; Penzien, Donald B.; Lipton, Richard B.; Houle, Timothy T.

2013-01-01

The objective of this series is to examine several threats to the interpretation of headache chronification studies that arise from methodological issues. The study of headache chronification has extensively used longitudinal designs with two or more measurement occasions. Unfortunately, application of these designs when combined with the common practice of extreme score selection as well as the extant challenges in measuring headache frequency rates (eg, unreliability, regression to the mean), induces substantive threats to accurate interpretation of findings. Partitioning the amount of observed variance in rates of chronification and remission attributable to regression artifacts is a critical yet previously overlooked step to learning more about headache as a potentially progressive disease. In this series on rethinking headache chronification, we provide an overview of methodological issues in this area (this paper), highlight the influence of rounding error on estimates of headache frequency (second paper), examine the influence of random error and regression artifacts on estimates of chronification and remission (third paper), and consider future directions for this line of research (fourth paper). PMID:23721237

Rating curve estimation of nutrient loads in Iowa rivers

USGS Publications Warehouse

Stenback, G.A.; Crumpton, W.G.; Schilling, K.E.; Helmers, M.J.

2011-01-01

Accurate estimation of nutrient loads in rivers and streams is critical for many applications including determination of sources of nutrient loads in watersheds, evaluating long-term trends in loads, and estimating loading to downstream waterbodies. Since in many cases nutrient concentrations are measured on a weekly or monthly frequency, there is a need to estimate concentration and loads during periods when no data is available. The objectives of this study were to: (i) document the performance of a multiple regression model to predict loads of nitrate and total phosphorus (TP) in Iowa rivers and streams; (ii) determine whether there is any systematic bias in the load prediction estimates for nitrate and TP; and (iii) evaluate streamflow and concentration factors that could affect the load prediction efficiency. A commonly cited rating curve regression is utilized to estimate riverine nitrate and TP loads for rivers in Iowa with watershed areas ranging from 17.4 to over 34,600km2. Forty-nine nitrate and 44 TP datasets each comprising 5-22years of approximately weekly to monthly concentrations were examined. Three nitrate data sets had sample collection frequencies averaging about three samples per week. The accuracy and precision of annual and long term riverine load prediction was assessed by direct comparison of rating curve load predictions with observed daily loads. Significant positive bias of annual and long term nitrate loads was detected. Long term rating curve nitrate load predictions exceeded observed loads by 25% or more at 33% of the 49 measurement sites. No bias was found for TP load prediction although 15% of the 44 cases either underestimated or overestimate observed long-term loads by more than 25%. The rating curve was found to poorly characterize nitrate and phosphorus variation in some rivers. ?? 2010 .

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

NASA Astrophysics Data System (ADS)

Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

2018-04-01

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

Comparison between artificial neural network and multilinear regression models in an evaluation of cognitive workload in a flight simulator.

PubMed

Hannula, Manne; Huttunen, Kerttu; Koskelo, Jukka; Laitinen, Tomi; Leino, Tuomo

2008-01-01

In this study, the performances of artificial neural network (ANN) analysis and multilinear regression (MLR) model-based estimation of heart rate were compared in an evaluation of individual cognitive workload. The data comprised electrocardiography (ECG) measurements and an evaluation of cognitive load that induces psychophysiological stress (PPS), collected from 14 interceptor fighter pilots during complex simulated F/A-18 Hornet air battles. In our data, the mean absolute error of the ANN estimate was 11.4 as a visual analog scale score, being 13-23% better than the mean absolute error of the MLR model in the estimation of cognitive workload.

Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis.

PubMed

Choi, Seung Hoan; Labadorf, Adam T; Myers, Richard H; Lunetta, Kathryn L; Dupuis, Josée; DeStefano, Anita L

2017-02-06

Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates. When the sample size is small or the expression levels of a gene are highly dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes logistic (BL) regressions are conservative. Firth's logistic (FL) regression performs well or is slightly conservative. Large sample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha levels of 0.05 and 0.01. However, Type-I error rates are controlled after applying the data adaptive method. The NB, BL, and FL regressions gain increased power with large sample size, large log2 fold-change, and low dispersion. The FL regression has comparable power to NB regression. We conclude that implementing the data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis. Firth's logistic regression provides a concise statistical inference process and reduces spurious associations from inaccurately estimated dispersion parameters in the negative binomial framework.

Mechanisms behind the estimation of photosynthesis traits from leaf reflectance observations

NASA Astrophysics Data System (ADS)

Dechant, Benjamin; Cuntz, Matthias; Doktor, Daniel; Vohland, Michael

2016-04-01

Many studies have investigated the reflectance-based estimation of leaf chlorophyll, water and dry matter contents of plants. Only few studies focused on photosynthesis traits, however. The maximum potential uptake of carbon dioxide under given environmental conditions is determined mainly by RuBisCO activity, limiting carboxylation, or the speed of photosynthetic electron transport. These two main limitations are represented by the maximum carboxylation capacity, V cmax,25, and the maximum electron transport rate, Jmax,25. These traits were estimated from leaf reflectance before but the mechanisms underlying the estimation remain rather speculative. The aim of this study was therefore to reveal the mechanisms behind reflectance-based estimation of V cmax,25 and Jmax,25. Leaf reflectance, photosynthetic response curves as well as nitrogen content per area, Narea, and leaf mass per area, LMA, were measured on 37 deciduous tree species. V cmax,25 and Jmax,25 were determined from the response curves. Partial Least Squares (PLS) regression models for the two photosynthesis traits V cmax,25 and Jmax,25 as well as Narea and LMA were studied using a cross-validation approach. Analyses of linear regression models based on Narea and other leaf traits estimated via PROSPECT inversion, PLS regression coefficients and model residuals were conducted in order to reveal the mechanisms behind the reflectance-based estimation. We found that V cmax,25 and Jmax,25 can be estimated from leaf reflectance with good to moderate accuracy for a large number of species and different light conditions. The dominant mechanism behind the estimations was the strong relationship between photosynthesis traits and leaf nitrogen content. This was concluded from very strong relationships between PLS regression coefficients, the model residuals as well as the prediction performance of Narea- based linear regression models compared to PLS regression models. While the PLS regression model for V cmax,25 was fully based on the correlation to Narea, the PLS regression model for Jmax,25 was not entirely based on it. Analyses of the contributions of different parts of the reflectance spectrum revealed that the information contributing to the Jmax,25 PLS regression model in addition to the main source of information, Narea, was mainly located in the visible part of the spectrum (500-900 nm). Estimated chlorophyll content could be excluded as potential source of this extra information. The PLS regression coefficients of the Jmax,25 model indicated possible contributions from chlorophyll fluorescence and cytochrome f content. In summary, we found that the main mechanism behind the estimation of V cmax,25 and Jmax,25 from leaf reflectance observations is the correlation to Narea but that there is additional information related to Jmax,25 mainly in the visible part of the spectrum.

A comparison of time dependent Cox regression, pooled logistic regression and cross sectional pooling with simulations and an application to the Framingham Heart Study.

PubMed

Ngwa, Julius S; Cabral, Howard J; Cheng, Debbie M; Pencina, Michael J; Gagnon, David R; LaValley, Michael P; Cupples, L Adrienne

2016-11-03

Typical survival studies follow individuals to an event and measure explanatory variables for that event, sometimes repeatedly over the course of follow up. The Cox regression model has been used widely in the analyses of time to diagnosis or death from disease. The associations between the survival outcome and time dependent measures may be biased unless they are modeled appropriately. In this paper we explore the Time Dependent Cox Regression Model (TDCM), which quantifies the effect of repeated measures of covariates in the analysis of time to event data. This model is commonly used in biomedical research but sometimes does not explicitly adjust for the times at which time dependent explanatory variables are measured. This approach can yield different estimates of association compared to a model that adjusts for these times. In order to address the question of how different these estimates are from a statistical perspective, we compare the TDCM to Pooled Logistic Regression (PLR) and Cross Sectional Pooling (CSP), considering models that adjust and do not adjust for time in PLR and CSP. In a series of simulations we found that time adjusted CSP provided identical results to the TDCM while the PLR showed larger parameter estimates compared to the time adjusted CSP and the TDCM in scenarios with high event rates. We also observed upwardly biased estimates in the unadjusted CSP and unadjusted PLR methods. The time adjusted PLR had a positive bias in the time dependent Age effect with reduced bias when the event rate is low. The PLR methods showed a negative bias in the Sex effect, a subject level covariate, when compared to the other methods. The Cox models yielded reliable estimates for the Sex effect in all scenarios considered. We conclude that survival analyses that explicitly account in the statistical model for the times at which time dependent covariates are measured provide more reliable estimates compared to unadjusted analyses. We present results from the Framingham Heart Study in which lipid measurements and myocardial infarction data events were collected over a period of 26 years.

Bioconcentration of sediment-associated fluoranthene by the filter-feeding bivalve mollusk, Crassostrea virginica

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

Siewicki, T.C.; Chandler, G.T.

1995-12-31

Eastern oysters (Crassostrea virginica) were continuously exposed to suspended {sup 14}C-fluoranthene spiked-sediment for either: (1) five days followed by 24 days deputation, or (2) 28 days exposure. Sediment less than 63 um contained fluoranthene concentrations one or ten times that measured at suburbanized sites in southeastern estuaries (133 or 1,300 ng/g). The data were evaluated both raw and normalized for tissue lipid and sediment organic carbon concentrations. Uptake rate constants were estimated using non-linear regression methods. Depuration rate constants were estimated by linear regression of the deputation phase following five-days exposure and as the second partial derivative of the non-linearmore » regression for the 28-day exposures. Uptake and deputation rate constants, bioconcentration factors and half-lives were similar regardless of exposure time, sediment fluoranthene concentration or use of data normalization. Uptake and deputation rate constants, bioconcentration factors and half-lives (days) were similar and low for all experiments, ranging from 0.02 to 0.10, 0.14 to 0.30, 0.09 to 0.46, and 2.4 to 5.0, respectively. Degradation by the mixed function oxidase system is not expected in oysters allowing the use of radiotracers for measuring very low concentrations of fluoranthene. The results suggest that short-term exposures followed by deputation are effective for estimating kinetic rate constants and that normalization provides little benefit in these controlled studies. The results further show that bioconcentration of sediment-associated fluoranthene, and possibly other polycyclic aromatic hydrocarbons, is very low compared to either dissolved forms or levels commonly used in regulatory actions.« less

Simple, Efficient Estimators of Treatment Effects in Randomized Trials Using Generalized Linear Models to Leverage Baseline Variables

PubMed Central

Rosenblum, Michael; van der Laan, Mark J.

2010-01-01

Models, such as logistic regression and Poisson regression models, are often used to estimate treatment effects in randomized trials. These models leverage information in variables collected before randomization, in order to obtain more precise estimates of treatment effects. However, there is the danger that model misspecification will lead to bias. We show that certain easy to compute, model-based estimators are asymptotically unbiased even when the working model used is arbitrarily misspecified. Furthermore, these estimators are locally efficient. As a special case of our main result, we consider a simple Poisson working model containing only main terms; in this case, we prove the maximum likelihood estimate of the coefficient corresponding to the treatment variable is an asymptotically unbiased estimator of the marginal log rate ratio, even when the working model is arbitrarily misspecified. This is the log-linear analog of ANCOVA for linear models. Our results demonstrate one application of targeted maximum likelihood estimation. PMID:20628636

Predicting the rate of change in timber value for forest stands infested with gypsy moth

Treesearch

David A. Gansner; Owen W. Herrick

1982-01-01

Presents a method for estimating the potential impact of gypsy moth attacks on forest-stand value. Robust regression analysis is used to develop an equation for predicting the rate of change in timber value from easy-to-measure key characteristics of stand condition.

Estimations of One Repetition Maximum and Isometric Peak Torque in Knee Extension Based on the Relationship Between Force and Velocity.

PubMed

Sugiura, Yoshito; Hatanaka, Yasuhiko; Arai, Tomoaki; Sakurai, Hiroaki; Kanada, Yoshikiyo

2016-04-01

We aimed to investigate whether a linear regression formula based on the relationship between joint torque and angular velocity measured using a high-speed video camera and image measurement software is effective for estimating 1 repetition maximum (1RM) and isometric peak torque in knee extension. Subjects comprised 20 healthy men (mean ± SD; age, 27.4 ± 4.9 years; height, 170.3 ± 4.4 cm; and body weight, 66.1 ± 10.9 kg). The exercise load ranged from 40% to 150% 1RM. Peak angular velocity (PAV) and peak torque were used to estimate 1RM and isometric peak torque. To elucidate the relationship between force and velocity in knee extension, the relationship between the relative proportion of 1RM (% 1RM) and PAV was examined using simple regression analysis. The concordance rate between the estimated value and actual measurement of 1RM and isometric peak torque was examined using intraclass correlation coefficients (ICCs). Reliability of the regression line of PAV and % 1RM was 0.95. The concordance rate between the actual measurement and estimated value of 1RM resulted in an ICC(2,1) of 0.93 and that of isometric peak torque had an ICC(2,1) of 0.87 and 0.86 for 6 and 3 levels of load, respectively. Our method for estimating 1RM was effective for decreasing the measurement time and reducing patients' burden. Additionally, isometric peak torque can be estimated using 3 levels of load, as we obtained the same results as those reported previously. We plan to expand the range of subjects and examine the generalizability of our results.

Accounting for individual differences and timing of events: estimating the effect of treatment on criminal convictions in heroin users.

PubMed

Røislien, Jo; Clausen, Thomas; Gran, Jon Michael; Bukten, Anne

2014-05-17

The reduction of crime is an important outcome of opioid maintenance treatment (OMT). Criminal intensity and treatment regimes vary among OMT patients, but this is rarely adjusted for in statistical analyses, which tend to focus on cohort incidence rates and rate ratios. The purpose of this work was to estimate the relationship between treatment and criminal convictions among OMT patients, adjusting for individual covariate information and timing of events, fitting time-to-event regression models of increasing complexity. National criminal records were cross linked with treatment data on 3221 patients starting OMT in Norway 1997-2003. In addition to calculating cohort incidence rates, criminal convictions was modelled as a recurrent event dependent variable, and treatment a time-dependent covariate, in Cox proportional hazards, Aalen's additive hazards, and semi-parametric additive hazards regression models. Both fixed and dynamic covariates were included. During OMT, the number of days with criminal convictions for the cohort as a whole was 61% lower than when not in treatment. OMT was associated with reduced number of days with criminal convictions in all time-to-event regression models, but the hazard ratio (95% CI) was strongly attenuated when adjusting for covariates; from 0.40 (0.35, 0.45) in a univariate model to 0.79 (0.72, 0.87) in a fully adjusted model. The hazard was lower for females and decreasing with older age, while increasing with high numbers of criminal convictions prior to application to OMT (all p < 0.001). The strongest predictors were level of criminal activity prior to entering into OMT, and having a recent criminal conviction (both p < 0.001). The effect of several predictors was significantly time-varying with their effects diminishing over time. Analyzing complex observational data regarding to fixed factors only overlooks important temporal information, and naïve cohort level incidence rates might result in biased estimates of the effect of interventions. Applying time-to-event regression models, properly adjusting for individual covariate information and timing of various events, allows for more precise and reliable effect estimates, as well as painting a more nuanced picture that can aid health care professionals and policy makers.

Establishing endangered species recovery criteria using predictive simulation modeling

USGS Publications Warehouse

McGowan, Conor P.; Catlin, Daniel H.; Shaffer, Terry L.; Gratto-Trevor, Cheri L.; Aron, Carol

2014-01-01

Listing a species under the Endangered Species Act (ESA) and developing a recovery plan requires U.S. Fish and Wildlife Service to establish specific and measurable criteria for delisting. Generally, species are listed because they face (or are perceived to face) elevated risk of extinction due to issues such as habitat loss, invasive species, or other factors. Recovery plans identify recovery criteria that reduce extinction risk to an acceptable level. It logically follows that the recovery criteria, the defined conditions for removing a species from ESA protections, need to be closely related to extinction risk. Extinction probability is a population parameter estimated with a model that uses current demographic information to project the population into the future over a number of replicates, calculating the proportion of replicated populations that go extinct. We simulated extinction probabilities of piping plovers in the Great Plains and estimated the relationship between extinction probability and various demographic parameters. We tested the fit of regression models linking initial abundance, productivity, or population growth rate to extinction risk, and then, using the regression parameter estimates, determined the conditions required to reduce extinction probability to some pre-defined acceptable threshold. Binomial regression models with mean population growth rate and the natural log of initial abundance were the best predictors of extinction probability 50 years into the future. For example, based on our regression models, an initial abundance of approximately 2400 females with an expected mean population growth rate of 1.0 will limit extinction risk for piping plovers in the Great Plains to less than 0.048. Our method provides a straightforward way of developing specific and measurable recovery criteria linked directly to the core issue of extinction risk. Published by Elsevier Ltd.

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

SPReM: Sparse Projection Regression Model For High-dimensional Linear Regression *

PubMed Central

Sun, Qiang; Zhu, Hongtu; Liu, Yufeng; Ibrahim, Joseph G.

2014-01-01

The aim of this paper is to develop a sparse projection regression modeling (SPReM) framework to perform multivariate regression modeling with a large number of responses and a multivariate covariate of interest. We propose two novel heritability ratios to simultaneously perform dimension reduction, response selection, estimation, and testing, while explicitly accounting for correlations among multivariate responses. Our SPReM is devised to specifically address the low statistical power issue of many standard statistical approaches, such as the Hotelling’s T2 test statistic or a mass univariate analysis, for high-dimensional data. We formulate the estimation problem of SPREM as a novel sparse unit rank projection (SURP) problem and propose a fast optimization algorithm for SURP. Furthermore, we extend SURP to the sparse multi-rank projection (SMURP) by adopting a sequential SURP approximation. Theoretically, we have systematically investigated the convergence properties of SURP and the convergence rate of SURP estimates. Our simulation results and real data analysis have shown that SPReM out-performs other state-of-the-art methods. PMID:26527844

Sampling effort and estimates of species richness based on prepositioned area electrofisher samples

USGS Publications Warehouse

Bowen, Z.H.; Freeman, Mary C.

1998-01-01

Estimates of species richness based on electrofishing data are commonly used to describe the structure of fish communities. One electrofishing method for sampling riverine fishes that has become popular in the last decade is the prepositioned area electrofisher (PAE). We investigated the relationship between sampling effort and fish species richness at seven sites in the Tallapoosa River system, USA based on 1,400 PAE samples collected during 1994 and 1995. First, we estimated species richness at each site using the first-order jackknife and compared observed values for species richness and jackknife estimates of species richness to estimates based on historical collection data. Second, we used a permutation procedure and nonlinear regression to examine rates of species accumulation. Third, we used regression to predict the number of PAE samples required to collect the jackknife estimate of species richness at each site during 1994 and 1995. We found that jackknife estimates of species richness generally were less than or equal to estimates based on historical collection data. The relationship between PAE electrofishing effort and species richness in the Tallapoosa River was described by a positive asymptotic curve as found in other studies using different electrofishing gears in wadable streams. Results from nonlinear regression analyses indicted that rates of species accumulation were variable among sites and between years. Across sites and years, predictions of sampling effort required to collect jackknife estimates of species richness suggested that doubling sampling effort (to 200 PAEs) would typically increase observed species richness by not more than six species. However, sampling effort beyond about 60 PAE samples typically increased observed species richness by < 10%. We recommend using historical collection data in conjunction with a preliminary sample size of at least 70 PAE samples to evaluate estimates of species richness in medium-sized rivers. Seventy PAE samples should provide enough information to describe the relationship between sampling effort and species richness and thus facilitate evaluation of a sampling effort.

Probability estimation with machine learning methods for dichotomous and multicategory outcome: theory.

PubMed

Kruppa, Jochen; Liu, Yufeng; Biau, Gérard; Kohler, Michael; König, Inke R; Malley, James D; Ziegler, Andreas

2014-07-01

Probability estimation for binary and multicategory outcome using logistic and multinomial logistic regression has a long-standing tradition in biostatistics. However, biases may occur if the model is misspecified. In contrast, outcome probabilities for individuals can be estimated consistently with machine learning approaches, including k-nearest neighbors (k-NN), bagged nearest neighbors (b-NN), random forests (RF), and support vector machines (SVM). Because machine learning methods are rarely used by applied biostatisticians, the primary goal of this paper is to explain the concept of probability estimation with these methods and to summarize recent theoretical findings. Probability estimation in k-NN, b-NN, and RF can be embedded into the class of nonparametric regression learning machines; therefore, we start with the construction of nonparametric regression estimates and review results on consistency and rates of convergence. In SVMs, outcome probabilities for individuals are estimated consistently by repeatedly solving classification problems. For SVMs we review classification problem and then dichotomous probability estimation. Next we extend the algorithms for estimating probabilities using k-NN, b-NN, and RF to multicategory outcomes and discuss approaches for the multicategory probability estimation problem using SVM. In simulation studies for dichotomous and multicategory dependent variables we demonstrate the general validity of the machine learning methods and compare it with logistic regression. However, each method fails in at least one simulation scenario. We conclude with a discussion of the failures and give recommendations for selecting and tuning the methods. Applications to real data and example code are provided in a companion article (doi:10.1002/bimj.201300077). © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Managed care and the diffusion of endoscopy in fee-for-service Medicare.

PubMed

Mobley, Lee Rivers; Subramanian, Sujha; Koschinsky, Julia; Frech, H E; Trantham, Laurel Clayton; Anselin, Luc

2011-12-01

To determine whether Medicare managed care penetration impacted the diffusion of endoscopy services (sigmoidoscopy, colonoscopy) among the fee-for-service (FFS) Medicare population during 2001-2006. We model utilization rates for colonoscopy or sigmoidoscopy as impacted by both market supply and demand factors. We use spatial regression to perform ecological analysis of county-area utilization rates over two time intervals (2001-2003, 2004-2006) following Medicare benefits expansion in 2001 to cover colonoscopy for persons of average risk. We examine each technology in separate cross-sectional regressions estimated over early and later periods to assess differential effects on diffusion over time. We discuss selection factors in managed care markets and how failure to control perfectly for market selection might impact our managed care spillover estimates. Areas with worse socioeconomic conditions have lower utilization rates, especially for colonoscopy. Holding constant statistically the socioeconomic factors, we find that managed care spillover effects onto FFS Medicare utilization rates are negative for colonoscopy and positive for sigmoidoscopy. The spatial lag estimates are conservative and interpreted as a lower bound on true effects. Our findings suggest that managed care presence fostered persistence of the older technology during a time when it was rapidly being replaced by the newer technology. © Health Research and Educational Trust.

Levels and trends of child and adult mortality rates in the Islamic Republic of Iran, 1990-2013; protocol of the NASBOD study.

PubMed

Mohammadi, Younes; Parsaeian, Mahboubeh; Farzadfar, Farshad; Kasaeian, Amir; Mehdipour, Parinaz; Sheidaei, Ali; Mansouri, Anita; Saeedi Moghaddam, Sahar; Djalalinia, Shirin; Mahmoudi, Mahmood; Khosravi, Ardeshir; Yazdani, Kamran

2014-03-01

Calculation of burden of diseases and risk factors is crucial to set priorities in the health care systems. Nevertheless, the reliable measurement of mortality rates is the main barrier to reach this goal. Unfortunately, in many developing countries the vital registration system (VRS) is either defective or does not exist at all. Consequently, alternative methods have been developed to measure mortality. This study is a subcomponent of NASBOD project, which is currently conducting in Iran. In this study, we aim to calculate incompleteness of the Death Registration System (DRS) and then to estimate levels and trends of child and adult mortality using reliable methods. In order to estimate mortality rates, first, we identify all possible data sources. Then, we calculate incompleteness of child and adult morality separately. For incompleteness of child mortality, we analyze summary birth history data using maternal age cohort and maternal age period methods. Then, we combine these two methods using LOESS regression. However, these estimates are not plausible for some provinces. We use additional information of covariates such as wealth index and years of schooling to make predictions for these provinces using spatio-temporal model. We generate yearly estimates of mortality using Gaussian process regression that covers both sampling and non-sampling errors within uncertainty intervals. By comparing the resulted estimates with mortality rates from DRS, we calculate child mortality incompleteness. For incompleteness of adult mortality, Generalized Growth Balance, Synthetic Extinct Generation and a hybrid of two mentioned methods are used. Afterwards, we combine incompleteness of three methods using GPR, and apply it to correct and adjust the number of deaths. In this study, we develop a conceptual framework to overcome the existing challenges for accurate measuring of mortality rates. The resulting estimates can be used to inform policy-makers about past, current and future mortality rates as a major indicator of health status of a population.

Introduction to the use of regression models in epidemiology.

PubMed

Bender, Ralf

2009-01-01

Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.

Parameter estimation in Cox models with missing failure indicators and the OPPERA study.

PubMed

Brownstein, Naomi C; Cai, Jianwen; Slade, Gary D; Bair, Eric

2015-12-30

In a prospective cohort study, examining all participants for incidence of the condition of interest may be prohibitively expensive. For example, the "gold standard" for diagnosing temporomandibular disorder (TMD) is a physical examination by a trained clinician. In large studies, examining all participants in this manner is infeasible. Instead, it is common to use questionnaires to screen for incidence of TMD and perform the "gold standard" examination only on participants who screen positively. Unfortunately, some participants may leave the study before receiving the "gold standard" examination. Within the framework of survival analysis, this results in missing failure indicators. Motivated by the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study, a large cohort study of TMD, we propose a method for parameter estimation in survival models with missing failure indicators. We estimate the probability of being an incident case for those lacking a "gold standard" examination using logistic regression. These estimated probabilities are used to generate multiple imputations of case status for each missing examination that are combined with observed data in appropriate regression models. The variance introduced by the procedure is estimated using multiple imputation. The method can be used to estimate both regression coefficients in Cox proportional hazard models as well as incidence rates using Poisson regression. We simulate data with missing failure indicators and show that our method performs as well as or better than competing methods. Finally, we apply the proposed method to data from the OPPERA study. Copyright © 2015 John Wiley & Sons, Ltd.

Are Alcohol Taxation and Pricing Policies Regressive? Product-Level Effects of a Specific Tax and a Minimum Unit Price for Alcohol.

PubMed

Vandenberg, Brian; Sharma, Anurag

2016-07-01

To compare estimated effects of two policy alternatives, (i) a minimum unit price (MUP) for alcohol and (ii) specific (per-unit) taxation, upon current product prices, per capita spending (A$), and per capita consumption by income quintile, consumption quintile and product type. Estimation of baseline spending and consumption, and modelling policy-to-price and price-to-consumption effects of policy changes using scanner data from a panel of demographically representative Australian households that includes product-level details of their off-trade alcohol spending (n = 885; total observations = 12,505). Robustness checks include alternative price elasticities, tax rates, minimum price thresholds and tax pass-through rates. Current alcohol taxes and alternative taxation and pricing policies are not highly regressive. Any regressive effects are small and concentrated among heavy consumers. The lowest-income consumers currently spend a larger proportion of income (2.3%) on alcohol taxes than the highest-income consumers (0.3%), but the mean amount is small in magnitude [A$5.50 per week (95%CI: 5.18-5.88)]. Both a MUP and specific taxation will have some regressive effects, but the effects are limited, as they are greatest for the heaviest consumers, irrespective of income. Among the policy alternatives, a MUP is more effective in reducing consumption than specific taxation, especially for consumers in the lowest-income quintile: an estimated mean per capita reduction of 11.9 standard drinks per week (95%CI: 11.3-12.6). Policies that increase the cost of the cheapest alcohol can be effective in reducing alcohol consumption, without having highly regressive effects. © The Author 2015. Medical Council on Alcohol and Oxford University Press. All rights reserved.

Uncovering state-dependent relationships in shallow lakes using Bayesian latent variable regression.

PubMed

Vitense, Kelsey; Hanson, Mark A; Herwig, Brian R; Zimmer, Kyle D; Fieberg, John

2018-03-01

Ecosystems sometimes undergo dramatic shifts between contrasting regimes. Shallow lakes, for instance, can transition between two alternative stable states: a clear state dominated by submerged aquatic vegetation and a turbid state dominated by phytoplankton. Theoretical models suggest that critical nutrient thresholds differentiate three lake types: highly resilient clear lakes, lakes that may switch between clear and turbid states following perturbations, and highly resilient turbid lakes. For effective and efficient management of shallow lakes and other systems, managers need tools to identify critical thresholds and state-dependent relationships between driving variables and key system features. Using shallow lakes as a model system for which alternative stable states have been demonstrated, we developed an integrated framework using Bayesian latent variable regression (BLR) to classify lake states, identify critical total phosphorus (TP) thresholds, and estimate steady state relationships between TP and chlorophyll a (chl a) using cross-sectional data. We evaluated the method using data simulated from a stochastic differential equation model and compared its performance to k-means clustering with regression (KMR). We also applied the framework to data comprising 130 shallow lakes. For simulated data sets, BLR had high state classification rates (median/mean accuracy >97%) and accurately estimated TP thresholds and state-dependent TP-chl a relationships. Classification and estimation improved with increasing sample size and decreasing noise levels. Compared to KMR, BLR had higher classification rates and better approximated the TP-chl a steady state relationships and TP thresholds. We fit the BLR model to three different years of empirical shallow lake data, and managers can use the estimated bifurcation diagrams to prioritize lakes for management according to their proximity to thresholds and chance of successful rehabilitation. Our model improves upon previous methods for shallow lakes because it allows classification and regression to occur simultaneously and inform one another, directly estimates TP thresholds and the uncertainty associated with thresholds and state classifications, and enables meaningful constraints to be built into models. The BLR framework is broadly applicable to other ecosystems known to exhibit alternative stable states in which regression can be used to establish relationships between driving variables and state variables. © 2017 by the Ecological Society of America.

Examination of universal purchase programs as a driver of vaccine uptake among US States, 1995-2014.

PubMed

Mulligan, Karen; Snider, Julia Thornton; Arthur, Phyllis; Frank, Gregory; Tebeka, Mahlet; Walker, Amy; Abrevaya, Jason

2018-06-01

Immunization against numerous potentially life-threatening illnesses has been a great public health achievement. In the United States, the Vaccines for Children (VFC) program has provided vaccines to uninsured and underinsured children since the early 1990s, increasing vaccination rates. In recent years, some states have adopted Universal Purchase (UP) programs with the stated aim of further increasing vaccination rates. Under UP programs, states also purchase vaccines for privately-insured children at federally-contracted VFC prices and bill private health insurers for the vaccines through assessments. In this study, we estimated the effect of UP adoption in a state on children's vaccination rates using state-level and individual-level data from the 1995-2014 National Immunization Survey. For the state-level analysis, we performed ordinary least squares regression to estimate the state's vaccination rate as a function of whether the state had UP in the given year, state demographic characteristics, other vaccination policies, state fixed effects, and a time trend. For the individual analysis, we performed logistic regression to estimate a child's likelihood of being vaccinated as a function of whether the state had UP in the given year, the child's demographic characteristics, state characteristics and vaccine policies, state fixed effects, and a time trend. We performed separate regressions for each of nine recommended vaccines, as well as composite measures on whether a child was up-to-date on all required vaccines. In the both the state-level and individual-level analyses, we found UP had no significant (p < 0.10) effect on any of the vaccines or composite measures in our base case specifications. Results were similar in alternative specifications. We hypothesize that UP was ineffective in increasing vaccination rates. Policymakers seeking to increase vaccination rates would do well to consider other policies such as addressing provider practice issues and vaccine hesitancy. Copyright © 2018. Published by Elsevier Ltd.

Beyond long memory in heart rate variability: An approach based on fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity

NASA Astrophysics Data System (ADS)

Leite, Argentina; Paula Rocha, Ana; Eduarda Silva, Maria

2013-06-01

Heart Rate Variability (HRV) series exhibit long memory and time-varying conditional variance. This work considers the Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. ARFIMA-GARCH models may be used to capture and remove long memory and estimate the conditional volatility in 24 h HRV recordings. The ARFIMA-GARCH approach is applied to fifteen long term HRV series available at Physionet, leading to the discrimination among normal individuals, heart failure patients, and patients with atrial fibrillation.

Effect of clinical response to active drugs and placebo on antipsychotics and mood stabilizers relative efficacy for bipolar depression and mania: A meta-regression analysis.

PubMed

Bartoli, Francesco; Clerici, Massimo; Di Brita, Carmen; Riboldi, Ilaria; Crocamo, Cristina; Carrà, Giuseppe

2018-04-01

Randomised placebo-controlled trials investigating treatments for bipolar disorder have been hampered by wide variations of active drugs and placebo clinical response rates. It is important to estimate whether the active drug or placebo response has a greater influence in determining the relative efficacy of drugs for psychosis (antipsychotics) and relapse prevention (mood stabilisers) for bipolar depression and mania. We identified 53 randomised, placebo-controlled trials assessing antipsychotic or mood stabiliser monotherapy ('active drugs') for bipolar depression or mania. We carried out random-effects meta-regressions, estimating the influence of active drugs and placebo response rates on treatment relative efficacy. Meta-regressions showed that treatment relative efficacy for bipolar mania was influenced by the magnitude of clinical response to active drugs ( p=0.002), but not to placebo ( p=0.60). On the other hand, treatment relative efficacy for bipolar depression was influenced by response to placebo ( p=0.047), but not to active drugs ( p=0.98). Despite several limitations, our unexpected findings showed that antipsychotics / mood stabilisers relative efficacy for bipolar depression seems unrelated to active drugs response rates, depending only on clinical response to placebo. Future research should explore strategies to reduce placebo-related issues in randomised, placebo-controlled trials for bipolar depression.

Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery.

PubMed

Liu, Han; Wang, Lie; Zhao, Tuo

2015-08-01

We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models. Compared with existing methods, CMR calibrates regularization for each regression task with respect to its noise level so that it simultaneously attains improved finite-sample performance and tuning insensitiveness. Theoretically, we provide sufficient conditions under which CMR achieves the optimal rate of convergence in parameter estimation. Computationally, we propose an efficient smoothed proximal gradient algorithm with a worst-case numerical rate of convergence O (1/ ϵ ), where ϵ is a pre-specified accuracy of the objective function value. We conduct thorough numerical simulations to illustrate that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR to solve a brain activity prediction problem and find that it is as competitive as a handcrafted model created by human experts. The R package camel implementing the proposed method is available on the Comprehensive R Archive Network http://cran.r-project.org/web/packages/camel/.

The relationship between cigarette taxes and child maltreatment.

PubMed

McLaughlin, Michael

2018-05-01

Prior research suggests that income and child maltreatment are related, but questions remain about the specific types of economic factors that affect the risk of maltreatment. The need to understand the role of economics in child welfare is critical, given the significant public health costs of child maltreatment. One factor that has been overlooked is regressive taxation. This study addresses this need by examining whether state-level changes in cigarette tax rates predict changes in state-level child maltreatment rates. The results of both a fixed effects (FE) and a fixed effects instrumental variables (FE-IV) estimator show that increases in state cigarette tax rates are followed by increases in child abuse and neglect. An additional test finds that increases in the sales tax (another tax deemed to be regressive) also predict increases in child maltreatment rates. Taken as a whole, the findings suggest that regressive taxes have a significant effect on the risk of child maltreatment. Copyright © 2018 Elsevier Ltd. All rights reserved.

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…

Estimation of factors from natural and anthropogenic radioactivity present in the surface soil and comparison with DCF values.

PubMed

Ranade, A K; Pandey, M; Datta, D

2013-01-01

A study was conducted to evaluate the absorbed rate coefficient of (238)U, (232)Th, (40)K and (137)Cs present in soil. A total of 31 soil samples and the corresponding terrestrial dose rates at 1 m from different locations were taken around the Anushaktinagar region, where the litho-logy is dominated by red soil. A linear regression model was developed for the estimation of these factors. The estimated coefficients (nGy h(-1) Bq(-1) kg(-1)) were 0.454, 0.586, 0.035 and 0.392, respectively. The factors calculated were in good agreement with the literature values.

Simulation of relationship between river discharge and sediment yield in the semi-arid river watersheds

NASA Astrophysics Data System (ADS)

Khaleghi, Mohammad Reza; Varvani, Javad

2018-02-01

Complex and variable nature of the river sediment yield caused many problems in estimating the long-term sediment yield and problems input into the reservoirs. Sediment Rating Curves (SRCs) are generally used to estimate the suspended sediment load of the rivers and drainage watersheds. Since the regression equations of the SRCs are obtained by logarithmic retransformation and have a little independent variable in this equation, they also overestimate or underestimate the true sediment load of the rivers. To evaluate the bias correction factors in Kalshor and Kashafroud watersheds, seven hydrometric stations of this region with suitable upstream watershed and spatial distribution were selected. Investigation of the accuracy index (ratio of estimated sediment yield to observed sediment yield) and the precision index of different bias correction factors of FAO, Quasi-Maximum Likelihood Estimator (QMLE), Smearing, and Minimum-Variance Unbiased Estimator (MVUE) with LSD test showed that FAO coefficient increases the estimated error in all of the stations. Application of MVUE in linear and mean load rating curves has not statistically meaningful effects. QMLE and smearing factors increased the estimated error in mean load rating curve, but that does not have any effect on linear rating curve estimation.

The increasing financial impact of chronic kidney disease in australia.

PubMed

Tucker, Patrick S; Kingsley, Michael I; Morton, R Hugh; Scanlan, Aaron T; Dalbo, Vincent J

2014-01-01

The aim of this investigation was to determine and compare current and projected expenditure associated with chronic kidney disease (CKD), renal replacement therapy (RRT), and cardiovascular disease (CVD) in Australia. Data published by Australia and New Zealand Dialysis and Transplant Registry, Australian Institute of Health and Welfare, and World Bank were used to compare CKD-, RRT-, and CVD-related expenditure and prevalence rates. Prevalence and expenditure predictions were made using a linear regression model. Direct statistical comparisons of rates of annual increase utilised indicator variables in combined regressions. Statistical significance was set at P < 0.05. Dollar amounts were adjusted for inflation prior to analysis. Between 2012 and 2020, prevalence, per-patient expenditure, and total disease expenditure associated with CKD and RRT are estimated to increase significantly more rapidly than CVD. RRT prevalence is estimated to increase by 29%, compared to 7% in CVD. Average annual RRT per-patient expenditure is estimated to increase by 16%, compared to 8% in CVD. Total CKD- and RRT-related expenditure had been estimated to increase by 37%, compared to 14% in CVD. Per-patient, CKD produces a considerably greater financial impact on Australia's healthcare system, compared to CVD. Research focusing on novel preventative/therapeutic interventions is warranted.

Something from nothing: Estimating consumption rates using propensity scores, with application to emissions reduction policies

PubMed Central

Büchs, Milena; Schnepf, Sylke V.

2017-01-01

Consumption surveys often record zero purchases of a good because of a short observation window. Measures of distribution are then precluded and only mean consumption rates can be inferred. We show that Propensity Score Matching can be applied to recover the distribution of consumption rates. We demonstrate the method using the UK National Travel Survey, in which c.40% of motorist households purchase no fuel. Estimated consumption rates are plausible judging by households’ annual mileages, and highly skewed. We apply the same approach to estimate CO2 emissions and outcomes of a carbon cap or tax. Reliance on means apparently distorts analysis of such policies because of skewness of the underlying distributions. The regressiveness of a simple tax or cap is overstated, and redistributive features of a revenue-neutral policy are understated. PMID:29020029

Estimates of self, parental, and partner multiple intelligence and their relationship with personality, values, and demographic variables: a study in Britain and France.

PubMed

Swami, Viren; Furnham, Adrian; Zilkha, Susan

2009-11-01

In the present study, 151 British and 151 French participants estimated their own, their parents' and their partner's overall intelligence and 13 'multiple intelligences.' In accordance with previous studies, men rated themselves as higher on almost all measures of intelligence, but there were few cross-national differences. There were also important sex differences in ratings of parental and partner intelligence. Participants generally believed they were more intelligent than their parents but not their partners. Regressions indicated that participants believed verbal, logical-mathematical, and spatial intelligence to be the main predictors of intelligence. Regressions also showed that participants' Big Five personality scores (in particular, Extraversion and Openness), but not values or beliefs about intelligence and intelligences tests, were good predictors of intelligence. Results were discussed in terms of the influence of gender-role stereotypes.

Less money, more problems: How changes in disposable income affect child maltreatment.

PubMed

McLaughlin, Michael

2017-05-01

A number of research studies have documented an association between child maltreatment and family income. Yet, little is known about the specific types of economic shocks that affect child maltreatment rates. The paucity of information is troubling given that more than six million children are reported for maltreatment annually in the U.S. alone. This study examines whether an exogenous shock to families' disposable income, a change in the price of gasoline, predicts changes in child maltreatment. The findings of a fixed-effects regression show that increases in state-level gas prices are associated with increases in state-level child maltreatment referral rates, even after controlling for demographic and other economic variables. The results are robust to the manner of estimation; random-effects and mixed-effects regressions produce similar estimates. The findings suggest that fluctuations in the price of gas may have important consequences for children. Copyright © 2017 Elsevier Ltd. All rights reserved.

Explaining cross-national differences in marriage, cohabitation, and divorce in Europe, 1990-2000.

PubMed

Kalmijn, Matthijs

2007-11-01

European countries differ considerably in their marriage patterns. The study presented in this paper describes these differences for the 1990s and attempts to explain them from a macro-level perspective. We find that different indicators of marriage (i.e., marriage rate, age at marriage, divorce rate, and prevalence of unmarried cohabitation) cannot be seen as indicators of an underlying concept such as the 'strength of marriage'. Multivariate ordinary least squares (OLS) regression analyses are estimated with countries as units and panel regression models are estimated in which annual time series for multiple countries are pooled. Using these models, we find that popular explanations of trends in the indicators - explanations that focus on gender roles, secularization, unemployment, and educational expansion - are also important for understanding differences among countries. We also find evidence for the role of historical continuity and societal disintegration in understanding cross-national differences.

The Association between Financial Aid Availability and the College Dropout Rates in Colombia

ERIC Educational Resources Information Center

Melguizo, Tatiana; Torres, Fabio Sanchez; Jaime, Haider

2011-01-01

The main objective of this study is to estimate the association between financial aid and college dropout rates of postsecondary students in Colombia. We use a unique dataset from the Colombian Ministry of Education that includes all enrolled college students in the country between 1998 and 2008. Logistic regression is used to identify the…

A Cross-National Study of the Relationship between Elderly Suicide Rates and Urbanization

ERIC Educational Resources Information Center

Shah, Ajit

2008-01-01

There is mixed evidence of a relationship between suicide rates in the general population and urbanization, and a paucity of studies examining this relationship in the elderly. A cross-national study with curve estimation regression model analysis, was undertaken to examine the a priori hypothesis that the relationship between elderly suicide…

[Effects of fundamental frequency and speech rate on impression formation].

PubMed

Uchida, Teruhisa; Nakaune, Naoko

2004-12-01

This study investigated the systematic relationship between nonverbal features of speech and personality trait ratings of the speaker. In Study 1, fundamental frequency (F0) in original speech was converted into five levels from 64% to 156.25%. Then 132 undergraduates rated each of the converted speeches in terms of personality traits. In Study 2 134 undergraduates similarly rated the speech stimuli, which had five speech rate levels as well as two F0 levels. Results showed that listener ratings along Big Five dimensions were mostly independent. Each dimension had a slightly different change profile over the five levels of F0 and speech rate. A quadratic regression equation provided a good approximation for each rating as a function of F0 or speech rate. The quadratic regression equations put together would provide us with a rough estimate of personality trait impression as a function of prosodic features. The functional relationship among F0, speech rate, and trait ratings was shown as a curved surface in the three-dimensional space.

Assessment and improvement of biotransfer models to cow's milk and beef used in exposure assessment tools for organic pollutants.

PubMed

Takaki, Koki; Wade, Andrew J; Collins, Chris D

2015-11-01

The aim of this study was to assess and improve the accuracy of biotransfer models for the organic pollutants (PCBs, PCDD/Fs, PBDEs, PFCAs, and pesticides) into cow's milk and beef used in human exposure assessment. Metabolic rate in cattle is known as a key parameter for this biotransfer, however few experimental data and no simulation methods are currently available. In this research, metabolic rate was estimated using existing QSAR biodegradation models of microorganisms (BioWIN) and fish (EPI-HL and IFS-HL). This simulated metabolic rate was then incorporated into the mechanistic cattle biotransfer models (RAIDAR, ACC-HUMAN, OMEGA, and CKow). The goodness of fit tests showed that RAIDAR, ACC-HUMAN, OMEGA model performances were significantly improved using either of the QSARs when comparing the new model outputs to observed data. The CKow model is the only one that separates the processes in the gut and liver. This model showed the lowest residual error of all the models tested when the BioWIN model was used to represent the ruminant metabolic process in the gut and the two fish QSARs were used to represent the metabolic process in the liver. Our testing included EUSES and CalTOX which are KOW-regression models that are widely used in regulatory assessment. New regressions based on the simulated rate of the two metabolic processes are also proposed as an alternative to KOW-regression models for a screening risk assessment. The modified CKow model is more physiologically realistic, but has equivalent usability to existing KOW-regression models for estimating cattle biotransfer of organic pollutants. Copyright © 2015. Published by Elsevier Ltd.

Demographic responses of Pinguicula ionantha to prescribed fire: a regression-design LTRE approach.

PubMed

Kesler, Herbert C; Trusty, Jennifer L; Hermann, Sharon M; Guyer, Craig

2008-06-01

This study describes the use of periodic matrix analysis and regression-design life table response experiments (LTRE) to investigate the effects of prescribed fire on demographic responses of Pinguicula ionantha, a federally listed plant endemic to the herb bog/savanna community in north Florida. Multi-state mark-recapture models with dead recoveries were used to estimate survival and transition probabilities for over 2,300 individuals in 12 populations of P. ionantha. These estimates were applied to parameterize matrix models used in further analyses. P. ionantha demographics were found to be strongly dependent on prescribed fire events. Periodic matrix models were used to evaluate season of burn (either growing or dormant season) for fire return intervals ranging from 1 to 20 years. Annual growing and biannual dormant season fires maximized population growth rates for this species. A regression design LTRE was used to evaluate the effect of number of days since last fire on population growth. Maximum population growth rates calculated using standard asymptotic analysis were realized shortly following a burn event (<2 years), and a regression design LTRE showed that short-term fire-mediated changes in vital rates translated into observed increases in population growth. The LTRE identified fecundity and individual growth as contributing most to increases in post-fire population growth. Our analyses found that the current four-year prescribed fire return intervals used at the study sites can be significantly shortened to increase the population growth rates of this rare species. Understanding the role of fire frequency and season in creating and maintaining appropriate habitat for this species may aid in the conservation of this and other rare herb bog/savanna inhabitants.

[Relationship between Electrical Conductivity and Decomposition Rate of Rat Postmortem Skeletal Muscle].

PubMed

Xia, Z Y; Zhai, X D; Liu, B B; Zheng, Z; Zhao, L L; Mo, Y N

2017-02-01

To analyze the relationship among electrical conductivity （EC）, total volatile basic nitrogen （TVB-N）, which is an index of decomposition rate for meat production, and postmortem interval （PMI）. To explore the feasibility of EC as an index of cadaveric skeletal muscle decomposition rate and lay the foundation for PMI estimation. Healthy Sprague-Dawley rats were sacrificed by cervical vertebrae dislocation and kept at 28 ℃. Muscle of rear limbs was removed at different PMI, homogenized in deionized water and then skeletal extraction liquid of mass concentration 0.1 g/mL was prepared. EC and TVB-N of extraction liquid were separately determined. The correlation between EC ( x ₁) and TVB-N ( x ₂) was analyzed, and their regression function was established. The relationship between PMI ( y ) and these two parameters were studied, and their regression functions were separately established. The change trends of EC and TVB-N of skeletal extraction liquid at different PMI were almost the same, and there was a linear positive correlation between them. The regression equation was x ₂=0.14 x ₁-164.91( R ²=0.982). EC and TVB-N of skeletal muscle changed significantly with PMI, and the regression functions were y =19.38 x ₁³-370.68 x ₁²+2 526.03 x ₁-717.06( R ²=0.994), and y =2.56 x ₂³-48.39 x ₂²+330.60 x ₂-255.04( R ²=0.997), respectively. EC and TVB-N of rat postmortem skeletal muscle show similar change trends, which can be used as an index for decomposition rate of cadaveric skeletal muscle and provide a method for further study of late PMI estimation. Copyright© by the Editorial Department of Journal of Forensic Medicine

Estimating Commute Distances of U.S. Army Reservists by Regional and Unit Characteristics

DTIC Science & Technology

1990-09-01

multiple regression equation is used to estimate the parameters of the commute distance distribution as a function of reserve center and market ...used to estimate the parameters of the commute distance distribution as a function of reserve center and market characteristics. The results of the...recruiting personnel to meet unit fill rates. An important objective of the USAREC is to identify market areas that will support new reserve units [Ref. 2:p

Determination of the minimal clinically important difference for seven fatigue measures in rheumatoid arthritis

PubMed Central

Pouchot, Jacques; Kherani, Raheem B.; Brant, Rollin; Lacaille, Diane; Lehman, Allen J.; Ensworth, Stephanie; Kopec, Jacek; Esdaile, John M.; Liang, Matthew H.

2008-01-01

Objective To estimate the minimal clinically important difference (MCID) of seven measures of fatigue in rheumatoid arthritis. Study Design and Setting A cross-sectional study design based on inter-individual comparisons was used. Six to eight subjects participated in a single meeting and completed seven fatigue questionnaires (nine sessions were organized and 61 subjects participated). After completion of the questionnaires, the subjects had five one-on-one 10-minute conversations with different people in the group to discuss their fatigue. After each conversation, each patient compared their fatigue to their conversational partner’s on a global rating. Ratings were compared to the scores of the fatigue measures to estimate the MCID. Both non-parametric and linear regression analyses were used. Results Non-parametric estimates for the MCID relative to “little more fatigue” tended to be smaller than those for “little less fatigue”. The global MCIDs estimated by linear regression were: FSS 20.2, VT 14.8, MAF 18.7, MFI 16.6, FACIT–F 15.9, CFS 9.9, RS 19.7, for normalized scores (0 to 100). The standardized MCIDs for the seven measures were roughly similar (0.67 to 0.76). Conclusion These estimates of MCID will help to interpret changes observed in a fatigue score and will be critical in estimating sample size requirements. PMID:18359189

Robust inference under the beta regression model with application to health care studies.

PubMed

Ghosh, Abhik

2017-01-01

Data on rates, percentages, or proportions arise frequently in many different applied disciplines like medical biology, health care, psychology, and several others. In this paper, we develop a robust inference procedure for the beta regression model, which is used to describe such response variables taking values in (0, 1) through some related explanatory variables. In relation to the beta regression model, the issue of robustness has been largely ignored in the literature so far. The existing maximum likelihood-based inference has serious lack of robustness against outliers in data and generate drastically different (erroneous) inference in the presence of data contamination. Here, we develop the robust minimum density power divergence estimator and a class of robust Wald-type tests for the beta regression model along with several applications. We derive their asymptotic properties and describe their robustness theoretically through the influence function analyses. Finite sample performances of the proposed estimators and tests are examined through suitable simulation studies and real data applications in the context of health care and psychology. Although we primarily focus on the beta regression models with a fixed dispersion parameter, some indications are also provided for extension to the variable dispersion beta regression models with an application.

GLOBALLY ADAPTIVE QUANTILE REGRESSION WITH ULTRA-HIGH DIMENSIONAL DATA

PubMed Central

Zheng, Qi; Peng, Limin; He, Xuming

2015-01-01

Quantile regression has become a valuable tool to analyze heterogeneous covaraite-response associations that are often encountered in practice. The development of quantile regression methodology for high dimensional covariates primarily focuses on examination of model sparsity at a single or multiple quantile levels, which are typically prespecified ad hoc by the users. The resulting models may be sensitive to the specific choices of the quantile levels, leading to difficulties in interpretation and erosion of confidence in the results. In this article, we propose a new penalization framework for quantile regression in the high dimensional setting. We employ adaptive L1 penalties, and more importantly, propose a uniform selector of the tuning parameter for a set of quantile levels to avoid some of the potential problems with model selection at individual quantile levels. Our proposed approach achieves consistent shrinkage of regression quantile estimates across a continuous range of quantiles levels, enhancing the flexibility and robustness of the existing penalized quantile regression methods. Our theoretical results include the oracle rate of uniform convergence and weak convergence of the parameter estimators. We also use numerical studies to confirm our theoretical findings and illustrate the practical utility of our proposal. PMID:26604424

The effect of high leverage points on the logistic ridge regression estimator having multicollinearity

NASA Astrophysics Data System (ADS)

Ariffin, Syaiba Balqish; Midi, Habshah

2014-06-01

This article is concerned with the performance of logistic ridge regression estimation technique in the presence of multicollinearity and high leverage points. In logistic regression, multicollinearity exists among predictors and in the information matrix. The maximum likelihood estimator suffers a huge setback in the presence of multicollinearity which cause regression estimates to have unduly large standard errors. To remedy this problem, a logistic ridge regression estimator is put forward. It is evident that the logistic ridge regression estimator outperforms the maximum likelihood approach for handling multicollinearity. The effect of high leverage points are then investigated on the performance of the logistic ridge regression estimator through real data set and simulation study. The findings signify that logistic ridge regression estimator fails to provide better parameter estimates in the presence of both high leverage points and multicollinearity.

Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

NASA Astrophysics Data System (ADS)

Minh, Vu Trieu; Katushin, Dmitri; Antonov, Maksim; Veinthal, Renno

2017-03-01

This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM) based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock brittleness index (BI), the distance between planes of weakness (DPW), and the alpha angle (Alpha) between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP). Four (4) statistical regression models (two linear and two nonlinear) are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2) of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.

Comparison of in situ and in vitro techniques for measuring ruminal degradation of animal by-product proteins.

PubMed

England, M L; Broderick, G A; Shaver, R D; Combs, D K

1997-11-01

Ruminally undegraded protein (RUP) values of blood meal (n = 2), hydrolyzed feather meal (n = 2), fish meal (n = 2), meat and bone meal, and soybean meal were estimated using an in situ method, an inhibitor in vitro method, and an inhibitor in vitro technique applying Michaelis-Menten saturation kinetics. Degradation rates for in situ and inhibitor in vitro methods were calculated by regression of the natural log of the proportion of crude protein (CP) remaining undegraded versus time. Nonlinear regression analysis of the integrated Michaelis-Menten equation was used to determine maximum velocity, the Michaelis constant, and degradation rate (the ratio of maximum velocity to the Michaelis constant). A ruminal passage rate of 0.06/h was assumed in the calculation of RUP. The in situ and inhibitor in vitro techniques yielded similar estimates of ruminal degradation. Mean RUP estimated for soybean meal, blood meal, hydrolyzed feather meal, fish meal, and meat and bone meal were, respectively, 28.6, 86.0, 77.4, 52.9, and 52.6% of CP by the in situ method and 26.4, 86.1, 76.0, 59.6, and 49.5% of CP by the inhibitor in vitro technique. The Michaelis-Menten inhibitor in vitro technique yielded more rapid CP degradation rates and decreased estimates of RUP. The inhibitor in vitro method required less time and labor than did the other two techniques to estimate the RUP values of animal by-product proteins. Results from in vitro incubations with pepsin.HCl suggested that low postruminal digestibility of hydrolyzed feather meal may impair its value as a source of RUP.

Accounting for individual differences and timing of events: estimating the effect of treatment on criminal convictions in heroin users

PubMed Central

2014-01-01

Background The reduction of crime is an important outcome of opioid maintenance treatment (OMT). Criminal intensity and treatment regimes vary among OMT patients, but this is rarely adjusted for in statistical analyses, which tend to focus on cohort incidence rates and rate ratios. The purpose of this work was to estimate the relationship between treatment and criminal convictions among OMT patients, adjusting for individual covariate information and timing of events, fitting time-to-event regression models of increasing complexity. Methods National criminal records were cross linked with treatment data on 3221 patients starting OMT in Norway 1997–2003. In addition to calculating cohort incidence rates, criminal convictions was modelled as a recurrent event dependent variable, and treatment a time-dependent covariate, in Cox proportional hazards, Aalen’s additive hazards, and semi-parametric additive hazards regression models. Both fixed and dynamic covariates were included. Results During OMT, the number of days with criminal convictions for the cohort as a whole was 61% lower than when not in treatment. OMT was associated with reduced number of days with criminal convictions in all time-to-event regression models, but the hazard ratio (95% CI) was strongly attenuated when adjusting for covariates; from 0.40 (0.35, 0.45) in a univariate model to 0.79 (0.72, 0.87) in a fully adjusted model. The hazard was lower for females and decreasing with older age, while increasing with high numbers of criminal convictions prior to application to OMT (all p < 0.001). The strongest predictors were level of criminal activity prior to entering into OMT, and having a recent criminal conviction (both p < 0.001). The effect of several predictors was significantly time-varying with their effects diminishing over time. Conclusions Analyzing complex observational data regarding to fixed factors only overlooks important temporal information, and naïve cohort level incidence rates might result in biased estimates of the effect of interventions. Applying time-to-event regression models, properly adjusting for individual covariate information and timing of various events, allows for more precise and reliable effect estimates, as well as painting a more nuanced picture that can aid health care professionals and policy makers. PMID:24886472

Linear and evolutionary polynomial regression models to forecast coastal dynamics: Comparison and reliability assessment

NASA Astrophysics Data System (ADS)

Bruno, Delia Evelina; Barca, Emanuele; Goncalves, Rodrigo Mikosz; de Araujo Queiroz, Heithor Alexandre; Berardi, Luigi; Passarella, Giuseppe

2018-01-01

In this paper, the Evolutionary Polynomial Regression data modelling strategy has been applied to study small scale, short-term coastal morphodynamics, given its capability for treating a wide database of known information, non-linearly. Simple linear and multilinear regression models were also applied to achieve a balance between the computational load and reliability of estimations of the three models. In fact, even though it is easy to imagine that the more complex the model, the more the prediction improves, sometimes a "slight" worsening of estimations can be accepted in exchange for the time saved in data organization and computational load. The models' outcomes were validated through a detailed statistical, error analysis, which revealed a slightly better estimation of the polynomial model with respect to the multilinear model, as expected. On the other hand, even though the data organization was identical for the two models, the multilinear one required a simpler simulation setting and a faster run time. Finally, the most reliable evolutionary polynomial regression model was used in order to make some conjecture about the uncertainty increase with the extension of extrapolation time of the estimation. The overlapping rate between the confidence band of the mean of the known coast position and the prediction band of the estimated position can be a good index of the weakness in producing reliable estimations when the extrapolation time increases too much. The proposed models and tests have been applied to a coastal sector located nearby Torre Colimena in the Apulia region, south Italy.

Predicting SF-6D utility scores from the Neck Disability Index and Numeric Rating Scales for Neck and Arm Pain

PubMed Central

Carreon, Leah Y.; Anderson, Paul A.; McDonough, Christine M.; Djurasovic, Mladen; Glassman, Steven D.

2010-01-01

Study Design Cross-sectional cohort Objective This study aims to provide an algorithm estimate SF-6D utilities using data from the NDI, neck pain and arm pain scores. Summary of Background Data Although cost-utility analysis is increasingly used to provide information about the relative value of alternative interventions, health state values or utilities are rarely available from clinical trial data. The Neck Disability Index (NDI) and numeric rating scales for neck and arm pain, are widely used disease-specific measures of symptoms, function and disability in patients with cervical degenerative disorders. The purpose of this study is to provide an algorithm to allow estimation of SF-6D utilities using data from the NDI, and numeric rating scales for neck and arm pain. Methods SF-36, NDI, neck and arm pain rating scale scores were prospectively collected pre-operatively, at 12 and 24 months post-operatively in 2080 patients undergoing cervical fusion for degenerative disorders. SF-6D utilities were computed and Spearman correlation coefficients were calculated for paired observations from multiple time points between NDI, neck and arm pain scores and SF-6D utility scores. SF-6D scores were estimated from the NDI, neck and arm pain scores using a linear regression model. Using a separate, independent dataset of 396 patients in which and NDI scores were available SF-6D was estimated for each subject and compared to their actual SF-6D. Results The mean age for those in the development sample, was 50.4 ± 11.0 years and 33% were male. In the validation sample the mean age was 53.1 ± 9.9 years and 35% were male. Correlations between the SF-6D and the NDI, neck and arm pain scores were statistically significant (p<0.0001) with correlation coefficients of 0.82, 0.62, and 0.50 respectively. The regression equation using NDI alone to predict SF-6D had an R2 of 0.66 and a root mean square error (RMSE) of 0.056. In the validation analysis, there was no statistically significant difference (p=0.961) between actual mean SF-6D (0.49 ± 0.08) and the estimated mean SF-6D score (0.49 ± 0.08) using the NDI regression model. Conclusion This regression-based algorithm may be a useful tool to predict SF-6D scores in studies of cervical degenerative disease that have collected NDI but not utility scores. PMID:20847713

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

Regression method for estimating long-term mean annual ground-water recharge rates from base flow in Pennsylvania

USGS Publications Warehouse

Risser, Dennis W.; Thompson, Ronald E.; Stuckey, Marla H.

2008-01-01

A method was developed for making estimates of long-term, mean annual ground-water recharge from streamflow data at 80 streamflow-gaging stations in Pennsylvania. The method relates mean annual base-flow yield derived from the streamflow data (as a proxy for recharge) to the climatic, geologic, hydrologic, and physiographic characteristics of the basins (basin characteristics) by use of a regression equation. Base-flow yield is the base flow of a stream divided by the drainage area of the basin, expressed in inches of water basinwide. Mean annual base-flow yield was computed for the period of available streamflow record at continuous streamflow-gaging stations by use of the computer program PART, which separates base flow from direct runoff on the streamflow hydrograph. Base flow provides a reasonable estimate of recharge for basins where streamflow is mostly unaffected by upstream regulation, diversion, or mining. Twenty-eight basin characteristics were included in the exploratory regression analysis as possible predictors of base-flow yield. Basin characteristics found to be statistically significant predictors of mean annual base-flow yield during 1971-2000 at the 95-percent confidence level were (1) mean annual precipitation, (2) average maximum daily temperature, (3) percentage of sand in the soil, (4) percentage of carbonate bedrock in the basin, and (5) stream channel slope. The equation for predicting recharge was developed using ordinary least-squares regression. The standard error of prediction for the equation on log-transformed data was 9.7 percent, and the coefficient of determination was 0.80. The equation can be used to predict long-term, mean annual recharge rates for ungaged basins, providing that the explanatory basin characteristics can be determined and that the underlying assumption is accepted that base-flow yield derived from PART is a reasonable estimate of ground-water recharge rates. For example, application of the equation for 370 hydrologic units in Pennsylvania predicted a range of ground-water recharge from about 6.0 to 22 inches per year. A map of the predicted recharge illustrates the general magnitude and variability of recharge throughout Pennsylvania.

Quantile regression models of animal habitat relationships

USGS Publications Warehouse

Cade, Brian S.

2003-01-01

Typically, all factors that limit an organism are not measured and included in statistical models used to investigate relationships with their environment. If important unmeasured variables interact multiplicatively with the measured variables, the statistical models often will have heterogeneous response distributions with unequal variances. Quantile regression is an approach for estimating the conditional quantiles of a response variable distribution in the linear model, providing a more complete view of possible causal relationships between variables in ecological processes. Chapter 1 introduces quantile regression and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of estimates for homogeneous and heterogeneous regression models. Chapter 2 evaluates performance of quantile rankscore tests used for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0 ≤ τ ≤ 1). A permutation F test maintained better Type I errors than the Chi-square T test for models with smaller n, greater number of parameters p, and more extreme quantiles τ. Both versions of the test required weighting to maintain correct Type I errors when there was heterogeneity under the alternative model. An example application related trout densities to stream channel width:depth. Chapter 3 evaluates a drop in dispersion, F-ratio like permutation test for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0 ≤ τ ≤ 1). Chapter 4 simulates from a large (N = 10,000) finite population representing grid areas on a landscape to demonstrate various forms of hidden bias that might occur when the effect of a measured habitat variable on some animal was confounded with the effect of another unmeasured variable (spatially and not spatially structured). Depending on whether interactions of the measured habitat and unmeasured variable were negative (interference interactions) or positive (facilitation interactions), either upper (τ > 0.5) or lower (τ < 0.5) quantile regression parameters were less biased than mean rate parameters. Sampling (n = 20 - 300) simulations demonstrated that confidence intervals constructed by inverting rankscore tests provided valid coverage of these biased parameters. Quantile regression was used to estimate effects of physical habitat resources on a bivalve mussel (Macomona liliana) in a New Zealand harbor by modeling the spatial trend surface as a cubic polynomial of location coordinates.

Estimating Children's Soil/Dust Ingestion Rates through ...

EPA Pesticide Factsheets

Background: Soil/dust ingestion rates are important variables in assessing children’s health risks in contaminated environments. Current estimates are based largely on soil tracer methodology, which is limited by analytical uncertainty, small sample size, and short study duration. Objectives: The objective was to estimate site-specific soil/dust ingestion rates through reevaluation of the lead absorption dose–response relationship using new bioavailability data from the Bunker Hill Mining and Metallurgical Complex Superfund Site (BHSS) in Idaho, USA. Methods: The U.S. Environmental Protection Agency (EPA) in vitro bioavailability methodology was applied to archived BHSS soil and dust samples. Using age-specific biokinetic slope factors, we related bioavailable lead from these sources to children’s blood lead levels (BLLs) monitored during cleanup from 1988 through 2002. Quantitative regression analyses and exposure assessment guidance were used to develop candidate soil/dust source partition scenarios estimating lead intake, allowing estimation of age-specific soil/dust ingestion rates. These ingestion rate and bioavailability estimates were simultaneously applied to the U.S. EPA Integrated Exposure Uptake Biokinetic Model for Lead in Children to determine those combinations best approximating observed BLLs. Results: Absolute soil and house dust bioavailability averaged 33% (SD ± 4%) and 28% (SD ± 6%), respectively. Estimated BHSS age-specific soil/du

A computer program (MODFLOWP) for estimating parameters of a transient, three-dimensional ground-water flow model using nonlinear regression

USGS Publications Warehouse

Hill, Mary Catherine

1992-01-01

This report documents a new version of the U.S. Geological Survey modular, three-dimensional, finite-difference, ground-water flow model (MODFLOW) which, with the new Parameter-Estimation Package that also is documented in this report, can be used to estimate parameters by nonlinear regression. The new version of MODFLOW is called MODFLOWP (pronounced MOD-FLOW*P), and functions nearly identically to MODFLOW when the ParameterEstimation Package is not used. Parameters are estimated by minimizing a weighted least-squares objective function by the modified Gauss-Newton method or by a conjugate-direction method. Parameters used to calculate the following MODFLOW model inputs can be estimated: Transmissivity and storage coefficient of confined layers; hydraulic conductivity and specific yield of unconfined layers; vertical leakance; vertical anisotropy (used to calculate vertical leakance); horizontal anisotropy; hydraulic conductance of the River, Streamflow-Routing, General-Head Boundary, and Drain Packages; areal recharge rates; maximum evapotranspiration; pumpage rates; and the hydraulic head at constant-head boundaries. Any spatial variation in parameters can be defined by the user. Data used to estimate parameters can include existing independent estimates of parameter values, observed hydraulic heads or temporal changes in hydraulic heads, and observed gains and losses along head-dependent boundaries (such as streams). Model output includes statistics for analyzing the parameter estimates and the model; these statistics can be used to quantify the reliability of the resulting model, to suggest changes in model construction, and to compare results of models constructed in different ways.

The need to control for regression to the mean in social psychology studies.

PubMed

Yu, Rongjun; Chen, Li

2014-01-01

It is common in repeated measurements for extreme values at the first measurement to approach the mean at the subsequent measurement, a phenomenon called regression to the mean (RTM). If RTM is not fully controlled, it will lead to erroneous conclusions. The wide use of repeated measurements in social psychology creates a risk that an RTM effect will influence results. However, insufficient attention is paid to RTM in most social psychological research. Notable cases include studies on the phenomena of social conformity and unrealistic optimism (Klucharev et al., 2009, 2011; Sharot et al., 2011, 2012b; Campbell-Meiklejohn et al., 2012; Kim et al., 2012; Garrett and Sharot, 2014). In Study 1, 13 university students rated and re-rated the facial attractiveness of a series of female faces as a test of the social conformity effect (Klucharev et al., 2009). In Study 2, 15 university students estimated and re-estimated their risk of experiencing a series of adverse life events as a test of the unrealistic optimism effect (Sharot et al., 2011). Although these studies used methodologies similar to those used in earlier research, the social conformity and unrealistic optimism effects were no longer evident after controlling for RTM. Based on these findings we suggest several ways to control for the RTM effect in social psychology studies, such as adding the initial rating as a covariate in regression analysis, selecting a subset of stimuli for which the participant' initial ratings were matched across experimental conditions, and using a control group.

Do alcohol excise taxes affect traffic accidents? Evidence from Estonia.

PubMed

Saar, Indrek

2015-01-01

This article examines the association between alcohol excise tax rates and alcohol-related traffic accidents in Estonia. Monthly time series of traffic accidents involving drunken motor vehicle drivers from 1998 through 2013 were regressed on real average alcohol excise tax rates while controlling for changes in economic conditions and the traffic environment. Specifically, regression models with autoregressive integrated moving average (ARIMA) errors were estimated in order to deal with serial correlation in residuals. Counterfactual models were also estimated in order to check the robustness of the results, using the level of non-alcohol-related traffic accidents as a dependent variable. A statistically significant (P <.01) strong negative relationship between the real average alcohol excise tax rate and alcohol-related traffic accidents was disclosed under alternative model specifications. For instance, the regression model with ARIMA (0, 1, 1)(0, 1, 1) errors revealed that a 1-unit increase in the tax rate is associated with a 1.6% decrease in the level of accidents per 100,000 population involving drunk motor vehicle drivers. No similar association was found in the cases of counterfactual models for non-alcohol-related traffic accidents. This article indicates that the level of alcohol-related traffic accidents in Estonia has been affected by changes in real average alcohol excise taxes during the period 1998-2013. Therefore, in addition to other measures, the use of alcohol taxation is warranted as a policy instrument in tackling alcohol-related traffic accidents.

Prediction of Cancer Incidence and Mortality in Korea, 2018.

PubMed

Jung, Kyu-Won; Won, Young-Joo; Kong, Hyun-Joo; Lee, Eun Sook

2018-04-01

This study aimed to report on cancer incidence and mortality for the year 2018 to estimate Korea's current cancer burden. Cancer incidence data from 1999 to 2015 were obtained from the Korea National Cancer Incidence Database, and cancer mortality data from 1993 to 2016 were acquired from Statistics Korea. Cancer incidence and mortality were projected by fitting a linear regression model to observed age-specific cancer rates against observed years, then multiplying the projected age-specific rates by the age-specific population. The Joinpoint regression model was used to determine at which year the linear trend changed significantly, we only used the data of the latest trend. A total of 204,909 new cancer cases and 82,155 cancer deaths are expected to occur in Korea in 2018. The most common cancer sites were lung, followed by stomach, colorectal, breast and liver. These five cancers represent half of the overall burden of cancer in Korea. For mortality, the most common sites were lung cancer, followed by liver, colorectal, stomach and pancreas. The incidence rate of all cancer in Korea are estimated to decrease gradually, mainly due to decrease of thyroid cancer. These up-to-date estimates of the cancer burden in Korea could be an important resource for planning and evaluation of cancer-control programs.

Field comparison of body composition techniques: hydrostatic weighing, skinfold thickness, and bioelectric impedance.

PubMed

Kirkendall, D T; Grogan, J W; Bowers, R G

1991-01-01

Body composition and appropriate playing weight are frequently requested by coaches. Numerous methods for estimating these figures are available, and each has its own limitation, be it technical or biological. A comparison of three common methods was made-underwater weighting (H2O, the criterion), skinfold thicknesses (SF), and commercial bioelectrical impedance analysis (BIA). Subjects were 29 professional football players measured by each of the three methods after an overnight fast. Data was collected 10 weeks preceding the players' formal training camp. There was no difference for percentage of weight as fat between SF (15.8%) and H2O (14.2%). Bioelectrical impedance analysis significantly (p < .05) overestimated percent fat (19.2%) compared to H20. Error rates when regressing SF on H2O were favorable, whether expressed for the whole sample (3.04%) or by race (1.78% or 3.56% for whites and blacks, respectively). Regression of BIA on H2O showed an elevated, overall error rate (14.12%) and elevated error rates for whites (11.57%) and blacks (13.81%). Of the two estimates of body composition on a racially mixed sample of males, SF provided the best estimate with the least amount of error. J Orthop Sports Phys Ther 1991;13(5):235-239.

An evaluation of bias in propensity score-adjusted non-linear regression models.

PubMed

Wan, Fei; Mitra, Nandita

2018-03-01

Propensity score methods are commonly used to adjust for observed confounding when estimating the conditional treatment effect in observational studies. One popular method, covariate adjustment of the propensity score in a regression model, has been empirically shown to be biased in non-linear models. However, no compelling underlying theoretical reason has been presented. We propose a new framework to investigate bias and consistency of propensity score-adjusted treatment effects in non-linear models that uses a simple geometric approach to forge a link between the consistency of the propensity score estimator and the collapsibility of non-linear models. Under this framework, we demonstrate that adjustment of the propensity score in an outcome model results in the decomposition of observed covariates into the propensity score and a remainder term. Omission of this remainder term from a non-collapsible regression model leads to biased estimates of the conditional odds ratio and conditional hazard ratio, but not for the conditional rate ratio. We further show, via simulation studies, that the bias in these propensity score-adjusted estimators increases with larger treatment effect size, larger covariate effects, and increasing dissimilarity between the coefficients of the covariates in the treatment model versus the outcome model.

Syndromic surveillance for evaluating the occurrence of healthcare-associated infections in equine hospitals.

PubMed

Ruple-Czerniak, A A; Aceto, H W; Bender, J B; Paradis, M R; Shaw, S P; Van Metre, D C; Weese, J S; Wilson, D A; Wilson, J; Morley, P S

2014-07-01

Methods that can be used to estimate rates of healthcare-associated infections and other nosocomial events have not been well established for use in equine hospitals. Traditional laboratory-based surveillance is expensive and cannot be applied in all of these settings. To evaluate the use of a syndromic surveillance system for estimating rates of occurrence of healthcare-associated infections among hospitalised equine cases. Multicentre, prospective longitudinal study. This study included weaned equids (n = 297) that were admitted for gastrointestinal disorders at one of 5 participating veterinary referral hospitals during a 12-week period in 2006. A survey form was completed by the primary clinician to summarise basic case information, procedures and treatments the horse received, and whether one or more of 7 predefined nosocomial syndromes were recognised at any point during hospitalisation. Adjusted rates of nosocomial events were estimated using Poisson regression. Risk factors associated with the risk of developing a nosocomial event were analysed using multivariable logistic regression. Among the study population, 95 nosocomial events were reported to have occurred in 65 horses. Controlling for differences among hospitals, 19.7% (95% confidence interval, 14.5-26.7) of the study population was reported to have had at least one nosocomial event recognised during hospitalisation. The most commonly reported nosocomial syndromes that were unrelated to the reason for hospitalisation were surgical site inflammation and i.v. catheter site inflammation. Syndromic surveillance systems can be standardised successfully for use across multiple hospitals without interfering with established organisational structures, in order to provide useful estimates of rates related to healthcare-associated infections. © 2013 EVJ Ltd.

Relationship between the evaporation rate and vapor pressure of moderately and highly volatile chemicals.

PubMed

van Wesenbeeck, Ian; Driver, Jeffrey; Ross, John

2008-04-01

Volatilization of chemicals can be an important form of dissipation in the environment. Rates of evaporative losses from plant and soil surfaces are useful for estimating the potential for food-related dietary residues and operator and bystander exposure, and can be used as source functions for screening models that predict off-site movement of volatile materials. A regression of evaporation on vapor pressure from three datasets containing 82 pesticidal active ingredients and co-formulants, ranging in vapor pressure from 0.0001 to >30,000 Pa was developed for this purpose with a regression correlation coefficient of 0.98.

A statistical methodology for estimating transport parameters: Theory and applications to one-dimensional advectivec-dispersive systems

USGS Publications Warehouse

Wagner, Brian J.; Gorelick, Steven M.

1986-01-01

A simulation nonlinear multiple-regression methodology for estimating parameters that characterize the transport of contaminants is developed and demonstrated. Finite difference contaminant transport simulation is combined with a nonlinear weighted least squares multiple-regression procedure. The technique provides optimal parameter estimates and gives statistics for assessing the reliability of these estimates under certain general assumptions about the distributions of the random measurement errors. Monte Carlo analysis is used to estimate parameter reliability for a hypothetical homogeneous soil column for which concentration data contain large random measurement errors. The value of data collected spatially versus data collected temporally was investigated for estimation of velocity, dispersion coefficient, effective porosity, first-order decay rate, and zero-order production. The use of spatial data gave estimates that were 2–3 times more reliable than estimates based on temporal data for all parameters except velocity. Comparison of estimated linear and nonlinear confidence intervals based upon Monte Carlo analysis showed that the linear approximation is poor for dispersion coefficient and zero-order production coefficient when data are collected over time. In addition, examples demonstrate transport parameter estimation for two real one-dimensional systems. First, the longitudinal dispersivity and effective porosity of an unsaturated soil are estimated using laboratory column data. We compare the reliability of estimates based upon data from individual laboratory experiments versus estimates based upon pooled data from several experiments. Second, the simulation nonlinear regression procedure is extended to include an additional governing equation that describes delayed storage during contaminant transport. The model is applied to analyze the trends, variability, and interrelationship of parameters in a mourtain stream in northern California.

Modeling Sea-Level Change using Errors-in-Variables Integrated Gaussian Processes

NASA Astrophysics Data System (ADS)

Cahill, Niamh; Parnell, Andrew; Kemp, Andrew; Horton, Benjamin

2014-05-01

We perform Bayesian inference on historical and late Holocene (last 2000 years) rates of sea-level change. The data that form the input to our model are tide-gauge measurements and proxy reconstructions from cores of coastal sediment. To accurately estimate rates of sea-level change and reliably compare tide-gauge compilations with proxy reconstructions it is necessary to account for the uncertainties that characterize each dataset. Many previous studies used simple linear regression models (most commonly polynomial regression) resulting in overly precise rate estimates. The model we propose uses an integrated Gaussian process approach, where a Gaussian process prior is placed on the rate of sea-level change and the data itself is modeled as the integral of this rate process. The non-parametric Gaussian process model is known to be well suited to modeling time series data. The advantage of using an integrated Gaussian process is that it allows for the direct estimation of the derivative of a one dimensional curve. The derivative at a particular time point will be representative of the rate of sea level change at that time point. The tide gauge and proxy data are complicated by multiple sources of uncertainty, some of which arise as part of the data collection exercise. Most notably, the proxy reconstructions include temporal uncertainty from dating of the sediment core using techniques such as radiocarbon. As a result of this, the integrated Gaussian process model is set in an errors-in-variables (EIV) framework so as to take account of this temporal uncertainty. The data must be corrected for land-level change known as glacio-isostatic adjustment (GIA) as it is important to isolate the climate-related sea-level signal. The correction for GIA introduces covariance between individual age and sea level observations into the model. The proposed integrated Gaussian process model allows for the estimation of instantaneous rates of sea-level change and accounts for all available sources of uncertainty in tide-gauge and proxy-reconstruction data. Our response variable is sea level after correction for GIA. By embedding the integrated process in an errors-in-variables (EIV) framework, and removing the estimate of GIA, we can quantify rates with better estimates of uncertainty than previously possible. The model provides a flexible fit and enables us to estimate rates of change at any given time point, thus observing how rates have been evolving from the past to present day.

Locoregional Control of Non-Small Cell Lung Cancer in Relation to Automated Early Assessment of Tumor Regression on Cone Beam Computed Tomography

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

Brink, Carsten, E-mail: carsten.brink@rsyd.dk; Laboratory of Radiation Physics, Odense University Hospital; Bernchou, Uffe

2014-07-15

Purpose: Large interindividual variations in volume regression of non-small cell lung cancer (NSCLC) are observable on standard cone beam computed tomography (CBCT) during fractionated radiation therapy. Here, a method for automated assessment of tumor volume regression is presented and its potential use in response adapted personalized radiation therapy is evaluated empirically. Methods and Materials: Automated deformable registration with calculation of the Jacobian determinant was applied to serial CBCT scans in a series of 99 patients with NSCLC. Tumor volume at the end of treatment was estimated on the basis of the first one third and two thirds of the scans.more » The concordance between estimated and actual relative volume at the end of radiation therapy was quantified by Pearson's correlation coefficient. On the basis of the estimated relative volume, the patients were stratified into 2 groups having volume regressions below or above the population median value. Kaplan-Meier plots of locoregional disease-free rate and overall survival in the 2 groups were used to evaluate the predictive value of tumor regression during treatment. Cox proportional hazards model was used to adjust for other clinical characteristics. Results: Automatic measurement of the tumor regression from standard CBCT images was feasible. Pearson's correlation coefficient between manual and automatic measurement was 0.86 in a sample of 9 patients. Most patients experienced tumor volume regression, and this could be quantified early into the treatment course. Interestingly, patients with pronounced volume regression had worse locoregional tumor control and overall survival. This was significant on patient with non-adenocarcinoma histology. Conclusions: Evaluation of routinely acquired CBCT images during radiation therapy provides biological information on the specific tumor. This could potentially form the basis for personalized response adaptive therapy.« less

A comparison of the performances of an artificial neural network and a regression model for GFR estimation.

PubMed

Liu, Xun; Li, Ning-shan; Lv, Lin-sheng; Huang, Jian-hua; Tang, Hua; Chen, Jin-xia; Ma, Hui-juan; Wu, Xiao-ming; Lou, Tan-qi

2013-12-01

Accurate estimation of glomerular filtration rate (GFR) is important in clinical practice. Current models derived from regression are limited by the imprecision of GFR estimates. We hypothesized that an artificial neural network (ANN) might improve the precision of GFR estimates. A study of diagnostic test accuracy. 1,230 patients with chronic kidney disease were enrolled, including the development cohort (n=581), internal validation cohort (n=278), and external validation cohort (n=371). Estimated GFR (eGFR) using a new ANN model and a new regression model using age, sex, and standardized serum creatinine level derived in the development and internal validation cohort, and the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) 2009 creatinine equation. Measured GFR (mGFR). GFR was measured using a diethylenetriaminepentaacetic acid renal dynamic imaging method. Serum creatinine was measured with an enzymatic method traceable to isotope-dilution mass spectrometry. In the external validation cohort, mean mGFR was 49±27 (SD) mL/min/1.73 m2 and biases (median difference between mGFR and eGFR) for the CKD-EPI, new regression, and new ANN models were 0.4, 1.5, and -0.5 mL/min/1.73 m2, respectively (P<0.001 and P=0.02 compared to CKD-EPI and P<0.001 comparing the new regression and ANN models). Precisions (IQRs for the difference) were 22.6, 14.9, and 15.6 mL/min/1.73 m2, respectively (P<0.001 for both compared to CKD-EPI and P<0.001 comparing the new ANN and new regression models). Accuracies (proportions of eGFRs not deviating >30% from mGFR) were 50.9%, 77.4%, and 78.7%, respectively (P<0.001 for both compared to CKD-EPI and P=0.5 comparing the new ANN and new regression models). Different methods for measuring GFR were a source of systematic bias in comparisons of new models to CKD-EPI, and both the derivation and validation cohorts consisted of a group of patients who were referred to the same institution. An ANN model using 3 variables did not perform better than a new regression model. Whether ANN can improve GFR estimation using more variables requires further investigation. Copyright © 2013 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

Diurnal variation in heat production related to some physical activities in laying hens.

PubMed

Li, Y Z; Ito, T; Yamamoto, S

1991-09-01

1. Heat production, standing and eating activities, and hourly food intake of 4 laying hens were observed simultaneously and the effects of activity and food intake on heat production were studied. 2. Average heat production during the dark period (20.00 to 06.00 h) was 18.9 kJ/kgW0.75 h which was 33% lower than that during the light period. About 76% of the light-dark difference in the rate of heat production was probably associated with activity and posture. 3. Standing time, which included a range of behavioural activities, occupied 90% of the light period and the increased rate of heat production associated with standing was estimated to be about 18% of daily heat production. 4. Eating time occupied 40% of the light period; the heat production associated with eating activity represented about 5% of daily heat production or 3% of ME intake. 5. Because the regression of heat production on time spent eating agreed with the regression of heat production on hourly food intake, it is suggested that the energy expenditure associated with ad libitum feeding can be estimated for hens from the regression of heat production on hourly food intake.

Mixed oxidizer hybrid propulsion system optimization under uncertainty using applied response surface methodology and Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Whitehead, James Joshua

The analysis documented herein provides an integrated approach for the conduct of optimization under uncertainty (OUU) using Monte Carlo Simulation (MCS) techniques coupled with response surface-based methods for characterization of mixture-dependent variables. This novel methodology provides an innovative means of conducting optimization studies under uncertainty in propulsion system design. Analytic inputs are based upon empirical regression rate information obtained from design of experiments (DOE) mixture studies utilizing a mixed oxidizer hybrid rocket concept. Hybrid fuel regression rate was selected as the target response variable for optimization under uncertainty, with maximization of regression rate chosen as the driving objective. Characteristic operational conditions and propellant mixture compositions from experimental efforts conducted during previous foundational work were combined with elemental uncertainty estimates as input variables. Response surfaces for mixture-dependent variables and their associated uncertainty levels were developed using quadratic response equations incorporating single and two-factor interactions. These analysis inputs, response surface equations and associated uncertainty contributions were applied to a probabilistic MCS to develop dispersed regression rates as a function of operational and mixture input conditions within design space. Illustrative case scenarios were developed and assessed using this analytic approach including fully and partially constrained operational condition sets over all of design mixture space. In addition, optimization sets were performed across an operationally representative region in operational space and across all investigated mixture combinations. These scenarios were selected as representative examples relevant to propulsion system optimization, particularly for hybrid and solid rocket platforms. Ternary diagrams, including contour and surface plots, were developed and utilized to aid in visualization. The concept of Expanded-Durov diagrams was also adopted and adapted to this study to aid in visualization of uncertainty bounds. Regions of maximum regression rate and associated uncertainties were determined for each set of case scenarios. Application of response surface methodology coupled with probabilistic-based MCS allowed for flexible and comprehensive interrogation of mixture and operating design space during optimization cases. Analyses were also conducted to assess sensitivity of uncertainty to variations in key elemental uncertainty estimates. The methodology developed during this research provides an innovative optimization tool for future propulsion design efforts.

Logistic Regression with Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages.

PubMed

Kim, Yoonsang; Choi, Young-Ku; Emery, Sherry

2013-08-01

Several statistical packages are capable of estimating generalized linear mixed models and these packages provide one or more of three estimation methods: penalized quasi-likelihood, Laplace, and Gauss-Hermite. Many studies have investigated these methods' performance for the mixed-effects logistic regression model. However, the authors focused on models with one or two random effects and assumed a simple covariance structure between them, which may not be realistic. When there are multiple correlated random effects in a model, the computation becomes intensive, and often an algorithm fails to converge. Moreover, in our analysis of smoking status and exposure to anti-tobacco advertisements, we have observed that when a model included multiple random effects, parameter estimates varied considerably from one statistical package to another even when using the same estimation method. This article presents a comprehensive review of the advantages and disadvantages of each estimation method. In addition, we compare the performances of the three methods across statistical packages via simulation, which involves two- and three-level logistic regression models with at least three correlated random effects. We apply our findings to a real dataset. Our results suggest that two packages-SAS GLIMMIX Laplace and SuperMix Gaussian quadrature-perform well in terms of accuracy, precision, convergence rates, and computing speed. We also discuss the strengths and weaknesses of the two packages in regard to sample sizes.

Logistic Regression with Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages

PubMed Central

Kim, Yoonsang; Emery, Sherry

2013-01-01

Several statistical packages are capable of estimating generalized linear mixed models and these packages provide one or more of three estimation methods: penalized quasi-likelihood, Laplace, and Gauss-Hermite. Many studies have investigated these methods’ performance for the mixed-effects logistic regression model. However, the authors focused on models with one or two random effects and assumed a simple covariance structure between them, which may not be realistic. When there are multiple correlated random effects in a model, the computation becomes intensive, and often an algorithm fails to converge. Moreover, in our analysis of smoking status and exposure to anti-tobacco advertisements, we have observed that when a model included multiple random effects, parameter estimates varied considerably from one statistical package to another even when using the same estimation method. This article presents a comprehensive review of the advantages and disadvantages of each estimation method. In addition, we compare the performances of the three methods across statistical packages via simulation, which involves two- and three-level logistic regression models with at least three correlated random effects. We apply our findings to a real dataset. Our results suggest that two packages—SAS GLIMMIX Laplace and SuperMix Gaussian quadrature—perform well in terms of accuracy, precision, convergence rates, and computing speed. We also discuss the strengths and weaknesses of the two packages in regard to sample sizes. PMID:24288415

An application of robust ridge regression model in the presence of outliers to real data problem

NASA Astrophysics Data System (ADS)

Shariff, N. S. Md.; Ferdaos, N. A.

2017-09-01

Multicollinearity and outliers are often leads to inconsistent and unreliable parameter estimates in regression analysis. The well-known procedure that is robust to multicollinearity problem is the ridge regression method. This method however is believed are affected by the presence of outlier. The combination of GM-estimation and ridge parameter that is robust towards both problems is on interest in this study. As such, both techniques are employed to investigate the relationship between stock market price and macroeconomic variables in Malaysia due to curiosity of involving the multicollinearity and outlier problem in the data set. There are four macroeconomic factors selected for this study which are Consumer Price Index (CPI), Gross Domestic Product (GDP), Base Lending Rate (BLR) and Money Supply (M1). The results demonstrate that the proposed procedure is able to produce reliable results towards the presence of multicollinearity and outliers in the real data.

Examining the relation between rock mass cuttability index and rock drilling properties

NASA Astrophysics Data System (ADS)

Yetkin, Mustafa E.; Özfırat, M. Kemal; Yenice, Hayati; Şimşir, Ferhan; Kahraman, Bayram

2016-12-01

Drilling rate is a substantial index value in drilling and excavation operations at mining. It is not only a help in determining physical and mechanical features of rocks, but also delivers strong estimations about instantaneous cutting rates. By this way, work durations to be finished on time, proper machine/equipment selection and efficient excavation works can be achieved. In this study, physical and mechanical properties of surrounding rocks and ore zones are determined by investigations carried out on specimens taken from an underground ore mine. Later, relationships among rock mass classifications, drillability rates, cuttability, and abrasivity have been investigated using multi regression analysis. As a result, equations having high regression rates have been found out among instantaneous cutting rates and geomechanical properties of rocks. Moreover, excavation machine selection for the study area has been made at the best possible interval.

SEPARABLE FACTOR ANALYSIS WITH APPLICATIONS TO MORTALITY DATA

PubMed Central

Fosdick, Bailey K.; Hoff, Peter D.

2014-01-01

Human mortality data sets can be expressed as multiway data arrays, the dimensions of which correspond to categories by which mortality rates are reported, such as age, sex, country and year. Regression models for such data typically assume an independent error distribution or an error model that allows for dependence along at most one or two dimensions of the data array. However, failing to account for other dependencies can lead to inefficient estimates of regression parameters, inaccurate standard errors and poor predictions. An alternative to assuming independent errors is to allow for dependence along each dimension of the array using a separable covariance model. However, the number of parameters in this model increases rapidly with the dimensions of the array and, for many arrays, maximum likelihood estimates of the covariance parameters do not exist. In this paper, we propose a submodel of the separable covariance model that estimates the covariance matrix for each dimension as having factor analytic structure. This model can be viewed as an extension of factor analysis to array-valued data, as it uses a factor model to estimate the covariance along each dimension of the array. We discuss properties of this model as they relate to ordinary factor analysis, describe maximum likelihood and Bayesian estimation methods, and provide a likelihood ratio testing procedure for selecting the factor model ranks. We apply this methodology to the analysis of data from the Human Mortality Database, and show in a cross-validation experiment how it outperforms simpler methods. Additionally, we use this model to impute mortality rates for countries that have no mortality data for several years. Unlike other approaches, our methodology is able to estimate similarities between the mortality rates of countries, time periods and sexes, and use this information to assist with the imputations. PMID:25489353

Estimating EQ-5D values from the Neck Disability Index and numeric rating scales for neck and arm pain.

PubMed

Carreon, Leah Y; Bratcher, Kelly R; Das, Nandita; Nienhuis, Jacob B; Glassman, Steven D

2014-09-01

The Neck Disability Index (NDI) and numeric rating scales (0 to 10) for neck pain and arm pain are widely used cervical spine disease-specific measures. Recent studies have shown that there is a strong relationship between the SF-6D and the NDI such that using a simple linear regression allows for the estimation of an SF-6D value from the NDI alone. Due to ease of administration and scoring, the EQ-5D is increasingly being used as a measure of utility in the clinical setting. The purpose of this study is to determine if the EQ-5D values can be estimated from commonly available cervical spine disease-specific health-related quality of life measures, much like the SF-6D. The EQ-5D, NDI, neck pain score, and arm pain score were prospectively collected in 3732 patients who presented to the authors' clinic with degenerative cervical spine disorders. Correlation coefficients for paired observations from multiple time points between the NDI, neck pain and arm pain scores, and EQ-5D were determined. Regression models were built to estimate the EQ-5D values from the NDI, neck pain, and arm pain scores. The mean age of the 3732 patients was 53.3 ± 12.2 years, and 43% were male. Correlations between the EQ-5D and the NDI, neck pain score, and arm pain score were statistically significant (p < 0.0001), with correlation coefficients of -0.77, -0.62, and -0.50, respectively. The regression equation 0.98947 + (-0.00705 × NDI) + (-0.00875 × arm pain score) + (-0.00877 × neck pain score) to predict EQ-5D had an R-square of 0.62 and a root mean square error (RMSE) of 0.146. The model using NDI alone had an R-square of 0.59 and a RMSE of 0.150. The model using the individual NDI items had an R-square of 0.46 and an RMSE of 0.172. The correlation coefficient between the observed and estimated EQ-5D scores was 0.79. There was no statistically significant difference between the actual EQ-5D score (0.603 ± 0.235) and the estimated EQ-5D score (0.603 ± 0.185) using the NDI, neck pain score, and arm pain score regression model. However, rounding off the coefficients to fewer than 5 decimal places produced less accurate results. The regression model estimating the EQ-5D from the NDI, neck pain score, and arm pain score accounted for 60% of the variability of the EQ-5D with a relatively large RMSE. This regression model may not be sufficient to accurately or reliably estimate actual EQ-5D values.

Nonparametric change point estimation for survival distributions with a partially constant hazard rate.

PubMed

Brazzale, Alessandra R; Küchenhoff, Helmut; Krügel, Stefanie; Schiergens, Tobias S; Trentzsch, Heiko; Hartl, Wolfgang

2018-04-05

We present a new method for estimating a change point in the hazard function of a survival distribution assuming a constant hazard rate after the change point and a decreasing hazard rate before the change point. Our method is based on fitting a stump regression to p values for testing hazard rates in small time intervals. We present three real data examples describing survival patterns of severely ill patients, whose excess mortality rates are known to persist far beyond hospital discharge. For designing survival studies in these patients and for the definition of hospital performance metrics (e.g. mortality), it is essential to define adequate and objective end points. The reliable estimation of a change point will help researchers to identify such end points. By precisely knowing this change point, clinicians can distinguish between the acute phase with high hazard (time elapsed after admission and before the change point was reached), and the chronic phase (time elapsed after the change point) in which hazard is fairly constant. We show in an extensive simulation study that maximum likelihood estimation is not robust in this setting, and we evaluate our new estimation strategy including bootstrap confidence intervals and finite sample bias correction.

Genetic parameters for body condition score, body weight, milk yield, and fertility estimated using random regression models.

PubMed

Berry, D P; Buckley, F; Dillon, P; Evans, R D; Rath, M; Veerkamp, R F

2003-11-01

Genetic (co)variances between body condition score (BCS), body weight (BW), milk yield, and fertility were estimated using a random regression animal model extended to multivariate analysis. The data analyzed included 81,313 BCS observations, 91,937 BW observations, and 100,458 milk test-day yields from 8725 multiparous Holstein-Friesian cows. A cubic random regression was sufficient to model the changing genetic variances for BCS, BW, and milk across different days in milk. The genetic correlations between BCS and fertility changed little over the lactation; genetic correlations between BCS and interval to first service and between BCS and pregnancy rate to first service varied from -0.47 to -0.31, and from 0.15 to 0.38, respectively. This suggests that maximum genetic gain in fertility from indirect selection on BCS should be based on measurements taken in midlactation when the genetic variance for BCS is largest. Selection for increased BW resulted in shorter intervals to first service, but more services and poorer pregnancy rates; genetic correlations between BW and pregnancy rate to first service varied from -0.52 to -0.45. Genetic selection for higher lactation milk yield alone through selection on increased milk yield in early lactation is likely to have a more deleterious effect on genetic merit for fertility than selection on higher milk yield in late lactation.

[A method to estimate the short-term fractal dimension of heart rate variability based on wavelet transform].

PubMed

Zhonggang, Liang; Hong, Yan

2006-10-01

A new method of calculating fractal dimension of short-term heart rate variability signals is presented. The method is based on wavelet transform and filter banks. The implementation of the method is: First of all we pick-up the fractal component from HRV signals using wavelet transform. Next, we estimate the power spectrum distribution of fractal component using auto-regressive model, and we estimate parameter 7 using the least square method. Finally according to formula D = 2- (gamma-1)/2 estimate fractal dimension of HRV signal. To validate the stability and reliability of the proposed method, using fractional brown movement simulate 24 fractal signals that fractal value is 1.6 to validate, the result shows that the method has stability and reliability.

Methods for estimating confidence intervals in interrupted time series analyses of health interventions.

PubMed

Zhang, Fang; Wagner, Anita K; Soumerai, Stephen B; Ross-Degnan, Dennis

2009-02-01

Interrupted time series (ITS) is a strong quasi-experimental research design, which is increasingly applied to estimate the effects of health services and policy interventions. We describe and illustrate two methods for estimating confidence intervals (CIs) around absolute and relative changes in outcomes calculated from segmented regression parameter estimates. We used multivariate delta and bootstrapping methods (BMs) to construct CIs around relative changes in level and trend, and around absolute changes in outcome based on segmented linear regression analyses of time series data corrected for autocorrelated errors. Using previously published time series data, we estimated CIs around the effect of prescription alerts for interacting medications with warfarin on the rate of prescriptions per 10,000 warfarin users per month. Both the multivariate delta method (MDM) and the BM produced similar results. BM is preferred for calculating CIs of relative changes in outcomes of time series studies, because it does not require large sample sizes when parameter estimates are obtained correctly from the model. Caution is needed when sample size is small.

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

Mackillop, William J., E-mail: william.mackillop@krcc.on.ca; Kong, Weidong; Brundage, Michael

Purpose: Estimates of the appropriate rate of use of radiation therapy (RT) are required for planning and monitoring access to RT. Our objective was to compare estimates of the appropriate rate of use of RT derived from mathematical models, with the rate observed in a population of patients with optimal access to RT. Methods and Materials: The rate of use of RT within 1 year of diagnosis (RT{sub 1Y}) was measured in the 134,541 cases diagnosed in Ontario between November 2009 and October 2011. The lifetime rate of use of RT (RT{sub LIFETIME}) was estimated by the multicohort utilization tablemore » method. Poisson regression was used to evaluate potential barriers to access to RT and to identify a benchmark subpopulation with unimpeded access to RT. Rates of use of RT were measured in the benchmark subpopulation and compared with published evidence-based estimates of the appropriate rates. Results: The benchmark rate for RT{sub 1Y}, observed under conditions of optimal access, was 33.6% (95% confidence interval [CI], 33.0%-34.1%), and the benchmark for RT{sub LIFETIME} was 41.5% (95% CI, 41.2%-42.0%). Benchmarks for RT{sub LIFETIME} for 4 of 5 selected sites and for all cancers combined were significantly lower than the corresponding evidence-based estimates. Australian and Canadian evidence-based estimates of RT{sub LIFETIME} for 5 selected sites differed widely. RT{sub LIFETIME} in the overall population of Ontario was just 7.9% short of the benchmark but 20.9% short of the Australian evidence-based estimate of the appropriate rate. Conclusions: Evidence-based estimates of the appropriate lifetime rate of use of RT may overestimate the need for RT in Ontario.« less

Relationship between neighborhood poverty rate and bloodstream infections in the critically ill.

PubMed

Mendu, Mallika L; Zager, Sam; Gibbons, Fiona K; Christopher, Kenneth B

2012-05-01

Poverty is associated with increased risk of chronic illness, but its contribution to bloodstream infections is not well-defined. We performed a multicenter observational study of 14,657 patients, aged 18 yrs or older, who received critical care and had blood cultures drawn between 1997 and 2007 in two hospitals in Boston, Massachusetts. Data sources included 1990 U.S. Census and hospital administrative data. Census tracts were used as the geographic units of analysis. The exposure of interest was neighborhood poverty rate categorized as <5%, 5%-10%, 10%-20%, 20%-40%, and >40%. Neighborhood poverty rate is the percentage of residents with income below the federal poverty line. The primary end point was bloodstream infection occurring 48 hrs before critical care initiation to 48 hrs after. Associations between neighborhood poverty rate and bloodstream infection were estimated by logistic regression models. Adjusted odds ratios were estimated by multivariable logistic regression models. Two thousand four-hundred thirty-five patients had bloodstream infections. Neighborhood poverty rate was a strong predictor of risk of bloodstream infection, with a significant risk gradient across neighborhood poverty rate quintiles. After multivariable analysis, neighborhood poverty rate in the highest quintiles (20%-40% and >40%) were associated with a 26% and 49% increase in bloodstream infection risk, respectively, relative to patients with neighborhood poverty rate of <5%. Within the limitations of our study design, increased neighborhood poverty rate, a proxy for decreased socioeconomic status, appears to be associated with risk of bloodstream infection among patients who receive critical care.

Analytic Methods for Adjusting Subjective Rating Schemes

DTIC Science & Technology

1976-06-01

individual performance. The approach developed here is a variant of the classical linear regression model. Specifically, it la proposed that...values of y and X. Moreover, this difference la gener- ally independent of sample size, so that LS estimates are different from ML estimates at...baervationa. H^ever, aa T. -. - ,„ aU . th(. Hit (4.10) la aatlafled, and EKV and ML eatlnatea are equlvalent A practical proble, in applying

Cyclical and noncyclical unemployment differences among demographic groups.

PubMed

Lynch, G J; Hyclak, T

1984-01-01

The objective of this study was to determine if 1) the full employment-unemployment rate, or natural unemployment rate, changed between 1954-79 differentially for various subgroups in the US population; 2) minimum wage laws and unemployment compensation impacted differentially on subgroups in the population; and 3) there were structural shifts in the determinants of unemployment and labor force participation rates among subgroups. The 6 subgroups investigated were white and nonwhite teenagers, white and nonwhite females, and white and nonwhite males. Trends and cycles in unemployment were analyzed using regression techniques and basic time series models, and structural changes in the unemployment rate were analyzed by using a technique developed by Brown, Durbin, and Evans to test for change in estimated regression coefficients. Results indicated that the natural unemployment rate in the US increased from 4.70% to 5.14% between 1959-79. This increase was due in part to the unemployment rate increases observed among different subgroups in the population, and expecially among teenagers. In 1979 the unemployment rates among teenagers were 13.6% for whites and 28.72% for nonwhites. Respective rates in 1979 for white and nonwhite adult females were 4.20% and 9.98%, and for white and nonwhite adult males they were 2.78% and 6.36%. Other findings were 1) increases in minimum wage had a positive impact on the nonwhite teenagers' jobless rates, no effect on the white teenager jobless rate, and a negative impact on the adult unemployment rate; 2) increased unemployment compensation was positively associated with higher jobless rates for adult males and nonwhite teenagers; 3) the jobless rate was not significantly related to changes between 1954-79 in the proportion of different age, sex, and race groups in the population; and 4) structural shifts in the determinants of unemployment were observed for secondary workers only. Tables provided the results of the regression analysis, estimates of unemployment rates, by race, sex, and age for 1959, 1969, and 1979, and labor force composition and employment rates by race, sex, and age for 1954 and 1981.

Reduced Lung Cancer Mortality With Lower Atmospheric Pressure.

PubMed

Merrill, Ray M; Frutos, Aaron

2018-01-01

Research has shown that higher altitude is associated with lower risk of lung cancer and improved survival among patients. The current study assessed the influence of county-level atmospheric pressure (a measure reflecting both altitude and temperature) on age-adjusted lung cancer mortality rates in the contiguous United States, with 2 forms of spatial regression. Ordinary least squares regression and geographically weighted regression models were used to evaluate the impact of climate and other selected variables on lung cancer mortality, based on 2974 counties. Atmospheric pressure was significantly positively associated with lung cancer mortality, after controlling for sunlight, precipitation, PM2.5 (µg/m 3 ), current smoker, and other selected variables. Positive county-level β coefficient estimates ( P < .05) for atmospheric pressure were observed throughout the United States, higher in the eastern half of the country. The spatial regression models showed that atmospheric pressure is positively associated with age-adjusted lung cancer mortality rates, after controlling for other selected variables.

Modeling of geogenic radon in Switzerland based on ordered logistic regression.

PubMed

Kropat, Georg; Bochud, François; Murith, Christophe; Palacios Gruson, Martha; Baechler, Sébastien

2017-01-01

The estimation of the radon hazard of a future construction site should ideally be based on the geogenic radon potential (GRP), since this estimate is free of anthropogenic influences and building characteristics. The goal of this study was to evaluate terrestrial gamma dose rate (TGD), geology, fault lines and topsoil permeability as predictors for the creation of a GRP map based on logistic regression. Soil gas radon measurements (SRC) are more suited for the estimation of GRP than indoor radon measurements (IRC) since the former do not depend on ventilation and heating habits or building characteristics. However, SRC have only been measured at a few locations in Switzerland. In former studies a good correlation between spatial aggregates of IRC and SRC has been observed. That's why we used IRC measurements aggregated on a 10 km × 10 km grid to calibrate an ordered logistic regression model for geogenic radon potential (GRP). As predictors we took into account terrestrial gamma doserate, regrouped geological units, fault line density and the permeability of the soil. The classification success rate of the model results to 56% in case of the inclusion of all 4 predictor variables. Our results suggest that terrestrial gamma doserate and regrouped geological units are more suited to model GRP than fault line density and soil permeability. Ordered logistic regression is a promising tool for the modeling of GRP maps due to its simplicity and fast computation time. Future studies should account for additional variables to improve the modeling of high radon hazard in the Jura Mountains of Switzerland. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Using Marginal Structural Modeling to Estimate the Cumulative Impact of an Unconditional Tax Credit on Self-Rated Health.

PubMed

Pega, Frank; Blakely, Tony; Glymour, M Maria; Carter, Kristie N; Kawachi, Ichiro

2016-02-15

In previous studies, researchers estimated short-term relationships between financial credits and health outcomes using conventional regression analyses, but they did not account for time-varying confounders affected by prior treatment (CAPTs) or the credits' cumulative impacts over time. In this study, we examined the association between total number of years of receiving New Zealand's Family Tax Credit (FTC) and self-rated health (SRH) in 6,900 working-age parents using 7 waves of New Zealand longitudinal data (2002-2009). We conducted conventional linear regression analyses, both unadjusted and adjusted for time-invariant and time-varying confounders measured at baseline, and fitted marginal structural models (MSMs) that more fully adjusted for confounders, including CAPTs. Of all participants, 5.1%-6.8% received the FTC for 1-3 years and 1.8%-3.6% for 4-7 years. In unadjusted and adjusted conventional regression analyses, each additional year of receiving the FTC was associated with 0.033 (95% confidence interval (CI): -0.047, -0.019) and 0.026 (95% CI: -0.041, -0.010) units worse SRH (on a 5-unit scale). In the MSMs, the average causal treatment effect also reflected a small decrease in SRH (unstabilized weights: β = -0.039 unit, 95% CI: -0.058, -0.020; stabilized weights: β = -0.031 unit, 95% CI: -0.050, -0.007). Cumulatively receiving the FTC marginally reduced SRH. Conventional regression analyses and MSMs produced similar estimates, suggesting little bias from CAPTs. © The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

The relationship between non-communicable disease occurrence and poverty-evidence from demographic surveillance in Matlab, Bangladesh.

PubMed

Mirelman, Andrew J; Rose, Sherri; Khan, Jahangir Am; Ahmed, Sayem; Peters, David H; Niessen, Louis W; Trujillo, Antonio J

2016-07-01

In low-income countries, a growing proportion of the disease burden is attributable to non-communicable diseases (NCDs). There is little knowledge, however, of their impact on wealth, human capital, economic growth or household poverty. This article estimates the risk of being poor after an NCD death in the rural, low-income area of Matlab, Bangladesh. In a matched cohort study, we estimated the 2-year relative risk (RR) of being poor in Matlab households with an NCD death in 2010. Three separate measures of household economic status were used as outcomes: an asset-based index, self-rated household economic condition and total household landholding. Several estimation methods were used including contingency tables, log-binomial regression and regression standardization and machine learning. Households with an NCD death had a large and significant risk of being poor. The unadjusted RR of being poor after death was 1.19, 1.14 and 1.10 for the asset quintile, self-rated condition and landholding outcomes. Adjusting for household and individual level independent variables with log-binomial regression gave RRs of 1.19 [standard error (SE) 0.09], 1.16 (SE 0.07) and 1.14 (SE 0.06), which were found to be exactly the same using regression standardization (SE: 0.09, 0.05, 0.03). Machine learning-based standardization produced slightly smaller RRs though still in the same order of magnitude. The findings show that efforts to address the burden of NCD may also combat household poverty and provide a return beyond improved health. Future work should attempt to disentangle the mechanisms through which economic impacts from an NCD death occur. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

A comparison of small-area hospitalisation rates, estimated morbidity and hospital access.

PubMed

Shulman, H; Birkin, M; Clarke, G P

2015-11-01

Published data on hospitalisation rates tend to reveal marked spatial variations within a city or region. Such variations may simply reflect corresponding variations in need at the small-area level. However, they might also be a consequence of poorer accessibility to medical facilities for certain communities within the region. To help answer this question it is important to compare these variable hospitalisation rates with small-area estimates of need. This paper first maps hospitalisation rates at the small-area level across the region of Yorkshire in the UK to show the spatial variations present. Then the Health Survey of England is used to explore the characteristics of persons with heart disease, using chi-square and logistic regression analysis. Using the most significant variables from this analysis the authors build a spatial microsimulation model of morbidity for heart disease for the Yorkshire region. We then compare these estimates of need with the patterns of hospitalisation rates seen across the region. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

Impact of transverse and longitudinal dispersion on first-order degradation rate constant estimation

NASA Astrophysics Data System (ADS)

Stenback, Greg A.; Ong, Say Kee; Rogers, Shane W.; Kjartanson, Bruce H.

2004-09-01

A two-dimensional analytical model is employed for estimating the first-order degradation rate constant of hydrophobic organic compounds (HOCs) in contaminated groundwater under steady-state conditions. The model may utilize all aqueous concentration data collected downgradient of a source area, but does not require that any data be collected along the plume centerline. Using a least squares fit of the model to aqueous concentrations measured in monitoring wells, degradation rate constants were estimated at a former manufactured gas plant (FMGP) site in the Midwest U.S. The estimated degradation rate constants are 0.0014, 0.0034, 0.0031, 0.0019, and 0.0053 day -1 for acenaphthene, naphthalene, benzene, ethylbenzene, and toluene, respectively. These estimated rate constants were as low as one-half those estimated with the one-dimensional (centerline) approach of Buscheck and Alcantar [Buscheck, T.E., Alcantar, C.M., 1995. Regression techniques and analytical solutions to demonstrate intrinsic bioremediation. In: Hinchee, R.E., Wilson, J.T., Downey, D.C. (Eds.), Intrinsic Bioremediation, Battelle Press, Columbus, OH, pp. 109-116] which does not account for transverse dispersivity. Varying the transverse and longitudinal dispersivity values over one order of magnitude for toluene data obtained from the FMGP site resulted in nearly a threefold variation in the estimated degradation rate constant—highlighting the importance of reliable estimates of the dispersion coefficients for obtaining reasonable estimates of the degradation rate constants. These results have significant implications for decision making and site management where overestimation of a degradation rate may result in remediation times and bioconversion factors that exceed expectations. For a complex source area or non-steady-state plume, a superposition of analytical models that incorporate longitudinal and transverse dispersion and time may be used at sites where the centerline method would not be applicable.

Competitive Swimming and Racial Disparities in Drowning

PubMed Central

Myers, Samuel L.; Cuesta, Ana M.; Lai, Yufeng

2018-01-01

This paper provides compelling evidence of an inverse relationship between competitive swimming rates and drowning rates using Centers for Disease Control and Prevention (CDC) data on fatal drowning rates and membership rates from USA Swimming, the governing organization of competitive swimming in the United States. Tobit and Poisson regression models are estimated using panel data by state from 1999–2007 separately for males, females, African Americans and whites. The strong inverse relationship between competitive swimming rates and unintentional deaths through fatal drowning is most pronounced among African Americans males.

Application of a parameter-estimation technique to modeling the regional aquifer underlying the eastern Snake River plain, Idaho

USGS Publications Warehouse

Garabedian, Stephen P.

1986-01-01

A nonlinear, least-squares regression technique for the estimation of ground-water flow model parameters was applied to the regional aquifer underlying the eastern Snake River Plain, Idaho. The technique uses a computer program to simulate two-dimensional, steady-state ground-water flow. Hydrologic data for the 1980 water year were used to calculate recharge rates, boundary fluxes, and spring discharges. Ground-water use was estimated from irrigated land maps and crop consumptive-use figures. These estimates of ground-water withdrawal, recharge rates, and boundary flux, along with leakance, were used as known values in the model calibration of transmissivity. Leakance values were adjusted between regression solutions by comparing model-calculated to measured spring discharges. In other simulations, recharge and leakance also were calibrated as prior-information regression parameters, which limits the variation of these parameters using a normalized standard error of estimate. Results from a best-fit model indicate a wide areal range in transmissivity from about 0.05 to 44 feet squared per second and in leakance from about 2.2x10 -9 to 6.0 x 10 -8 feet per second per foot. Along with parameter values, model statistics also were calculated, including the coefficient of correlation between calculated and observed head (0.996), the standard error of the estimates for head (40 feet), and the parameter coefficients of variation (about 10-40 percent). Additional boundary flux was added in some areas during calibration to achieve proper fit to ground-water flow directions. Model fit improved significantly when areas that violated model assumptions were removed. It also improved slightly when y-direction (northwest-southeast) transmissivity values were larger than x-direction (northeast-southwest) transmissivity values. The model was most sensitive to changes in recharge, and in some areas, to changes in transmissivity, particularly near the spring discharge area from Milner Dam to King Hill.

Predicting spatio-temporal failure in large scale observational and micro scale experimental systems

NASA Astrophysics Data System (ADS)

de las Heras, Alejandro; Hu, Yong

2006-10-01

Forecasting has become an essential part of modern thought, but the practical limitations still are manifold. We addressed future rates of change by comparing models that take into account time, and models that focus more on space. Cox regression confirmed that linear change can be safely assumed in the short-term. Spatially explicit Poisson regression, provided a ceiling value for the number of deforestation spots. With several observed and estimated rates, it was decided to forecast using the more robust assumptions. A Markov-chain cellular automaton thus projected 5-year deforestation in the Amazonian Arc of Deforestation, showing that even a stable rate of change would largely deplete the forest area. More generally, resolution and implementation of the existing models could explain many of the modelling difficulties still affecting forecasting.

Estimating The Rate of Technology Adoption for Cockpit Weather Information Systems

NASA Technical Reports Server (NTRS)

Kauffmann, Paul; Stough, H. P.

2000-01-01

In February 1997, President Clinton announced a national goal to reduce the weather related fatal accident rate for aviation by 80% in ten years. To support that goal, NASA established an Aviation Weather Information Distribution and Presentation Project to develop technologies that will provide timely and intuitive information to pilots, dispatchers, and air traffic controllers. This information should enable the detection and avoidance of atmospheric hazards and support an improvement in the fatal accident rate related to weather. A critical issue in the success of NASA's weather information program is the rate at which the market place will adopt this new weather information technology. This paper examines that question by developing estimated adoption curves for weather information systems in five critical aviation segments: commercial, commuter, business, general aviation, and rotorcraft. The paper begins with development of general product descriptions. Using this data, key adopters are surveyed and estimates of adoption rates are obtained. These estimates are regressed to develop adoption curves and equations for weather related information systems. The paper demonstrates the use of adoption rate curves in product development and research planning to improve managerial decision processes and resource allocation.

Quasi-Likelihood Techniques in a Logistic Regression Equation for Identifying Simulium damnosum s.l. Larval Habitats Intra-cluster Covariates in Togo.

PubMed

Jacob, Benjamin G; Novak, Robert J; Toe, Laurent; Sanfo, Moussa S; Afriyie, Abena N; Ibrahim, Mohammed A; Griffith, Daniel A; Unnasch, Thomas R

2012-01-01

The standard methods for regression analyses of clustered riverine larval habitat data of Simulium damnosum s.l. a major black-fly vector of Onchoceriasis, postulate models relating observational ecological-sampled parameter estimators to prolific habitats without accounting for residual intra-cluster error correlation effects. Generally, this correlation comes from two sources: (1) the design of the random effects and their assumed covariance from the multiple levels within the regression model; and, (2) the correlation structure of the residuals. Unfortunately, inconspicuous errors in residual intra-cluster correlation estimates can overstate precision in forecasted S.damnosum s.l. riverine larval habitat explanatory attributes regardless how they are treated (e.g., independent, autoregressive, Toeplitz, etc). In this research, the geographical locations for multiple riverine-based S. damnosum s.l. larval ecosystem habitats sampled from 2 pre-established epidemiological sites in Togo were identified and recorded from July 2009 to June 2010. Initially the data was aggregated into proc genmod. An agglomerative hierarchical residual cluster-based analysis was then performed. The sampled clustered study site data was then analyzed for statistical correlations using Monthly Biting Rates (MBR). Euclidean distance measurements and terrain-related geomorphological statistics were then generated in ArcGIS. A digital overlay was then performed also in ArcGIS using the georeferenced ground coordinates of high and low density clusters stratified by Annual Biting Rates (ABR). This data was overlain onto multitemporal sub-meter pixel resolution satellite data (i.e., QuickBird 0.61m wavbands ). Orthogonal spatial filter eigenvectors were then generated in SAS/GIS. Univariate and non-linear regression-based models (i.e., Logistic, Poisson and Negative Binomial) were also employed to determine probability distributions and to identify statistically significant parameter estimators from the sampled data. Thereafter, Durbin-Watson test statistics were used to test the null hypothesis that the regression residuals were not autocorrelated against the alternative that the residuals followed an autoregressive process in AUTOREG. Bayesian uncertainty matrices were also constructed employing normal priors for each of the sampled estimators in PROC MCMC. The residuals revealed both spatially structured and unstructured error effects in the high and low ABR-stratified clusters. The analyses also revealed that the estimators, levels of turbidity and presence of rocks were statistically significant for the high-ABR-stratified clusters, while the estimators distance between habitats and floating vegetation were important for the low-ABR-stratified cluster. Varying and constant coefficient regression models, ABR- stratified GIS-generated clusters, sub-meter resolution satellite imagery, a robust residual intra-cluster diagnostic test, MBR-based histograms, eigendecomposition spatial filter algorithms and Bayesian matrices can enable accurate autoregressive estimation of latent uncertainity affects and other residual error probabilities (i.e., heteroskedasticity) for testing correlations between georeferenced S. damnosum s.l. riverine larval habitat estimators. The asymptotic distribution of the resulting residual adjusted intra-cluster predictor error autocovariate coefficients can thereafter be established while estimates of the asymptotic variance can lead to the construction of approximate confidence intervals for accurately targeting productive S. damnosum s.l habitats based on spatiotemporal field-sampled count data.

Estimating life expectancies for US small areas: a regression framework

NASA Astrophysics Data System (ADS)

Congdon, Peter

2014-01-01

Analysis of area mortality variations and estimation of area life tables raise methodological questions relevant to assessing spatial clustering, and socioeconomic inequalities in mortality. Existing small area analyses of US life expectancy variation generally adopt ad hoc amalgamations of counties to alleviate potential instability of mortality rates involved in deriving life tables, and use conventional life table analysis which takes no account of correlated mortality for adjacent areas or ages. The alternative strategy here uses structured random effects methods that recognize correlations between adjacent ages and areas, and allows retention of the original county boundaries. This strategy generalizes to include effects of area category (e.g. poverty status, ethnic mix), allowing estimation of life tables according to area category, and providing additional stabilization of estimated life table functions. This approach is used here to estimate stabilized mortality rates, derive life expectancies in US counties, and assess trends in clustering and in inequality according to county poverty category.

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.

Testing a single regression coefficient in high dimensional linear models

PubMed Central

Zhong, Ping-Shou; Li, Runze; Wang, Hansheng; Tsai, Chih-Ling

2017-01-01

In linear regression models with high dimensional data, the classical z-test (or t-test) for testing the significance of each single regression coefficient is no longer applicable. This is mainly because the number of covariates exceeds the sample size. In this paper, we propose a simple and novel alternative by introducing the Correlated Predictors Screening (CPS) method to control for predictors that are highly correlated with the target covariate. Accordingly, the classical ordinary least squares approach can be employed to estimate the regression coefficient associated with the target covariate. In addition, we demonstrate that the resulting estimator is consistent and asymptotically normal even if the random errors are heteroscedastic. This enables us to apply the z-test to assess the significance of each covariate. Based on the p-value obtained from testing the significance of each covariate, we further conduct multiple hypothesis testing by controlling the false discovery rate at the nominal level. Then, we show that the multiple hypothesis testing achieves consistent model selection. Simulation studies and empirical examples are presented to illustrate the finite sample performance and the usefulness of the proposed method, respectively. PMID:28663668

Testing a single regression coefficient in high dimensional linear models.

PubMed

Lan, Wei; Zhong, Ping-Shou; Li, Runze; Wang, Hansheng; Tsai, Chih-Ling

2016-11-01

In linear regression models with high dimensional data, the classical z -test (or t -test) for testing the significance of each single regression coefficient is no longer applicable. This is mainly because the number of covariates exceeds the sample size. In this paper, we propose a simple and novel alternative by introducing the Correlated Predictors Screening (CPS) method to control for predictors that are highly correlated with the target covariate. Accordingly, the classical ordinary least squares approach can be employed to estimate the regression coefficient associated with the target covariate. In addition, we demonstrate that the resulting estimator is consistent and asymptotically normal even if the random errors are heteroscedastic. This enables us to apply the z -test to assess the significance of each covariate. Based on the p -value obtained from testing the significance of each covariate, we further conduct multiple hypothesis testing by controlling the false discovery rate at the nominal level. Then, we show that the multiple hypothesis testing achieves consistent model selection. Simulation studies and empirical examples are presented to illustrate the finite sample performance and the usefulness of the proposed method, respectively.

Humidity compensation of bad-smell sensing system using a detector tube and a built-in camera

NASA Astrophysics Data System (ADS)

Hirano, Hiroyuki; Nakamoto, Takamichi

2011-09-01

We developed a low-cost sensing system robust against humidity change for detecting and estimating concentration of bad smell, such as hydrogen sulfide and ammonia. In the previous study, we developed automated measurement system for a gas detector tube using a built-in camera instead of the conventional manual inspection of the gas detector tube. Concentration detectable by the developed system ranges from a few tens of ppb to a few tens of ppm. However, we previously found that the estimated concentration depends not only on actual concentration, but on humidity. Here, we established the method to correct the influence of humidity by creating regression function with its inputs of discoloration rate and humidity. We studied 2 methods (Backpropagation, Radial basis function network) to get regression function and evaluated them. Consequently, the system successfully estimated the concentration on a practical level even when humidity changes.

Regression analysis of sparse asynchronous longitudinal data.

PubMed

Cao, Hongyuan; Zeng, Donglin; Fine, Jason P

2015-09-01

We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or time-dependent coefficients under smoothness assumptions for the covariate processes which are similar to those for synchronous data. For models with either time invariant or time-dependent coefficients, the estimators are consistent and asymptotically normal but converge at slower rates than those achieved with synchronous data. Simulation studies evidence that the methods perform well with realistic sample sizes and may be superior to a naive application of methods for synchronous data based on an ad hoc last value carried forward approach. The practical utility of the methods is illustrated on data from a study on human immunodeficiency virus.

Calibrating the Decline Rate - Peak Luminosity Relation for Type Ia Supernovae

NASA Astrophysics Data System (ADS)

Rust, Bert W.; Pruzhinskaya, Maria V.; Thijsse, Barend J.

2015-08-01

The correlation between peak luminosity and rate of decline in luminosity for Type I supernovae was first studied by B. W. Rust [Ph.D. thesis, Univ. of Illinois (1974) ORNL-4953] and Yu. P. Pskovskii [Sov. Astron., 21 (1977) 675] in the 1970s. Their work was little-noted until Phillips rediscovered the correlation in 1993 [ApJ, 413 (1993) L105] and attempted to derive a calibration relation using a difference quotient approximation Δm15(B) to the decline rate after peak luminosity Mmax(B). Numerical differentiation of data containing measuring errors is a notoriously unstable calculation, but Δm15(B) remains the parameter of choice for most calibration methods developed since 1993. To succeed, it should be computed from good functional fits to the lightcurves, but most workers never exhibit their fits. In the few instances where they have, the fits are not very good. Some of the 9 supernovae in the Phillips study required extinction corrections in their estimates of the Mmax(B), and so were not appropriate for establishing a calibration relation. Although the relative uncertainties in his Δm15(B) estimates were comparable to those in his Mmax(B) estimates, he nevertheless used simple linear regression of the latter on the former, rather than major-axis regression (total least squares) which would have been more appropriate.Here we determine some new calibration relations using a sample of nearby "pure" supernovae suggested by M. V. Pruzhinskaya [Astron. Lett., 37 (2011) 663]. Their parent galaxies are all in the NED collection, with good distance estimates obtained by several different methods. We fit each lightcurve with an optimal regression spline obtained by B. J. Thijsse's spline2 [Comp. in Sci. & Eng., 10 (2008) 49]. The fits, which explain more that 99% of the variance in each case, are better than anything heretofore obtained by stretching "template" lightcurves or fitting combinations of standard lightcurves. We use the fits to compute estimates of Δm15(B) and some other calibration parameters suggested by Pskovskii [Sov. Astron., 28 (1984) 858] and compare their utility for cosmological testing.

A note on variance estimation in random effects meta-regression.

PubMed

Sidik, Kurex; Jonkman, Jeffrey N

2005-01-01

For random effects meta-regression inference, variance estimation for the parameter estimates is discussed. Because estimated weights are used for meta-regression analysis in practice, the assumed or estimated covariance matrix used in meta-regression is not strictly correct, due to possible errors in estimating the weights. Therefore, this note investigates the use of a robust variance estimation approach for obtaining variances of the parameter estimates in random effects meta-regression inference. This method treats the assumed covariance matrix of the effect measure variables as a working covariance matrix. Using an example of meta-analysis data from clinical trials of a vaccine, the robust variance estimation approach is illustrated in comparison with two other methods of variance estimation. A simulation study is presented, comparing the three methods of variance estimation in terms of bias and coverage probability. We find that, despite the seeming suitability of the robust estimator for random effects meta-regression, the improved variance estimator of Knapp and Hartung (2003) yields the best performance among the three estimators, and thus may provide the best protection against errors in the estimated weights.

The need to control for regression to the mean in social psychology studies

PubMed Central

Yu, Rongjun; Chen, Li

2014-01-01

It is common in repeated measurements for extreme values at the first measurement to approach the mean at the subsequent measurement, a phenomenon called regression to the mean (RTM). If RTM is not fully controlled, it will lead to erroneous conclusions. The wide use of repeated measurements in social psychology creates a risk that an RTM effect will influence results. However, insufficient attention is paid to RTM in most social psychological research. Notable cases include studies on the phenomena of social conformity and unrealistic optimism (Klucharev et al., 2009, 2011; Sharot et al., 2011, 2012b; Campbell-Meiklejohn et al., 2012; Kim et al., 2012; Garrett and Sharot, 2014). In Study 1, 13 university students rated and re-rated the facial attractiveness of a series of female faces as a test of the social conformity effect (Klucharev et al., 2009). In Study 2, 15 university students estimated and re-estimated their risk of experiencing a series of adverse life events as a test of the unrealistic optimism effect (Sharot et al., 2011). Although these studies used methodologies similar to those used in earlier research, the social conformity and unrealistic optimism effects were no longer evident after controlling for RTM. Based on these findings we suggest several ways to control for the RTM effect in social psychology studies, such as adding the initial rating as a covariate in regression analysis, selecting a subset of stimuli for which the participant' initial ratings were matched across experimental conditions, and using a control group. PMID:25620951

Age estimation using pulp/tooth area ratio in maxillary canines-A digital image analysis.

PubMed

Juneja, Manjushree; Devi, Yashoda B K; Rakesh, N; Juneja, Saurabh

2014-09-01

Determination of age of a subject is one of the most important aspects of medico-legal cases and anthropological research. Radiographs can be used to indirectly measure the rate of secondary dentine deposition which is depicted by reduction in the pulp area. In this study, 200 patients of Karnataka aged between 18-72 years were selected for the study. Panoramic radiographs were made and indirectly digitized. Radiographic images of maxillary canines (RIC) were processed using a computer-aided drafting program (ImageJ). The variables pulp/root length (p), pulp/tooth length (r), pulp/root width at enamel-cementum junction (ECJ) level (a), pulp/root width at mid-root level (c), pulp/root width at midpoint level between ECJ level and mid-root level (b) and pulp/tooth area ratio (AR) were recorded. All the morphological variables including gender were statistically analyzed to derive regression equation for estimation of age. It was observed that 2 variables 'AR' and 'b' contributed significantly to the fit and were included in the regression model, yielding the formula: Age = 87.305-480.455(AR)+48.108(b). Statistical analysis indicated that the regression equation with selected variables explained 96% of total variance with the median of the residuals of 0.1614 years and standard error of estimate of 3.0186 years. There is significant correlation between age and morphological variables 'AR' and 'b' and the derived population specific regression equation can be potentially used for estimation of chronological age of individuals of Karnataka origin.

Female Labor Supply and Fertility in Iran: A Comparison Between Developed, Semi Developed and Less Developed Regions.

PubMed

Emamgholipour Sefiddashti, Sara; Homaie Rad, Enayatollah; Arab, Mohamad; Bordbar, Shima

2016-02-01

Female labor supply has been changed dramatically in the recent yr. In this study, we examined the effects of development on the relationship between fertility and female labor supply. We used data of population and housing census of Iran and estimated three separate models. To do this we employed Logistic Regressions (BLR). The estimation results of our study showed that there was a negative relationship between fertility rate and female labor supply and there are some differences for this relationship in three models. When fertility rate increases, FLS would decreases. In addition, for higher fertility rates, the woman might be forced to work more because of the economic conditions of her family; and negative coefficients of the fertility rate effects on FLS would increase with a diminishing rate.

Simple agrometeorological models for estimating Guineagrass yield in Southeast Brazil.

PubMed

Pezzopane, José Ricardo Macedo; da Cruz, Pedro Gomes; Santos, Patricia Menezes; Bosi, Cristiam; de Araujo, Leandro Coelho

2014-09-01

The objective of this work was to develop and evaluate agrometeorological models to simulate the production of Guineagrass. For this purpose, we used forage yield from 54 growing periods between December 2004-January 2007 and April 2010-March 2012 in irrigated and non-irrigated pastures in São Carlos, São Paulo state, Brazil (latitude 21°57'42″ S, longitude 47°50'28″ W and altitude 860 m). Initially we performed linear regressions between the agrometeorological variables and the average dry matter accumulation rate for irrigated conditions. Then we determined the effect of soil water availability on the relative forage yield considering irrigated and non-irrigated pastures, by means of segmented linear regression among water balance and relative production variables (dry matter accumulation rates with and without irrigation). The models generated were evaluated with independent data related to 21 growing periods without irrigation in the same location, from eight growing periods in 2000 and 13 growing periods between December 2004-January 2007 and April 2010-March 2012. The results obtained show the satisfactory predictive capacity of the agrometeorological models under irrigated conditions based on univariate regression (mean temperature, minimum temperature and potential evapotranspiration or degreedays) or multivariate regression. The response of irrigation on production was well correlated with the climatological water balance variables (ratio between actual and potential evapotranspiration or between actual and maximum soil water storage). The models that performed best for estimating Guineagrass yield without irrigation were based on minimum temperature corrected by relative soil water storage, determined by the ratio between the actual soil water storage and the soil water holding capacity.irrigation in the same location, in 2000, 2010 and 2011. The results obtained show the satisfactory predictive capacity of the agrometeorological models under irrigated conditions based on univariate regression (mean temperature, potential evapotranspiration or degree-days) or multivariate regression. The response of irrigation on production was well correlated with the climatological water balance variables (ratio between actual and potential evapotranspiration or between actual and maximum soil water storage). The models that performed best for estimating Guineagrass yield without irrigation were based on degree-days corrected by the water deficit factor.

Enhancing the estimation of blood pressure using pulse arrival time and two confounding factors.

PubMed

Baek, Hyun Jae; Kim, Ko Keun; Kim, Jung Soo; Lee, Boreom; Park, Kwang Suk

2010-02-01

A new method of blood pressure (BP) estimation using multiple regression with pulse arrival time (PAT) and two confounding factors was evaluated in clinical and unconstrained monitoring situations. For the first analysis with clinical data, electrocardiogram (ECG), photoplethysmogram (PPG) and invasive BP signals were obtained by a conventional patient monitoring device during surgery. In the second analysis, ECG, PPG and non-invasive BP were measured using systems developed to obtain data under conditions in which the subject was not constrained. To enhance the performance of BP estimation methods, heart rate (HR) and arterial stiffness were considered as confounding factors in regression analysis. The PAT and HR were easily extracted from ECG and PPG signals. For arterial stiffness, the duration from the maximum derivative point to the maximum of the dicrotic notch in the PPG signal, a parameter called TDB, was employed. In two experiments that normally cause BP variation, the correlation between measured BP and the estimated BP was investigated. Multiple-regression analysis with the two confounding factors improved correlation coefficients for diastolic blood pressure and systolic blood pressure to acceptable confidence levels, compared to existing methods that consider PAT only. In addition, reproducibility for the proposed method was determined using constructed test sets. Our results demonstrate that non-invasive, non-intrusive BP estimation can be obtained using methods that can be applied in both clinical and daily healthcare situations.

Efficient parameter estimation in longitudinal data analysis using a hybrid GEE method.

PubMed

Leung, Denis H Y; Wang, You-Gan; Zhu, Min

2009-07-01

The method of generalized estimating equations (GEEs) provides consistent estimates of the regression parameters in a marginal regression model for longitudinal data, even when the working correlation model is misspecified (Liang and Zeger, 1986). However, the efficiency of a GEE estimate can be seriously affected by the choice of the working correlation model. This study addresses this problem by proposing a hybrid method that combines multiple GEEs based on different working correlation models, using the empirical likelihood method (Qin and Lawless, 1994). Analyses show that this hybrid method is more efficient than a GEE using a misspecified working correlation model. Furthermore, if one of the working correlation structures correctly models the within-subject correlations, then this hybrid method provides the most efficient parameter estimates. In simulations, the hybrid method's finite-sample performance is superior to a GEE under any of the commonly used working correlation models and is almost fully efficient in all scenarios studied. The hybrid method is illustrated using data from a longitudinal study of the respiratory infection rates in 275 Indonesian children.

Analysing Twitter and web queries for flu trend prediction.

PubMed

Santos, José Carlos; Matos, Sérgio

2014-05-07

Social media platforms encourage people to share diverse aspects of their daily life. Among these, shared health related information might be used to infer health status and incidence rates for specific conditions or symptoms. In this work, we present an infodemiology study that evaluates the use of Twitter messages and search engine query logs to estimate and predict the incidence rate of influenza like illness in Portugal. Based on a manually classified dataset of 2704 tweets from Portugal, we selected a set of 650 textual features to train a Naïve Bayes classifier to identify tweets mentioning flu or flu-like illness or symptoms. We obtained a precision of 0.78 and an F-measure of 0.83, based on cross validation over the complete annotated set. Furthermore, we trained a multiple linear regression model to estimate the health-monitoring data from the Influenzanet project, using as predictors the relative frequencies obtained from the tweet classification results and from query logs, and achieved a correlation ratio of 0.89 (p<0.001). These classification and regression models were also applied to estimate the flu incidence in the following flu season, achieving a correlation of 0.72. Previous studies addressing the estimation of disease incidence based on user-generated content have mostly focused on the english language. Our results further validate those studies and show that by changing the initial steps of data preprocessing and feature extraction and selection, the proposed approaches can be adapted to other languages. Additionally, we investigated whether the predictive model created can be applied to data from the subsequent flu season. In this case, although the prediction result was good, an initial phase to adapt the regression model could be necessary to achieve more robust results.

Prediction of Cancer Incidence and Mortality in Korea, 2018

PubMed Central

Jung, Kyu-Won; Won, Young-Joo; Kong, Hyun-Joo; Lee, Eun Sook

2018-01-01

Purpose This study aimed to report on cancer incidence and mortality for the year 2018 to estimate Korea’s current cancer burden. Materials and Methods Cancer incidence data from 1999 to 2015 were obtained from the Korea National Cancer Incidence Database, and cancer mortality data from 1993 to 2016 were acquired from Statistics Korea. Cancer incidence and mortality were projected by fitting a linear regression model to observed age-specific cancer rates against observed years, then multiplying the projected age-specific rates by the age-specific population. The Joinpoint regression model was used to determine at which year the linear trend changed significantly, we only used the data of the latest trend. Results A total of 204,909 new cancer cases and 82,155 cancer deaths are expected to occur in Korea in 2018. The most common cancer sites were lung, followed by stomach, colorectal, breast and liver. These five cancers represent half of the overall burden of cancer in Korea. For mortality, the most common sites were lung cancer, followed by liver, colorectal, stomach and pancreas. Conclusion The incidence rate of all cancer in Korea are estimated to decrease gradually, mainly due to decrease of thyroid cancer. These up-to-date estimates of the cancer burden in Korea could be an important resource for planning and evaluation of cancer-control programs. PMID:29566480

The impact of healthcare spending on health outcomes: A meta-regression analysis.

PubMed

Gallet, Craig A; Doucouliagos, Hristos

2017-04-01

While numerous studies assess the impact of healthcare spending on health outcomes, typically reporting multiple estimates of the elasticity of health outcomes (most often measured by a mortality rate or life expectancy) with respect to healthcare spending, the extent to which study attributes influence these elasticity estimates is unclear. Accordingly, we utilize a meta-data set (consisting of 65 studies completed over the 1969-2014 period) to examine these elasticity estimates using meta-regression analysis (MRA). Correcting for a number of issues, including publication selection bias, healthcare spending is found to have the greatest impact on the mortality rate compared to life expectancy. Indeed, conditional on several features of the literature, the spending elasticity for mortality is near -0.13, whereas it is near to +0.04 for life expectancy. MRA results reveal that the spending elasticity for the mortality rate is particularly sensitive to data aggregation, the specification of the health production function, and the nature of healthcare spending. The spending elasticity for life expectancy is particularly sensitive to the age at which life expectancy is measured, as well as the decision to control for the endogeneity of spending in the health production function. With such results in hand, we have a better understanding of how modeling choices influence results reported in this literature. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predicting SF-6D utility scores from the Oswestry disability index and numeric rating scales for back and leg pain.

PubMed

Carreon, Leah Y; Glassman, Steven D; McDonough, Christine M; Rampersaud, Raja; Berven, Sigurd; Shainline, Michael

2009-09-01

Cross-sectional cohort. The purpose of this study is to provide a model to allow estimation of utility from the Short Form (SF)-6D using data from the Oswestry Disability Index (ODI), Back Pain Numeric Rating Scale (BPNRS), and the Leg Pain Numeric Rating Scale (LPNRS). Cost-utility analysis provides important information about the relative value of interventions and requires a measure of utility not often available from clinical trial data. The ODI and numeric rating scales for back (BPNRS) and leg pain (LPNRS), are widely used disease-specific measures for health-related quality of life in patients with lumbar degenerative disorders. The purpose of this study is to provide a model to allow estimation of utility from the SF-6D using data from the ODI, BPNRS, and the LPNRS. SF-36, ODI, BPNRS, and LPNRS were prospectively collected before surgery, at 12 and 24 months after surgery in 2640 patients undergoing lumbar fusion for degenerative disorders. Spearman correlation coefficients for paired observations from multiple time points between ODI, BPNRS, and LPNRS, and SF-6D utility scores were determined. Regression modeling was done to compute the SF-6D score from the ODI, BPNRS, and LPNRS. Using a separate, independent dataset of 2174 patients in which actual SF-6D and ODI scores were available, the SF-6D was estimated for each subject and compared to their actual SF-6D. In the development sample, the mean age was 52.5 +/- 15 years and 34% were male. In the validation sample, the mean age was 52.9 +/- 14.2 years and 44% were male. Correlations between the SF-6D and the ODI, BPNRS, and LPNRS were statistically significant (P < 0.0001) with correlation coefficients of 0.82, 0.78, and 0.72, respectively. The regression equation using ODI, BPNRS,and LPNRS to predict SF-6D had an R of 0.69 and a root mean square error of 0.076. The model using ODI alone had an R of 0.67 and a root mean square error of 0.078. The correlation coefficient between the observed and estimated SF-6D score was 0.80. In the validation analysis, there was no statistically significant difference (P = 0.11) between actual mean SF-6D (0.55 +/- 0.12) and the estimated mean SF-6D score (0.55 +/- 0.10) using the ODI regression model. This regression-based algorithm may be used to predict SF-6D scores in studies of lumbar degenerative disease that have collected ODI but not utility scores.

Incidence and player risk factors for injury in youth football.

PubMed

Malina, Robert M; Morano, Peter J; Barron, Mary; Miller, Susan J; Cumming, Sean P; Kontos, Anthony P

2006-05-01

To estimate the incidence of injuries in youth football and to assess the relationship between player-related risk factors (age, body size, biological maturity status) and the occurrence of injury in youth football. Prospective over two seasons. Two communities in central Michigan. Subjects were 678 youth, 9-14 years of age, who were members of 33 youth football teams in two central Michigan communities in the 2000 and 2001 seasons. Certified athletic trainers (ATCs) were on site to record the number of players at all practices and home games (exposures) and injuries as they occurred. A reportable injury (RI) was defined by the criteria used in the National Athletic Trainers' Association (NATA) survey of several high school sports. Estimated injury rates (95% confidence intervals) per athlete exposures (AE) and per number of athletes were calculated for practices and games by grade. Player risk factors included age, height, weight, BMI and estimated maturity status. Estimated injury rates and relative risks of injury during practices and games by grade; logistic regression to evaluate relationships between player-related risk factors and risk of injury. A total of 259 RIs, 178 in practice and 81 in games, were recorded during the two seasons. Practice injury rates increased with grade level, while game injury rates were similar among fourth through fifth grade and sixth grade players and about twice as high among seventh and eighth grade players. The majority of RIs during the two seasons was minor (64%); the remainder was moderate (18%) and major (13%). Injured fourth through fifth grade players were significantly lighter in weight and had a lower BMI; otherwise, injured and non-injured players within each grade did not differ in age, body size and estimated biological maturity status. Logistic regressions within grade revealed no significant associations between injury and age, height, BMI, and maturity status. Game injury rates are higher than practice injury rates, and the incidence of injury tends to increase with grade level. Age, height, BMI and maturity status were not related to the risk of injury in youth football players.

Risk factors for measles mortality and the importance of decentralized case management during an unusually large measles epidemic in eastern Democratic Republic of Congo in 2013

PubMed Central

Polonsky, Jonathan; Ciglenecki, Iza; Bichet, Mathieu; Coldiron, Matthew; Thuambe Lwiyo, Enoch; Akonda, Innocent; Serafini, Micaela; Porten, Klaudia

2018-01-01

In 2013, a large measles epidemic occurred in the Aketi Health Zone of the Democratic Republic of Congo. We conducted a two-stage, retrospective cluster survey to estimate the attack rate, the case fatality rate, and the measles-specific mortality rate during the epidemic. 1424 households containing 7880 individuals were included. The estimated attack rate was 14.0%, (35.0% among children aged <5 years). The estimated case fatality rate was 4.2% (6.1% among children aged <5 years). Spatial analysis and linear regression showed that younger children, those who did not receive care, and those living farther away from Aketi Hospital early in the epidemic had a higher risk of measles related death. Vaccination coverage prior to the outbreak was low (76%), and a delayed reactive vaccination campaign contributed to the high attack rate. We provide evidences suggesting that a comprehensive case management approach reduced measles fatality during this epidemic in rural, inaccessible resource-poor setting. PMID:29538437

Risk factors for measles mortality and the importance of decentralized case management during an unusually large measles epidemic in eastern Democratic Republic of Congo in 2013.

PubMed

Gignoux, Etienne; Polonsky, Jonathan; Ciglenecki, Iza; Bichet, Mathieu; Coldiron, Matthew; Thuambe Lwiyo, Enoch; Akonda, Innocent; Serafini, Micaela; Porten, Klaudia

2018-01-01

In 2013, a large measles epidemic occurred in the Aketi Health Zone of the Democratic Republic of Congo. We conducted a two-stage, retrospective cluster survey to estimate the attack rate, the case fatality rate, and the measles-specific mortality rate during the epidemic. 1424 households containing 7880 individuals were included. The estimated attack rate was 14.0%, (35.0% among children aged <5 years). The estimated case fatality rate was 4.2% (6.1% among children aged <5 years). Spatial analysis and linear regression showed that younger children, those who did not receive care, and those living farther away from Aketi Hospital early in the epidemic had a higher risk of measles related death. Vaccination coverage prior to the outbreak was low (76%), and a delayed reactive vaccination campaign contributed to the high attack rate. We provide evidences suggesting that a comprehensive case management approach reduced measles fatality during this epidemic in rural, inaccessible resource-poor setting.

Performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data.

PubMed

Yelland, Lisa N; Salter, Amy B; Ryan, Philip

2011-10-15

Modified Poisson regression, which combines a log Poisson regression model with robust variance estimation, is a useful alternative to log binomial regression for estimating relative risks. Previous studies have shown both analytically and by simulation that modified Poisson regression is appropriate for independent prospective data. This method is often applied to clustered prospective data, despite a lack of evidence to support its use in this setting. The purpose of this article is to evaluate the performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data, by using generalized estimating equations to account for clustering. A simulation study is conducted to compare log binomial regression and modified Poisson regression for analyzing clustered data from intervention and observational studies. Both methods generally perform well in terms of bias, type I error, and coverage. Unlike log binomial regression, modified Poisson regression is not prone to convergence problems. The methods are contrasted by using example data sets from 2 large studies. The results presented in this article support the use of modified Poisson regression as an alternative to log binomial regression for analyzing clustered prospective data when clustering is taken into account by using generalized estimating equations.

Survival Analysis of Patients with End Stage Renal Disease

NASA Astrophysics Data System (ADS)

Urrutia, J. D.; Gayo, W. S.; Bautista, L. A.; Baccay, E. B.

2015-06-01

This paper provides a survival analysis of End Stage Renal Disease (ESRD) under Kaplan-Meier Estimates and Weibull Distribution. The data were obtained from the records of V. L. MakabaliMemorial Hospital with respect to time t (patient's age), covariates such as developed secondary disease (Pulmonary Congestion and Cardiovascular Disease), gender, and the event of interest: the death of ESRD patients. Survival and hazard rates were estimated using NCSS for Weibull Distribution and SPSS for Kaplan-Meier Estimates. These lead to the same conclusion that hazard rate increases and survival rate decreases of ESRD patient diagnosed with Pulmonary Congestion, Cardiovascular Disease and both diseases with respect to time. It also shows that female patients have a greater risk of death compared to males. The probability risk was given the equation R = 1 — e-H(t) where e-H(t) is the survival function, H(t) the cumulative hazard function which was created using Cox-Regression.

Diabatic forcing and intialization with assimilation of cloud water and rainwater in a forecast model

NASA Technical Reports Server (NTRS)

Raymond, William H.; Olson, William S.; Callan, Geary

1995-01-01

In this study, diabatic forcing, and liquid water assimilation techniques are tested in a semi-implicit hydrostatic regional forecast model containing explicit representations of grid-scale cloud water and rainwater. Diabatic forcing, in conjunction with diabatic contributions in the initialization, is found to help the forecast retain the diabatic signal found in the liquid water or heating rate data, consequently reducing the spinup time associated with grid-scale precipitation processes. Both observational Special Sensor Microwave/Imager (SSM/I) and model-generated data are used. A physical retrieval method incorporating SSM/I radiance data is utilized to estimate the 3D distribution of precipitating storms. In the retrieval method the relationship between precipitation distributions and upwelling microwave radiances is parameterized, based upon cloud ensemble-radiative model simulations. Regression formulae relating vertically integrated liquid and ice-phase precipitation amounts to latent heating rates are also derived from the cloud ensemble simulations. Thus, retrieved SSM/I precipitation structures can be used in conjunction with the regression-formulas to infer the 3D distribution of latent heating rates. These heating rates are used directly in the forecast model to help initiate Tropical Storm Emily (21 September 1987). The 14-h forecast of Emily's development yields atmospheric precipitation water contents that compare favorably with coincident SSM/I estimates.

Estimating chronic hepatitis C prognosis using transient elastography-based liver stiffness: A systematic review and meta-analysis.

PubMed

Erman, A; Sathya, A; Nam, A; Bielecki, J M; Feld, J J; Thein, H-H; Wong, W W L; Grootendorst, P; Krahn, M D

2018-05-01

Chronic hepatitis C (CHC) is a leading cause of hepatic fibrosis and cirrhosis. The level of fibrosis is traditionally established by histology, and prognosis is estimated using fibrosis progression rates (FPRs; annual probability of progressing across histological stages). However, newer noninvasive alternatives are quickly replacing biopsy. One alternative, transient elastography (TE), quantifies fibrosis by measuring liver stiffness (LSM). Given these developments, the purpose of this study was (i) to estimate prognosis in treatment-naïve CHC patients using TE-based liver stiffness progression rates (LSPR) as an alternative to FPRs and (ii) to compare consistency between LSPRs and FPRs. A systematic literature search was performed using multiple databases (January 1990 to February 2016). LSPRs were calculated using either a direct method (given the difference in serial LSMs and time elapsed) or an indirect method given a single LSM and the estimated duration of infection and pooled using random-effects meta-analyses. For validation purposes, FPRs were also estimated. Heterogeneity was explored by random-effects meta-regression. Twenty-seven studies reporting on 39 groups of patients (N = 5874) were identified with 35 groups allowing for indirect and 8 for direct estimation of LSPR. The majority (~58%) of patients were HIV/HCV-coinfected. The estimated time-to-cirrhosis based on TE vs biopsy was 39 and 38 years, respectively. In univariate meta-regressions, male sex and HIV were positively and age at assessment, negatively associated with LSPRs. Noninvasive prognosis of HCV is consistent with FPRs in predicting time-to-cirrhosis, but more longitudinal studies of liver stiffness are needed to obtain refined estimates. © 2017 John Wiley & Sons Ltd.

The effect of social deprivation on local authority sickness absence rates.

PubMed

Wynn, P; Low, A

2008-06-01

There is an extensive body of research relating to the association between ergonomic and psychosocial factors on sickness absence rates. The impact of deprivation on health indices has also been extensively investigated. However, published research has not investigated the extent of any association between standard measures of deprivation and sickness absence and ill-health retirement rates. To establish if a relationship exists between standard measures of deprivation, used by the UK central government to determine regional health and social welfare funding, and sickness absence and ill-health early retirement rates in English local government employers. Local authority sickness absence rates for 2001-02 were regressed against the 2004 Indices of Multiple Deprivation in a multiple regression model that also included size and type of organization as independent variables. A second model using ill-health retirement as the dependent variable was also estimated. In the full regression models, organization size was not significant and reduced models with deprivation and organization type (depending on whether teachers were employed by the organization or not) were estimated. For the sickness absence model, the adjusted R(2) was 0.20, with 17% of the variation in sickness absence rates being explained by deprivation rank. Ill-health retirement showed a similar relationship with deprivation. In both models, the deprivation coefficients were highly significant: for sickness absence [t = -7.85 (P = 0.00)] and for ill-health retirement [t = -4.79 (P = 0.00)]. A significant proportion of variation in sickness absence and ill-health retirement rates in local government in England are associated with local measures of deprivation. Recognition of the impact of deprivation on sickness absence has implications for a number of different areas of work. These include target setting for Local Government Best Value Performance Indicators, history taking in sickness absence consultations and the role of deprivation as a confounding factor in sickness absence intervention studies.

Trophic transfer efficiency of methylmercury and inorganic mercury to lake trout Salvelinus namaycush from its prey

USGS Publications Warehouse

Madenijian, C.P.; David, S.R.; Krabbenhoft, D.P.

2012-01-01

Based on a laboratory experiment, we estimated the net trophic transfer efficiency of methylmercury to lake trout Salvelinus namaycush from its prey to be equal to 76.6 %. Under the assumption that gross trophic transfer efficiency of methylmercury to lake trout from its prey was equal to 80 %, we estimated that the rate at which lake trout eliminated methylmercury was 0.000244 day−1. Our laboratory estimate of methylmercury elimination rate was 5.5 times lower than the value predicted by a published regression equation developed from estimates of methylmercury elimination rates for fish available from the literature. Thus, our results, in conjunction with other recent findings, suggested that methylmercury elimination rates for fish have been overestimated in previous studies. In addition, based on our laboratory experiment, we estimated that the net trophic transfer efficiency of inorganic mercury to lake trout from its prey was 63.5 %. The lower net trophic transfer efficiency for inorganic mercury compared with that for methylmercury was partly attributable to the greater elimination rate for inorganic mercury. We also found that the efficiency with which lake trout retained either methylmercury or inorganic mercury from their food did not appear to be significantly affected by the degree of their swimming activity.

Estimating the Societal Benefits of THA After Accounting for Work Status and Productivity: A Markov Model Approach.

PubMed

Koenig, Lane; Zhang, Qian; Austin, Matthew S; Demiralp, Berna; Fehring, Thomas K; Feng, Chaoling; Mather, Richard C; Nguyen, Jennifer T; Saavoss, Asha; Springer, Bryan D; Yates, Adolph J

2016-12-01

Demand for total hip arthroplasty (THA) is high and expected to continue to grow during the next decade. Although much of this growth includes working-aged patients, cost-effectiveness studies on THA have not fully incorporated the productivity effects from surgery. We asked: (1) What is the expected effect of THA on patients' employment and earnings? (2) How does accounting for these effects influence the cost-effectiveness of THA relative to nonsurgical treatment? Taking a societal perspective, we used a Markov model to assess the overall cost-effectiveness of THA compared with nonsurgical treatment. We estimated direct medical costs using Medicare claims data and indirect costs (employment status and worker earnings) using regression models and nonparametric simulations. For direct costs, we estimated average spending 1 year before and after surgery. Spending estimates included physician and related services, hospital inpatient and outpatient care, and postacute care. For indirect costs, we estimated the relationship between functional status and productivity, using data from the National Health Interview Survey and regression analysis. Using regression coefficients and patient survey data, we ran a nonparametric simulation to estimate productivity (probability of working multiplied by earnings if working minus the value of missed work days) before and after THA. We used the Australian Orthopaedic Association National Joint Replacement Registry to obtain revision rates because it contained osteoarthritis-specific THA revision rates by age and gender, which were unavailable in other registry reports. Other model assumptions were extracted from a previously published cost-effectiveness analysis that included a comprehensive literature review. We incorporated all parameter estimates into Markov models to assess THA effects on quality-adjusted life years and lifetime costs. We conducted threshold and sensitivity analyses on direct costs, indirect costs, and revision rates to assess the robustness of our Markov model results. Compared with nonsurgical treatments, THA increased average annual productivity of patients by USD 9503 (95% CI, USD 1446-USD 17,812). We found that THA increases average lifetime direct costs by USD 30,365, which were offset by USD 63,314 in lifetime savings from increased productivity. With net societal savings of USD 32,948 per patient, total lifetime societal savings were estimated at almost USD 10 billion from more than 300,000 THAs performed in the United States each year. Using a Markov model approach, we show that THA produces societal benefits that can offset the costs of THA. When comparing THA with other nonsurgical treatments, policymakers should consider the long-term benefits associated with increased productivity from surgery. Level III, economic and decision analysis.

Relations between continuous real-time turbidity data and discrete suspended-sediment concentration samples in the Neosho and Cottonwood Rivers, east-central Kansas, 2009-2012

USGS Publications Warehouse

Foster, Guy M.

2014-01-01

The Neosho River and its primary tributary, the Cottonwood River, are the primary sources of inflow to the John Redmond Reservoir in east-central Kansas. Sedimentation rate in the John Redmond Reservoir was estimated as 743 acre-feet per year for 1964–2006. This estimated sedimentation rate is more than 80 percent larger than the projected design sedimentation rate of 404 acre-feet per year, and resulted in a loss of 40 percent of the conservation pool since its construction in 1964. To reduce sediment input into the reservoir, the Kansas Water Office implemented stream bank stabilization techniques along an 8.3 mile reach of the Neosho River during 2010 through 2011. The U.S. Geological Survey, in cooperation with the Kansas Water Office and funded in part through the Kansas State Water Plan Fund, operated continuous real-time water-quality monitors upstream and downstream from stream bank stabilization efforts before, during, and after construction. Continuously measured water-quality properties include streamflow, specific conductance, water temperature, and turbidity. Discrete sediment samples were collected from June 2009 through September 2012 and analyzed for suspended-sediment concentration (SSC), percentage of sediments less than 63 micrometers (sand-fine break), and loss of material on ignition (analogous to amount of organic matter). Regression models were developed to establish relations between discretely measured SSC samples, and turbidity or streamflow to estimate continuously SSC. Continuous water-quality monitors represented between 96 and 99 percent of the cross-sectional variability for turbidity, and had slopes between 0.91 and 0.98. Because consistent bias was not observed, values from continuous water-quality monitors were considered representative of stream conditions. On average, turbidity-based SSC models explained 96 percent of the variance in SSC. Streamflow-based regressions explained 53 to 60 percent of the variance. Mean squared prediction error for turbidity-based regression relations ranged from -32 to 48 percent, whereas mean square prediction error for streamflow-based regressions ranged from -69 to 218 percent. These models are useful for evaluating the variability of SSC during rapidly changing conditions, computing loads and yields to assess SSC transport through the watershed, and for providing more accurate load estimates compared to streamflow-only based estimation methods used in the past. These models can be used to evaluate the efficacy of streambank stabilization efforts.

Estimating energy expenditure from heart rate in older adults: a case for calibration.

PubMed

Schrack, Jennifer A; Zipunnikov, Vadim; Goldsmith, Jeff; Bandeen-Roche, Karen; Crainiceanu, Ciprian M; Ferrucci, Luigi

2014-01-01

Accurate measurement of free-living energy expenditure is vital to understanding changes in energy metabolism with aging. The efficacy of heart rate as a surrogate for energy expenditure is rooted in the assumption of a linear function between heart rate and energy expenditure, but its validity and reliability in older adults remains unclear. To assess the validity and reliability of the linear function between heart rate and energy expenditure in older adults using different levels of calibration. Heart rate and energy expenditure were assessed across five levels of exertion in 290 adults participating in the Baltimore Longitudinal Study of Aging. Correlation and random effects regression analyses assessed the linearity of the relationship between heart rate and energy expenditure and cross-validation models assessed predictive performance. Heart rate and energy expenditure were highly correlated (r=0.98) and linear regardless of age or sex. Intra-person variability was low but inter-person variability was high, with substantial heterogeneity of the random intercept (s.d. =0.372) despite similar slopes. Cross-validation models indicated individual calibration data substantially improves accuracy predictions of energy expenditure from heart rate, reducing the potential for considerable measurement bias. Although using five calibration measures provided the greatest reduction in the standard deviation of prediction errors (1.08 kcals/min), substantial improvement was also noted with two (0.75 kcals/min). These findings indicate standard regression equations may be used to make population-level inferences when estimating energy expenditure from heart rate in older adults but caution should be exercised when making inferences at the individual level without proper calibration.

Estimating Pneumonia Deaths of Post-Neonatal Children in Countries of Low or No Death Certification in 2008

PubMed Central

Theodoratou, Evropi; Zhang, Jian Shayne F.; Kolcic, Ivana; Davis, Andrew M.; Bhopal, Sunil; Nair, Harish; Chan, Kit Yee; Liu, Li; Johnson, Hope; Rudan, Igor; Campbell, Harry

2011-01-01

Background Pneumonia is the leading cause of child deaths globally. The aims of this study were to: a) estimate the number and global distribution of pneumonia deaths for children 1–59 months for 2008 for countries with low (<85%) or no coverage of death certification using single-cause regression models and b) compare these country estimates with recently published ones based on multi-cause regression models. Methods and Findings For 35 low child-mortality countries with <85% coverage of death certification, a regression model based on vital registration data of low child-mortality and >85% coverage of death certification countries was used. For 87 high child-mortality countries pneumonia death estimates were obtained by applying a regression model developed from published and unpublished verbal autopsy data from high child-mortality settings. The total number of 1–59 months pneumonia deaths for the year 2008 for these 122 countries was estimated to be 1.18 M (95% CI 0.77 M–1.80 M), which represented 23.27% (95% CI 17.15%–32.75%) of all 1–59 month child deaths. The country level estimation correlation coefficient between these two methods was 0.40. Interpretation Although the overall number of post-neonatal pneumonia deaths was similar irrespective to the method of estimation used, the country estimate correlation coefficient was low, and therefore country-specific estimates should be interpreted with caution. Pneumonia remains the leading cause of child deaths and is greatest in regions of poverty and high child-mortality. Despite the concerns about gender inequity linked with childhood mortality we could not estimate sex-specific pneumonia mortality rates due to the inadequate data. Life-saving interventions effective in preventing and treating pneumonia mortality exist but few children in high pneumonia disease burden regions are able to access them. To achieve the United Nations Millennium Development Goal 4 target to reduce child deaths by two-thirds in year 2015 will require the scale-up of access to these effective pneumonia interventions. PMID:21966425

Use of geostationary meteorological satellite images in convective rain estimation for flash-flood forecasting

NASA Astrophysics Data System (ADS)

Wardah, T.; Abu Bakar, S. H.; Bardossy, A.; Maznorizan, M.

2008-07-01

SummaryFrequent flash-floods causing immense devastation in the Klang River Basin of Malaysia necessitate an improvement in the real-time forecasting systems being used. The use of meteorological satellite images in estimating rainfall has become an attractive option for improving the performance of flood forecasting-and-warning systems. In this study, a rainfall estimation algorithm using the infrared (IR) information from the Geostationary Meteorological Satellite-5 (GMS-5) is developed for potential input in a flood forecasting system. Data from the records of GMS-5 IR images have been retrieved for selected convective cells to be trained with the radar rain rate in a back-propagation neural network. The selected data as inputs to the neural network, are five parameters having a significant correlation with the radar rain rate: namely, the cloud-top brightness-temperature of the pixel of interest, the mean and the standard deviation of the temperatures of the surrounding five by five pixels, the rate of temperature change, and the sobel operator that indicates the temperature gradient. In addition, three numerical weather prediction (NWP) products, namely the precipitable water content, relative humidity, and vertical wind, are also included as inputs. The algorithm is applied for the areal rainfall estimation in the upper Klang River Basin and compared with another technique that uses power-law regression between the cloud-top brightness-temperature and radar rain rate. Results from both techniques are validated against previously recorded Thiessen areal-averaged rainfall values with coefficient correlation values of 0.77 and 0.91 for the power-law regression and the artificial neural network (ANN) technique, respectively. An extra lead time of around 2 h is gained when the satellite-based ANN rainfall estimation is coupled with a rainfall-runoff model to forecast a flash-flood event in the upper Klang River Basin.

Energy metabolism and hematology of white-tailed deer fawns

USGS Publications Warehouse

Rawson, R.E.; DelGiudice, G.D.; Dziuk, H.E.; Mech, L.D.

1992-01-01

Resting metabolic rates, weight gains and hematologic profiles of six newborn, captive white-tailed deer (Odocoileus virginianus) fawns (four females, two males) were determined during the first 3 mo of life. Estimated mean daily weight gain of fawns was 0.2 kg. The regression equation for metabolic rate was: Metabolic rate (kcal/kg0.75/day) = 56.1 +/- 1.3 (age in days), r = 0.65, P less than 0.001). Regression equations were also used to relate age to red blood cell count (RBC), hemoglobin concentration (Hb), packed cell volume, white blood cell count, mean corpuscular volume, mean corpuscular hemoglobin concentration (MCHC), and mean corpuscular hemoglobin. The age relationships of Hb, MCHC, and smaller RBC's were indicative of an increasing and more efficient oxygen-carrying and exchange capacity to fulfill the increasing metabolic demands for oxygen associated with increasing body size.

Accuracy of pulse oximeters in estimating heart rate at rest and during exercise.

PubMed Central

Iyriboz, Y; Powers, S; Morrow, J; Ayers, D; Landry, G

1991-01-01

Pulse oximeters are being widely used for non-invasive, simultaneous assessment of haemoglobin oxygen saturation. They are reliable, accurate, relatively inexpensive and portable. Pulse oximeters are often used for estimating heart rate at rest and during exercise. However, at present the data available to validate their use as heart rate monitors are not sufficient. We evaluated the accuracy of two oximeters (Radiometer, ear and finger probe; Ohmeda 3700, ear probe) in monitoring heart rate during incremental exercise by comparing the pulse oximeters with simultaneous ECG readings. Data were collected on eight men (713 heart rate readings) during graded cycle ergometer and treadmill exercise to volitional fatigue. Analysis by linear regression revealed that general oximeter readings significantly correlated with those of ECG (r = 0.91, P less than 0.0001). However, comparison of heart rate at each level of work showed that oximeter readings significantly (P less than 0.05) under-estimated rates above 155 beats/min. These results indicate that the use of pulse oximeters as heart rate monitors during strenuous exercise is questionable. This inaccuracy may well originate from the instability of the probes, sweating, other artefacts during exercise, and measurement of different components in the cardiovascular cycle. PMID:1777787

Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival.

PubMed

Ishwaran, Hemant; Lu, Min

2018-06-04

Random forests are a popular nonparametric tree ensemble procedure with broad applications to data analysis. While its widespread popularity stems from its prediction performance, an equally important feature is that it provides a fully nonparametric measure of variable importance (VIMP). A current limitation of VIMP, however, is that no systematic method exists for estimating its variance. As a solution, we propose a subsampling approach that can be used to estimate the variance of VIMP and for constructing confidence intervals. The method is general enough that it can be applied to many useful settings, including regression, classification, and survival problems. Using extensive simulations, we demonstrate the effectiveness of the subsampling estimator and in particular find that the delete-d jackknife variance estimator, a close cousin, is especially effective under low subsampling rates due to its bias correction properties. These 2 estimators are highly competitive when compared with the .164 bootstrap estimator, a modified bootstrap procedure designed to deal with ties in out-of-sample data. Most importantly, subsampling is computationally fast, thus making it especially attractive for big data settings. Copyright © 2018 John Wiley & Sons, Ltd.

PREDICTION OF SOLAR FLARE SIZE AND TIME-TO-FLARE USING SUPPORT VECTOR MACHINE REGRESSION

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

Boucheron, Laura E.; Al-Ghraibah, Amani; McAteer, R. T. James

We study the prediction of solar flare size and time-to-flare using 38 features describing magnetic complexity of the photospheric magnetic field. This work uses support vector regression to formulate a mapping from the 38-dimensional feature space to a continuous-valued label vector representing flare size or time-to-flare. When we consider flaring regions only, we find an average error in estimating flare size of approximately half a geostationary operational environmental satellite (GOES) class. When we additionally consider non-flaring regions, we find an increased average error of approximately three-fourths a GOES class. We also consider thresholding the regressed flare size for the experimentmore » containing both flaring and non-flaring regions and find a true positive rate of 0.69 and a true negative rate of 0.86 for flare prediction. The results for both of these size regression experiments are consistent across a wide range of predictive time windows, indicating that the magnetic complexity features may be persistent in appearance long before flare activity. This is supported by our larger error rates of some 40 hr in the time-to-flare regression problem. The 38 magnetic complexity features considered here appear to have discriminative potential for flare size, but their persistence in time makes them less discriminative for the time-to-flare problem.« less

Regularized quantile regression for SNP marker estimation of pig growth curves.

PubMed

Barroso, L M A; Nascimento, M; Nascimento, A C C; Silva, F F; Serão, N V L; Cruz, C D; Resende, M D V; Silva, F L; Azevedo, C F; Lopes, P S; Guimarães, S E F

2017-01-01

Genomic growth curves are generally defined only in terms of population mean; an alternative approach that has not yet been exploited in genomic analyses of growth curves is the Quantile Regression (QR). This methodology allows for the estimation of marker effects at different levels of the variable of interest. We aimed to propose and evaluate a regularized quantile regression for SNP marker effect estimation of pig growth curves, as well as to identify the chromosome regions of the most relevant markers and to estimate the genetic individual weight trajectory over time (genomic growth curve) under different quantiles (levels). The regularized quantile regression (RQR) enabled the discovery, at different levels of interest (quantiles), of the most relevant markers allowing for the identification of QTL regions. We found the same relevant markers simultaneously affecting different growth curve parameters (mature weight and maturity rate): two (ALGA0096701 and ALGA0029483) for RQR(0.2), one (ALGA0096701) for RQR(0.5), and one (ALGA0003761) for RQR(0.8). Three average genomic growth curves were obtained and the behavior was explained by the curve in quantile 0.2, which differed from the others. RQR allowed for the construction of genomic growth curves, which is the key to identifying and selecting the most desirable animals for breeding purposes. Furthermore, the proposed model enabled us to find, at different levels of interest (quantiles), the most relevant markers for each trait (growth curve parameter estimates) and their respective chromosomal positions (identification of new QTL regions for growth curves in pigs). These markers can be exploited under the context of marker assisted selection while aiming to change the shape of pig growth curves.

Validation of the Omni Scale of Perceived Exertion in a sample of Spanish-speaking youth from the USA.

PubMed

Suminski, Richard R; Robertson, Robert J; Goss, Fredric L; Olvera, Norma

2008-08-01

Whether the translation of verbal descriptors from English to Spanish affects the validity of the Children's OMNI Scale of Perceived Exertion is not known, so the validity of a Spanish version of the OMNI was examined with 32 boys and 36 girls (9 to 12 years old) for whom Spanish was the primary language. Oxygen consumption, ventilation, respiratory rate, respiratory exchange ratio, heart rate, and ratings of perceived exertion for the overall body (RPE-O) were measured during an incremental treadmill test. All response values displayed significant linear increases across test stages. The linear regression analyses indicated RPE-O values were distributed as positive linear functions of oxygen consumption, ventilation, respiratory rate, respiratory exchange ratio, heart rate, and percent of maximal oxygen consumption. All regression models were statistically significant. The Spanish OMNI Scale is valid for estimating exercise effort during walking and running amongst Hispanic youth whose primary language is Spanish.

Predicting seasonal influenza transmission using functional regression models with temporal dependence.

PubMed

Oviedo de la Fuente, Manuel; Febrero-Bande, Manuel; Muñoz, María Pilar; Domínguez, Àngela

2018-01-01

This paper proposes a novel approach that uses meteorological information to predict the incidence of influenza in Galicia (Spain). It extends the Generalized Least Squares (GLS) methods in the multivariate framework to functional regression models with dependent errors. These kinds of models are useful when the recent history of the incidence of influenza are readily unavailable (for instance, by delays on the communication with health informants) and the prediction must be constructed by correcting the temporal dependence of the residuals and using more accessible variables. A simulation study shows that the GLS estimators render better estimations of the parameters associated with the regression model than they do with the classical models. They obtain extremely good results from the predictive point of view and are competitive with the classical time series approach for the incidence of influenza. An iterative version of the GLS estimator (called iGLS) was also proposed that can help to model complicated dependence structures. For constructing the model, the distance correlation measure [Formula: see text] was employed to select relevant information to predict influenza rate mixing multivariate and functional variables. These kinds of models are extremely useful to health managers in allocating resources in advance to manage influenza epidemics.

Using heart rate to predict energy expenditure in large domestic dogs.

PubMed

Gerth, N; Ruoß, C; Dobenecker, B; Reese, S; Starck, J M

2016-06-01

The aim of this study was to establish heart rate as a measure of energy expenditure in large active kennel dogs (28 ± 3 kg bw). Therefore, the heart rate (HR)-oxygen consumption (V˙O2) relationship was analysed in Foxhound-Boxer-Ingelheim-Labrador cross-breds (FBI dogs) at rest and graded levels of exercise on a treadmill up to 60-65% of maximal aerobic capacity. To test for effects of training, HR and V˙O2 were measured in female dogs, before and after a training period, and after an adjacent training pause to test for reversibility of potential effects. Least squares regression was applied to describe the relationship between HR and V˙O2. The applied training had no statistically significant effect on the HR-V˙O2 regression. A general regression line from all data collected was prepared to establish a general predictive equation for energy expenditure from HR in FBI dogs. The regression equation established in this study enables fast estimation of energy requirement for running activity. The equation is valid for large dogs weighing around 30 kg that run at ground level up to 15 km/h with a heart rate maximum of 190 bpm irrespective of the training level. Journal of Animal Physiology and Animal Nutrition © 2015 Blackwell Verlag GmbH.

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.

Using Appendicitis to Improve Estimates of Childhood Medicaid Participation Rates.

PubMed

Silber, Jeffrey H; Zeigler, Ashley E; Reiter, Joseph G; Hochman, Lauren L; Ludwig, Justin M; Wang, Wei; Calhoun, Shawna R; Pati, Susmita

2018-03-23

Administrative data are often used to estimate state Medicaid/Children's Health Insurance Program duration of enrollment and insurance continuity, but they are generally not used to estimate participation (the fraction of eligible children enrolled) because administrative data do not include reasons for disenrollment and cannot observe eligible never-enrolled children, causing estimates of eligible unenrolled to be inaccurate. Analysts are therefore forced to either utilize survey information that is not generally linkable to administrative claims or rely on duration and continuity measures derived from administrative data and forgo estimating claims-based participation. We introduce appendectomy-based participation (ABP) to estimate statewide participation rates using claims by taking advantage of a natural experiment around statewide appendicitis admissions to improve the accuracy of participation rate estimates. We used Medicaid Analytic eXtract (MAX) for 2008-2010; and the American Community Survey for 2008-2010 from 43 states to calculate ABP, continuity ratio, duration, and participation based on the American Community Survey (ACS). In the validation study, median participation rate using ABP was 86% versus 87% for ACS-based participation estimates using logical edits and 84% without logical edits. Correlations between ABP and ACS with or without logical edits was 0.86 (P < .0001). Using regression analysis, ABP alone was a significant predictor of ACS (P < .0001) with or without logical edits, and adding duration and/or the continuity ratio did not significantly improve the model. Using the ABP rate derived from administrative claims (MAX) is a valid method to estimate statewide public insurance participation rates in children. Copyright © 2018 Academic Pediatric Association. Published by Elsevier Inc. All rights reserved.

Estimating CHD prevalence by small area: integrating information from health surveys and area mortality.

PubMed

Congdon, Peter

2008-03-01

The risk of coronary heart disease (CHD) is strongly linked both to deprivation and ethnicity and so prevalence will vary considerably between areas. Variations in prevalence are important in assessing health care needs and how far CHD service provision and surgical intervention rates match need. This paper uses a regression model of prevalence rates by age, sex, region and ethnicity from the 1999 and 2003 Health Surveys for England to estimate CHD prevalence for 354 English local authority areas. To allow for the impact of social factors on prevalence, survey information on the deprivation quintile in the respondents' micro-area of residence is also used. Allowance is also made for area CHD mortality rates (obtained from aggregated vital statistics data) which are positively correlated with, and hence a proxy for, CHD prevalence rates. An application involves assessment of surgical intervention rates in relation to prevalence at the level of 28 Strategic Health Authorities.

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.

Analysis of volumetric response of pituitary adenomas receiving adjuvant CyberKnife stereotactic radiosurgery with the application of an exponential fitting model.

PubMed

Yu, Yi-Lin; Yang, Yun-Ju; Lin, Chin; Hsieh, Chih-Chuan; Li, Chiao-Zhu; Feng, Shao-Wei; Tang, Chi-Tun; Chung, Tzu-Tsao; Ma, Hsin-I; Chen, Yuan-Hao; Ju, Da-Tong; Hueng, Dueng-Yuan

2017-01-01

Tumor control rates of pituitary adenomas (PAs) receiving adjuvant CyberKnife stereotactic radiosurgery (CK SRS) are high. However, there is currently no uniform way to estimate the time course of the disease. The aim of this study was to analyze the volumetric responses of PAs after CK SRS and investigate the application of an exponential decay model in calculating an accurate time course and estimation of the eventual outcome.A retrospective review of 34 patients with PAs who received adjuvant CK SRS between 2006 and 2013 was performed. Tumor volume was calculated using the planimetric method. The percent change in tumor volume and tumor volume rate of change were compared at median 4-, 10-, 20-, and 36-month intervals. Tumor responses were classified as: progression for >15% volume increase, regression for ≤15% decrease, and stabilization for ±15% of the baseline volume at the time of last follow-up. For each patient, the volumetric change versus time was fitted with an exponential model.The overall tumor control rate was 94.1% in the 36-month (range 18-87 months) follow-up period (mean volume change of -43.3%). Volume regression (mean decrease of -50.5%) was demonstrated in 27 (79%) patients, tumor stabilization (mean change of -3.7%) in 5 (15%) patients, and tumor progression (mean increase of 28.1%) in 2 (6%) patients (P = 0.001). Tumors that eventually regressed or stabilized had a temporary volume increase of 1.07% and 41.5% at 4 months after CK SRS, respectively (P = 0.017). The tumor volume estimated using the exponential fitting equation demonstrated high positive correlation with the actual volume calculated by magnetic resonance imaging (MRI) as tested by Pearson correlation coefficient (0.9).Transient progression of PAs post-CK SRS was seen in 62.5% of the patients receiving CK SRS, and it was not predictive of eventual volume regression or progression. A three-point exponential model is of potential predictive value according to relative distribution. An exponential decay model can be used to calculate the time course of tumors that are ultimately controlled.

A Simultaneous Equation Demand Model for Block Rates

NASA Astrophysics Data System (ADS)

Agthe, Donald E.; Billings, R. Bruce; Dobra, John L.; Raffiee, Kambiz

1986-01-01

This paper examines the problem of simultaneous-equations bias in estimation of the water demand function under an increasing block rate structure. The Hausman specification test is used to detect the presence of simultaneous-equations bias arising from correlation of the price measures with the regression error term in the results of a previously published study of water demand in Tucson, Arizona. An alternative simultaneous equation model is proposed for estimating the elasticity of demand in the presence of block rate pricing structures and availability of service charges. This model is used to reestimate the price and rate premium elasticities of demand in Tucson, Arizona for both the usual long-run static model and for a simple short-run demand model. The results from these simultaneous equation models are consistent with a priori expectations and are unbiased.

Structured functional additive regression in reproducing kernel Hilbert spaces.

PubMed

Zhu, Hongxiao; Yao, Fang; Zhang, Hao Helen

2014-06-01

Functional additive models (FAMs) provide a flexible yet simple framework for regressions involving functional predictors. The utilization of data-driven basis in an additive rather than linear structure naturally extends the classical functional linear model. However, the critical issue of selecting nonlinear additive components has been less studied. In this work, we propose a new regularization framework for the structure estimation in the context of Reproducing Kernel Hilbert Spaces. The proposed approach takes advantage of the functional principal components which greatly facilitates the implementation and the theoretical analysis. The selection and estimation are achieved by penalized least squares using a penalty which encourages the sparse structure of the additive components. Theoretical properties such as the rate of convergence are investigated. The empirical performance is demonstrated through simulation studies and a real data application.

Estimates of Self, Parental and Partner Multiple Intelligences in Iran: A replication and extension

PubMed Central

Kosari, Afrooz; Swami, Viren

2012-01-01

Two hundred and fifty-eight Iranian university students estimated their own, parents’, and partners’ overall (general) intelligence, and also estimated 13 ‘multiple intelligences’ on a simple, two-page questionnaire which was previously used in many similar studies. In accordance with previous research, men rated themselves higher than women on logical-mathematical, spatial and musical intelligence. There were, however, no sex differences in ratings of parental and partner multiple intelligences, which is inconsistent with the extant literature. Participants also believed that they were more intelligent than their parents and partners, and that their fathers were more intelligent than their mothers. Multiple regressions indicated that participants’ Big Five personality typologies and test experience were significant predictors of self-estimated intelligence. These results are discussed in terms of the cross-cultural literature in the field. Implications of the results are also considered. PMID:22952548

Estimates of Self, Parental and Partner Multiple Intelligences in Iran: A replication and extension.

PubMed

Furnham, Adrian; Kosari, Afrooz; Swami, Viren

2012-01-01

Two hundred and fifty-eight Iranian university students estimated their own, parents', and partners' overall (general) intelligence, and also estimated 13 'multiple intelligences' on a simple, two-page questionnaire which was previously used in many similar studies. In accordance with previous research, men rated themselves higher than women on logical-mathematical, spatial and musical intelligence. There were, however, no sex differences in ratings of parental and partner multiple intelligences, which is inconsistent with the extant literature. Participants also believed that they were more intelligent than their parents and partners, and that their fathers were more intelligent than their mothers. Multiple regressions indicated that participants' Big Five personality typologies and test experience were significant predictors of self-estimated intelligence. These results are discussed in terms of the cross-cultural literature in the field. Implications of the results are also considered.

Estimation of Rainfall Rates from Passive Microwave Remote Sensing.

NASA Astrophysics Data System (ADS)

Sharma, Awdhesh Kumar

Rainfall rates have been estimated using the passive microwave and visible/infrared remote sensing techniques. Data of September 14, 1978 from the Scanning Multichannel Microwave Radiometer (SMMR) on board SEA SAT-A and the Visible and Infrared Spin Scan Radiometer (VISSR) on board GOES-W (Geostationary Operational Environmental Satellite - West) was obtained and analyzed for rainfall rate retrieval. Microwave brightness temperatures (MBT) are simulated, using the microwave radiative transfer model (MRTM) and atmospheric scattering models. These MBT were computed as a function of rates of rainfall from precipitating clouds which are in a combined phase of ice and water. Microwave extinction due to ice and liquid water are calculated using Mie-theory and Gamma drop size distributions. Microwave absorption due to oxygen and water vapor are based on the schemes given by Rosenkranz, and Barret and Chung. The scattering phase matrix involved in the MRTM is found using Eddington's two stream approximation. The surface effects due to winds and foam are included through the ocean surface emissivity model. Rainfall rates are then inverted from MBT using the optimization technique "Leaps and Bounds" and multiple linear regression leading to a relationship between the rainfall rates and MBT. This relationship has been used to infer the oceanic rainfall rates from SMMR data. The VISSR data has been inverted for the rainfall rates using Griffith's scheme. This scheme provides an independent means of estimating rainfall rates for cross checking SMMR estimates. The inferred rainfall rates from both techniques have been plotted on a world map for comparison. A reasonably good correlation has been obtained between the two estimates.

Ridge: a computer program for calculating ridge regression estimates

Treesearch

Donald E. Hilt; Donald W. Seegrist

1977-01-01

Least-squares coefficients for multiple-regression models may be unstable when the independent variables are highly correlated. Ridge regression is a biased estimation procedure that produces stable estimates of the coefficients. Ridge regression is discussed, and a computer program for calculating the ridge coefficients is presented.

Addressing the identification problem in age-period-cohort analysis: a tutorial on the use of partial least squares and principal components analysis.

PubMed

Tu, Yu-Kang; Krämer, Nicole; Lee, Wen-Chung

2012-07-01

In the analysis of trends in health outcomes, an ongoing issue is how to separate and estimate the effects of age, period, and cohort. As these 3 variables are perfectly collinear by definition, regression coefficients in a general linear model are not unique. In this tutorial, we review why identification is a problem, and how this problem may be tackled using partial least squares and principal components regression analyses. Both methods produce regression coefficients that fulfill the same collinearity constraint as the variables age, period, and cohort. We show that, because the constraint imposed by partial least squares and principal components regression is inherent in the mathematical relation among the 3 variables, this leads to more interpretable results. We use one dataset from a Taiwanese health-screening program to illustrate how to use partial least squares regression to analyze the trends in body heights with 3 continuous variables for age, period, and cohort. We then use another dataset of hepatocellular carcinoma mortality rates for Taiwanese men to illustrate how to use partial least squares regression to analyze tables with aggregated data. We use the second dataset to show the relation between the intrinsic estimator, a recently proposed method for the age-period-cohort analysis, and partial least squares regression. We also show that the inclusion of all indicator variables provides a more consistent approach. R code for our analyses is provided in the eAppendix.

[Evaluation of estimation of prevalence ratio using bayesian log-binomial regression model].

PubMed

Gao, W L; Lin, H; Liu, X N; Ren, X W; Li, J S; Shen, X P; Zhu, S L

2017-03-10

To evaluate the estimation of prevalence ratio ( PR ) by using bayesian log-binomial regression model and its application, we estimated the PR of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea in their infants by using bayesian log-binomial regression model in Openbugs software. The results showed that caregivers' recognition of infant' s risk signs of diarrhea was associated significantly with a 13% increase of medical care-seeking. Meanwhile, we compared the differences in PR 's point estimation and its interval estimation of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea and convergence of three models (model 1: not adjusting for the covariates; model 2: adjusting for duration of caregivers' education, model 3: adjusting for distance between village and township and child month-age based on model 2) between bayesian log-binomial regression model and conventional log-binomial regression model. The results showed that all three bayesian log-binomial regression models were convergence and the estimated PRs were 1.130(95 %CI : 1.005-1.265), 1.128(95 %CI : 1.001-1.264) and 1.132(95 %CI : 1.004-1.267), respectively. Conventional log-binomial regression model 1 and model 2 were convergence and their PRs were 1.130(95 % CI : 1.055-1.206) and 1.126(95 % CI : 1.051-1.203), respectively, but the model 3 was misconvergence, so COPY method was used to estimate PR , which was 1.125 (95 %CI : 1.051-1.200). In addition, the point estimation and interval estimation of PRs from three bayesian log-binomial regression models differed slightly from those of PRs from conventional log-binomial regression model, but they had a good consistency in estimating PR . Therefore, bayesian log-binomial regression model can effectively estimate PR with less misconvergence and have more advantages in application compared with conventional log-binomial regression model.

Ensemble of trees approaches to risk adjustment for evaluating a hospital's performance.

PubMed

Liu, Yang; Traskin, Mikhail; Lorch, Scott A; George, Edward I; Small, Dylan

2015-03-01

A commonly used method for evaluating a hospital's performance on an outcome is to compare the hospital's observed outcome rate to the hospital's expected outcome rate given its patient (case) mix and service. The process of calculating the hospital's expected outcome rate given its patient mix and service is called risk adjustment (Iezzoni 1997). Risk adjustment is critical for accurately evaluating and comparing hospitals' performances since we would not want to unfairly penalize a hospital just because it treats sicker patients. The key to risk adjustment is accurately estimating the probability of an Outcome given patient characteristics. For cases with binary outcomes, the method that is commonly used in risk adjustment is logistic regression. In this paper, we consider ensemble of trees methods as alternatives for risk adjustment, including random forests and Bayesian additive regression trees (BART). Both random forests and BART are modern machine learning methods that have been shown recently to have excellent performance for prediction of outcomes in many settings. We apply these methods to carry out risk adjustment for the performance of neonatal intensive care units (NICU). We show that these ensemble of trees methods outperform logistic regression in predicting mortality among babies treated in NICU, and provide a superior method of risk adjustment compared to logistic regression.

Estimating Children's Soil/Dust Ingestion Rates through Retrospective Analyses of Blood Lead Biomonitoring from the Bunker Hill Superfund Site in Idaho.

PubMed

von Lindern, Ian; Spalinger, Susan; Stifelman, Marc L; Stanek, Lindsay Wichers; Bartrem, Casey

2016-09-01

Soil/dust ingestion rates are important variables in assessing children's health risks in contaminated environments. Current estimates are based largely on soil tracer methodology, which is limited by analytical uncertainty, small sample size, and short study duration. The objective was to estimate site-specific soil/dust ingestion rates through reevaluation of the lead absorption dose-response relationship using new bioavailability data from the Bunker Hill Mining and Metallurgical Complex Superfund Site (BHSS) in Idaho, USA. The U.S. Environmental Protection Agency (EPA) in vitro bioavailability methodology was applied to archived BHSS soil and dust samples. Using age-specific biokinetic slope factors, we related bioavailable lead from these sources to children's blood lead levels (BLLs) monitored during cleanup from 1988 through 2002. Quantitative regression analyses and exposure assessment guidance were used to develop candidate soil/dust source partition scenarios estimating lead intake, allowing estimation of age-specific soil/dust ingestion rates. These ingestion rate and bioavailability estimates were simultaneously applied to the U.S. EPA Integrated Exposure Uptake Biokinetic Model for Lead in Children to determine those combinations best approximating observed BLLs. Absolute soil and house dust bioavailability averaged 33% (SD ± 4%) and 28% (SD ± 6%), respectively. Estimated BHSS age-specific soil/dust ingestion rates are 86-94 mg/day for 6-month- to 2-year-old children and 51-67 mg/day for 2- to 9-year-old children. Soil/dust ingestion rate estimates for 1- to 9-year-old children at the BHSS are lower than those commonly used in human health risk assessment. A substantial component of children's exposure comes from sources beyond the immediate home environment. von Lindern I, Spalinger S, Stifelman ML, Stanek LW, Bartrem C. 2016. Estimating children's soil/dust ingestion rates through retrospective analyses of blood lead biomonitoring from the Bunker Hill Superfund Site in Idaho. Environ Health Perspect 124:1462-1470; http://dx.doi.org/10.1289/ehp.1510144.

Eastern Baltic region vs. Western Europe: modelling age related changes in the pubic symphysis and the auricular surface.

PubMed

Jatautis, Šarūnas; Jankauskas, Rimantas

2018-02-01

Objectives. The present study addresses the following two main questions: a) Is the pattern of skeletal ageing observed in well-known western European reference collections applicable to modern eastern Baltic populations, or are population-specific standards needed? b) What are the consequences for estimating the age-at-death distribution in the target population when differences in the estimates from reference data are not taken into account? Materials and methods. The dataset consists of a modern Lithuanian osteological reference collection, which is the only collection of this type in the eastern Baltic countries (n = 381); and two major western European reference collections, Coimbra (n = 264) and Spitalfields (n = 239). The age-related changes were evaluated using the scoring systems of Suchey-Brooks (Brooks & Suchey 1990) and Lovejoy et al. (1985), and were modelled via regression models for multinomial responses. A controlled experiment based on simulations and the Rostock Manifesto estimation protocol (Wood et al. 2002) was then carried out to assess the effect of using estimates from different reference samples and different regression models on estimates of the age-at-death distribution in the hypothetical target population. Results. The following key results were obtained in this study. a) The morphological alterations in the pubic symphysis were much faster among women than among men at comparable ages in all three reference samples. In contrast, we found no strong evidence in any of the reference samples that sex is an important factor to explain rate of changes in the auricular surface. b) The rate of ageing in the pubic symphysis seems to be similar across the three reference samples, but there is little evidence of a similar pattern in the auricular surface. That is, the estimated rate of age-related changes in the auricular surface was much faster in the LORC and the Coimbra samples than in the Spitalfields sample. c) The results of simulations showed that the differences in the estimates from the reference data result in noticeably different age-at-death distributions in the target population. Thus, a degree bias may be expected if estimates from the western European reference data are used to collect information on ages at death in the eastern Baltic region based on the changes in the auricular surface. d) Moreover, the bias is expected to be more pronounced if the fitted regression model improperly describes the reference data. Conclusions. Differences in the timing of age-related changes in skeletal traits are to be expected among European reference samples, and cannot be ignored when seeking to reliably estimate an age-at-death distribution in the target population. This form of bias should be taken into consideration in further studies of skeletal samples from the eastern Baltic region.

Short communication: Effect of heat stress on nonreturn rate of Italian Holstein cows.

PubMed

Biffani, S; Bernabucci, U; Vitali, A; Lacetera, N; Nardone, A

2016-07-01

The data set consisted of 1,016,856 inseminations of 191,012 first, second, and third parity Holstein cows from 484 farms. Data were collected from year 2001 through 2007 and included meteorological data from 35 weather stations. Nonreturn rate at 56 d after first insemination (NR56) was considered. A logit model was used to estimate the effect of temperature-humidity index (THI) on reproduction across parities. Then, least squares means were used to detect the THI breakpoints using a 2-phase linear regression procedure. Finally, a multiple-trait threshold model was used to estimate variance components for NR56 in first and second parity cows. A dummy regression variable (t) was used to estimate NR56 decline due to heat stress. The NR56, both for first and second parity cows, was significantly (unfavorable) affected by THI from 4 d before 5 d after the insemination date. Additive genetic variances for NR56 increased from first to second parity both for general and heat stress effect. Genetic correlations between general and heat stress effects were -0.31 for first parity and -0.45 for second parity cows. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Estimation of Subjective Mental Work Load Level with Heart Rate Variability by Tolerance to Driver's Mental Load

NASA Astrophysics Data System (ADS)

Yokoi, Toshiyuki; Itoh, Michimasa; Oguri, Koji

Most of the traffic accidents have been caused by inappropriate driver's mental state. Therefore, driver monitoring is one of the most important challenges to prevent traffic accidents. Some studies for evaluating the driver's mental state while driving have been reported; however driver's mental state should be estimated in real-time in the future. This paper proposes a way to estimate quantitatively driver's mental workload using heart rate variability. It is assumed that the tolerance to driver's mental workload is different depending on the individual. Therefore, we classify people based on their individual tolerance to mental workload. Our estimation method is multiple linear regression analysis, and we compare it to NASA-TLX which is used as the evaluation method of subjective mental workload. As a result, the coefficient of correlation improved from 0.83 to 0.91, and the standard deviation of error also improved. Therefore, our proposed method demonstrated the possibility to estimate mental workload.

An EM-based semi-parametric mixture model approach to the regression analysis of competing-risks data.

PubMed

Ng, S K; McLachlan, G J

2003-04-15

We consider a mixture model approach to the regression analysis of competing-risks data. Attention is focused on inference concerning the effects of factors on both the probability of occurrence and the hazard rate conditional on each of the failure types. These two quantities are specified in the mixture model using the logistic model and the proportional hazards model, respectively. We propose a semi-parametric mixture method to estimate the logistic and regression coefficients jointly, whereby the component-baseline hazard functions are completely unspecified. Estimation is based on maximum likelihood on the basis of the full likelihood, implemented via an expectation-conditional maximization (ECM) algorithm. Simulation studies are performed to compare the performance of the proposed semi-parametric method with a fully parametric mixture approach. The results show that when the component-baseline hazard is monotonic increasing, the semi-parametric and fully parametric mixture approaches are comparable for mildly and moderately censored samples. When the component-baseline hazard is not monotonic increasing, the semi-parametric method consistently provides less biased estimates than a fully parametric approach and is comparable in efficiency in the estimation of the parameters for all levels of censoring. The methods are illustrated using a real data set of prostate cancer patients treated with different dosages of the drug diethylstilbestrol. Copyright 2003 John Wiley & Sons, Ltd.

The measurement of linear frequency drift in oscillators

NASA Astrophysics Data System (ADS)

Barnes, J. A.

1985-04-01

A linear drift in frequency is an important element in most stochastic models of oscillator performance. Quartz crystal oscillators often have drifts in excess of a part in ten to the tenth power per day. Even commercial cesium beam devices often show drifts of a few parts in ten to the thirteenth per year. There are many ways to estimate the drift rates from data samples (e.g., regress the phase on a quadratic; regress the frequency on a linear; compute the simple mean of the first difference of frequency; use Kalman filters with a drift term as one element in the state vector; and others). Although most of these estimators are unbiased, they vary in efficiency (i.e., confidence intervals). Further, the estimation of confidence intervals using the standard analysis of variance (typically associated with the specific estimating technique) can give amazingly optimistic results. The source of these problems is not an error in, say, the regressions techniques, but rather the problems arise from correlations within the residuals. That is, the oscillator model is often not consistent with constraints on the analysis technique or, in other words, some specific analysis techniques are often inappropriate for the task at hand. The appropriateness of a specific analysis technique is critically dependent on the oscillator model and can often be checked with a simple whiteness test on the residuals.

A National Study of the Association between Food Environments and County-Level Health Outcomes

ERIC Educational Resources Information Center

Ahern, Melissa; Brown, Cheryl; Dukas, Stephen

2011-01-01

Purpose: This national, county-level study examines the relationship between food availability and access, and health outcomes (mortality, diabetes, and obesity rates) in both metro and non-metro areas. Methods: This is a secondary, cross-sectional analysis using Food Environment Atlas and CDC data. Linear regression models estimate relationships…

Subjective frequency estimates for 2,938 monosyllabic words.

PubMed

Balota, D A; Pilotti, M; Cortese, M J

2001-06-01

Subjective frequency estimates for large sample of monosyllabic English words were collected from 574 young adults (undergraduate students) and from a separate group of 1,590 adults of varying ages and educational backgrounds. Estimates from the latter group were collected via the internet. In addition, 90 healthy older adults provided estimates for a random sample of 480 of these words. All groups rated words with respect to the estimated frequency of encounters of each word on a 7-point scale, ranging from never encountered to encountered several times a day. The young and older groups also rated each word with respect to the frequency of encounters in different perceptual domains (e.g., reading, hearing, writing, or speaking). The results of regression analyses indicated that objective log frequency and meaningfulness accounted for most of the variance in subjective frequency estimates, whereas neighborhood size accounted for the least amount of variance in the ratings. The predictive power of log frequency and meaningfulness were dependent on the level of subjective frequency estimates. Meaningfulness was a better predictor of subjective frequency for uncommon words, whereas log frequency was a better predictor of subjective frequency for common words. Our discussion focuses on the utility of subjective frequency estimates compared with other estimates of familiarity. The raw subjective frequency data for all words are available at http://www.artsci.wustl.edu/dbalota/labpub.html.

Coestimation of recombination, substitution and molecular adaptation rates by approximate Bayesian computation.

PubMed

Lopes, J S; Arenas, M; Posada, D; Beaumont, M A

2014-03-01

The estimation of parameters in molecular evolution may be biased when some processes are not considered. For example, the estimation of selection at the molecular level using codon-substitution models can have an upward bias when recombination is ignored. Here we address the joint estimation of recombination, molecular adaptation and substitution rates from coding sequences using approximate Bayesian computation (ABC). We describe the implementation of a regression-based strategy for choosing subsets of summary statistics for coding data, and show that this approach can accurately infer recombination allowing for intracodon recombination breakpoints, molecular adaptation and codon substitution rates. We demonstrate that our ABC approach can outperform other analytical methods under a variety of evolutionary scenarios. We also show that although the choice of the codon-substitution model is important, our inferences are robust to a moderate degree of model misspecification. In addition, we demonstrate that our approach can accurately choose the evolutionary model that best fits the data, providing an alternative for when the use of full-likelihood methods is impracticable. Finally, we applied our ABC method to co-estimate recombination, substitution and molecular adaptation rates from 24 published human immunodeficiency virus 1 coding data sets.

Obstructive sleep apnea severity estimation: Fusion of speech-based systems.

PubMed

Ben Or, D; Dafna, E; Tarasiuk, A; Zigel, Y

2016-08-01

Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder. Previous studies associated OSA with anatomical abnormalities of the upper respiratory tract that may be reflected in the acoustic characteristics of speech. We tested the hypothesis that the speech signal carries essential information that can assist in early assessment of OSA severity by estimating apnea-hypopnea index (AHI). 198 men referred to routine polysomnography (PSG) were recorded shortly prior to sleep onset while reading a one-minute speech protocol. The different parts of the speech recordings, i.e., sustained vowels, short-time frames of fluent speech, and the speech recording as a whole, underwent separate analyses, using sustained vowels features, short-term features, and long-term features, respectively. Applying support vector regression and regression trees, these features were used in order to estimate AHI. The fusion of the outputs of the three subsystems resulted in a diagnostic agreement of 67.3% between the speech-estimated AHI and the PSG-determined AHI, and an absolute error rate of 10.8 events/hr. Speech signal analysis may assist in the estimation of AHI, thus allowing the development of a noninvasive tool for OSA screening.

Estimating irrigation water use in the humid eastern United States

USGS Publications Warehouse

Levin, Sara B.; Zarriello, Phillip J.

2013-01-01

Accurate accounting of irrigation water use is an important part of the U.S. Geological Survey National Water-Use Information Program and the WaterSMART initiative to help maintain sustainable water resources in the Nation. Irrigation water use in the humid eastern United States is not well characterized because of inadequate reporting and wide variability associated with climate, soils, crops, and farming practices. To better understand irrigation water use in the eastern United States, two types of predictive models were developed and compared by using metered irrigation water-use data for corn, cotton, peanut, and soybean crops in Georgia and turf farms in Rhode Island. Reliable metered irrigation data were limited to these areas. The first predictive model that was developed uses logistic regression to predict the occurrence of irrigation on the basis of antecedent climate conditions. Logistic regression equations were developed for corn, cotton, peanut, and soybean crops by using weekly irrigation water-use data from 36 metered sites in Georgia in 2009 and 2010 and turf farms in Rhode Island from 2000 to 2004. For the weeks when irrigation was predicted to take place, the irrigation water-use volume was estimated by multiplying the average metered irrigation application rate by the irrigated acreage for a given crop. The second predictive model that was developed is a crop-water-demand model that uses a daily soil water balance to estimate the water needs of a crop on a given day based on climate, soil, and plant properties. Crop-water-demand models were developed independently of reported irrigation water-use practices and relied on knowledge of plant properties that are available in the literature. Both modeling approaches require accurate accounting of irrigated area and crop type to estimate total irrigation water use. Water-use estimates from both modeling methods were compared to the metered irrigation data from Rhode Island and Georgia that were used to develop the models as well as two independent validation datasets from Georgia and Virginia that were not used in model development. Irrigation water-use estimates from the logistic regression method more closely matched mean reported irrigation rates than estimates from the crop-water-demand model when compared to the irrigation data used to develop the equations. The root mean squared errors (RMSEs) for the logistic regression estimates of mean annual irrigation ranged from 0.3 to 2.0 inches (in.) for the five crop types; RMSEs for the crop-water-demand models ranged from 1.4 to 3.9 in. However, when the models were applied and compared to the independent validation datasets from southwest Georgia from 2010, and from Virginia from 1999 to 2007, the crop-water-demand model estimates were as good as or better at predicting the mean irrigation volume than the logistic regression models for most crop types. RMSEs for logistic regression estimates of mean annual irrigation ranged from 1.0 to 7.0 in. for validation data from Georgia and from 1.8 to 4.9 in. for validation data from Virginia; RMSEs for crop-water-demand model estimates ranged from 2.1 to 5.8 in. for Georgia data and from 2.0 to 3.9 in. for Virginia data. In general, regression-based models performed better in areas that had quality daily or weekly irrigation data from which the regression equations were developed; however, the regression models were less reliable than the crop-water-demand models when applied outside the area for which they were developed. In most eastern coastal states that do not have quality irrigation data, the crop-water-demand model can be used more reliably. The development of predictive models of irrigation water use in this study was hindered by a lack of quality irrigation data. Many mid-Atlantic and New England states do not require irrigation water use to be reported. A survey of irrigation data from 14 eastern coastal states from Maine to Georgia indicated that, with the exception of the data in Georgia, irrigation data in the states that do require reporting commonly did not contain requisite ancillary information such as irrigated area or crop type, lacked precision, or were at an aggregated temporal scale making them unsuitable for use in the development of predictive models. Confidence in the reliability of either modeling method is affected by uncertainty in the reported data from which the models were developed or validated. Only through additional collection of quality data and further study can the accuracy and uncertainty of irrigation water-use estimates be improved in the humid eastern United States.

Tobit analysis of vehicle accident rates on interstate highways.

PubMed

Anastasopoulos, Panagiotis Ch; Tarko, Andrew P; Mannering, Fred L

2008-03-01

There has been an abundance of research that has used Poisson models and its variants (negative binomial and zero-inflated models) to improve our understanding of the factors that affect accident frequencies on roadway segments. This study explores the application of an alternate method, tobit regression, by viewing vehicle accident rates directly (instead of frequencies) as a continuous variable that is left-censored at zero. Using data from vehicle accidents on Indiana interstates, the estimation results show that many factors relating to pavement condition, roadway geometrics and traffic characteristics significantly affect vehicle accident rates.

Sediment acoustic index method for computing continuous suspended-sediment concentrations

USGS Publications Warehouse

Landers, Mark N.; Straub, Timothy D.; Wood, Molly S.; Domanski, Marian M.

2016-07-11

Once developed, sediment acoustic index ratings must be validated with additional suspended-sediment samples, beyond the period of record used in the rating development, to verify that the regression model continues to adequately represent sediment conditions within the stream. Changes in ADVM configuration or installation, or replacement with another ADVM, may require development of a new rating. The best practices described in this report can be used to develop continuous estimates of suspended-sediment concentration and load using sediment acoustic surrogates to enable more informed and accurate responses to diverse sedimentation issues.

Regression analysis of sparse asynchronous longitudinal data

PubMed Central

Cao, Hongyuan; Zeng, Donglin; Fine, Jason P.

2015-01-01

Summary We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or time-dependent coefficients under smoothness assumptions for the covariate processes which are similar to those for synchronous data. For models with either time invariant or time-dependent coefficients, the estimators are consistent and asymptotically normal but converge at slower rates than those achieved with synchronous data. Simulation studies evidence that the methods perform well with realistic sample sizes and may be superior to a naive application of methods for synchronous data based on an ad hoc last value carried forward approach. The practical utility of the methods is illustrated on data from a study on human immunodeficiency virus. PMID:26568699

Comparison of anchor-based and distributional approaches in estimating important difference in common cold.

PubMed

Barrett, Bruce; Brown, Roger; Mundt, Marlon

2008-02-01

Evaluative health-related quality-of-life instruments used in clinical trials should be able to detect small but important changes in health status. Several approaches to minimal important difference (MID) and responsiveness have been developed. To compare anchor-based and distributional approaches to important difference and responsiveness for the Wisconsin Upper Respiratory Symptom Survey (WURSS), an illness-specific quality of life outcomes instrument. Participants with community-acquired colds self-reported daily using the WURSS-44. Distribution-based methods calculated standardized effect size (ES) and standard error of measurement (SEM). Anchor-based methods compared daily interval changes to global ratings of change, using: (1) standard MID methods based on correspondence to ratings of "a little better" or "somewhat better," and (2) two-level multivariate regression models. About 150 adults were monitored throughout their colds (1,681 sick days.): 88% were white, 69% were women, and 50% had completed college. The mean age was 35.5 years (SD = 14.7). WURSS scores increased 2.2 points from the first to second day, and then dropped by an average of 8.2 points per day from days 2 to 7. The SEM averaged 9.1 during these 7 days. Standard methods yielded a between day MID of 22 points. Regression models of MID projected 11.3-point daily changes. Dividing these estimates of small-but-important-difference by pooled SDs yielded coefficients of .425 for standard MID, .218 for regression model, .177 for SEM, and .157 for ES. These imply per-group sample sizes of 870 using ES, 616 for SEM, 302 for regression model, and 89 for standard MID, assuming alpha = .05, beta = .20 (80% power), and two-tailed testing. Distribution and anchor-based approaches provide somewhat different estimates of small but important difference, which in turn can have substantial impact on trial design.

Using multi-year national survey cohorts for period estimates: an application of weighted discrete Poisson regression for assessing annual national mortality in US adults with and without diabetes, 2000-2006.

PubMed

Cheng, Yiling J; Gregg, Edward W; Rolka, Deborah B; Thompson, Theodore J

2016-12-15

Monitoring national mortality among persons with a disease is important to guide and evaluate progress in disease control and prevention. However, a method to estimate nationally representative annual mortality among persons with and without diabetes in the United States does not currently exist. The aim of this study is to demonstrate use of weighted discrete Poisson regression on national survey mortality follow-up data to estimate annual mortality rates among adults with diabetes. To estimate mortality among US adults with diabetes, we applied a weighted discrete time-to-event Poisson regression approach with post-stratification adjustment to national survey data. Adult participants aged 18 or older with and without diabetes in the National Health Interview Survey 1997-2004 were followed up through 2006 for mortality status. We estimated mortality among all US adults, and by self-reported diabetes status at baseline. The time-varying covariates used were age and calendar year. Mortality among all US adults was validated using direct estimates from the National Vital Statistics System (NVSS). Using our approach, annual all-cause mortality among all US adults ranged from 8.8 deaths per 1,000 person-years (95% confidence interval [CI]: 8.0, 9.6) in year 2000 to 7.9 (95% CI: 7.6, 8.3) in year 2006. By comparison, the NVSS estimates ranged from 8.6 to 7.9 (correlation = 0.94). All-cause mortality among persons with diabetes decreased from 35.7 (95% CI: 28.4, 42.9) in 2000 to 31.8 (95% CI: 28.5, 35.1) in 2006. After adjusting for age, sex, and race/ethnicity, persons with diabetes had 2.1 (95% CI: 2.01, 2.26) times the risk of death of those without diabetes. Period-specific national mortality can be estimated for people with and without a chronic condition using national surveys with mortality follow-up and a discrete time-to-event Poisson regression approach with post-stratification adjustment.

Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions.

PubMed

Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

2012-05-01

Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

The natural history of hepatitis C infection acquired through injection drug use: meta-analysis and meta-regression.

PubMed

John-Baptiste, Ava; Krahn, Murray; Heathcote, Jenny; Laporte, Audery; Tomlinson, George

2010-08-01

Our aim was to estimate the rate of progression to cirrhosis for those infected with hepatitis C virus (HCV) through injection drug use. We searched the published literature for articles assessing cirrhosis in this population and abstracted data on cirrhosis prevalence, mean duration of infection, mean age, mean alanine aminotransferase (ALT) enzyme levels, proportion of males, proportion HIV co-infected, proportion consuming excessive alcohol, and study setting. Summary progression rates were estimated using weighted averages and random effects Poisson meta-regression. The impact of co-variates was assessed by estimating the posterior probability that the relative risk (RR) of progression exceeded 1.0. A total of 47 published articles were identified. After adjusting for covariates in 44 studies representing 6457 patients, the estimated rate of progression to cirrhosis, was 8.1 per 1000 person-years (95% credible region (CR), 3.9-14.7). This corresponds to a 20-year cirrhosis prevalence of 14.8% (95% CR, 7.5-25.5). A 5% increase in the proportion of male participants and a 5% increase in the proportion consuming excessive alcohol were associated with faster progression (probability RR>1=0.97 and 0.92, respectively). A 5% increase in the proportion of HIV co-infected, an increase in ALT of 5 IU/L and studies in settings with a high risk of referral bias were not associated with faster progression (probability RR>1=0.42, 0.65, and 0.43, respectively). Analysis of aggregate level data suggests that for patients who contracted HCV through injection drug use prognosis is poor in populations with many male patients and high levels of alcohol consumption. Copyright 2010 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

Effect of insurance parity on substance abuse treatment.

PubMed

Azzone, Vanessa; Frank, Richard G; Normand, Sharon-Lise T; Burnam, M Audrey

2011-02-01

This study examined the impact of insurance parity on the use, cost, and quality of substance abuse treatment. The authors compared substance abuse treatment spending and utilization from 1999 to 2002 for continuously enrolled beneficiaries covered by Federal Employees Health Benefit (FEHB) plans, which require parity coverage of mental health and substance use disorders, with spending and utilization among beneficiaries in a matched set of health plans without parity coverage. Logistic regression models estimated the probability of any substance abuse service use. Conditional on use, linear models estimated total and out-of-pocket spending. Logistic regression models for three quality indicators for substance abuse treatment were also estimated: identification of adult enrollees with a new substance abuse diagnosis, treatment initiation, and treatment engagement. Difference-in-difference estimates were computed as (postparity - preparity) differences in outcomes in plans without parity subtracted from those in FEHB plans. There were no significant differences between FEHB and non-FEHB plans in rates of change in average utilization of substance abuse services. Conditional on service utilization, the rate of substance abuse treatment out-of-pocket spending declined significantly in the FEHB plans compared with the non-FEHB plans (mean difference=-$101.09, 95% confidence interval [CI]=-$198.06 to -$4.12), whereas changes in total plan spending per user did not differ significantly. With parity, more patients had new diagnoses of a substance use disorder (difference-in-difference risk=.10%, CI=.02% to .19%). No statistically significant differences were found for rates of initiation and engagement in substance abuse treatment. Findings suggest that for continuously enrolled populations, providing parity of substance abuse treatment coverage improved insurance protection but had little impact on utilization, costs for plans, or quality of care.

Machine Learning to Improve Energy Expenditure Estimation in Children With Disabilities: A Pilot Study in Duchenne Muscular Dystrophy.

PubMed

Pande, Amit; Mohapatra, Prasant; Nicorici, Alina; Han, Jay J

2016-07-19

Children with physical impairments are at a greater risk for obesity and decreased physical activity. A better understanding of physical activity pattern and energy expenditure (EE) would lead to a more targeted approach to intervention. This study focuses on studying the use of machine-learning algorithms for EE estimation in children with disabilities. A pilot study was conducted on children with Duchenne muscular dystrophy (DMD) to identify important factors for determining EE and develop a novel algorithm to accurately estimate EE from wearable sensor-collected data. There were 7 boys with DMD, 6 healthy control boys, and 22 control adults recruited. Data were collected using smartphone accelerometer and chest-worn heart rate sensors. The gold standard EE values were obtained from the COSMED K4b2 portable cardiopulmonary metabolic unit worn by boys (aged 6-10 years) with DMD and controls. Data from this sensor setup were collected simultaneously during a series of concurrent activities. Linear regression and nonlinear machine-learning-based approaches were used to analyze the relationship between accelerometer and heart rate readings and COSMED values. Existing calorimetry equations using linear regression and nonlinear machine-learning-based models, developed for healthy adults and young children, give low correlation to actual EE values in children with disabilities (14%-40%). The proposed model for boys with DMD uses ensemble machine learning techniques and gives a 91% correlation with actual measured EE values (root mean square error of 0.017). Our results confirm that the methods developed to determine EE using accelerometer and heart rate sensor values in normal adults are not appropriate for children with disabilities and should not be used. A much more accurate model is obtained using machine-learning-based nonlinear regression specifically developed for this target population. ©Amit Pande, Prasant Mohapatra, Alina Nicorici, Jay J Han. Originally published in JMIR Rehabilitation and Assistive Technology (http://rehab.jmir.org), 19.07.2016.

Assessing the risk of bovine fasciolosis using linear regression analysis for the state of Rio Grande do Sul, Brazil.

PubMed

Silva, Ana Elisa Pereira; Freitas, Corina da Costa; Dutra, Luciano Vieira; Molento, Marcelo Beltrão

2016-02-15

Fasciola hepatica is the causative agent of fasciolosis, a disease that triggers a chronic inflammatory process in the liver affecting mainly ruminants and other animals including humans. In Brazil, F. hepatica occurs in larger numbers in the most Southern state of Rio Grande do Sul. The objective of this study was to estimate areas at risk using an eight-year (2002-2010) time series of climatic and environmental variables that best relate to the disease using a linear regression method to municipalities in the state of Rio Grande do Sul. The positivity index of the disease, which is the rate of infected animal per slaughtered animal, was divided into three risk classes: low, medium and high. The accuracy of the known sample classification on the confusion matrix for the low, medium and high rates produced by the estimated model presented values between 39 and 88% depending of the year. The regression analysis showed the importance of the time-based data for the construction of the model, considering the two variables of the previous year of the event (positivity index and maximum temperature). The generated data is important for epidemiological and parasite control studies mainly because F. hepatica is an infection that can last from months to years. Copyright © 2015 Elsevier B.V. All rights reserved.

A statistical model to estimate the impact of a hepatitis A vaccination programme.

PubMed

Oviedo, Manuel; Pilar Muñoz, M; Domínguez, Angela; Borras, Eva; Carmona, Gloria

2008-11-11

A program of routine hepatitis A+B vaccination in preadolescents was introduced in 1998 in Catalonia, a region situated in the northeast of Spain. The objective of this study was to quantify the reduction in the incidence of hepatitis A in order to differentiate the natural reduction of the incidence of hepatitis A from that produced due to the vaccination programme and to predict the evolution of the disease in forthcoming years. A generalized linear model (GLM) using negative binomial regression was used to estimate the incidence rates of hepatitis A in Catalonia by year, age group and vaccination. Introduction of the vaccine reduced cases by 5.5 by year (p-value<0.001), but there was a significant interaction between the year of report and vaccination that smoothed this reduction (p-value<0.001). The reduction was not equal in all age groups, being greater in the 12-18 years age group, which fell from a mean rate of 8.15 per 100,000 person/years in the pre-vaccination period (1992-1998) to 1.4 in the vaccination period (1999-2005). The model predicts the evolution accurately for the group of vaccinated subjects. Negative binomial regression is more appropriate than Poisson regression when observed variance exceeds the observed mean (overdispersed count data), can cause a variable apparently contribute more on the model of what really makes it.

Annual estimates of recharge, quick-flow runoff, and ET for the contiguous U.S. using empirical regression equations

USGS Publications Warehouse

Reitz, Meredith; Sanford, Ward E.; Senay, Gabriel; Cazenas, J.

2017-01-01

This study presents new data-driven, annual estimates of the division of precipitation into the recharge, quick-flow runoff, and evapotranspiration (ET) water budget components for 2000-2013 for the contiguous United States (CONUS). The algorithms used to produce these maps ensure water budget consistency over this broad spatial scale, with contributions from precipitation influx attributed to each component at 800 m resolution. The quick-flow runoff estimates for the contribution to the rapidly varying portion of the hydrograph are produced using data from 1,434 gaged watersheds, and depend on precipitation, soil saturated hydraulic conductivity, and surficial geology type. Evapotranspiration estimates are produced from a regression using water balance data from 679 gaged watersheds and depend on land cover, temperature, and precipitation. The quick-flow and ET estimates are combined to calculate recharge as the remainder of precipitation. The ET and recharge estimates are checked against independent field data, and the results show good agreement. Comparisons of recharge estimates with groundwater extraction data show that in 15% of the country, groundwater is being extracted at rates higher than the local recharge. These maps of the internally consistent water budget components of recharge, quick-flow runoff, and ET, being derived from and tested against data, are expected to provide reliable first-order estimates of these quantities across the CONUS, even where field measurements are sparse.

Summary of groundwater-recharge estimates for Pennsylvania

USGS Publications Warehouse

Stuart O. Reese,; Risser, Dennis W.

2010-01-01

Groundwater recharge is water that infiltrates through the subsurface to the zone of saturation beneath the water table. Because recharge is a difficult parameter to quantify, it is typically estimated from measurements of other parameters like streamflow and precipitation. This report provides a general overview of processes affecting recharge in Pennsylvania and presents estimates of recharge rates from studies at various scales.The most common method for estimating recharge in Pennsylvania has been to estimate base flow from measurements of streamflow and assume that base flow (expressed in inches over the basin) approximates recharge. Statewide estimates of mean annual groundwater recharge were developed by relating base flow to basin characteristics of HUC10 watersheds (a fifth-level classification that uses 10 digits to define unique hydrologic units) using a regression equation. The regression analysis indicated that mean annual precipitation, average daily maximum temperature, percent of sand in soil, percent of carbonate rock in the watershed, and average stream-channel slope were significant factors in the explaining the variability of groundwater recharge across the Commonwealth.Several maps are included in this report to illustrate the principal factors affecting recharge and provide additional information about the spatial distribution of recharge in Pennsylvania. The maps portray the patterns of precipitation, temperature, prevailing winds across Pennsylvania’s varied physiography; illustrate the error associated with recharge estimates; and show the spatial variability of recharge as a percent of precipitation. National, statewide, regional, and local values of recharge, based on numerous studies, are compiled to allow comparison of estimates from various sources. Together these plates provide a synopsis of groundwater-recharge estimations and factors in Pennsylvania.Areas that receive the most recharge are typically those that get the most rainfall, have favorable surface conditions for infiltration, and are less susceptible to the influences of high temperatures, and thus, evapotranspiration. Areas that have less recharge in Pennsylvania are typically those with less precipitation, less permeable soils, and higher temperatures that are conducive to greater rates of evapotranspiration.

Accelerating rates of cognitive decline and imaging markers associated with β-amyloid pathology.

PubMed

Insel, Philip S; Mattsson, Niklas; Mackin, R Scott; Schöll, Michael; Nosheny, Rachel L; Tosun, Duygu; Donohue, Michael C; Aisen, Paul S; Jagust, William J; Weiner, Michael W

2016-05-17

To estimate points along the spectrum of β-amyloid pathology at which rates of change of several measures of neuronal injury and cognitive decline begin to accelerate. In 460 patients with mild cognitive impairment (MCI), we estimated the points at which rates of florbetapir PET, fluorodeoxyglucose (FDG) PET, MRI, and cognitive and functional decline begin to accelerate with respect to baseline CSF Aβ42. Points of initial acceleration in rates of decline were estimated using mixed-effects regression. Rates of neuronal injury and cognitive and even functional decline accelerate substantially before the conventional threshold for amyloid positivity, with rates of florbetapir PET and FDG PET accelerating early. Temporal lobe atrophy rates also accelerate prior to the threshold, but not before the acceleration of cognitive and functional decline. A considerable proportion of patients with MCI would not meet inclusion criteria for a trial using the current threshold for amyloid positivity, even though on average, they are experiencing cognitive/functional decline associated with prethreshold levels of CSF Aβ42. Future trials in early Alzheimer disease might consider revising the criteria regarding β-amyloid thresholds to include the range of amyloid associated with the first signs of accelerating rates of decline. © 2016 American Academy of Neurology.

Accelerating rates of cognitive decline and imaging markers associated with β-amyloid pathology

PubMed Central

Mattsson, Niklas; Mackin, R. Scott; Schöll, Michael; Nosheny, Rachel L.; Tosun, Duygu; Donohue, Michael C.; Aisen, Paul S.; Jagust, William J.; Weiner, Michael W.

2016-01-01

Objective: To estimate points along the spectrum of β-amyloid pathology at which rates of change of several measures of neuronal injury and cognitive decline begin to accelerate. Methods: In 460 patients with mild cognitive impairment (MCI), we estimated the points at which rates of florbetapir PET, fluorodeoxyglucose (FDG) PET, MRI, and cognitive and functional decline begin to accelerate with respect to baseline CSF Aβ42. Points of initial acceleration in rates of decline were estimated using mixed-effects regression. Results: Rates of neuronal injury and cognitive and even functional decline accelerate substantially before the conventional threshold for amyloid positivity, with rates of florbetapir PET and FDG PET accelerating early. Temporal lobe atrophy rates also accelerate prior to the threshold, but not before the acceleration of cognitive and functional decline. Conclusions: A considerable proportion of patients with MCI would not meet inclusion criteria for a trial using the current threshold for amyloid positivity, even though on average, they are experiencing cognitive/functional decline associated with prethreshold levels of CSF Aβ42. Future trials in early Alzheimer disease might consider revising the criteria regarding β-amyloid thresholds to include the range of amyloid associated with the first signs of accelerating rates of decline. PMID:27164667

Accelerating rates of cognitive decline and imaging markers associated with β-amyloid pathology

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

Insel, Philip S.; Mattsson, Niklas; Mackin, R. Scott

Objective: Our objective is to estimate points along the spectrum of β-amyloid pathology at which rates of change of several measures of neuronal injury and cognitive decline begin to accelerate. Methods: In 460 patients with mild cognitive impairment (MCI), we estimated the points at which rates of florbetapir PET, fluorodeoxyglucose (FDG) PET, MRI, and cognitive and functional decline begin to accelerate with respect to baseline CSF Aβ 42. Points of initial acceleration in rates of decline were estimated using mixed-effects regression. Results: Rates of neuronal injury and cognitive and even functional decline accelerate substantially before the conventional threshold for amyloidmore » positivity, with rates of florbetapir PET and FDG PET accelerating early. Temporal lobe atrophy rates also accelerate prior to the threshold, but not before the acceleration of cognitive and functional decline. Conclusions: A considerable proportion of patients with MCI would not meet inclusion criteria for a trial using the current threshold for amyloid positivity, even though on average, they are experiencing cognitive/functional decline associated with prethreshold levels of CSF Aβ 42. Lastly, future trials in early Alzheimer disease might consider revising the criteria regarding β-amyloid thresholds to include the range of amyloid associated with the first signs of accelerating rates of decline.« less

Accelerating rates of cognitive decline and imaging markers associated with β-amyloid pathology

DOE PAGES

Insel, Philip S.; Mattsson, Niklas; Mackin, R. Scott; ...

2016-04-15

Objective: Our objective is to estimate points along the spectrum of β-amyloid pathology at which rates of change of several measures of neuronal injury and cognitive decline begin to accelerate. Methods: In 460 patients with mild cognitive impairment (MCI), we estimated the points at which rates of florbetapir PET, fluorodeoxyglucose (FDG) PET, MRI, and cognitive and functional decline begin to accelerate with respect to baseline CSF Aβ 42. Points of initial acceleration in rates of decline were estimated using mixed-effects regression. Results: Rates of neuronal injury and cognitive and even functional decline accelerate substantially before the conventional threshold for amyloidmore » positivity, with rates of florbetapir PET and FDG PET accelerating early. Temporal lobe atrophy rates also accelerate prior to the threshold, but not before the acceleration of cognitive and functional decline. Conclusions: A considerable proportion of patients with MCI would not meet inclusion criteria for a trial using the current threshold for amyloid positivity, even though on average, they are experiencing cognitive/functional decline associated with prethreshold levels of CSF Aβ 42. Lastly, future trials in early Alzheimer disease might consider revising the criteria regarding β-amyloid thresholds to include the range of amyloid associated with the first signs of accelerating rates of decline.« less

Structured functional additive regression in reproducing kernel Hilbert spaces

PubMed Central

Zhu, Hongxiao; Yao, Fang; Zhang, Hao Helen

2013-01-01

Summary Functional additive models (FAMs) provide a flexible yet simple framework for regressions involving functional predictors. The utilization of data-driven basis in an additive rather than linear structure naturally extends the classical functional linear model. However, the critical issue of selecting nonlinear additive components has been less studied. In this work, we propose a new regularization framework for the structure estimation in the context of Reproducing Kernel Hilbert Spaces. The proposed approach takes advantage of the functional principal components which greatly facilitates the implementation and the theoretical analysis. The selection and estimation are achieved by penalized least squares using a penalty which encourages the sparse structure of the additive components. Theoretical properties such as the rate of convergence are investigated. The empirical performance is demonstrated through simulation studies and a real data application. PMID:25013362

A geographic information system tool to solve regression equations and estimate flow-frequency characteristics of Vermont Streams

USGS Publications Warehouse

Olson, Scott A.; Tasker, Gary D.; Johnston, Craig M.

2003-01-01

Estimates of the magnitude and frequency of streamflow are needed to safely and economically design bridges, culverts, and other structures in or near streams. These estimates also are used for managing floodplains, identifying flood-hazard areas, and establishing flood-insurance rates, but may be required at ungaged sites where no observed flood data are available for streamflow-frequency analysis. This report describes equations for estimating flow-frequency characteristics at ungaged, unregulated streams in Vermont. In the past, regression equations developed to estimate streamflow statistics required users to spend hours manually measuring basin characteristics for the stream site of interest. This report also describes the accompanying customized geographic information system (GIS) tool that automates the measurement of basin characteristics and calculation of corresponding flow statistics. The tool includes software that computes the accuracy of the results and adjustments for expected probability and for streamflow data of a nearby stream-gaging station that is either upstream or downstream and within 50 percent of the drainage area of the site where the flow-frequency characteristics are being estimated. The custom GIS can be linked to the National Flood Frequency program, adding the ability to plot peak-flow-frequency curves and synthetic hydrographs and to compute adjustments for urbanization.

Caregivers who refuse preventive care for their children: the relationship between immunization and topical fluoride refusal.

PubMed

Chi, Donald L

2014-07-01

The aim of this study was to examine caregivers' refusal of preventive medical and dental care for children. Prevalence rates of topical fluoride refusal based on dental records and caregiver self-reports were estimated for children treated in 3 dental clinics in Washington State. A 60-item survey was administered to 1024 caregivers to evaluate the association between immunization and topical fluoride refusal. Modified Poisson regression models were used to estimate prevalence rate ratios (PRRs). The prevalence of topical fluoride refusal was 4.9% according to dental records and 12.7% according to caregiver self-reports. The rate of immunization refusal was 27.4%. In the regression models, immunization refusal was significantly associated with topical fluoride refusal (dental record PRR = 1.61; 95% confidence interval [CI] = 1.32, 1.96; P < .001; caregiver self-report PRR = 6.20; 95% CI = 3.21, 11.98; P < .001). Caregivers younger than 35 years were significantly more likely than older caregivers to refuse both immunizations and topical fluoride (P < .05). Caregiver refusal of immunizations is associated with topical fluoride refusal. Future research should identify the behavioral and social factors related to caregiver refusal of preventive care with the goal of developing multidisciplinary strategies to help caregivers make optimal preventive care decisions for children.

Global burden of road traffic accidents in older adults: A systematic review and meta-regression analysis.

PubMed

Ang, Boon Hong; Chen, Won Sun; Lee, Shaun Wen Huey

2017-09-01

This study aims to estimate the burden of road traffic accidents and death among older adults. A systematic literature review was conducted on 10 electronic databases for articles describing Road Traffic Accident(RTA) mortality in older adults until September 2016. A random-effects meta-regression analyses was conducted to estimate the pooled rates of road traffic accidents and death. A total 5018 studies were identified and 23 studies were included. Most of the reported older adults were aged between 60 and 74 years, with majority being male gender and sustained minor trauma due to Motor-Vehicle Collision (MVC). The overall pooled mortality rate was 14% (95% Confidence Interval, CI: 11%, 16%), with higher mortality rates in studies conducted in North America (15%, 95% CI: 12%, 18%) and older adults admitted to trauma centers (17%, 95% CI: 14%, 21%). Secondary analysis showed that the very elderly adults (aged >75years) and pedestrians had higher odds of mortality death (Odds Ratio, OR: 2.05, 95% CI: 1.25, 3.38; OR: 2.08, 95% CI: 1.63, 2.66, respectively). A new comprehensive trauma management guidelines tailored to older adults should be established in low and middle-income countries where such guidelines are still lacking. Copyright © 2017 Elsevier B.V. All rights reserved.

RBF kernel based support vector regression to estimate the blood volume and heart rate responses during hemodialysis.

PubMed

Javed, Faizan; Chan, Gregory S H; Savkin, Andrey V; Middleton, Paul M; Malouf, Philip; Steel, Elizabeth; Mackie, James; Lovell, Nigel H

2009-01-01

This paper uses non-linear support vector regression (SVR) to model the blood volume and heart rate (HR) responses in 9 hemodynamically stable kidney failure patients during hemodialysis. Using radial bias function (RBF) kernels the non-parametric models of relative blood volume (RBV) change with time as well as percentage change in HR with respect to RBV were obtained. The e-insensitivity based loss function was used for SVR modeling. Selection of the design parameters which includes capacity (C), insensitivity region (e) and the RBF kernel parameter (sigma) was made based on a grid search approach and the selected models were cross-validated using the average mean square error (AMSE) calculated from testing data based on a k-fold cross-validation technique. Linear regression was also applied to fit the curves and the AMSE was calculated for comparison with SVR. For the model based on RBV with time, SVR gave a lower AMSE for both training (AMSE=1.5) as well as testing data (AMSE=1.4) compared to linear regression (AMSE=1.8 and 1.5). SVR also provided a better fit for HR with RBV for both training as well as testing data (AMSE=15.8 and 16.4) compared to linear regression (AMSE=25.2 and 20.1).

Treatment rates for injectable tiamulin and lincomycin as an estimate of morbidity in a swine herd with endemic swine dysentery.

PubMed

Walczak, Krysia; Friendship, Robert; Brockoff, Egan; Greer, Amy; Poljak, Zvonimir

2017-05-01

Treatment can be used as an indirect measure of morbidity, and treatment records can be used to describe disease patterns in a population. The aim of this study was to describe the rates of treatments with tiamulin and lincomycin by the intramuscular route in cohorts of pigs affected by swine dysentery. Data from treatment records from 19 cohorts of a 1500-head grower-finisher barn were analyzed using Poisson regression to determine factors associated with rates of treatment. Serial interval and reproductive numbers were extracted. Treatment rates displayed marked seasonality. The mean serial interval was estimated at 17 d with variability among batches. In the early period of most cohorts, the effective reproductive number did not exceed 1, and the highest estimate was 2.15 (95% CI: 1.46, 3.20). The average days-to-first treatment was 4.8 which suggests that pigs could have been infected at time of entry. The information about possible sources of infection and likely seasonality should be considered when developing disease and infection control measures in affected barns.

Treatment rates for injectable tiamulin and lincomycin as an estimate of morbidity in a swine herd with endemic swine dysentery

PubMed Central

Walczak, Krysia; Friendship, Robert; Brockoff, Egan; Greer, Amy; Poljak, Zvonimir

2017-01-01

Treatment can be used as an indirect measure of morbidity, and treatment records can be used to describe disease patterns in a population. The aim of this study was to describe the rates of treatments with tiamulin and lincomycin by the intramuscular route in cohorts of pigs affected by swine dysentery. Data from treatment records from 19 cohorts of a 1500-head grower-finisher barn were analyzed using Poisson regression to determine factors associated with rates of treatment. Serial interval and reproductive numbers were extracted. Treatment rates displayed marked seasonality. The mean serial interval was estimated at 17 d with variability among batches. In the early period of most cohorts, the effective reproductive number did not exceed 1, and the highest estimate was 2.15 (95% CI: 1.46, 3.20). The average days-to-first treatment was 4.8 which suggests that pigs could have been infected at time of entry. The information about possible sources of infection and likely seasonality should be considered when developing disease and infection control measures in affected barns. PMID:28487591

Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models.

PubMed

Yilmaz, Banu; Aras, Egemen; Nacar, Sinan; Kankal, Murat

2018-05-23

The functional life of a dam is often determined by the rate of sediment delivery to its reservoir. Therefore, an accurate estimate of the sediment load in rivers with dams is essential for designing and predicting a dam's useful lifespan. The most credible method is direct measurements of sediment input, but this can be very costly and it cannot always be implemented at all gauging stations. In this study, we tested various regression models to estimate suspended sediment load (SSL) at two gauging stations on the Çoruh River in Turkey, including artificial bee colony (ABC), teaching-learning-based optimization algorithm (TLBO), and multivariate adaptive regression splines (MARS). These models were also compared with one another and with classical regression analyses (CRA). Streamflow values and previously collected data of SSL were used as model inputs with predicted SSL data as output. Two different training and testing dataset configurations were used to reinforce the model accuracy. For the MARS method, the root mean square error value was found to range between 35% and 39% for the test two gauging stations, which was lower than errors for other models. Error values were even lower (7% to 15%) using another dataset. Our results indicate that simultaneous measurements of streamflow with SSL provide the most effective parameter for obtaining accurate predictive models and that MARS is the most accurate model for predicting SSL. Copyright © 2017 Elsevier B.V. All rights reserved.

Comparison of athlete-coach perceptions of internal and external load markers for elite junior tennis training.

PubMed

Murphy, Alistair P; Duffield, Rob; Kellett, Aaron; Reid, Machar

2014-09-01

To investigate the discrepancy between coach and athlete perceptions of internal load and notational analysis of external load in elite junior tennis. Fourteen elite junior tennis players and 6 international coaches were recruited. Ratings of perceived exertion (RPEs) were recorded for individual drills and whole sessions, along with a rating of mental exertion, coach rating of intended session exertion, and athlete heart rate (HR). Furthermore, total stroke count and unforced-error count were notated using video coding after each session, alongside coach and athlete estimations of shots and errors made. Finally, regression analyses explained the variance in the criterion variables of athlete and coach RPE. Repeated-measures analyses of variance and interclass correlation coefficients revealed that coaches significantly (P < .01) underestimated athlete session RPE, with only moderate correlation (r = .59) demonstrated between coach and athlete. However, athlete drill RPE (P = .14; r = .71) and mental exertion (P = .44; r = .68) were comparable and substantially correlated. No significant differences in estimated stroke count were evident between athlete and coach (P = .21), athlete notational analysis (P = .06), or coach notational analysis (P = .49). Coaches estimated significantly greater unforced errors than either athletes or notational analysis (P < .01). Regression analyses found that 54.5% of variance in coach RPE was explained by intended session exertion and coach drill RPE, while drill RPE and peak HR explained 45.3% of the variance in athlete session RPE. Coaches misinterpreted session RPE but not drill RPE, while inaccurately monitoring error counts. Improved understanding of external- and internal-load monitoring may help coach-athlete relationships in individual sports like tennis avoid maladaptive training.

Prevalence of psychotic disorders and its association with methodological issues. A systematic review and meta-analyses

PubMed Central

Martín, Carlos; Pastor, Loly

2018-01-01

Objectives The purpose of this study is to provide an updated systematic review to identify studies describing the prevalence of psychosis in order to explore methodological factors that could account for the variation in prevalence estimates. Methods Studies with original data related to the prevalence of psychosis (published between 1990 and 2015) were identified via searching electronic databases and reviewing manual citations. Prevalence estimates were sorted according to prevalence type (point, 12-months and lifetime). The independent association between key methodological variables and the mean effect of prevalence was examined (prevalence type, case-finding setting, method of confirming diagnosis, international classification of diseases, diagnosis category, and study quality) by meta-analytical techniques and random-effects meta-regression. Results Seventy-three primary studies were included, providing a total of 101 estimates of prevalence rates of psychosis. Across these studies, the pooled median point and 12-month prevalence for persons was 3.89 and 4.03 per 1000 respectively; and the median lifetime prevalence was 7.49 per 1000. The result of the random-effects meta-regression analysis revealed a significant effect for the prevalence type, with higher rates of lifetime prevalence than 12-month prevalence (p<0.001). Studies conducted in the general population presented higher prevalence rates than those carried out in populations attended in health/social services (p = 0.006). Compared to the diagnosis of schizophrenia only, prevalence rates were higher in the probable psychotic disorder (p = 0.022) and non-affective psychosis (p = 0.009). Finally, a higher study quality is associated with a lower estimated prevalence of psychotic disorders (p<0.001). Conclusions This systematic review provides a comprehensive comparison of methodologies used in studies of the prevalence of psychosis, which can provide insightful information for future epidemiological studies in adopting the most relevant methodological approach. PMID:29649252

Genital Chlamydia Prevalence in Europe and Non-European High Income Countries: Systematic Review and Meta-Analysis

PubMed Central

Redmond, Shelagh M.; Alexander-Kisslig, Karin; Woodhall, Sarah C.; van den Broek, Ingrid V. F.; van Bergen, Jan; Ward, Helen; Uusküla, Anneli; Herrmann, Björn; Andersen, Berit; Götz, Hannelore M.; Sfetcu, Otilia; Low, Nicola

2015-01-01

Background Accurate information about the prevalence of Chlamydia trachomatis is needed to assess national prevention and control measures. Methods We systematically reviewed population-based cross-sectional studies that estimated chlamydia prevalence in European Union/European Economic Area (EU/EEA) Member States and non-European high income countries from January 1990 to August 2012. We examined results in forest plots, explored heterogeneity using the I2 statistic, and conducted random effects meta-analysis if appropriate. Meta-regression was used to examine the relationship between study characteristics and chlamydia prevalence estimates. Results We included 25 population-based studies from 11 EU/EEA countries and 14 studies from five other high income countries. Four EU/EEA Member States reported on nationally representative surveys of sexually experienced adults aged 18–26 years (response rates 52–71%). In women, chlamydia point prevalence estimates ranged from 3.0–5.3%; the pooled average of these estimates was 3.6% (95% CI 2.4, 4.8, I2 0%). In men, estimates ranged from 2.4–7.3% (pooled average 3.5%; 95% CI 1.9, 5.2, I2 27%). Estimates in EU/EEA Member States were statistically consistent with those in other high income countries (I2 0% for women, 6% for men). There was statistical evidence of an association between survey response rate and estimated chlamydia prevalence; estimates were higher in surveys with lower response rates, (p = 0.003 in women, 0.018 in men). Conclusions Population-based surveys that estimate chlamydia prevalence are at risk of participation bias owing to low response rates. Estimates obtained in nationally representative samples of the general population of EU/EEA Member States are similar to estimates from other high income countries. PMID:25615574

Evaluation of procedures for estimating ruminal particle turnover and diet digestibility in ruminant animals

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

Cochran, R.C.

1985-01-01

Procedures used in estimating ruminal particle turnover and diet digestibility were evaluated in a series of independent experiments. Experiment 1 and 2 evaluated the influence of sampling site, mathematical model and intraruminal mixing on estimates of ruminal particle turnover in beef steers grazing crested wheatgrass or offered ad libitum levels of prairie hay once daily, respectively. Particle turnover rate constants were estimated by intraruminal administration (via rumen cannula) of ytterbium (Yb)-labeled forage, followed by serial collection of rumen digesta or fecal samples. Rumen Yb concentrations were transformed to natural logarithms and regressed on time. Influence of sampling site (rectum versusmore » rumen) on turnover estimates was modified by the model used to fit fecal marker excretion curves in the grazing study. In contrast, estimated turnover rate constants from rumen sampling were smaller (P < 0.05) than rectally derived rate constants, regardless of fecal model used, when steers were fed once daily. In Experiment 3, in vitro residues subjected to acid or neutral detergent fiber extraction (IVADF and IVNDF), acid detergent fiber incubated in cellulase (ADFIC) and acid detergent lignin (ADL) were evaluated as internal markers for predicting diet digestibility. Both IVADF and IVNDF displayed variable accuracy for prediction of in vivo digestibility whereas ADL and ADFIC inaccurately predicted digestibility of all diets.« less

Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data.

PubMed

Holsclaw, Tracy; Hallgren, Kevin A; Steyvers, Mark; Smyth, Padhraic; Atkins, David C

2015-12-01

Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased Type I and Type II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in online supplemental materials. (c) 2016 APA, all rights reserved).

Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data

PubMed Central

Holsclaw, Tracy; Hallgren, Kevin A.; Steyvers, Mark; Smyth, Padhraic; Atkins, David C.

2015-01-01

Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non-normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased type-I and type-II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally-technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in supplementary materials. PMID:26098126

CART (Classification and Regression Trees) Program: The Implementation of the CART Program and Its Application to Estimating Attrition Rates.

DTIC Science & Technology

1985-12-01

consists of the node t and all descendants of t in T. (3) Definition 3. Pruning a branch Tt from a tree T con- sists of deleting from T all...The default is 1.0 so that actually, this keyword did not need to appear in the above file. (5) DELETE . This keyword does not appear in our example, but...when it is used associated with some variable names, it indicates that we want to delete these vari- ables from the regression. If this keyword is

A Heckman selection model for the safety analysis of signalized intersections

PubMed Central

Wong, S. C.; Zhu, Feng; Pei, Xin; Huang, Helai; Liu, Youjun

2017-01-01

Purpose The objective of this paper is to provide a new method for estimating crash rate and severity simultaneously. Methods This study explores a Heckman selection model of the crash rate and severity simultaneously at different levels and a two-step procedure is used to investigate the crash rate and severity levels. The first step uses a probit regression model to determine the sample selection process, and the second step develops a multiple regression model to simultaneously evaluate the crash rate and severity for slight injury/kill or serious injury (KSI), respectively. The model uses 555 observations from 262 signalized intersections in the Hong Kong metropolitan area, integrated with information on the traffic flow, geometric road design, road environment, traffic control and any crashes that occurred during two years. Results The results of the proposed two-step Heckman selection model illustrate the necessity of different crash rates for different crash severity levels. Conclusions A comparison with the existing approaches suggests that the Heckman selection model offers an efficient and convenient alternative method for evaluating the safety performance at signalized intersections. PMID:28732050

Association of food environment and food retailers with obesity in US adults.

PubMed

Yan, Renfei; Bastian, Nathaniel D; Griffin, Paul M

2015-05-01

The food environment has been shown to be a factor affecting the obesity rate. We studied the association of density of food retailer type with obesity rate in U.S. adults in local regions controlling for socioeconomic factors. Parametric nonlinear regression was used on publically available data (year=2009) at the county level. We used the results of this association to estimate the impact of the addition of a new food retailer type in a geographic region. Obesity rate increased in supercenters (0.25-0.28%) and convenience stores (0.05%) and decreased in grocery stores (0.08%) and specialized food stores (0.27-0.36%). The marginal measures estimated in this work could be useful in identifying regions where interventions based on food retailer type would be most effective. Copyright © 2015 Elsevier Ltd. All rights reserved.

Re-analysis of a banding study to test the effects of an experimental increase in bag limits of mourning doves

USGS Publications Warehouse

Otis, D.L.; White, Gary C.

2002-01-01

In 1966-1971, eastern US states with hunting seasons on mourning doves (Zenaida macroura) participated in a study designed to estimate the effects of bag limit increases on population survival rates. More than 400 000 adult and juvenile birds were banded and released during this period, and subsequent harvest and return of bands, together with total harvest estimates from mail and telephone surveys of hunters, provided the database for analysis. The original analysis used an ANOVA framework, and resulted in inferences of no effect of bag limit increase on population parameters (Hayne 1975). We used a logistic regression analysis to infer that the bag limit increase did not cause a biologically significant increase in harvest rate and thus the experiment could not provide any insight into the relationship between harvest and annual survival rates. Harvest rate estimates of breeding populations from geographical subregions were used as covariates in a Program MARK analysis and revealed an association between annual survival and harvest rates, although this relationship is potentially confounded by a latitudinal gradient in survival rates of dove populations. We discuss methodological problems encountered in the analysis of these data, and provide recommendations for future studies of the relationship between harvest and annual survival rates of mourning dove populations.

Estimating top-of-atmosphere thermal infrared radiance using MERRA-2 atmospheric data

NASA Astrophysics Data System (ADS)

Kleynhans, Tania; Montanaro, Matthew; Gerace, Aaron; Kanan, Christopher

2017-05-01

Thermal infrared satellite images have been widely used in environmental studies. However, satellites have limited temporal resolution, e.g., 16 day Landsat or 1 to 2 day Terra MODIS. This paper investigates the use of the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data product, produced by NASA's Global Modeling and Assimilation Office (GMAO) to predict global topof-atmosphere (TOA) thermal infrared radiance. The high temporal resolution of the MERRA-2 data product presents opportunities for novel research and applications. Various methods were applied to estimate TOA radiance from MERRA-2 variables namely (1) a parameterized physics based method, (2) Linear regression models and (3) non-linear Support Vector Regression. Model prediction accuracy was evaluated using temporally and spatially coincident Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data as reference data. This research found that Support Vector Regression with a radial basis function kernel produced the lowest error rates. Sources of errors are discussed and defined. Further research is currently being conducted to train deep learning models to predict TOA thermal radiance

A Bayesian methodological framework for accommodating interannual variability of nutrient loading with the SPARROW model

NASA Astrophysics Data System (ADS)

Wellen, Christopher; Arhonditsis, George B.; Labencki, Tanya; Boyd, Duncan

2012-10-01

Regression-type, hybrid empirical/process-based models (e.g., SPARROW, PolFlow) have assumed a prominent role in efforts to estimate the sources and transport of nutrient pollution at river basin scales. However, almost no attempts have been made to explicitly accommodate interannual nutrient loading variability in their structure, despite empirical and theoretical evidence indicating that the associated source/sink processes are quite variable at annual timescales. In this study, we present two methodological approaches to accommodate interannual variability with the Spatially Referenced Regressions on Watershed attributes (SPARROW) nonlinear regression model. The first strategy uses the SPARROW model to estimate a static baseline load and climatic variables (e.g., precipitation) to drive the interannual variability. The second approach allows the source/sink processes within the SPARROW model to vary at annual timescales using dynamic parameter estimation techniques akin to those used in dynamic linear models. Model parameterization is founded upon Bayesian inference techniques that explicitly consider calibration data and model uncertainty. Our case study is the Hamilton Harbor watershed, a mixed agricultural and urban residential area located at the western end of Lake Ontario, Canada. Our analysis suggests that dynamic parameter estimation is the more parsimonious of the two strategies tested and can offer insights into the temporal structural changes associated with watershed functioning. Consistent with empirical and theoretical work, model estimated annual in-stream attenuation rates varied inversely with annual discharge. Estimated phosphorus source areas were concentrated near the receiving water body during years of high in-stream attenuation and dispersed along the main stems of the streams during years of low attenuation, suggesting that nutrient source areas are subject to interannual variability.

Determination of the minimal clinically important difference for seven measures of fatigue in Swedish patients with systemic lupus erythematosus.

PubMed

Pettersson, S; Lundberg, I E; Liang, M H; Pouchot, J; Henriksson, E Welin

2015-05-01

To estimate the minimal clinically important difference (MCID) in seven self-administered measures assessing fatigue in Swedish patients with systemic lupus erythematosus (SLE). The participants (n = 51, women 98%, age 52.8 ± 12.1 years, disease duration 18.7 ± 13.6 years) met in groups of six to nine persons. After completing seven fatigue questionnaires [the Fatigue Severity Scale (FSS); the Multidimensional Assessment of Fatigue (MAF) scale; the 20-item Multidimensional Fatigue Inventory (MFI); the Chalder Fatigue Scale (CFS); the Short Form-36 Vitality subscale (VT); the Functional Assessment of Chronic Illness Therapy - Fatigue (FACIT-F) scale; and the Numeric Rating Scale (NRS)], each respondent had a minimum of five face-to-face discussions, followed by an individual comparative assessment of their own level of fatigue (seven-grade scale). This method resulted in 260 contrasting assessments; MCIDs were first calculated using the paired differences and then established by a regression approach. Patients were asked to comment on their experience with the questionnaires and whether they captured their fatigue adequately. The paired approach (using 'little more fatigue' as an anchor for MCID during the face-to-face comparative assessments) provided estimates of 4.6-17.0; the regression approach provided estimates of 4.3-10.8. Estimates using the regression approach were consistently lower than those using the paired model. The MCID estimates were least favourable and fewer respondents supported the use of the NRS compared to the other self-reported questionnaires. All seven instruments detect MCIDs for fatigue in Swedish patients with SLE. However, the single-question measure was not supported by the MCID estimates or by comments from the respondents.

Deletion Diagnostics for Alternating Logistic Regressions

PubMed Central

Preisser, John S.; By, Kunthel; Perin, Jamie; Qaqish, Bahjat F.

2013-01-01

Deletion diagnostics are introduced for the regression analysis of clustered binary outcomes estimated with alternating logistic regressions, an implementation of generalized estimating equations (GEE) that estimates regression coefficients in a marginal mean model and in a model for the intracluster association given by the log odds ratio. The diagnostics are developed within an estimating equations framework that recasts the estimating functions for association parameters based upon conditional residuals into equivalent functions based upon marginal residuals. Extensions of earlier work on GEE diagnostics follow directly, including computational formulae for one-step deletion diagnostics that measure the influence of a cluster of observations on the estimated regression parameters and on the overall marginal mean or association model fit. The diagnostic formulae are evaluated with simulations studies and with an application concerning an assessment of factors associated with health maintenance visits in primary care medical practices. The application and the simulations demonstrate that the proposed cluster-deletion diagnostics for alternating logistic regressions are good approximations of their exact fully iterated counterparts. PMID:22777960

Comprehensive investigation into historical pipeline construction costs and engineering economic analysis of Alaska in-state gas pipeline

NASA Astrophysics Data System (ADS)

Rui, Zhenhua

This study analyzes historical cost data of 412 pipelines and 220 compressor stations. On the basis of this analysis, the study also evaluates the feasibility of an Alaska in-state gas pipeline using Monte Carlo simulation techniques. Analysis of pipeline construction costs shows that component costs, shares of cost components, and learning rates for material and labor costs vary by diameter, length, volume, year, and location. Overall average learning rates for pipeline material and labor costs are 6.1% and 12.4%, respectively. Overall average cost shares for pipeline material, labor, miscellaneous, and right of way (ROW) are 31%, 40%, 23%, and 7%, respectively. Regression models are developed to estimate pipeline component costs for different lengths, cross-sectional areas, and locations. An analysis of inaccuracy in pipeline cost estimation demonstrates that the cost estimation of pipeline cost components is biased except for in the case of total costs. Overall overrun rates for pipeline material, labor, miscellaneous, ROW, and total costs are 4.9%, 22.4%, -0.9%, 9.1%, and 6.5%, respectively, and project size, capacity, diameter, location, and year of completion have different degrees of impacts on cost overruns of pipeline cost components. Analysis of compressor station costs shows that component costs, shares of cost components, and learning rates for material and labor costs vary in terms of capacity, year, and location. Average learning rates for compressor station material and labor costs are 12.1% and 7.48%, respectively. Overall average cost shares of material, labor, miscellaneous, and ROW are 50.6%, 27.2%, 21.5%, and 0.8%, respectively. Regression models are developed to estimate compressor station component costs in different capacities and locations. An investigation into inaccuracies in compressor station cost estimation demonstrates that the cost estimation for compressor stations is biased except for in the case of material costs. Overall average overrun rates for compressor station material, labor, miscellaneous, land, and total costs are 3%, 60%, 2%, -14%, and 11%, respectively, and cost overruns for cost components are influenced by location and year of completion to different degrees. Monte Carlo models are developed and simulated to evaluate the feasibility of an Alaska in-state gas pipeline by assigning triangular distribution of the values of economic parameters. Simulated results show that the construction of an Alaska in-state natural gas pipeline is feasible at three scenarios: 500 million cubic feet per day (mmcfd), 750 mmcfd, and 1000 mmcfd.

Comparison of statistical models to estimate parasite growth rate in the induced blood stage malaria model.

PubMed

Wockner, Leesa F; Hoffmann, Isabell; O'Rourke, Peter; McCarthy, James S; Marquart, Louise

2017-08-25

The efficacy of vaccines aimed at inhibiting the growth of malaria parasites in the blood can be assessed by comparing the growth rate of parasitaemia in the blood of subjects treated with a test vaccine compared to controls. In studies using induced blood stage malaria (IBSM), a type of controlled human malaria infection, parasite growth rate has been measured using models with the intercept on the y-axis fixed to the inoculum size. A set of statistical models was evaluated to determine an optimal methodology to estimate parasite growth rate in IBSM studies. Parasite growth rates were estimated using data from 40 subjects published in three IBSM studies. Data was fitted using 12 statistical models: log-linear, sine-wave with the period either fixed to 48 h or not fixed; these models were fitted with the intercept either fixed to the inoculum size or not fixed. All models were fitted by individual, and overall by study using a mixed effects model with a random effect for the individual. Log-linear models and sine-wave models, with the period fixed or not fixed, resulted in similar parasite growth rate estimates (within 0.05 log 10 parasites per mL/day). Average parasite growth rate estimates for models fitted by individual with the intercept fixed to the inoculum size were substantially lower by an average of 0.17 log 10 parasites per mL/day (range 0.06-0.24) compared with non-fixed intercept models. Variability of parasite growth rate estimates across the three studies analysed was substantially higher (3.5 times) for fixed-intercept models compared with non-fixed intercept models. The same tendency was observed in models fitted overall by study. Modelling data by individual or overall by study had minimal effect on parasite growth estimates. The analyses presented in this report confirm that fixing the intercept to the inoculum size influences parasite growth estimates. The most appropriate statistical model to estimate the growth rate of blood-stage parasites in IBSM studies appears to be a log-linear model fitted by individual and with the intercept estimated in the log-linear regression. Future studies should use this model to estimate parasite growth rates.

Net-infiltration map of the Navajo Sandstone outcrop area in western Washington County, Utah

USGS Publications Warehouse

Heilweil, Victor M.; McKinney, Tim S.

2007-01-01

As populations grow in the arid southwestern United States and desert bedrock aquifers are increasingly targeted for future development, understanding and quantifying the spatial variability of net infiltration and recharge becomes critically important for inventorying groundwater resources and mapping contamination vulnerability. A Geographic Information System (GIS)-based model utilizing readily available soils, topographic, precipitation, and outcrop data has been developed for predicting net infiltration to exposed and soil-covered areas of the Navajo Sandstone outcrop of southwestern Utah. The Navajo Sandstone is an important regional bedrock aquifer. The GIS model determines the net-infiltration percentage of precipitation by using an empirical equation. This relation is derived from least squares linear regression between three surficial parameters (soil coarseness, topographic slope, and downgradient distance from outcrop) and the percentage of estimated net infiltration based on environmental tracer data from excavations and boreholes at Sand Hollow Reservoir in the southeastern part of the study area.Processed GIS raster layers are applied as parameters in the empirical equation for determining net infiltration for soil-covered areas as a percentage of precipitation. This net-infiltration percentage is multiplied by average annual Parameter-elevation Regressions on Independent Slopes Model (PRISM) precipitation data to obtain an infiltration rate for each model cell. Additionally, net infiltration on exposed outcrop areas is set to 10 percent of precipitation on the basis of borehole net-infiltration estimates. Soils and outcrop net-infiltration rates are merged to form a final map.Areas of low, medium, and high potential for ground-water recharge have been identified, and estimates of net infiltration range from 0.1 to 66 millimeters per year (mm/yr). Estimated net-infiltration rates of less than 10 mm/yr are considered low, rates of 10 to 50 mm/yr are considered medium, and rates of more than 50 mm/yr are considered high. A comparison of estimated net-infiltration rates (determined from tritium data) to predicted rates (determined from GIS methods) at 12 sites in Sand Hollow and at Anderson Junction indicates an average difference of about 50 percent. Two of the predicted values were lower, five were higher, and five were within the estimated range. While such uncertainty is relatively small compared with the three order-of-magnitude range in predicted net-infiltration rates, the net-infiltration map is best suited for evaluating relative spatial distribution rather than for precise quantification of recharge to the Navajo aquifer at specific locations. An important potential use for this map is land-use zoning for protecting high net-infiltration parts of the aquifer from potential surface contamination.

The comparison between several robust ridge regression estimators in the presence of multicollinearity and multiple outliers

NASA Astrophysics Data System (ADS)

Zahari, Siti Meriam; Ramli, Norazan Mohamed; Moktar, Balkiah; Zainol, Mohammad Said

2014-09-01

In the presence of multicollinearity and multiple outliers, statistical inference of linear regression model using ordinary least squares (OLS) estimators would be severely affected and produces misleading results. To overcome this, many approaches have been investigated. These include robust methods which were reported to be less sensitive to the presence of outliers. In addition, ridge regression technique was employed to tackle multicollinearity problem. In order to mitigate both problems, a combination of ridge regression and robust methods was discussed in this study. The superiority of this approach was examined when simultaneous presence of multicollinearity and multiple outliers occurred in multiple linear regression. This study aimed to look at the performance of several well-known robust estimators; M, MM, RIDGE and robust ridge regression estimators, namely Weighted Ridge M-estimator (WRM), Weighted Ridge MM (WRMM), Ridge MM (RMM), in such a situation. Results of the study showed that in the presence of simultaneous multicollinearity and multiple outliers (in both x and y-direction), the RMM and RIDGE are more or less similar in terms of superiority over the other estimators, regardless of the number of observation, level of collinearity and percentage of outliers used. However, when outliers occurred in only single direction (y-direction), the WRMM estimator is the most superior among the robust ridge regression estimators, by producing the least variance. In conclusion, the robust ridge regression is the best alternative as compared to robust and conventional least squares estimators when dealing with simultaneous presence of multicollinearity and outliers.

Shrinkage regression-based methods for microarray missing value imputation.

PubMed

Wang, Hsiuying; Chiu, Chia-Chun; Wu, Yi-Ching; Wu, Wei-Sheng

2013-01-01

Missing values commonly occur in the microarray data, which usually contain more than 5% missing values with up to 90% of genes affected. Inaccurate missing value estimation results in reducing the power of downstream microarray data analyses. Many types of methods have been developed to estimate missing values. Among them, the regression-based methods are very popular and have been shown to perform better than the other types of methods in many testing microarray datasets. To further improve the performances of the regression-based methods, we propose shrinkage regression-based methods. Our methods take the advantage of the correlation structure in the microarray data and select similar genes for the target gene by Pearson correlation coefficients. Besides, our methods incorporate the least squares principle, utilize a shrinkage estimation approach to adjust the coefficients of the regression model, and then use the new coefficients to estimate missing values. Simulation results show that the proposed methods provide more accurate missing value estimation in six testing microarray datasets than the existing regression-based methods do. Imputation of missing values is a very important aspect of microarray data analyses because most of the downstream analyses require a complete dataset. Therefore, exploring accurate and efficient methods for estimating missing values has become an essential issue. Since our proposed shrinkage regression-based methods can provide accurate missing value estimation, they are competitive alternatives to the existing regression-based methods.

Panel regressions to estimate low-flow response to rainfall variability in ungaged basins

USGS Publications Warehouse

Bassiouni, Maoya; Vogel, Richard M.; Archfield, Stacey A.

2016-01-01

Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.

Panel regressions to estimate low-flow response to rainfall variability in ungaged basins

NASA Astrophysics Data System (ADS)

Bassiouni, Maoya; Vogel, Richard M.; Archfield, Stacey A.

2016-12-01

Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.

Nonparametric instrumental regression with non-convex constraints

NASA Astrophysics Data System (ADS)

Grasmair, M.; Scherzer, O.; Vanhems, A.

2013-03-01

This paper considers the nonparametric regression model with an additive error that is dependent on the explanatory variables. As is common in empirical studies in epidemiology and economics, it also supposes that valid instrumental variables are observed. A classical example in microeconomics considers the consumer demand function as a function of the price of goods and the income, both variables often considered as endogenous. In this framework, the economic theory also imposes shape restrictions on the demand function, such as integrability conditions. Motivated by this illustration in microeconomics, we study an estimator of a nonparametric constrained regression function using instrumental variables by means of Tikhonov regularization. We derive rates of convergence for the regularized model both in a deterministic and stochastic setting under the assumption that the true regression function satisfies a projected source condition including, because of the non-convexity of the imposed constraints, an additional smallness condition.

Bias in Estimation of Hippocampal Atrophy using Deformation-Based Morphometry Arises from Asymmetric Global Normalization: An Illustration in ADNI 3 Tesla MRI Data

PubMed Central

Yushkevich, Paul A.; Avants, Brian B.; Das, Sandhitsu R.; Pluta, John; Altinay, Murat; Craige, Caryne

2009-01-01

Measurement of brain change due to neurodegenerative disease and treatment is one of the fundamental tasks of neuroimaging. Deformation-based morphometry (DBM) has been long recognized as an effective and sensitive tool for estimating the change in the volume of brain regions over time. This paper demonstrates that a straightforward application of DBM to estimate the change in the volume of the hippocampus can result in substantial bias, i.e., an overestimation of the rate of change in hippocampal volume. In ADNI data, this bias is manifested as a non-zero intercept of the regression line fitted to the 6 and 12 month rates of hippocampal atrophy. The bias is further confirmed by applying DBM to repeat scans of subjects acquired on the same day. This bias appears to be the result of asymmetry in the interpolation of baseline and followup images during longitudinal image registration. Correcting this asymmetry leads to bias-free atrophy estimation. PMID:20005963

Best Fitting Prediction Equations for Basal Metabolic Rate: Informing Obesity Interventions in Diverse Populations

PubMed Central

Sabounchi, Nasim S.; Rahmandad, Hazhir; Ammerman, Alice

2014-01-01

Basal Metabolic Rate (BMR) represents the largest component of total energy expenditure and is a major contributor to energy balance. Therefore, accurately estimating BMR is critical for developing rigorous obesity prevention and control strategies. Over the past several decades, numerous BMR formulas have been developed targeted to different population groups. A comprehensive literature search revealed 248 BMR estimation equations developed using diverse ranges of age, gender, race, fat free mass, fat mass, height, waist-to-hip ratio, body mass index, and weight. A subset of 47 studies included enough detail to allow for development of meta-regression equations. Utilizing these studies, meta-equations were developed targeted to twenty specific population groups. This review provides a comprehensive summary of available BMR equations and an estimate of their accuracy. An accompanying online BMR prediction tool (available at http://www.sdl.ise.vt.edu/tutorials.html) was developed to automatically estimate BMR based on the most appropriate equation after user-entry of individual age, race, gender, and weight. PMID:23318720

Combining Decision Rules from Classification Tree Models and Expert Assessment to Estimate Occupational Exposure to Diesel Exhaust for a Case-Control Study

PubMed Central

Friesen, Melissa C.; Wheeler, David C.; Vermeulen, Roel; Locke, Sarah J.; Zaebst, Dennis D.; Koutros, Stella; Pronk, Anjoeka; Colt, Joanne S.; Baris, Dalsu; Karagas, Margaret R.; Malats, Nuria; Schwenn, Molly; Johnson, Alison; Armenti, Karla R.; Rothman, Nathanial; Stewart, Patricia A.; Kogevinas, Manolis; Silverman, Debra T.

2016-01-01

Objectives: To efficiently and reproducibly assess occupational diesel exhaust exposure in a Spanish case-control study, we examined the utility of applying decision rules that had been extracted from expert estimates and questionnaire response patterns using classification tree (CT) models from a similar US study. Methods: First, previously extracted CT decision rules were used to obtain initial ordinal (0–3) estimates of the probability, intensity, and frequency of occupational exposure to diesel exhaust for the 10 182 jobs reported in a Spanish case-control study of bladder cancer. Second, two experts reviewed the CT estimates for 350 jobs randomly selected from strata based on each CT rule’s agreement with the expert ratings in the original study [agreement rate, from 0 (no agreement) to 1 (perfect agreement)]. Their agreement with each other and with the CT estimates was calculated using weighted kappa (κ w) and guided our choice of jobs for subsequent expert review. Third, an expert review comprised all jobs with lower confidence (low-to-moderate agreement rates or discordant assignments, n = 931) and a subset of jobs with a moderate to high CT probability rating and with moderately high agreement rates (n = 511). Logistic regression was used to examine the likelihood that an expert provided a different estimate than the CT estimate based on the CT rule agreement rates, the CT ordinal rating, and the availability of a module with diesel-related questions. Results: Agreement between estimates made by two experts and between estimates made by each of the experts and the CT estimates was very high for jobs with estimates that were determined by rules with high CT agreement rates (κ w: 0.81–0.90). For jobs with estimates based on rules with lower agreement rates, moderate agreement was observed between the two experts (κ w: 0.42–0.67) and poor-to-moderate agreement was observed between the experts and the CT estimates (κ w: 0.09–0.57). In total, the expert review of 1442 jobs changed 156 probability estimates, 128 intensity estimates, and 614 frequency estimates. The expert was more likely to provide a different estimate when the CT rule agreement rate was <0.8, when the CT ordinal ratings were low to moderate, or when a module with diesel questions was available. Conclusions: Our reliability assessment provided important insight into where to prioritize additional expert review; as a result, only 14% of the jobs underwent expert review, substantially reducing the exposure assessment burden. Overall, we found that we could efficiently, reproducibly, and reliably apply CT decision rules from one study to assess exposure in another study. PMID:26732820

Downward trends in surgical site and urinary tract infections after cesarean delivery in a French surveillance network, 1997-2003.

PubMed

Vincent, Agnès; Ayzac, Louis; Girard, Raphaële; Caillat-Vallet, Emmanuelle; Chapuis, Catherine; Depaix, Florence; Dumas, Anne-Marie; Gignoux, Chantal; Haond, Catherine; Lafarge-Leboucher, Joëlle; Launay, Carine; Tissot-Guerraz, Françoise; Fabry, Jacques

2008-03-01

To evaluate whether the adjusted rates of surgical site infection (SSI) and urinary tract infection (UTI) after cesarean delivery decrease in maternity units that perform active healthcare-associated infection surveillance. Trend analysis by means of multiple logistic regression. A total of 80 maternity units participating in the Mater Sud-Est surveillance network. A total of 37,074 cesarean deliveries were included in the surveillance from January 1, 1997, through December 31, 2003. We used a logistic regression model to estimate risk-adjusted post-cesarean delivery infection odds ratios. The variables included were the maternity units' annual rate of operative procedures, the level of dispensed neonatal care, the year of delivery, maternal risk factors, and the characteristics of cesarean delivery. The trend of risk-adjusted odds ratios for SSI and UTI during the study period was studied by linear regression. The crude rates of SSI and UTI after cesarean delivery were 1.5% (571 of 37,074 patients) and 1.8% (685 of 37,074 patients), respectively. During the study period, the decrease in SSI and UTI adjusted odds ratios was statistically significant (R=-0.823 [P=.023] and R=-0.906 [P=.005], respectively). Reductions of 48% in the SSI rate and 52% in the UTI rate were observed in the maternity units. These unbiased trends could be related to progress in preventive practices as a result of the increased dissemination of national standards and a collaborative surveillance with benchmarking of rates.

Estimating self, parental, and partner multiple intelligences: a replication in Malaysia.

PubMed

Swami, Viren; Furnham, Adrian; Kannan, Kumaraswami

2006-12-01

Participants were 230 adult Malaysians who estimated their own, their parents', and their partners' overall IQs and 10 multiple intelligences. In accordance with both the previous literature and the authors' hypotheses, men rated themselves higher than did women on overall, verbal, logical-mathematical, and spatial intelligences. There were fewer gender differences in ratings of parents and in those of partners. Participants believed that they were more intelligent than both parents (but not their partners) and that their fathers were more intelligent than their mothers. Regressions indicated that participants believed that verbal intelligence and--to a lesser extent--logical-mathematical intelligence were the main predictors of overall intelligence. The authors discussed results in terms of the extant cross-cultural literature in the field.

Predictors of survival among hemodialysis patients: effect of perceived family support.

PubMed

Christensen, A J; Wiebe, J S; Smith, T W; Turner, C W

1994-11-01

The authors examined the role of perceived family support and symptoms of depression as predictors of survival in a sample of 78 in-center hemodialysis patients. Cox regression analysis revealed significant effects for family support (p < .005), blood urea nitrogen (p < .01), and age (p < .005). The effect for depression was not significant. The Cox model indicated that a 1-point increase on the family support measure was associated with a 13% reduction in the hazard rate (i.e., mortality). Estimated 5-year mortality rates among low family support patients were approximately 3 times higher than estimated mortality for high support patients. Differences in patient adherence to the dietary and medication regimens failed to explain the significant effect of family support.

Comparing facility-level methane emission rate estimates at natural gas gathering and boosting stations

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

Vaughn, Timothy L.; Bell, Clay S.; Yacovitch, Tara I.

Coordinated dual-tracer, aircraft-based, and direct component-level measurements were made at midstream natural gas gathering and boosting stations in the Fayetteville shale (Arkansas, USA). On-site component-level measurements were combined with engineering estimates to generate comprehensive facility-level methane emission rate estimates ('study on-site estimates (SOE)') comparable to tracer and aircraft measurements. Combustion slip (unburned fuel entrained in compressor engine exhaust), which was calculated based on 111 recent measurements of representative compressor engines, accounts for an estimated 75% of cumulative SOEs at gathering stations included in comparisons. Measured methane emissions from regenerator vents on glycol dehydrator units were substantially larger than predicted bymore » modelling software; the contribution of dehydrator regenerator vents to the cumulative SOE would increase from 1% to 10% if based on direct measurements. Concurrent measurements at 14 normally-operating facilities show relative agreement between tracer and SOE, but indicate that tracer measurements estimate lower emissions (regression of tracer to SOE = 0.91 (95% CI = 0.83-0.99), R2 = 0.89). Tracer and SOE 95% confidence intervals overlap at 11/14 facilities. Contemporaneous measurements at six facilities suggest that aircraft measurements estimate higher emissions than SOE. Aircraft and study on-site estimate 95% confidence intervals overlap at 3/6 facilities. The average facility level emission rate (FLER) estimated by tracer measurements in this study is 17-73% higher than a prior national study by Marchese et al.« less

Comparing facility-level methane emission rate estimates at natural gas gathering and boosting stations

DOE PAGES

Vaughn, Timothy L.; Bell, Clay S.; Yacovitch, Tara I.; ...

2017-02-09

Coordinated dual-tracer, aircraft-based, and direct component-level measurements were made at midstream natural gas gathering and boosting stations in the Fayetteville shale (Arkansas, USA). On-site component-level measurements were combined with engineering estimates to generate comprehensive facility-level methane emission rate estimates ('study on-site estimates (SOE)') comparable to tracer and aircraft measurements. Combustion slip (unburned fuel entrained in compressor engine exhaust), which was calculated based on 111 recent measurements of representative compressor engines, accounts for an estimated 75% of cumulative SOEs at gathering stations included in comparisons. Measured methane emissions from regenerator vents on glycol dehydrator units were substantially larger than predicted bymore » modelling software; the contribution of dehydrator regenerator vents to the cumulative SOE would increase from 1% to 10% if based on direct measurements. Concurrent measurements at 14 normally-operating facilities show relative agreement between tracer and SOE, but indicate that tracer measurements estimate lower emissions (regression of tracer to SOE = 0.91 (95% CI = 0.83-0.99), R2 = 0.89). Tracer and SOE 95% confidence intervals overlap at 11/14 facilities. Contemporaneous measurements at six facilities suggest that aircraft measurements estimate higher emissions than SOE. Aircraft and study on-site estimate 95% confidence intervals overlap at 3/6 facilities. The average facility level emission rate (FLER) estimated by tracer measurements in this study is 17-73% higher than a prior national study by Marchese et al.« less

Median and Low-Flow Characteristics for Streams under Natural and Diverted Conditions, Northeast Maui, Hawaii

USGS Publications Warehouse

Gingerich, Stephen B.

2005-01-01

Flow-duration statistics under natural (undiverted) and diverted flow conditions were estimated for gaged and ungaged sites on 21 streams in northeast Maui, Hawaii. The estimates were made using the optimal combination of continuous-record gaging-station data, low-flow measurements, and values determined from regression equations developed as part of this study. Estimated 50- and 95-percent flow duration statistics for streams are presented and the analyses done to develop and evaluate the methods used in estimating the statistics are described. Estimated streamflow statistics are presented for sites where various amounts of streamflow data are available as well as for locations where no data are available. Daily mean flows were used to determine flow-duration statistics for continuous-record stream-gaging stations in the study area following U.S. Geological Survey established standard methods. Duration discharges of 50- and 95-percent were determined from total flow and base flow for each continuous-record station. The index-station method was used to adjust all of the streamflow records to a common, long-term period. The gaging station on West Wailuaiki Stream (16518000) was chosen as the index station because of its record length (1914-2003) and favorable geographic location. Adjustments based on the index-station method resulted in decreases to the 50-percent duration total flow, 50-percent duration base flow, 95-percent duration total flow, and 95-percent duration base flow computed on the basis of short-term records that averaged 7, 3, 4, and 1 percent, respectively. For the drainage basin of each continuous-record gaged site and selected ungaged sites, morphometric, geologic, soil, and rainfall characteristics were quantified using Geographic Information System techniques. Regression equations relating the non-diverted streamflow statistics to basin characteristics of the gaged basins were developed using ordinary-least-squares regression analyses. Rainfall rate, maximum basin elevation, and the elongation ratio of the basin were the basin characteristics used in the final regression equations for 50-percent duration total flow and base flow. Rainfall rate and maximum basin elevation were used in the final regression equations for the 95-percent duration total flow and base flow. The relative errors between observed and estimated flows ranged from 10 to 20 percent for the 50-percent duration total flow and base flow, and from 29 to 56 percent for the 95-percent duration total flow and base flow. The regression equations developed for this study were used to determine the 50-percent duration total flow, 50-percent duration base flow, 95-percent duration total flow, and 95-percent duration base flow at selected ungaged diverted and undiverted sites. Estimated streamflow, prediction intervals, and standard errors were determined for 48 ungaged sites in the study area and for three gaged sites west of the study area. Relative errors were determined for sites where measured values of 95-percent duration discharge of total flow were available. East of Keanae Valley, the 95-percent duration discharge equation generally underestimated flow, and within and west of Keanae Valley, the equation generally overestimated flow. Reduction in 50- and 95-percent flow-duration values in stream reaches affected by diversions throughout the study area average 58 to 60 percent.

Simultaneous estimation of local-scale and flow path-scale dual-domain mass transfer parameters using geoelectrical monitoring

USGS Publications Warehouse

Briggs, Martin A.; Day-Lewis, Frederick D.; Ong, John B.; Curtis, Gary P.; Lane, John W.

2013-01-01

Anomalous solute transport, modeled as rate-limited mass transfer, has an observable geoelectrical signature that can be exploited to infer the controlling parameters. Previous experiments indicate the combination of time-lapse geoelectrical and fluid conductivity measurements collected during ionic tracer experiments provides valuable insight into the exchange of solute between mobile and immobile porosity. Here, we use geoelectrical measurements to monitor tracer experiments at a former uranium mill tailings site in Naturita, Colorado. We use nonlinear regression to calibrate dual-domain mass transfer solute-transport models to field data. This method differs from previous approaches by calibrating the model simultaneously to observed fluid conductivity and geoelectrical tracer signals using two parameter scales: effective parameters for the flow path upgradient of the monitoring point and the parameters local to the monitoring point. We use regression statistics to rigorously evaluate the information content and sensitivity of fluid conductivity and geophysical data, demonstrating multiple scales of mass transfer parameters can simultaneously be estimated. Our results show, for the first time, field-scale spatial variability of mass transfer parameters (i.e., exchange-rate coefficient, porosity) between local and upgradient effective parameters; hence our approach provides insight into spatial variability and scaling behavior. Additional synthetic modeling is used to evaluate the scope of applicability of our approach, indicating greater range than earlier work using temporal moments and a Lagrangian-based Damköhler number. The introduced Eulerian-based Damköhler is useful for estimating tracer injection duration needed to evaluate mass transfer exchange rates that range over several orders of magnitude.

Addressing the unemployment-mortality conundrum: non-linearity is the answer.

PubMed

Bonamore, Giorgio; Carmignani, Fabrizio; Colombo, Emilio

2015-02-01

The effect of unemployment on mortality is the object of a lively literature. However, this literature is characterized by sharply conflicting results. We revisit this issue and suggest that the relationship might be non-linear. We use data for 265 territorial units (regions) within 23 European countries over the period 2000-2012 to estimate a multivariate regression of mortality. The estimating equation allows for a quadratic relationship between unemployment and mortality. We control for various other determinants of mortality at regional and national level and we include region-specific and time-specific fixed effects. The model is also extended to account for the dynamic adjustment of mortality and possible lagged effects of unemployment. We find that the relationship between mortality and unemployment is U shaped. In the benchmark regression, when the unemployment rate is low, at 3%, an increase by one percentage point decreases average mortality by 0.7%. As unemployment increases, the effect decays: when the unemployment rate is 8% (sample average) a further increase by one percentage point decreases average mortality by 0.4%. The effect changes sign, turning from negative to positive, when unemployment is around 17%. When the unemployment rate is 25%, a further increase by one percentage point raises average mortality by 0.4%. Results hold for different causes of death and across different specifications of the estimating equation. We argue that the non-linearity arises because the level of unemployment affects the psychological and behavioural response of individuals to worsening economic conditions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Independent contrasts and PGLS regression estimators are equivalent.

PubMed

Blomberg, Simon P; Lefevre, James G; Wells, Jessie A; Waterhouse, Mary

2012-05-01

We prove that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameter of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution. This equivalence has several implications: 1. Understanding the structure of the linear model for GLS regression provides insight into when and why phylogeny is important in comparative studies. 2. The limitations of the PIC regression analysis are the same as the limitations of the GLS model. In particular, phylogenetic covariance applies only to the response variable in the regression and the explanatory variable should be regarded as fixed. Calculation of PICs for explanatory variables should be treated as a mathematical idiosyncrasy of the PIC regression algorithm. 3. Since the GLS estimator is the best linear unbiased estimator (BLUE), the slope parameter estimated using PICs is also BLUE. 4. If the slope is estimated using different branch lengths for the explanatory and response variables in the PIC algorithm, the estimator is no longer the BLUE, so this is not recommended. Finally, we discuss whether or not and how to accommodate phylogenetic covariance in regression analyses, particularly in relation to the problem of phylogenetic uncertainty. This discussion is from both frequentist and Bayesian perspectives.

Population drinking and fatal injuries in Eastern Europe: a time-series analysis of six countries.

PubMed

Landberg, Jonas

2010-01-01

To estimate to what extent injury mortality rates in 6 Eastern European countries are affected by changes in population drinking during the post-war period. The analysis included injury mortality rates and per capita alcohol consumption in Russia, Belarus, Poland, Hungary, Bulgaria and the former Czechoslovakia. Total population and gender-specific models were estimated using auto regressive integrated moving average time-series modelling. The estimates for the total population were generally positive and significant. For Russia and Belarus, a 1-litre increase in per capita consumption was associated with an increase in injury mortality of 7.5 and 5.5 per 100,000 inhabitants, respectively. The estimates for the remaining countries ranged between 1.4 and 2.0. The gender-specific estimates displayed national variations similar to the total population estimates although the estimates for males were higher than for females in all countries. The results suggest that changes in per capita consumption have a significant impact on injury mortality in these countries, but the strength of the association tends to be stronger in countries where intoxication-oriented drinking is more common. Copyright 2009 S. Karger AG, Basel.

Heritability estimations for inner muscular fat in Hereford cattle using random regressions

USDA-ARS?s Scientific Manuscript database

Random regressions make possible to make genetic predictions and parameters estimation across a gradient of environments, allowing a more accurate and beneficial use of animals as breeders in specific environments. The objective of this study was to use random regression models to estimate heritabil...

A Comparison of Methods for Nonparametric Estimation of Item Characteristic Curves for Binary Items

ERIC Educational Resources Information Center

Lee, Young-Sun

2007-01-01

This study compares the performance of three nonparametric item characteristic curve (ICC) estimation procedures: isotonic regression, smoothed isotonic regression, and kernel smoothing. Smoothed isotonic regression, employed along with an appropriate kernel function, provides better estimates and also satisfies the assumption of strict…

Data Analysis & Statistical Methods for Command File Errors

NASA Technical Reports Server (NTRS)

Meshkat, Leila; Waggoner, Bruce; Bryant, Larry

2014-01-01

This paper explains current work on modeling for managing the risk of command file errors. It is focused on analyzing actual data from a JPL spaceflight mission to build models for evaluating and predicting error rates as a function of several key variables. We constructed a rich dataset by considering the number of errors, the number of files radiated, including the number commands and blocks in each file, as well as subjective estimates of workload and operational novelty. We have assessed these data using different curve fitting and distribution fitting techniques, such as multiple regression analysis, and maximum likelihood estimation to see how much of the variability in the error rates can be explained with these. We have also used goodness of fit testing strategies and principal component analysis to further assess our data. Finally, we constructed a model of expected error rates based on the what these statistics bore out as critical drivers to the error rate. This model allows project management to evaluate the error rate against a theoretically expected rate as well as anticipate future error rates.

Estimating parasitic sea lamprey abundance in Lake Huron from heterogenous data sources

USGS Publications Warehouse

Young, Robert J.; Jones, Michael L.; Bence, James R.; McDonald, Rodney B.; Mullett, Katherine M.; Bergstedt, Roger A.

2003-01-01

The Great Lakes Fishery Commission uses time series of transformer, parasitic, and spawning population estimates to evaluate the effectiveness of its sea lamprey (Petromyzon marinus) control program. This study used an inverse variance weighting method to integrate Lake Huron sea lamprey population estimates derived from two estimation procedures: 1) prediction of the lake-wide spawning population from a regression model based on stream size and, 2) whole-lake mark and recapture estimates. In addition, we used a re-sampling procedure to evaluate the effect of trading off sampling effort between the regression and mark-recapture models. Population estimates derived from the regression model ranged from 132,000 to 377,000 while mark-recapture estimates of marked recently metamorphosed juveniles and parasitic sea lampreys ranged from 536,000 to 634,000 and 484,000 to 1,608,000, respectively. The precision of the estimates varied greatly among estimation procedures and years. The integrated estimate of the mark-recapture and spawner regression procedures ranged from 252,000 to 702,000 transformers. The re-sampling procedure indicated that the regression model is more sensitive to reduction in sampling effort than the mark-recapture model. Reliance on either the regression or mark-recapture model alone could produce misleading estimates of abundance of sea lampreys and the effect of the control program on sea lamprey abundance. These analyses indicate that the precision of the lakewide population estimate can be maximized by re-allocating sampling effort from marking sea lampreys to trapping additional streams.

Nomograms for predicting graft function and survival in living donor kidney transplantation based on the UNOS Registry.

PubMed

Tiong, H Y; Goldfarb, D A; Kattan, M W; Alster, J M; Thuita, L; Yu, C; Wee, A; Poggio, E D

2009-03-01

We developed nomograms that predict transplant renal function at 1 year (Modification of Diet in Renal Disease equation [estimated glomerular filtration rate]) and 5-year graft survival after living donor kidney transplantation. Data for living donor renal transplants were obtained from the United Network for Organ Sharing registry for 2000 to 2003. Nomograms were designed using linear or Cox regression models to predict 1-year estimated glomerular filtration rate and 5-year graft survival based on pretransplant information including demographic factors, immunosuppressive therapy, immunological factors and organ procurement technique. A third nomogram was constructed to predict 5-year graft survival using additional information available by 6 months after transplantation. These data included delayed graft function, any treated rejection episodes and the 6-month estimated glomerular filtration rate. The nomograms were internally validated using 10-fold cross-validation. The renal function nomogram had an r-square value of 0.13. It worked best when predicting estimated glomerular filtration rate values between 50 and 70 ml per minute per 1.73 m(2). The 5-year graft survival nomograms had a concordance index of 0.71 for the pretransplant nomogram and 0.78 for the 6-month posttransplant nomogram. Calibration was adequate for all nomograms. Nomograms based on data from the United Network for Organ Sharing registry have been validated to predict the 1-year estimated glomerular filtration rate and 5-year graft survival. These nomograms may facilitate individualized patient care in living donor kidney transplantation.

Long-Term Renal Function Recovery following Radical Nephrectomy for Kidney Cancer: Results from a Multicenter Confirmatory Study.

PubMed

Zabor, Emily C; Furberg, Helena; Lee, Byron; Campbell, Steven; Lane, Brian R; Thompson, R Houston; Antonio, Elvis Caraballo; Noyes, Sabrina L; Zaid, Harras; Jaimes, Edgar A; Russo, Paul

2018-04-01

We sought to confirm the findings from a previous single institution study of 572 patients from Memorial Sloan Kettering Cancer Center in which we found that 49% of patients recovered to the preoperative estimated glomerular filtration rate within 2 years following radical nephrectomy for renal cell carcinoma. A multicenter retrospective study was performed in 1,928 patients using data contributed from 3 independent centers. The outcome of interest was postoperative recovery to the preoperative estimated glomerular filtration rate. Data were analyzed using cumulative incidence and competing risks regression with death from any cause treated as a competing event. This study demonstrated that 45% of patients had recovered to the preoperative estimated glomerular filtration rate by 2 years following radical nephrectomy. Furthermore, this study confirmed that recovery of renal function differed according to preoperative renal function such that patients with a lower preoperative estimated glomerular filtration rate had an increased chance of recovery. This study also suggested that larger tumor size and female gender were significantly associated with an increased chance of renal function recovery. In this multicenter retrospective study we confirmed that in the long term a large proportion of patients recover to preoperative renal function following radical nephrectomy for kidney tumors. Recovery is more likely among those with a lower preoperative estimated glomerular filtration rate. Copyright © 2018 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Analysis of volumetric response of pituitary adenomas receiving adjuvant CyberKnife stereotactic radiosurgery with the application of an exponential fitting model

PubMed Central

Yu, Yi-Lin; Yang, Yun-Ju; Lin, Chin; Hsieh, Chih-Chuan; Li, Chiao-Zhu; Feng, Shao-Wei; Tang, Chi-Tun; Chung, Tzu-Tsao; Ma, Hsin-I; Chen, Yuan-Hao; Ju, Da-Tong; Hueng, Dueng-Yuan

2017-01-01

Abstract Tumor control rates of pituitary adenomas (PAs) receiving adjuvant CyberKnife stereotactic radiosurgery (CK SRS) are high. However, there is currently no uniform way to estimate the time course of the disease. The aim of this study was to analyze the volumetric responses of PAs after CK SRS and investigate the application of an exponential decay model in calculating an accurate time course and estimation of the eventual outcome. A retrospective review of 34 patients with PAs who received adjuvant CK SRS between 2006 and 2013 was performed. Tumor volume was calculated using the planimetric method. The percent change in tumor volume and tumor volume rate of change were compared at median 4-, 10-, 20-, and 36-month intervals. Tumor responses were classified as: progression for >15% volume increase, regression for ≤15% decrease, and stabilization for ±15% of the baseline volume at the time of last follow-up. For each patient, the volumetric change versus time was fitted with an exponential model. The overall tumor control rate was 94.1% in the 36-month (range 18–87 months) follow-up period (mean volume change of −43.3%). Volume regression (mean decrease of −50.5%) was demonstrated in 27 (79%) patients, tumor stabilization (mean change of −3.7%) in 5 (15%) patients, and tumor progression (mean increase of 28.1%) in 2 (6%) patients (P = 0.001). Tumors that eventually regressed or stabilized had a temporary volume increase of 1.07% and 41.5% at 4 months after CK SRS, respectively (P = 0.017). The tumor volume estimated using the exponential fitting equation demonstrated high positive correlation with the actual volume calculated by magnetic resonance imaging (MRI) as tested by Pearson correlation coefficient (0.9). Transient progression of PAs post-CK SRS was seen in 62.5% of the patients receiving CK SRS, and it was not predictive of eventual volume regression or progression. A three-point exponential model is of potential predictive value according to relative distribution. An exponential decay model can be used to calculate the time course of tumors that are ultimately controlled. PMID:28121913

A multivariate method for estimating mortality rates among children under 5 years from health and social indicators in Iraq.

PubMed

Garfield, R; Leu, C S

2000-06-01

Many reports on Iraq suggest that a rise in rates of death and disease have occurred since the Gulf War of January/February 1991 and the economic sanctions that followed it. Four preliminary models, based on unadjusted projections, were developed. A logistic regression model was then developed on the basis of six social variables in Iraq and comparable information from countries in the State of the World's Children report. Missing data were estimated for this model by a multiple imputation procedure. The final model depends on three socio-medical indicators: adult literacy, nutritional stunting of children under 5 years, and access to piped water. The model successfully predicted both the mortality rate in 1990, under stable conditions, and in 1991, following the Gulf War. For 1996, after 5 years of sanctions and prior to receipt of humanitarian food via the oil for food programme, this model shows mortality among children under 5 to have reached an estimated 87 per 1000, a rate last experienced more than 30 years ago. Accurate and timely estimates of mortality levels in developing countries are costly and require considerable methodological expertise. A rapid estimation technique like the one developed here may be a useful tool for quick and efficient estimation of mortality rates among under 5 year olds in countries where good mortality data are not routinely available. This is especially true for countries with complex humanitarian emergencies where information on mortality changes can guide interventions and the social stability to use standard demographic methods does not exist.

Time vs. Money: A Quantitative Evaluation of Monitoring Frequency vs. Monitoring Duration.

PubMed

McHugh, Thomas E; Kulkarni, Poonam R; Newell, Charles J

2016-09-01

The National Research Council has estimated that over 126,000 contaminated groundwater sites are unlikely to achieve low ug/L clean-up goals in the foreseeable future. At these sites, cost-effective, long-term monitoring schemes are needed in order to understand the long-term changes in contaminant concentrations. Current monitoring optimization schemes rely on site-specific evaluations to optimize groundwater monitoring frequency. However, when using linear regression to estimate the long-term zero-order or first-order contaminant attenuation rate, the effect of monitoring frequency and monitoring duration on the accuracy and confidence for the estimated attenuation rate is not site-specific. For a fixed number of monitoring events, doubling the time between monitoring events (e.g., changing from quarterly monitoring to semi-annual monitoring) will double the accuracy of estimated attenuation rate. For a fixed monitoring frequency (e.g., semi-annual monitoring), increasing the number of monitoring events by 60% will double the accuracy of the estimated attenuation rate. Combining these two factors, doubling the time between monitoring events (e.g., quarterly monitoring to semi-annual monitoring) while decreasing the total number of monitoring events by 38% will result in no change in the accuracy of the estimated attenuation rate. However, the time required to collect this dataset will increase by 25%. Understanding that the trade-off between monitoring frequency and monitoring duration is not site-specific should simplify the process of optimizing groundwater monitoring frequency at contaminated groundwater sites. © 2016 The Authors. Groundwater published by Wiley Periodicals, Inc. on behalf of National Ground Water Association.

Decomposition Technique for Remaining Useful Life Prediction

NASA Technical Reports Server (NTRS)

Saha, Bhaskar (Inventor); Goebel, Kai F. (Inventor); Saxena, Abhinav (Inventor); Celaya, Jose R. (Inventor)

2014-01-01

The prognostic tool disclosed here decomposes the problem of estimating the remaining useful life (RUL) of a component or sub-system into two separate regression problems: the feature-to-damage mapping and the operational conditions-to-damage-rate mapping. These maps are initially generated in off-line mode. One or more regression algorithms are used to generate each of these maps from measurements (and features derived from these), operational conditions, and ground truth information. This decomposition technique allows for the explicit quantification and management of different sources of uncertainty present in the process. Next, the maps are used in an on-line mode where run-time data (sensor measurements and operational conditions) are used in conjunction with the maps generated in off-line mode to estimate both current damage state as well as future damage accumulation. Remaining life is computed by subtracting the instance when the extrapolated damage reaches the failure threshold from the instance when the prediction is made.

An assessment of the suspended sediment rating curve approach for load estimation on the Rivers Bandon and Owenabue, Ireland

NASA Astrophysics Data System (ADS)

Harrington, Seán T.; Harrington, Joseph R.

2013-03-01

This paper presents an assessment of the suspended sediment rating curve approach for load estimation on the Rivers Bandon and Owenabue in Ireland. The rivers, located in the South of Ireland, are underlain by sandstone, limestones and mudstones, and the catchments are primarily agricultural. A comprehensive database of suspended sediment data is not available for rivers in Ireland. For such situations, it is common to estimate suspended sediment concentrations from the flow rate using the suspended sediment rating curve approach. These rating curves are most commonly constructed by applying linear regression to the logarithms of flow and suspended sediment concentration or by applying a power curve to normal data. Both methods are assessed in this paper for the Rivers Bandon and Owenabue. Turbidity-based suspended sediment loads are presented for each river based on continuous (15 min) flow data and the use of turbidity as a surrogate for suspended sediment concentration is investigated. A database of paired flow rate and suspended sediment concentration values, collected between the years 2004 and 2011, is used to generate rating curves for each river. From these, suspended sediment load estimates using the rating curve approach are estimated and compared to the turbidity based loads for each river. Loads are also estimated using stage and seasonally separated rating curves and daily flow data, for comparison purposes. The most accurate load estimate on the River Bandon is found using a stage separated power curve, while the most accurate load estimate on the River Owenabue is found using a general power curve. Maximum full monthly errors of - 76% to + 63% are found on the River Bandon with errors of - 65% to + 359% found on the River Owenabue. The average monthly error on the River Bandon is - 12% with an average error of + 87% on the River Owenabue. The use of daily flow data in the load estimation process does not result in a significant loss of accuracy on either river. Historic load estimates (with a 95% confidence interval) were hindcast from the flow record and average annual loads of 7253 ± 673 tonnes on the River Bandon and 1935 ± 325 tonnes on the River Owenabue were estimated to be passing the gauging stations.

Self-rated health in different social classes of Slovenian adult population: nationwide cross-sectional study.

PubMed

Farkas, Jerneja; Pahor, Majda; Zaletel-Kragelj, Lijana

2011-02-01

Self-rated health can be influenced by several characteristics of the social environment. The aim of this study was to evaluate the relationship between self-rated health and self-assessed social class in Slovenian adult population. The study was based on the Countrywide Integrated Non-communicable Diseases Intervention Health Monitor database. During 2004, 8,741/15,297 (57.1%) participants aged 25-64 years returned posted self-administered questionnaire. Logistic regression was used to determine unadjusted and adjusted estimates of association between poor self-rated health and self-assessed social class. Poor self-rated health was reported by 9.6% of participants with a decrease from lower to upper-middle/upper self-assessed social class (35.9 vs. 3.7%). Logistic regression showed significant association between self-rated health and all self-assessed social classes. In an adjusted model, poor self-rated health remained associated with self-assessed social class (odds ratio for lower vs. upper-middle/upper self-assessed social class 4.23, 95% confidence interval 2.46-7.25; P < 0.001). Our study confirmed differences in the prevalence of poor self-rated health across self-assessed social classes. Participants from lower self-assessed social class reported poor self-rated health most often and should comprise the focus of multisectoral interventions.

Recent Improvements in Estimating Convective and Stratiform Rainfall in Amazonia

NASA Technical Reports Server (NTRS)

Negri, Andrew J.

1999-01-01

In this paper we present results from the application of a satellite infrared (IR) technique for estimating rainfall over northern South America. Our main objectives are to examine the diurnal variability of rainfall and to investigate the relative contributions from the convective and stratiform components. We apply the technique of Anagnostou et al (1999). In simple functional form, the estimated rain area A(sub rain) may be expressed as: A(sub rain) = f(A(sub mode),T(sub mode)), where T(sub mode) is the mode temperature of a cloud defined by 253 K, and A(sub mode) is the area encompassed by T(sub mode). The technique was trained by a regression between coincident microwave estimates from the Goddard Profiling (GPROF) algorithm (Kummerow et al, 1996) applied to SSM/I data and GOES IR (11 microns) observations. The apportionment of the rainfall into convective and stratiform components is based on the microwave technique described by Anagnostou and Kummerow (1997). The convective area from this technique was regressed against an IR structure parameter (the Convective Index) defined by Anagnostou et al (1999). Finally, rainrates are assigned to the Am.de proportional to (253-temperature), with different rates for the convective and stratiform

Locally Weighted Score Estimation for Quantile Classification in Binary Regression Models

PubMed Central

Rice, John D.; Taylor, Jeremy M. G.

2016-01-01

One common use of binary response regression methods is classification based on an arbitrary probability threshold dictated by the particular application. Since this is given to us a priori, it is sensible to incorporate the threshold into our estimation procedure. Specifically, for the linear logistic model, we solve a set of locally weighted score equations, using a kernel-like weight function centered at the threshold. The bandwidth for the weight function is selected by cross validation of a novel hybrid loss function that combines classification error and a continuous measure of divergence between observed and fitted values; other possible cross-validation functions based on more common binary classification metrics are also examined. This work has much in common with robust estimation, but diers from previous approaches in this area in its focus on prediction, specifically classification into high- and low-risk groups. Simulation results are given showing the reduction in error rates that can be obtained with this method when compared with maximum likelihood estimation, especially under certain forms of model misspecification. Analysis of a melanoma data set is presented to illustrate the use of the method in practice. PMID:28018492

Estimating Children’s Soil/Dust Ingestion Rates through Retrospective Analyses of Blood Lead Biomonitoring from the Bunker Hill Superfund Site in Idaho

PubMed Central

von Lindern, Ian; Spalinger, Susan; Stifelman, Marc L.; Stanek, Lindsay Wichers; Bartrem, Casey

2016-01-01

Background: Soil/dust ingestion rates are important variables in assessing children’s health risks in contaminated environments. Current estimates are based largely on soil tracer methodology, which is limited by analytical uncertainty, small sample size, and short study duration. Objectives: The objective was to estimate site-specific soil/dust ingestion rates through reevaluation of the lead absorption dose–response relationship using new bioavailability data from the Bunker Hill Mining and Metallurgical Complex Superfund Site (BHSS) in Idaho, USA. Methods: The U.S. Environmental Protection Agency (EPA) in vitro bioavailability methodology was applied to archived BHSS soil and dust samples. Using age-specific biokinetic slope factors, we related bioavailable lead from these sources to children’s blood lead levels (BLLs) monitored during cleanup from 1988 through 2002. Quantitative regression analyses and exposure assessment guidance were used to develop candidate soil/dust source partition scenarios estimating lead intake, allowing estimation of age-specific soil/dust ingestion rates. These ingestion rate and bioavailability estimates were simultaneously applied to the U.S. EPA Integrated Exposure Uptake Biokinetic Model for Lead in Children to determine those combinations best approximating observed BLLs. Results: Absolute soil and house dust bioavailability averaged 33% (SD ± 4%) and 28% (SD ± 6%), respectively. Estimated BHSS age-specific soil/dust ingestion rates are 86–94 mg/day for 6-month- to 2-year-old children and 51–67 mg/day for 2- to 9-year-old children. Conclusions: Soil/dust ingestion rate estimates for 1- to 9-year-old children at the BHSS are lower than those commonly used in human health risk assessment. A substantial component of children’s exposure comes from sources beyond the immediate home environment. Citation: von Lindern I, Spalinger S, Stifelman ML, Stanek LW, Bartrem C. 2016. Estimating children’s soil/dust ingestion rates through retrospective analyses of blood lead biomonitoring from the Bunker Hill Superfund Site in Idaho. Environ Health Perspect 124:1462–1470; http://dx.doi.org/10.1289/ehp.1510144 PMID:26745545

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.

Cesarean delivery rates among family physicians versus obstetricians: a population-based cohort study using instrumental variable methods

PubMed Central

Dawe, Russell Eric; Bishop, Jessica; Pendergast, Amanda; Avery, Susan; Monaghan, Kelly; Duggan, Norah; Aubrey-Bassler, Kris

2017-01-01

Background: Previous research suggests that family physicians have rates of cesarean delivery that are lower than or equivalent to those for obstetricians, but adjustments for risk differences in these analyses may have been inadequate. We used an econometric method to adjust for observed and unobserved factors affecting the risk of cesarean delivery among women attended by family physicians versus obstetricians. Methods: This retrospective population-based cohort study included all Canadian (except Quebec) hospital deliveries by family physicians and obstetricians between Apr. 1, 2006, and Mar. 31, 2009. We excluded women with multiple gestations, and newborns with a birth weight less than 500 g or gestational age less than 20 weeks. We estimated the relative risk of cesarean delivery using instrumental-variable-adjusted and logistic regression. Results: The final cohort included 776 299 women who gave birth in 390 hospitals. The risk of cesarean delivery was 27.3%, and the mean proportion of deliveries by family physicians was 26.9% (standard deviation 23.8%). The relative risk of cesarean delivery for family physicians versus obstetricians was 0.48 (95% confidence interval [CI] 0.41-0.56) with logistic regression and 1.27 (95% CI 1.02-1.57) with instrumental-variable-adjusted regression. Interpretation: Our conventional analyses suggest that family physicians have a lower rate of cesarean delivery than obstetricians, but instrumental variable analyses suggest the opposite. Because instrumental variable methods adjust for unmeasured factors and traditional methods do not, the large discrepancy between these estimates of risk suggests that clinical and/or sociocultural factors affecting the decision to perform cesarean delivery may not be accounted for in our database. PMID:29233843

Quantifying the individual-level association between income and mortality risk in the United States using the National Longitudinal Mortality Study.

PubMed

Brodish, Paul Henry; Hakes, Jahn K

2016-12-01

Policy makers would benefit from being able to estimate the likely impact of potential interventions to reverse the effects of rapidly rising income inequality on mortality rates. Using multiple cohorts of the National Longitudinal Mortality Study (NLMS), we estimate the absolute income effect on premature mortality in the United States. A multivariate Poisson regression using the natural logarithm of equivilized household income establishes the magnitude of the absolute income effect on mortality. We calculate mortality rates for each income decile of the study sample and mortality rate ratios relative to the decile containing mean income. We then apply the estimated income effect to two kinds of hypothetical interventions that would redistribute income. The first lifts everyone with an equivalized household income at or below the U.S. poverty line (in 2000$) out of poverty, to the income category just above the poverty line. The second shifts each family's equivalized income by, in turn, 10%, 20%, 30%, or 40% toward the mean household income, equivalent to reducing the Gini coefficient by the same percentage in each scenario. We also assess mortality disparities of the hypothetical interventions using ratios of mortality rates of the ninth and second income deciles, and test sensitivity to the assumption of causality of income on mortality by halving the mortality effect per unit of equivalized household income. The estimated absolute income effect would produce a three to four percent reduction in mortality for a 10% reduction in the Gini coefficient. Larger mortality reductions result from larger reductions in the Gini, but with diminishing returns. Inequalities in estimated mortality rates are reduced by a larger percentage than overall estimated mortality rates under the same hypothetical redistributions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Sources of variability in satellite-derived estimates of phytoplankton production in the eastern tropical Pacific

NASA Technical Reports Server (NTRS)

Banse, Karl; Yong, Marina

1990-01-01

As a proxy for satellite CZCS observations and concurrent measurements of primary production rates, data from 138 stations occupied seasonally during 1967-1968 in the offshore eastern tropical Pacific were analyzed in terms of six temporal groups and our current regimes. Multiple linear regressions on column production Pt show that simulated satellite pigment is generally weakly correlated, but sometimes not correlated with Pt, and that incident irradiance, sea surface temperature, nitrate, transparency, and depths of mixed layer or nitracline assume little or no importance. After a proxy for the light-saturated chlorophyll-specific photosynthetic rate P(max) is added, the coefficient of determination ranges from 0.55 to 0.91 (median of 0.85) for the 10 cases. In stepwise multiple linear regressions the P(max) proxy is the best predictor for Pt.

Bias correction of risk estimates in vaccine safety studies with rare adverse events using a self-controlled case series design.

PubMed

Zeng, Chan; Newcomer, Sophia R; Glanz, Jason M; Shoup, Jo Ann; Daley, Matthew F; Hambidge, Simon J; Xu, Stanley

2013-12-15

The self-controlled case series (SCCS) method is often used to examine the temporal association between vaccination and adverse events using only data from patients who experienced such events. Conditional Poisson regression models are used to estimate incidence rate ratios, and these models perform well with large or medium-sized case samples. However, in some vaccine safety studies, the adverse events studied are rare and the maximum likelihood estimates may be biased. Several bias correction methods have been examined in case-control studies using conditional logistic regression, but none of these methods have been evaluated in studies using the SCCS design. In this study, we used simulations to evaluate 2 bias correction approaches-the Firth penalized maximum likelihood method and Cordeiro and McCullagh's bias reduction after maximum likelihood estimation-with small sample sizes in studies using the SCCS design. The simulations showed that the bias under the SCCS design with a small number of cases can be large and is also sensitive to a short risk period. The Firth correction method provides finite and less biased estimates than the maximum likelihood method and Cordeiro and McCullagh's method. However, limitations still exist when the risk period in the SCCS design is short relative to the entire observation period.

Genetic parameters for test day milk yields of first lactation Holstein cows by random regression models.

PubMed

de Melo, C M R; Packer, I U; Costa, C N; Machado, P F

2007-03-01

Covariance components for test day milk yield using 263 390 first lactation records of 32 448 Holstein cows were estimated using random regression animal models by restricted maximum likelihood. Three functions were used to adjust the lactation curve: the five-parameter logarithmic Ali and Schaeffer function (AS), the three-parameter exponential Wilmink function in its standard form (W) and in a modified form (W*), by reducing the range of covariate, and the combination of Legendre polynomial and W (LEG+W). Heterogeneous residual variance (RV) for different classes (4 and 29) of days in milk was considered in adjusting the functions. Estimates of RV were quite similar, rating from 4.15 to 5.29 kg2. Heritability estimates for AS (0.29 to 0.42), LEG+W (0.28 to 0.42) and W* (0.33 to 0.40) were similar, but heritability estimates used W (0.25 to 0.65) were highest than those estimated by the other functions, particularly at the end of lactation. Genetic correlations between milk yield on consecutive test days were close to unity, but decreased as the interval between test days increased. The AS function with homogeneous RV model had the best fit among those evaluated.

Bootstrap-based methods for estimating standard errors in Cox's regression analyses of clustered event times.

PubMed

Xiao, Yongling; Abrahamowicz, Michal

2010-03-30

We propose two bootstrap-based methods to correct the standard errors (SEs) from Cox's model for within-cluster correlation of right-censored event times. The cluster-bootstrap method resamples, with replacement, only the clusters, whereas the two-step bootstrap method resamples (i) the clusters, and (ii) individuals within each selected cluster, with replacement. In simulations, we evaluate both methods and compare them with the existing robust variance estimator and the shared gamma frailty model, which are available in statistical software packages. We simulate clustered event time data, with latent cluster-level random effects, which are ignored in the conventional Cox's model. For cluster-level covariates, both proposed bootstrap methods yield accurate SEs, and type I error rates, and acceptable coverage rates, regardless of the true random effects distribution, and avoid serious variance under-estimation by conventional Cox-based standard errors. However, the two-step bootstrap method over-estimates the variance for individual-level covariates. We also apply the proposed bootstrap methods to obtain confidence bands around flexible estimates of time-dependent effects in a real-life analysis of cluster event times.

SEMIPARAMETRIC QUANTILE REGRESSION WITH HIGH-DIMENSIONAL COVARIATES

PubMed Central

Zhu, Liping; Huang, Mian; Li, Runze

2012-01-01

This paper is concerned with quantile regression for a semiparametric regression model, in which both the conditional mean and conditional variance function of the response given the covariates admit a single-index structure. This semiparametric regression model enables us to reduce the dimension of the covariates and simultaneously retains the flexibility of nonparametric regression. Under mild conditions, we show that the simple linear quantile regression offers a consistent estimate of the index parameter vector. This is a surprising and interesting result because the single-index model is possibly misspecified under the linear quantile regression. With a root-n consistent estimate of the index vector, one may employ a local polynomial regression technique to estimate the conditional quantile function. This procedure is computationally efficient, which is very appealing in high-dimensional data analysis. We show that the resulting estimator of the quantile function performs asymptotically as efficiently as if the true value of the index vector were known. The methodologies are demonstrated through comprehensive simulation studies and an application to a real dataset. PMID:24501536

Linked versus unlinked estimates of mortality and length of life by education and marital status: evidence from the first record linkage study in Lithuania.

PubMed

Shkolnikov, Vladimir M; Jasilionis, Domantas; Andreev, Evgeny M; Jdanov, Dmitri A; Stankuniene, Vladislava; Ambrozaitiene, Dalia

2007-04-01

Earlier studies have found large and increasing with time differences in mortality by education and marital status in post-Soviet countries. Their results are based on independent tabulations of population and deaths counts (unlinked data). The present study provides the first census-linked estimates of group-specific mortality and the first comparison between census-linked and unlinked mortality estimates for a post-Soviet country. The study is based on a data set linking 140,000 deaths occurring in 2001-2004 in Lithuania with the population census of 2001. The same socio-demographic information about the deceased is available from both the census and death records. Cross-tabulations and Poisson regressions are used to compare linked and unlinked data. Linked and unlinked estimates of life expectancies and mortality rate ratios are calculated with standard life table techniques and Poisson regressions. For the two socio-demographic variables under study, the values from the death records partly differ from those from the census records. The deviations are especially significant for education, with 72-73%, 66-67%, and 82-84% matching for higher education, secondary education, and lower education, respectively. For marital status, deviations are less frequent. For education and marital status, unlinked estimates tend to overstate mortality in disadvantaged groups and they understate mortality in advantaged groups. The differences in inter-group life expectancy and the mortality rate ratios thus are significantly overestimated in the unlinked data. Socio-demographic differences in mortality previously observed in Lithuania and possibly other post-Soviet countries are overestimated. The growth in inequalities over the 1990s is real but might be overstated. The results of this study confirm the existence of large and widening health inequalities but call for better data.

Effects of serum creatinine calibration on estimated renal function in african americans: the Jackson heart study.

PubMed

Wang, Wei; Young, Bessie A; Fülöp, Tibor; de Boer, Ian H; Boulware, L Ebony; Katz, Ronit; Correa, Adolfo; Griswold, Michael E

2015-05-01

The calibration to isotope dilution mass spectrometry-traceable creatinine is essential for valid use of the new Chronic Kidney Disease Epidemiology Collaboration equation to estimate the glomerular filtration rate. For 5,210 participants in the Jackson Heart Study (JHS), serum creatinine was measured with a multipoint enzymatic spectrophotometric assay at the baseline visit (2000-2004) and remeasured using the Roche enzymatic method, traceable to isotope dilution mass spectrometry in a subset of 206 subjects. The 200 eligible samples (6 were excluded, 1 for failure of the remeasurement and 5 for outliers) were divided into 3 disjoint sets-training, validation and test-to select a calibration model, estimate true errors and assess performance of the final calibration equation. The calibration equation was applied to serum creatinine measurements of 5,210 participants to estimate glomerular filtration rate and the prevalence of chronic kidney disease (CKD). The selected Deming regression model provided a slope of 0.968 (95% confidence interval [CI], 0.904-1.053) and intercept of -0.0248 (95% CI, -0.0862 to 0.0366) with R value of 0.9527. Calibrated serum creatinine showed high agreement with actual measurements when applying to the unused test set (concordance correlation coefficient 0.934, 95% CI, 0.894-0.960). The baseline prevalence of CKD in the JHS (2000-2004) was 6.30% using calibrated values compared with 8.29% using noncalibrated serum creatinine with the Chronic Kidney Disease Epidemiology Collaboration equation (P < 0.001). A Deming regression model was chosen to optimally calibrate baseline serum creatinine measurements in the JHS, and the calibrated values provide a lower CKD prevalence estimate.

Regional regression of flood characteristics employing historical information

USGS Publications Warehouse

Tasker, Gary D.; Stedinger, J.R.

1987-01-01

Streamflow gauging networks provide hydrologic information for use in estimating the parameters of regional regression models. The regional regression models can be used to estimate flood statistics, such as the 100 yr peak, at ungauged sites as functions of drainage basin characteristics. A recent innovation in regional regression is the use of a generalized least squares (GLS) estimator that accounts for unequal station record lengths and sample cross correlation among the flows. However, this technique does not account for historical flood information. A method is proposed here to adjust this generalized least squares estimator to account for possible information about historical floods available at some stations in a region. The historical information is assumed to be in the form of observations of all peaks above a threshold during a long period outside the systematic record period. A Monte Carlo simulation experiment was performed to compare the GLS estimator adjusted for historical floods with the unadjusted GLS estimator and the ordinary least squares estimator. Results indicate that using the GLS estimator adjusted for historical information significantly improves the regression model. ?? 1987.

The Reliability and Validity of Using Regression Residuals to Measure Institutional Effectiveness in Promoting Degree Completion

ERIC Educational Resources Information Center

Horn, Aaron S.; Lee, Giljae

2016-01-01

A relatively simple way of measuring institutional effectiveness in relation to degree completion is to estimate the difference between an actual and predicted graduation rate, but the reliability and validity of this method have not been thoroughly examined. Longitudinal data were obtained from IPEDS for both public and private not-for-profit…

Skipping Class in College and Exam Performance: Evidence from a Regression Discontinuity Classroom Experiment

ERIC Educational Resources Information Center

Dobkin, Carlos; Gil, Ricard; Marion, Justin

2010-01-01

In this paper we estimate the effect of class attendance on exam performance by implementing a policy in three large economics classes that required students scoring below the median on the midterm exam to attend class. This policy generated a large discontinuity in the rate of post-midterm attendance at the median of the midterm score. We…

Estimating V0[subscript 2]max Using a Personalized Step Test

ERIC Educational Resources Information Center

Webb, Carrie; Vehrs, Pat R.; George, James D.; Hager, Ronald

2014-01-01

The purpose of this study was to develop a step test with a personalized step rate and step height to predict cardiorespiratory fitness in 80 college-aged males and females using the self-reported perceived functional ability scale and data collected during the step test. Multiple linear regression analysis yielded a model (R = 0.90, SEE = 3.43…

Curriculum-Based Measurement of Reading: An Evaluation of Frequentist and Bayesian Methods to Model Progress Monitoring Data

ERIC Educational Resources Information Center

Christ, Theodore J.; Desjardins, Christopher David

2018-01-01

Curriculum-Based Measurement of Oral Reading (CBM-R) is often used to monitor student progress and guide educational decisions. Ordinary least squares regression (OLSR) is the most widely used method to estimate the slope, or rate of improvement (ROI), even though published research demonstrates OLSR's lack of validity and reliability, and…

Estimation Methods for Non-Homogeneous Regression - Minimum CRPS vs Maximum Likelihood

NASA Astrophysics Data System (ADS)

Gebetsberger, Manuel; Messner, Jakob W.; Mayr, Georg J.; Zeileis, Achim

2017-04-01

Non-homogeneous regression models are widely used to statistically post-process numerical weather prediction models. Such regression models correct for errors in mean and variance and are capable to forecast a full probability distribution. In order to estimate the corresponding regression coefficients, CRPS minimization is performed in many meteorological post-processing studies since the last decade. In contrast to maximum likelihood estimation, CRPS minimization is claimed to yield more calibrated forecasts. Theoretically, both scoring rules used as an optimization score should be able to locate a similar and unknown optimum. Discrepancies might result from a wrong distributional assumption of the observed quantity. To address this theoretical concept, this study compares maximum likelihood and minimum CRPS estimation for different distributional assumptions. First, a synthetic case study shows that, for an appropriate distributional assumption, both estimation methods yield to similar regression coefficients. The log-likelihood estimator is slightly more efficient. A real world case study for surface temperature forecasts at different sites in Europe confirms these results but shows that surface temperature does not always follow the classical assumption of a Gaussian distribution. KEYWORDS: ensemble post-processing, maximum likelihood estimation, CRPS minimization, probabilistic temperature forecasting, distributional regression models

Anthropometric change: implications for office ergonomics.

PubMed

Gordon, Claire C; Bradtmiller, Bruce

2012-01-01

Well-designed office workspaces require good anthropometric data in order to accommodate variability in the worker population. The recent obesity epidemic carries with it a number of anthropometric changes that have significant impact on design. We examine anthropometric change among US civilians over the last 50 years, and then examine that change in a subset of the US population--the US military--as military data sets often have more ergonomic dimensions than civilian ones. The civilian mean stature increased throughout the period 1962 to 2006 for both males and females. However, the rate of increase in mean weight was considerably faster. As a result, the male obesity rate changed from 10.7% in 1962 to 31.3% in 2006. The female change for the same period was 15.8% to 33.2%. In the Army, the proportion of obesity increased from 3.6% to 20.9%, in males. In the absence of national US ergonomic data, we demonstrate one approach to tracking civilian change in these dimensions, applying military height/weight regression equations to the civilian population estimates. This approach is useful for population monitoring but is not suitable for establishing new design limits, as regression estimates likely underestimate the change at the ends of the distribution.

Probability and predictors of cannabis use disorders relapse: results of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC).

PubMed

Flórez-Salamanca, Ludwing; Secades-Villa, Roberto; Budney, Alan J; García-Rodríguez, Olaya; Wang, Shuai; Blanco, Carlos

2013-09-01

This study aims to estimate the odds and predictors of Cannabis Use Disorders (CUD) relapse among individuals in remission. Analyses were done on the subsample of individuals with lifetime history of a CUD (abuse or dependence) who were in full remission at baseline (Wave 1) of the National Epidemiological Survey of Alcohol and Related Conditions (NESARC) (n=2350). Univariate logistic regression models and hierarchical logistic regression model were implemented to estimate odds of relapse and identify predictors of relapse at 3 years follow up (Wave 2). The relapse rate of CUD was 6.63% over an average of 3.6 year follow-up period. In the multivariable model, the odds of relapse were inversely related to time in remission, whereas having a history of conduct disorder or a major depressive disorder after Wave 1 increased the risk of relapse. Our findings suggest that maintenance of remission is the most common outcome for individuals in remission from a CUD. Treatment approaches may improve rates of sustained remission of individuals with CUD and conduct disorder or major depressive disorder. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Use of near infrared/red radiance ratios for estimating vegetation biomass and physiological status

NASA Technical Reports Server (NTRS)

Tucker, C. J.

1977-01-01

The application of photographic infrared/red (ir/red) reflectance or radiance ratios for the estimation of vegetation biomass and physiological status were investigated by analyzing in situ spectral reflectance data from experimental grass plots. Canopy biological samples were taken for total wet biomass, total dry biomass, leaf water content, dry green biomass, dry brown biomass, and total chlorophyll content at each sampling date. Integrated red and photographic infrared radiances were regressed against the various canopy or plot variables to determine the relative significance between the red, photographic infrared, and the ir/red ratio and the canopy variables. The ir/red ratio is sensitive to the photosynthetically active or green biomass, the rate of primary production, and actually measures the interaction between the green biomass and the rate of primary production within a given species type. The ir/red ratio resulted in improved regression significance over the red or the ir/radiances taken separately. Only slight differences were found between ir/red ratio, the ir-red difference, the vegetation index, and the transformed vegetation index. The asymptotic spectral radiance properties of the ir, red, ir/red ratio, and the various transformations were evaluated.

WHO systematic review of prevalence of chronic pelvic pain: a neglected reproductive health morbidity

PubMed Central

Latthe, Pallavi; Latthe, Manish; Say, Lale; Gülmezoglu, Metin; Khan, Khalid S

2006-01-01

Background Health care planning for chronic pelvic pain (CPP), an important cause of morbidity amongst women is hampered due to lack of clear collated summaries of its basic epidemiological data. We systematically reviewed worldwide literature on the prevalence of different types of CPP to assess the geographical distribution of data, and to explore sources of variation in its estimates. Methods We identified data available from Medline (1966 to 2004), Embase (1980 to 2004), PsycINFO (1887 to 2003), LILACS (1982 to 2004), Science Citation index, CINAHL (January 1980 to 2004) and hand searching of reference lists. Two reviewers extracted data independently, using a piloted form, on participants' characteristics, study quality and rates of CPP. We considered a study to be of high quality (valid) if had at least three of the following features: prospective design, validated measurement tool, adequate sampling method, sample size estimation and response rate >80%. We performed both univariate and multivariate meta-regression analysis to explore heterogeneity of results across studies. Results There were 178 studies (459975 participants) in 148 articles. Of these, 106 studies were (124259 participants) on dysmenorrhoea, 54 (35973 participants) on dyspareunia and 18 (301756 participants) on noncyclical pain. There were only 19/95 (20%) less developed and 1/45 (2.2%) least developed countries with relevant data in contrast to 22/43 (51.2%) developed countries. Meta-regression analysis showed that rates of pain varied according to study quality features. There were 40 (22.5%) high quality studies with representative samples. Amongst them, the rate of dysmenorrhoea was 16.8 to 81%, that of dyspareunia was 8 to 21.8%, and that for noncyclical pain was 2.1 to 24%. Conclusion There were few valid population based estimates of disease burden due to CPP from less developed countries. The variation in rates of CPP worldwide was due to variable study quality. Where valid data were available, a high disease burden of all types of pelvic pain was found. PMID:16824213

Normalization Regression Estimation With Application to a Nonorthogonal, Nonrecursive Model of School Learning.

ERIC Educational Resources Information Center

Bulcock, J. W.; And Others

Advantages of normalization regression estimation over ridge regression estimation are demonstrated by reference to Bloom's model of school learning. Theoretical concern centered on the structure of scholastic achievement at grade 10 in Canadian high schools. Data on 886 students were randomly sampled from the Carnegie Human Resources Data Bank.…

Trends and spatial distribution of deaths of children aged 12-60 months in São Paulo, Brazil, 1980-98.

PubMed Central

Antunes, José Leopoldo Ferreira; Waldman, Eliseu Alves

2002-01-01

OBJECTIVE: To describe trends in the mortality of children aged 12-60 months and to perform spatial data analysis of its distribution at the inner city district level in São Paulo from 1980 to 1998. METHODS: Official mortality data were analysed in relation to the underlying causes of death. The population of children aged 12-60 months, disaggregated by sex and age, was estimated for each year. Educational levels, income, employment status, and other socioeconomic indices were also assessed. Statistical Package for Social Sciences software was used for the statistical processing of time series. The Cochrane-Orcutt procedure of generalized least squares regression analysis was used to estimate the regression parameters with control of first-order autocorrelation. Spatial data analysis employed the discrimination of death rates and socioeconomic indices at the inner city district level. For classifying area-level death rates the method of K-means cluster analysis was used. Spatial correlation between variables was analysed by the simultaneous autoregressive regression method. FINDINGS: There was a steady decline in death rates during the 1980s at an average rate of 3.08% per year, followed by a levelling off. Infectious diseases remained the major cause of mortality, accounting for 43.1% of deaths during the last three years of the study. Injuries accounted for 16.5% of deaths. Mortality rates at the area level clearly demonstrated inequity in the city's health profile: there was an increasing difference between the rich and the underprivileged social strata in this respect. CONCLUSION: The overall mortality rate among children aged 12-60 months dropped by almost 30% during the study period. Most of the decline happened during the 1980s. Many people still live in a state of deprivation in underserved areas. Time-series and spatial data analysis provided indications of potential value in the planning of social policies promoting well-being, through the identification of factors affecting child survival and the regions with the worst health profiles, to which programmes and resources should be preferentially directed. PMID:12077615

Application of nonlinear least-squares regression to ground-water flow modeling, west-central Florida

USGS Publications Warehouse

Yobbi, D.K.

2000-01-01

A nonlinear least-squares regression technique for estimation of ground-water flow model parameters was applied to an existing model of the regional aquifer system underlying west-central Florida. The regression technique minimizes the differences between measured and simulated water levels. Regression statistics, including parameter sensitivities and correlations, were calculated for reported parameter values in the existing model. Optimal parameter values for selected hydrologic variables of interest are estimated by nonlinear regression. Optimal estimates of parameter values are about 140 times greater than and about 0.01 times less than reported values. Independently estimating all parameters by nonlinear regression was impossible, given the existing zonation structure and number of observations, because of parameter insensitivity and correlation. Although the model yields parameter values similar to those estimated by other methods and reproduces the measured water levels reasonably accurately, a simpler parameter structure should be considered. Some possible ways of improving model calibration are to: (1) modify the defined parameter-zonation structure by omitting and/or combining parameters to be estimated; (2) carefully eliminate observation data based on evidence that they are likely to be biased; (3) collect additional water-level data; (4) assign values to insensitive parameters, and (5) estimate the most sensitive parameters first, then, using the optimized values for these parameters, estimate the entire data set.

Regional interpretation of water-quality monitoring data

USGS Publications Warehouse

Smith, Richard A.; Schwarz, Gregory E.; Alexander, Richard B.

1997-01-01

We describe a method for using spatially referenced regressions of contaminant transport on watershed attributes (SPARROW) in regional water-quality assessment. The method is designed to reduce the problems of data interpretation caused by sparse sampling, network bias, and basin heterogeneity. The regression equation relates measured transport rates in streams to spatially referenced descriptors of pollution sources and land-surface and stream-channel characteristics. Regression models of total phosphorus (TP) and total nitrogen (TN) transport are constructed for a region defined as the nontidal conterminous United States. Observed TN and TP transport rates are derived from water-quality records for 414 stations in the National Stream Quality Accounting Network. Nutrient sources identified in the equations include point sources, applied fertilizer, livestock waste, nonagricultural land, and atmospheric deposition (TN only). Surface characteristics found to be significant predictors of land-water delivery include soil permeability, stream density, and temperature (TN only). Estimated instream decay coefficients for the two contaminants decrease monotonically with increasing stream size. TP transport is found to be significantly reduced by reservoir retention. Spatial referencing of basin attributes in relation to the stream channel network greatly increases their statistical significance and model accuracy. The method is used to estimate the proportion of watersheds in the conterminous United States (i.e., hydrologic cataloging units) with outflow TP concentrations less than the criterion of 0.1 mg/L, and to classify cataloging units according to local TN yield (kg/km2/yr).

Including information about comorbidity in estimates of disease burden: Results from the WHO World Mental Health Surveys

PubMed Central

Alonso, Jordi; Vilagut, Gemma; Chatterji, Somnath; Heeringa, Steven; Schoenbaum, Michael; Üstün, T. Bedirhan; Rojas-Farreras, Sonia; Angermeyer, Matthias; Bromet, Evelyn; Bruffaerts, Ronny; de Girolamo, Giovanni; Gureje, Oye; Haro, Josep Maria; Karam, Aimee N.; Kovess, Viviane; Levinson, Daphna; Liu, Zhaorui; Mora, Maria Elena Medina; Ormel, J.; Posada-Villa, Jose; Uda, Hidenori; Kessler, Ronald C.

2010-01-01

Background The methodology commonly used to estimate disease burden, featuring ratings of severity of individual conditions, has been criticized for ignoring comorbidity. A methodology that addresses this problem is proposed and illustrated here with data from the WHO World Mental Health Surveys. Although the analysis is based on self-reports about one’s own conditions in a community survey, the logic applies equally well to analysis of hypothetical vignettes describing comorbid condition profiles. Methods Face-to-face interviews in 13 countries (six developing, nine developed; n = 31,067; response rate = 69.6%) assessed 10 classes of chronic physical and 9 of mental conditions. A visual analog scale (VAS) was used to assess overall perceived health. Multiple regression analysis with interactions for comorbidity was used to estimate associations of conditions with VAS. Simulation was used to estimate condition-specific effects. Results The best-fitting model included condition main effects and interactions of types by numbers of conditions. Neurological conditions, insomnia, and major depression were rated most severe. Adjustment for comorbidity reduced condition-specific estimates with substantial between-condition variation (.24–.70 ratios of condition-specific estimates with and without adjustment for comorbidity). The societal-level burden rankings were quite different from the individual-level rankings, with the highest societal-level rankings associated with conditions having high prevalence rather than high individual-level severity. Conclusions Plausible estimates of disorder-specific effects on VAS can be obtained using methods that adjust for comorbidity. These adjustments substantially influence condition-specific ratings. PMID:20553636

Deep learning for biomarker regression: application to osteoporosis and emphysema on chest CT scans

NASA Astrophysics Data System (ADS)

González, Germán.; Washko, George R.; San José Estépar, Raúl

2018-03-01

Introduction: Biomarker computation using deep-learning often relies on a two-step process, where the deep learning algorithm segments the region of interest and then the biomarker is measured. We propose an alternative paradigm, where the biomarker is estimated directly using a regression network. We showcase this image-tobiomarker paradigm using two biomarkers: the estimation of bone mineral density (BMD) and the estimation of lung percentage of emphysema from CT scans. Materials and methods: We use a large database of 9,925 CT scans to train, validate and test the network for which reference standard BMD and percentage emphysema have been already computed. First, the 3D dataset is reduced to a set of canonical 2D slices where the organ of interest is visible (either spine for BMD or lungs for emphysema). This data reduction is performed using an automatic object detector. Second, The regression neural network is composed of three convolutional layers, followed by a fully connected and an output layer. The network is optimized using a momentum optimizer with an exponential decay rate, using the root mean squared error as cost function. Results: The Pearson correlation coefficients obtained against the reference standards are r = 0.940 (p < 0.00001) and r = 0.976 (p < 0.00001) for BMD and percentage emphysema respectively. Conclusions: The deep-learning regression architecture can learn biomarkers from images directly, without indicating the structures of interest. This approach simplifies the development of biomarker extraction algorithms. The proposed data reduction based on object detectors conveys enough information to compute the biomarkers of interest.

CALIBRATING NON-CONVEX PENALIZED REGRESSION IN ULTRA-HIGH DIMENSION.

PubMed

Wang, Lan; Kim, Yongdai; Li, Runze

2013-10-01

We investigate high-dimensional non-convex penalized regression, where the number of covariates may grow at an exponential rate. Although recent asymptotic theory established that there exists a local minimum possessing the oracle property under general conditions, it is still largely an open problem how to identify the oracle estimator among potentially multiple local minima. There are two main obstacles: (1) due to the presence of multiple minima, the solution path is nonunique and is not guaranteed to contain the oracle estimator; (2) even if a solution path is known to contain the oracle estimator, the optimal tuning parameter depends on many unknown factors and is hard to estimate. To address these two challenging issues, we first prove that an easy-to-calculate calibrated CCCP algorithm produces a consistent solution path which contains the oracle estimator with probability approaching one. Furthermore, we propose a high-dimensional BIC criterion and show that it can be applied to the solution path to select the optimal tuning parameter which asymptotically identifies the oracle estimator. The theory for a general class of non-convex penalties in the ultra-high dimensional setup is established when the random errors follow the sub-Gaussian distribution. Monte Carlo studies confirm that the calibrated CCCP algorithm combined with the proposed high-dimensional BIC has desirable performance in identifying the underlying sparsity pattern for high-dimensional data analysis.

Accounting for the decrease of photosystem photochemical efficiency with increasing irradiance to estimate quantum yield of leaf photosynthesis.

PubMed

Yin, Xinyou; Belay, Daniel W; van der Putten, Peter E L; Struik, Paul C

2014-12-01

Maximum quantum yield for leaf CO2 assimilation under limiting light conditions (Φ CO2LL) is commonly estimated as the slope of the linear regression of net photosynthetic rate against absorbed irradiance over a range of low-irradiance conditions. Methodological errors associated with this estimation have often been attributed either to light absorptance by non-photosynthetic pigments or to some data points being beyond the linear range of the irradiance response, both causing an underestimation of Φ CO2LL. We demonstrate here that a decrease in photosystem (PS) photochemical efficiency with increasing irradiance, even at very low levels, is another source of error that causes a systematic underestimation of Φ CO2LL. A model method accounting for this error was developed, and was used to estimate Φ CO2LL from simultaneous measurements of gas exchange and chlorophyll fluorescence on leaves using various combinations of species, CO2, O2, or leaf temperature levels. The conventional linear regression method under-estimated Φ CO2LL by ca. 10-15%. Differences in the estimated Φ CO2LL among measurement conditions were generally accounted for by different levels of photorespiration as described by the Farquhar-von Caemmerer-Berry model. However, our data revealed that the temperature dependence of PSII photochemical efficiency under low light was an additional factor that should be accounted for in the model.

Using the Violence Risk Scale-Sexual Offense version in sexual violence risk assessments: Updated risk categories and recidivism estimates from a multisite sample of treated sexual offenders.

PubMed

Olver, Mark E; Mundt, James C; Thornton, David; Beggs Christofferson, Sarah M; Kingston, Drew A; Sowden, Justina N; Nicholaichuk, Terry P; Gordon, Audrey; Wong, Stephen C P

2018-04-30

The present study sought to develop updated risk categories and recidivism estimates for the Violence Risk Scale-Sexual Offense version (VRS-SO; Wong, Olver, Nicholaichuk, & Gordon, 2003-2017), a sexual offender risk assessment and treatment planning tool. The overarching purpose was to increase the clarity and accuracy of communicating risk assessment information that includes a systematic incorporation of new information (i.e., change) to modify risk estimates. Four treated samples of sexual offenders with VRS-SO pretreatment, posttreatment, and Static-99R ratings were combined with a minimum follow-up period of 10-years postrelease (N = 913). Logistic regression was used to model 5- and 10-year sexual and violent (including sexual) recidivism estimates across 6 different regression models employing specific risk and change score information from the VRS-SO and/or Static-99R. A rationale is presented for clinical applications of select models and the necessity of controlling for baseline risk when utilizing change information across repeated assessments. Information concerning relative risk (percentiles) and absolute risk (recidivism estimates) is integrated with common risk assessment language guidelines to generate new risk categories for the VRS-SO. Guidelines for model selection and forensic clinical application of the risk estimates are discussed. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

CALIBRATING NON-CONVEX PENALIZED REGRESSION IN ULTRA-HIGH DIMENSION

PubMed Central

Wang, Lan; Kim, Yongdai; Li, Runze

2014-01-01

We investigate high-dimensional non-convex penalized regression, where the number of covariates may grow at an exponential rate. Although recent asymptotic theory established that there exists a local minimum possessing the oracle property under general conditions, it is still largely an open problem how to identify the oracle estimator among potentially multiple local minima. There are two main obstacles: (1) due to the presence of multiple minima, the solution path is nonunique and is not guaranteed to contain the oracle estimator; (2) even if a solution path is known to contain the oracle estimator, the optimal tuning parameter depends on many unknown factors and is hard to estimate. To address these two challenging issues, we first prove that an easy-to-calculate calibrated CCCP algorithm produces a consistent solution path which contains the oracle estimator with probability approaching one. Furthermore, we propose a high-dimensional BIC criterion and show that it can be applied to the solution path to select the optimal tuning parameter which asymptotically identifies the oracle estimator. The theory for a general class of non-convex penalties in the ultra-high dimensional setup is established when the random errors follow the sub-Gaussian distribution. Monte Carlo studies confirm that the calibrated CCCP algorithm combined with the proposed high-dimensional BIC has desirable performance in identifying the underlying sparsity pattern for high-dimensional data analysis. PMID:24948843

Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model

NASA Astrophysics Data System (ADS)

Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami

2017-06-01

A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.

The allometric relationship between resting metabolic rate and body mass in wild waterfowl (Anatidae) and an application to estimation of winter habitat requirements

USGS Publications Warehouse

Miller, M.R.; Eadie, J. McA

2006-01-01

We examined the allometric relationship between resting metabolic rate (RMR; kJ day-1) and body mass (kg) in wild waterfowl (Anatidae) by regressing RMR on body mass using species means from data obtained from published literature (18 sources, 54 measurements, 24 species; all data from captive birds). There was no significant difference among measurements from the rest (night; n = 37), active (day; n = 14), and unspecified (n = 3) phases of the daily cycle (P > 0.10), and we pooled these measurements for analysis. The resulting power function (aMassb) for all waterfowl (swans, geese, and ducks) had an exponent (b; slope of the regression) of 0.74, indistinguishable from that determined with commonly used general equations for nonpasserine birds (0.72-0.73). In contrast, the mass proportionality coefficient (b; y-intercept at mass = 1 kg) of 422 exceeded that obtained from the nonpasserine equations by 29%-37%. Analyses using independent contrasts correcting for phylogeny did not substantially alter the equation. Our results suggest the waterfowl equation provides a more appropriate estimate of RMR for bioenergetics analyses of waterfowl than do the general nonpasserine equations. When adjusted with a multiple to account for energy costs of free living, the waterfowl equation better estimates daily energy expenditure. Using this equation, we estimated that the extent of wetland habitat required to support wintering waterfowl populations could be 37%-50% higher than previously predicted using general nonpasserine equations. ?? The Cooper Ornithological Society 2006.

Doula care, birth outcomes, and costs among Medicaid beneficiaries.

PubMed

Kozhimannil, Katy Backes; Hardeman, Rachel R; Attanasio, Laura B; Blauer-Peterson, Cori; O'Brien, Michelle

2013-04-01

We compared childbirth-related outcomes for Medicaid recipients who received prenatal education and childbirth support from trained doulas with outcomes from a national sample of similar women and estimated potential cost savings. We calculated descriptive statistics for Medicaid-funded births nationally (from the 2009 Nationwide Inpatient Sample; n = 279,008) and births supported by doula care (n = 1079) in Minneapolis, Minnesota, in 2010 to 2012; used multivariate regression to estimate impacts of doula care; and modeled potential cost savings associated with reductions in cesarean delivery for doula-supported births. The cesarean rate was 22.3% among doula-supported births and 31.5% among Medicaid beneficiaries nationally. The corresponding preterm birth rates were 6.1% and 7.3%, respectively. After control for clinical and sociodemographic factors, odds of cesarean delivery were 40.9% lower for doula-supported births (adjusted odds ratio = 0.59; P < .001). Potential cost savings to Medicaid programs associated with such cesarean rate reductions are substantial but depend on states' reimbursement rates, birth volume, and current cesarean rates. State Medicaid programs should consider offering coverage for birth doulas to realize potential cost savings associated with reduced cesarean rates.

Using population models to evaluate management alternatives for Gulf Striped Bass

USGS Publications Warehouse

Aspinwall, Alexander P.; Irwin, Elise R.; Lloyd, M. Clint

2017-01-01

Interstate management of Gulf Striped Bass Morone saxatilis has involved a thirty-year cooperative effort involving Federal and State agencies in Georgia, Florida and Alabama (Apalachicola-Chattahoochee-Flint Gulf Striped Bass Technical Committee). The Committee has recently focused on developing an adaptive framework for conserving and restoring Gulf Striped Bass in the Apalachicola, Chattahoochee, and Flint River (ACF) system. To evaluate the consequences and tradeoffs among management activities, population models were used to inform management decisions. Stochastic matrix models were constructed with varying recruitment and stocking rates to simulate effects of management alternatives on Gulf Striped Bass population objectives. An age-classified matrix model that incorporated stock fecundity estimates and survival estimates was used to project population growth rate. In addition, combinations of management alternatives (stocking rates, Hydrilla control, harvest regulations) were evaluated with respect to how they influenced Gulf Striped Bass population growth. Annual survival and mortality rates were estimated from catch-curve analysis, while fecundity was estimated and predicted using a linear least squares regression analysis of fish length versus egg number from hatchery brood fish data. Stocking rates and stocked-fish survival rates were estimated from census data. Results indicated that management alternatives could be an effective approach to increasing the Gulf Striped Bass population. Population abundance was greatest under maximum stocking effort, maximum Hydrilla control and a moratorium. Conversely, population abundance was lowest under no stocking, no Hydrilla control and the current harvest regulation. Stocking rates proved to be an effective management strategy; however, low survival estimates of stocked fish (1%) limited the potential for population growth. Hydrilla control increased the survival rate of stocked fish and provided higher estimates of population abundances than maximizing the stocking rate. A change in the current harvest regulation (50% harvest regulation) was not an effective alternative to increasing the Gulf Striped Bass population size. Applying a moratorium to the Gulf Striped Bass fishery increased survival rates from 50% to 74% and resulted in the largest population growth of the individual management alternatives. These results could be used by the Committee to inform management decisions for other populations of Striped Bass in the Gulf Region.

Variability of individual genetic load: consequences for the detection of inbreeding depression.

PubMed

Restoux, Gwendal; Huot de Longchamp, Priscille; Fady, Bruno; Klein, Etienne K

2012-03-01

Inbreeding depression is a key factor affecting the persistence of natural populations, particularly when they are fragmented. In species with mixed mating systems, inbreeding depression can be estimated at the population level by regressing the average progeny fitness by the selfing rate of their mothers. We applied this method using simulated populations to investigate how population genetic parameters can affect the detection power of inbreeding depression. We simulated individual selfing rates and genetic loads from which we computed fitness values. The regression method yielded high statistical power, inbreeding depression being detected as significant (5 % level) in 92 % of the simulations. High individual variation in selfing rate and high mean genetic load led to better detection of inbreeding depression while high among-individual variation in genetic load made it more difficult to detect inbreeding depression. For a constant sampling effort, increasing the number of progenies while decreasing the number of individuals per progeny enhanced the detection power of inbreeding depression. We discuss the implication of among-mother variability of genetic load and selfing rate on inbreeding depression studies.

Increasing educational inequalities in self-rated health in Brazil, 1998-2013.

PubMed

Andrade, Flavia Cristina Drumond; Mehta, Jeenal Deepak

2018-01-01

The objectives of this study are to analyze the associations between educational levels and poor self-rated health (SRH) among adults in Brazil and to assess trends in the prevalence of poor self-rated health across educational groups between 1998 and 2013. Individual-level data came from the 1998, 2003 and 2008 Brazilian National Household Survey and the 2013 National Health Survey. We estimate prevalence rates of poor SRH by education. Using multivariable regressions, we assess the associations between educational levels and poor self-rated health. We use these regressions to predict the estimated ratios between the prevalence rates of those in low vs. high education in order to assess if relative changes in poor SRH have narrowed over time. Finally, we tested for statistically significant time trends in adult chronic disease inequalities by education. Results indicate a clear educational gradient in poor SRH. Prevalence ratios show that Brazilian adults with no education have levels of poor SRH that are 7 to 9 times higher than those with some college or more. The difference between those with lowest and highest education increased from 1998 to 2013. Compared to those with no education, there were increases in the prevalence of poor SRH among those with primary and secondary incomplete as well as among those with secondary complete in 2008 and 2013. In conclusion, there is a positive association between poor SRH and low education. Brazil has many social and geographic inequalities in health. Even though educational levels are increasing, there is no improvement in the general subjective health of Brazilians. Health inequalities by race and region highlight the need to improve the health of socially disadvantaged groups in Brazil. Addressing chronic conditions and mental health is needed to improve self-perceptions of health in Brazil as well.

Improved amputation-free survival in unreconstructable critical limb ischemia and its implications for clinical trial design and quality measurement.

PubMed

Benoit, Eric; O'Donnell, Thomas F; Kitsios, Georgios D; Iafrati, Mark D

2012-03-01

Amputation-free survival (AFS), a composite endpoint of mortality and amputation, is the preferred outcome measure in critical limb ischemia (CLI). Given the improvements in systemic management of atherosclerosis and interventional management of limb ischemia over the past 2 decades, we examined whether these outcomes have changed in patients with CLI without revascularization options (no option-critical limb ischemia [NO-CLI]). We reviewed the literature for published 1-year AFS, mortality, and amputation rates from control groups in NO-CLI trials. Summary proportions of events were estimated by conducting a random effects meta-analysis of proportions. To determine whether there had been any change in event rates over time, we performed a random effects meta-regression and a mixed effects logistic regression, both regressed against the variable "final year of recruitment." Eleven trials consisting of 886 patients satisfied search criteria, 7 of which presented AFS data. Summary proportion of events (95% confidence interval) were 0.551 (0.399 to 0.693) for AFS; 0.198 (0.116 to 0.317) for death; and 0.341 (0.209 to 0.487) for amputation. Regression analyses demonstrated that AFS has risen over time as mortality rates have fallen, and these improvements are statistically significant. The decrease in amputation rates failed to reach statistical significance. The lack of published data precluded a quantitative evaluation of any change in the clinical severity or comorbidities in the NO-CLI population. AFS and mortality rates in NO-CLI have improved over the past 2 decades. Due to declining event rates, clinical trials may underestimate treatment effects and thus fail to reach statistical significance unless sample sizes are increased or unless a subgroup with a higher event rate can be identified. Alternatively, comparing outcomes to historical values for quality measurement may overestimate treatment effects. Benchmark values of AFS and morality require periodic review and updating. Copyright © 2012 Society for Vascular Surgery. Published by Mosby, Inc. All rights reserved.

Caregivers Who Refuse Preventive Care for Their Children: The Relationship Between Immunization and Topical Fluoride Refusal

PubMed Central

2014-01-01

Objectives. The aim of this study was to examine caregivers’ refusal of preventive medical and dental care for children. Methods. Prevalence rates of topical fluoride refusal based on dental records and caregiver self-reports were estimated for children treated in 3 dental clinics in Washington State. A 60-item survey was administered to 1024 caregivers to evaluate the association between immunization and topical fluoride refusal. Modified Poisson regression models were used to estimate prevalence rate ratios (PRRs). Results. The prevalence of topical fluoride refusal was 4.9% according to dental records and 12.7% according to caregiver self-reports. The rate of immunization refusal was 27.4%. In the regression models, immunization refusal was significantly associated with topical fluoride refusal (dental record PRR = 1.61; 95% confidence interval [CI] = 1.32, 1.96; P < .001; caregiver self-report PRR = 6.20; 95% CI = 3.21, 11.98; P < .001). Caregivers younger than 35 years were significantly more likely than older caregivers to refuse both immunizations and topical fluoride (P < .05). Conclusions. Caregiver refusal of immunizations is associated with topical fluoride refusal. Future research should identify the behavioral and social factors related to caregiver refusal of preventive care with the goal of developing multidisciplinary strategies to help caregivers make optimal preventive care decisions for children. PMID:24832428

Estimating regression coefficients from clustered samples: Sampling errors and optimum sample allocation

NASA Technical Reports Server (NTRS)

Kalton, G.

1983-01-01

A number of surveys were conducted to study the relationship between the level of aircraft or traffic noise exposure experienced by people living in a particular area and their annoyance with it. These surveys generally employ a clustered sample design which affects the precision of the survey estimates. Regression analysis of annoyance on noise measures and other variables is often an important component of the survey analysis. Formulae are presented for estimating the standard errors of regression coefficients and ratio of regression coefficients that are applicable with a two- or three-stage clustered sample design. Using a simple cost function, they also determine the optimum allocation of the sample across the stages of the sample design for the estimation of a regression coefficient.

On the design of classifiers for crop inventories

NASA Technical Reports Server (NTRS)

Heydorn, R. P.; Takacs, H. C.

1986-01-01

Crop proportion estimators that use classifications of satellite data to correct, in an additive way, a given estimate acquired from ground observations are discussed. A linear version of these estimators is optimal, in terms of minimum variance, when the regression of the ground observations onto the satellite observations in linear. When this regression is not linear, but the reverse regression (satellite observations onto ground observations) is linear, the estimator is suboptimal but still has certain appealing variance properties. In this paper expressions are derived for those regressions which relate the intercepts and slopes to conditional classification probabilities. These expressions are then used to discuss the question of classifier designs that can lead to low-variance crop proportion estimates. Variance expressions for these estimates in terms of classifier omission and commission errors are also derived.

Using Robust Standard Errors to Combine Multiple Regression Estimates with Meta-Analysis

ERIC Educational Resources Information Center

Williams, Ryan T.

2012-01-01

Combining multiple regression estimates with meta-analysis has continued to be a difficult task. A variety of methods have been proposed and used to combine multiple regression slope estimates with meta-analysis, however, most of these methods have serious methodological and practical limitations. The purpose of this study was to explore the use…

The Collinearity Free and Bias Reduced Regression Estimation Project: The Theory of Normalization Ridge Regression. Report No. 2.

ERIC Educational Resources Information Center

Bulcock, J. W.; And Others

Multicollinearity refers to the presence of highly intercorrelated independent variables in structural equation models, that is, models estimated by using techniques such as least squares regression and maximum likelihood. There is a problem of multicollinearity in both the natural and social sciences where theory formulation and estimation is in…

Change in the discontinuation pattern of tumour necrosis factor antagonists in rheumatoid arthritis over 10 years: data from the Spanish registry BIOBADASER 2.0.

PubMed

Gómez-Reino, Juan J; Rodríguez-Lozano, Carlos; Campos-Fernández, Cristina; Montoro, María; Descalzo, Miguel Ángel; Carmona, Loreto

2012-03-01

To investigate in rheumatoid arthritis (RA) the rate and reason of discontinuation of tumour necrosis factor (TNF) antagonists over the past decade. RA patients in BIOBADASER 2.0 were stratified according to the start date of their first TNF antagonist into 2000-3, 2004-6 and 2007-9 interval years. Cumulative incidence function of discontinuation for inefficacy or toxicity was estimated with the alternative reason as competing risk. Competing risks regression models were used to measure the association of study groups with covariates and reasons for discontinuation. Association is expressed as subhazard ratios (SHR). 2907 RA patients were included in the study. Competing risk regression for inefficacy shows larger SHR for patients starting treatment in 2004-6 (SHR 2.57; 95% CI 1.55 to 4.25) and 2007-9 (SHR 3.4; 95% CI 2.08 to 5.55) than for those starting in 2000-3, after adjusting for TNF antagonists, clinical activity and concomitant treatment. Competing risk regression analysis for adverse events revealed no differences across the three time intervals. In RA, the discontinuation rate of TNF antagonists in the first year of treatment is higher more recently than a decade ago, inefficacy being the main reason for the increased rate. The rate of discontinuation for adverse events has remained stable.

Linear regression techniques for use in the EC tracer method of secondary organic aerosol estimation

NASA Astrophysics Data System (ADS)

Saylor, Rick D.; Edgerton, Eric S.; Hartsell, Benjamin E.

A variety of linear regression techniques and simple slope estimators are evaluated for use in the elemental carbon (EC) tracer method of secondary organic carbon (OC) estimation. Linear regression techniques based on ordinary least squares are not suitable for situations where measurement uncertainties exist in both regressed variables. In the past, regression based on the method of Deming [1943. Statistical Adjustment of Data. Wiley, London] has been the preferred choice for EC tracer method parameter estimation. In agreement with Chu [2005. Stable estimate of primary OC/EC ratios in the EC tracer method. Atmospheric Environment 39, 1383-1392], we find that in the limited case where primary non-combustion OC (OC non-comb) is assumed to be zero, the ratio of averages (ROA) approach provides a stable and reliable estimate of the primary OC-EC ratio, (OC/EC) pri. In contrast with Chu [2005. Stable estimate of primary OC/EC ratios in the EC tracer method. Atmospheric Environment 39, 1383-1392], however, we find that the optimal use of Deming regression (and the more general York et al. [2004. Unified equations for the slope, intercept, and standard errors of the best straight line. American Journal of Physics 72, 367-375] regression) provides excellent results as well. For the more typical case where OC non-comb is allowed to obtain a non-zero value, we find that regression based on the method of York is the preferred choice for EC tracer method parameter estimation. In the York regression technique, detailed information on uncertainties in the measurement of OC and EC is used to improve the linear best fit to the given data. If only limited information is available on the relative uncertainties of OC and EC, then Deming regression should be used. On the other hand, use of ROA in the estimation of secondary OC, and thus the assumption of a zero OC non-comb value, generally leads to an overestimation of the contribution of secondary OC to total measured OC.

[Incidence of congenital syphilis and factors associated with vertical transmission: data from the Birth in Brazil study].

PubMed

Domingues, Rosa Maria Soares Madeira; Leal, Maria do Carmo

2016-06-20

The objectives were to estimate incidence of congenital syphilis and verify factors associated with vertical transmission. A national hospital-based study was performed in 2011-2012 with 23,894 postpartum women using an in-hospital interview and data from patient charts and prenatal cards. Univariate logistic regression was performed to verify factors associated with congenital syphilis. Estimated incidence of congenital syphilis was 3.51 per 1,000 live births (95%CI: 2.29-5.37) and vertical transmission rate was 34.3% (95%CI: 24.7-45.4). Congenital syphilis was associated with lower maternal schooling, black skin color, higher rate of risk factors for prematurity, late initiation of prenatal care, fewer prenatal visits, and lower rate of prenatal serological testing. Fetal mortality was six times higher in congenital syphilis, and newborns with congenital syphilis showed higher hospital admission rates. Congenital syphilis is a persistent public health problem in Brazil and is associated with greater social vulnerability and gaps in prenatal care.

Accounting for Relatedness in Family Based Genetic Association Studies

PubMed Central

McArdle, P.F.; O’Connell, J.R.; Pollin, T.I.; Baumgarten, M.; Shuldiner, A.R.; Peyser, P.A.; Mitchell, B.D.

2007-01-01

Objective Assess the differences in point estimates, power and type 1 error rates when accounting for and ignoring family structure in genetic tests of association. Methods We compare by simulation the performance of analytic models using variance components to account for family structure and regression models that ignore relatedness for a range of possible family based study designs (i.e., sib pairs vs. large sibships vs. nuclear families vs. extended families). Results Our analyses indicate that effect size estimates and power are not significantly affected by ignoring family structure. Type 1 error rates increase when family structure is ignored, as density of family structures increases, and as trait heritability increases. For discrete traits with moderate levels of heritability and across many common sampling designs, type 1 error rates rise from a nominal 0.05 to 0.11. Conclusion Ignoring family structure may be useful in screening although it comes at a cost of a increased type 1 error rate, the magnitude of which depends on trait heritability and pedigree configuration. PMID:17570925

Annual regression-based estimates of evapotranspiration for the contiguous United States based on climate, remote sensing, and stream gage data

NASA Astrophysics Data System (ADS)

Reitz, M. D.; Sanford, W. E.; Senay, G. B.; Cazenas, J.

2015-12-01

Evapotranspiration (ET) is a key quantity in the hydrologic cycle, accounting for ~70% of precipitation across the contiguous United States (CONUS). However, it is a challenge to estimate, due to difficulty in making direct measurements and gaps in our theoretical understanding. Here we present a new data-driven, ~1km2 resolution map of long-term average actual evapotranspiration rates across the CONUS. The new ET map is a function of the USGS Landsat-derived National Land Cover Database (NLCD), precipitation, temperature, and daily average temperature range (from the PRISM climate dataset), and is calibrated to long-term water balance data from 679 watersheds. It is unique from previously presented ET maps in that (1) it was co-developed with estimates of runoff and recharge; (2) the regression equation was chosen from among many tested, previously published and newly proposed functional forms for its optimal description of long-term water balance ET data; (3) it has values over open-water areas that are derived from separate mass-transfer and humidity equations; and (4) the data include additional precipitation representing amounts converted from 2005 USGS water-use census irrigation data. The regression equation is calibrated using data from 2000-2013, but can also be applied to individual years with their corresponding input datasets. Comparisons among this new map, the more detailed remote-sensing-based estimates of MOD16 and SSEBop, and AmeriFlux ET tower measurements shows encouraging consistency, and indicates that the empirical ET estimate approach presented here produces closer agreement with independent flux tower data for annual average actual ET than other more complex remote sensing approaches.

The Influence of Hispanic Ethnicity and Nativity Status on 2009 H1N1 Pandemic Vaccination Uptake in the United States.

PubMed

Burger, Andrew E; Reither, Eric N; Hofmann, Erin Trouth; Mamelund, Svenn-Erik

2018-06-01

Previous research suggests Hispanic vaccination rates for H1N1 were similar to non-Hispanic whites. These previous estimates do not take into account nativity status. Using the 2010 National Health Interview Survey, we estimate adult H1N1 vaccination rates for non-Hispanic whites (n = 8780), U.S.-born Hispanics (n = 1142), and foreign-born Hispanics (n = 1912). To test Fundamental Cause Theory, we estimate odds of H1N1 vaccination while controlling for flexible resources (e.g., educational and economic capital), ethnicity, and nativity status. Foreign-born Hispanics experienced the lowest rates of H1N1 vaccination (15%), followed by U.S.-born Hispanics (18%) and non-Hispanic whites (21%). Regression models show odds of H1N1 vaccination did not differ among these three groups after controlling for sociodemographic characteristics. Insufficient access to flexible resources and healthcare coverage among foreign-born Hispanics was responsible for relatively low rates of H1N1 vaccination. Addressing resource disparities among Hispanics could increase vaccination uptake in the future, reducing inequities in disease burden.

Using Robust Variance Estimation to Combine Multiple Regression Estimates with Meta-Analysis

ERIC Educational Resources Information Center

Williams, Ryan

2013-01-01

The purpose of this study was to explore the use of robust variance estimation for combining commonly specified multiple regression models and for combining sample-dependent focal slope estimates from diversely specified models. The proposed estimator obviates traditionally required information about the covariance structure of the dependent…

A robust ridge regression approach in the presence of both multicollinearity and outliers in the data

NASA Astrophysics Data System (ADS)

Shariff, Nurul Sima Mohamad; Ferdaos, Nur Aqilah

2017-08-01

Multicollinearity often leads to inconsistent and unreliable parameter estimates in regression analysis. This situation will be more severe in the presence of outliers it will cause fatter tails in the error distributions than the normal distributions. The well-known procedure that is robust to multicollinearity problem is the ridge regression method. This method however is expected to be affected by the presence of outliers due to some assumptions imposed in the modeling procedure. Thus, the robust version of existing ridge method with some modification in the inverse matrix and the estimated response value is introduced. The performance of the proposed method is discussed and comparisons are made with several existing estimators namely, Ordinary Least Squares (OLS), ridge regression and robust ridge regression based on GM-estimates. The finding of this study is able to produce reliable parameter estimates in the presence of both multicollinearity and outliers in the data.

CNS infections in Greenland: A nationwide register-based cohort study

PubMed Central

Nordholm, Anne Christine; Søborg, Bolette; Andersson, Mikael; Hoffmann, Steen; Skinhøj, Peter; Koch, Anders

2017-01-01

Background Indigenous Arctic people suffer from high rates of infectious diseases. However, the burden of central nervous system (CNS) infections is poorly documented. This study aimed to estimate incidence rates and mortality of CNS infections among Inuits and non-Inuits in Greenland and in Denmark. Methods We conducted a nationwide cohort study using the populations of Greenland and Denmark 1990–2012. Information on CNS infection hospitalizations and pathogens was retrieved from national registries and laboratories. Incidence rates were estimated as cases per 100,000 person-years. Incidence rate ratios were calculated using log-linear Poisson-regression. Mortality was estimated using Kaplan-Meier curves and Log Rank test. Results The incidence rate of CNS infections was twice as high in Greenland (35.6 per 100,000 person years) as in Denmark (17.7 per 100,000 person years), but equally high among Inuits in Greenland and Denmark (38.2 and 35.4, respectively). Mortality from CNS infections was 2 fold higher among Inuits (10.5%) than among non-Inuits (4.8%) with a fivefold higher case fatality rate in Inuit toddlers. Conclusion Overall, Inuits living in Greenland and Denmark suffer from twice the rate of CNS infections compared with non-Inuits, and Inuit toddlers carried the highest risk of mortality. Further studies regarding risk factors such as genetic susceptibility, life style and socioeconomic factors are warranted. PMID:28158207

Normalization Ridge Regression in Practice I: Comparisons Between Ordinary Least Squares, Ridge Regression and Normalization Ridge Regression.

ERIC Educational Resources Information Center

Bulcock, J. W.

The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…

On the use and misuse of scalar scores of confounders in design and analysis of observational studies.

PubMed

Pfeiffer, R M; Riedl, R

2015-08-15

We assess the asymptotic bias of estimates of exposure effects conditional on covariates when summary scores of confounders, instead of the confounders themselves, are used to analyze observational data. First, we study regression models for cohort data that are adjusted for summary scores. Second, we derive the asymptotic bias for case-control studies when cases and controls are matched on a summary score, and then analyzed either using conditional logistic regression or by unconditional logistic regression adjusted for the summary score. Two scores, the propensity score (PS) and the disease risk score (DRS) are studied in detail. For cohort analysis, when regression models are adjusted for the PS, the estimated conditional treatment effect is unbiased only for linear models, or at the null for non-linear models. Adjustment of cohort data for DRS yields unbiased estimates only for linear regression; all other estimates of exposure effects are biased. Matching cases and controls on DRS and analyzing them using conditional logistic regression yields unbiased estimates of exposure effect, whereas adjusting for the DRS in unconditional logistic regression yields biased estimates, even under the null hypothesis of no association. Matching cases and controls on the PS yield unbiased estimates only under the null for both conditional and unconditional logistic regression, adjusted for the PS. We study the bias for various confounding scenarios and compare our asymptotic results with those from simulations with limited sample sizes. To create realistic correlations among multiple confounders, we also based simulations on a real dataset. Copyright © 2015 John Wiley & Sons, Ltd.

Orthogonal Projection in Teaching Regression and Financial Mathematics

ERIC Educational Resources Information Center

Kachapova, Farida; Kachapov, Ilias

2010-01-01

Two improvements in teaching linear regression are suggested. The first is to include the population regression model at the beginning of the topic. The second is to use a geometric approach: to interpret the regression estimate as an orthogonal projection and the estimation error as the distance (which is minimized by the projection). Linear…

Gender, g, Gender Identity Concepts, and Self-Constructs as Predictors of the Self-Estimated IQ

PubMed Central

Storek, Josephine

2013-01-01

In all 102 participants completed 2 intelligence tests, a self-estimated domain-masculine (DMIQ) intelligence rating (which is a composite of self-rated mathematical–logical and spatial intelligence), a measure of self-esteem, and of self-control. The aim was to confirm and extend previous findings about the role of general intelligence and gender identity in self-assessed intelligence. It aimed to examine further correlates of the Hubris–Humility Effect that shows men believe they are more intelligent than women. The DMIQ scores were correlated significantly with gender, psychometrically assessed IQ, and masculinity but not self-esteem or self-control. Stepwise regressions indicated that gender and gender role were the strongest predictors of DMIQ accounting for a third of the variance. PMID:24303578

An empirical model for dissolution profile and its application to floating dosage forms.

PubMed

Weiss, Michael; Kriangkrai, Worawut; Sungthongjeen, Srisagul

2014-06-02

A sum of two inverse Gaussian functions is proposed as a highly flexible empirical model for fitting of in vitro dissolution profiles. The model was applied to quantitatively describe theophylline release from effervescent multi-layer coated floating tablets containing different amounts of the anti-tacking agents talc or glyceryl monostearate. Model parameters were estimated by nonlinear regression (mixed-effects modeling). The estimated parameters were used to determine the mean dissolution time, as well as to reconstruct the time course of release rate for each formulation, whereby the fractional release rate can serve as a diagnostic tool for classification of dissolution processes. The approach allows quantification of dissolution behavior and could provide additional insights into the underlying processes. Copyright © 2014 Elsevier B.V. All rights reserved.

Gender, g, gender identity concepts, and self-constructs as predictors of the self-estimated IQ.

PubMed

Storek, Josephine; Furnham, Adrian

2013-01-01

In all 102 participants completed 2 intelligence tests, a self-estimated domain-masculine (DMIQ) intelligence rating (which is a composite of self-rated mathematical-logical and spatial intelligence), a measure of self-esteem, and of self-control. The aim was to confirm and extend previous findings about the role of general intelligence and gender identity in self-assessed intelligence. It aimed to examine further correlates of the Hubris-Humility Effect that shows men believe they are more intelligent than women. The DMIQ scores were correlated significantly with gender, psychometrically assessed IQ, and masculinity but not self-esteem or self-control. Stepwise regressions indicated that gender and gender role were the strongest predictors of DMIQ accounting for a third of the variance.

Moderation analysis using a two-level regression model.

PubMed

Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

2014-10-01

Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

Alarm Limits for Intraoperative Drug Infusions: A Report From the Multicenter Perioperative Outcomes Group.

PubMed

Berman, Mitchell F; Iyer, Nikhil; Freudzon, Leon; Wang, Shuang; Freundlich, Robert E; Housey, Michelle; Kheterpal, Sachin

2017-10-01

Continuous medication infusions are commonly used during surgical procedures. Alarm settings for infusion pumps are considered important for patient safety, but limits are not created in a standardized manner from actual usage data. We estimated 90th and 95th percentile infusion rates from a national database for potential use as upper limit alarm settings. We extracted infusion rate data from 17 major hospitals using intraoperative records provided by Multicenter Perioperative Outcomes Group for adult surgery between 2008 and 2014. Seven infusions were selected for study: propofol, remifentanil, dexmedetomidine, norepinephrine, phenylephrine, nitroglycerin, and esmolol. Each dosage entry for an infusion during a procedure was included. We estimated the 50th, 90th, and 95th percentile levels for each infusion across institutions, and performed quantile regression to examine factors that might affect the percentiles rates, such as use in general anesthesia versus sedation. The median 90th and 95th percentile infusion rates (with interquartile range) for propofol were 150 (140-150) and 170 (150-200) μg/kg/min. Quantile regression demonstrated higher 90th and 95th percentile rates during sedation for gastrointestinal endoscopy than for all surgical procedures performed under general anesthesia. For selected vasoactive medications, the corresponding median 90th and 95th percentile rates (with interquartile range) were norepinephrine 14.0 (9.8-18.1) and 18.3 (12.6-23.9) μg/min, and phenylephrine 60 (55-80) and 80 (75-100) μg/min. Alarm settings based on infusion rate percentile limits would be triggered at predictable rates; ie, the 95th percentile would be exceeded and an alarm sounded during 1 in 20 infusion rate entries. As a result, institutions could establish pump alarm settings consistent with desired alarm frequency using their own or externally validated usage data. Further study will be needed to determine the optimal percentile for infusion alarm settings.

The political economy of farmers’ suicides in India: indebted cash-crop farmers with marginal landholdings explain state-level variation in suicide rates

PubMed Central

2014-01-01

Background A recent Lancet article reported the first reliable estimates of suicide rates in India. National-level suicide rates are among the highest in the world, but suicide rates vary sharply between states and the causes of these differences are disputed. We test whether differences in the structure of agricultural production explain inter-state variation in suicides rates. This hypothesis is supported by a large number of qualitative studies, which argue that the liberalization of the agricultural sector in the early-1990s led to an agrarian crisis and that consequently farmers with certain socioeconomic characteristics–cash crops cultivators, with marginal landholdings, and debts–are at particular risk of committing suicide. The recent Lancet study, however, contends that there is no evidence to support this hypothesis. Methods We report scatter diagrams and linear regression models that combine the new state-level suicide rate estimates and the proportion of marginal farmers, cash crop cultivation, and indebted farmers. Results When we include all variables in the regression equation there is a significant positive relationship between the percentage of marginal farmers, cash crop production, and indebted farmers, and suicide rates. This model accounts for almost 75% of inter-state variation in suicide rates. If the proportion of marginal farmers, cash crops, or indebted farmers were reduced by 1%, the suicide rate–suicides per 100,000 per year–would fall by 0 · 437, 0 · 518 or 0 · 549 respectively, when all other variables are held constant. Conclusions Even if the Indian state is unable to enact land reforms due to the power of local elites, interventions to stabilize the price of cash crops and relieve indebted farmers may be effective at reducing suicide rates. PMID:24669945

Estimating linear temporal trends from aggregated environmental monitoring data

USGS Publications Warehouse

Erickson, Richard A.; Gray, Brian R.; Eager, Eric A.

2017-01-01

Trend estimates are often used as part of environmental monitoring programs. These trends inform managers (e.g., are desired species increasing or undesired species decreasing?). Data collected from environmental monitoring programs is often aggregated (i.e., averaged), which confounds sampling and process variation. State-space models allow sampling variation and process variations to be separated. We used simulated time-series to compare linear trend estimations from three state-space models, a simple linear regression model, and an auto-regressive model. We also compared the performance of these five models to estimate trends from a long term monitoring program. We specifically estimated trends for two species of fish and four species of aquatic vegetation from the Upper Mississippi River system. We found that the simple linear regression had the best performance of all the given models because it was best able to recover parameters and had consistent numerical convergence. Conversely, the simple linear regression did the worst job estimating populations in a given year. The state-space models did not estimate trends well, but estimated population sizes best when the models converged. We found that a simple linear regression performed better than more complex autoregression and state-space models when used to analyze aggregated environmental monitoring data.

Comparison of age estimation between 15-25 years using a modified form of Demirjian’s ten stage method and two teeth regression formula

NASA Astrophysics Data System (ADS)

Amiroh; Priaminiarti, M.; Syahraini, S. I.

2017-08-01

Age estimation of individuals, both dead and living, is important for victim identification and legal certainty. The Demirjian method uses the third molar for age estimation of individuals above 15 years old. The aim is to compare age estimation between 15-25 years using two Demirjian methods. Development stage of third molars in panoramic radiographs of 50 male and female samples were assessed by two observers using Demirjian’s ten stages and two teeth regression formula. Reliability was calculated using Cohen’s kappa coefficient and the significance of the observations was obtained from Wilcoxon tests. Deviations of age estimation were calculated using various methods. The deviation of age estimation with the two teeth regression formula was ±1.090 years; with ten stages, it was ±1.191 years. The deviation of age estimation using the two teeth regression formula was less than with the ten stages method. The age estimations using the two teeth regression formula or the ten stages method are significantly different until the age of 25, but they can be applied up to the age of 22.

Hybrid Support Vector Regression and Autoregressive Integrated Moving Average Models Improved by Particle Swarm Optimization for Property Crime Rates Forecasting with Economic Indicators

PubMed Central

Alwee, Razana; Hj Shamsuddin, Siti Mariyam; Sallehuddin, Roselina

2013-01-01

Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models. PMID:23766729

Effect of motivational interviewing on rates of early childhood caries: a randomized trial.

PubMed

Harrison, Rosamund; Benton, Tonya; Everson-Stewart, Siobhan; Weinstein, Phil

2007-01-01

The purposes of this randomized controlled trial were to: (1) test motivational interviewing (MI) to prevent early childhood caries; and (2) use Poisson regression for data analysis. A total of 240 South Asian children 6 to 18 months old were enrolled and randomly assigned to either the MI or control condition. Children had a dental exam, and their mothers completed pretested instruments at baseline and 1 and 2 years postintervention. Other covariates that might explain outcomes over and above treatment differences were modeled using Poisson regression. Hazard ratios were produced. Analyses included all participants whenever possible. Poisson regression supported a protective effect of MI (hazard ratio [HR]=0.54 (95%CI=035-0.84)-that is, the M/ group had about a 46% lower rate of dmfs at 2 years than did control children. Similar treatment effect estimates were obtained from models that included, as alternative outcomes, ds, dms, and dmfs, including "white spot lesions." Exploratory analyses revealed that rates of dmfs were higher in children whose mothers had: (1) prechewed their food; (2) been raised in a rural environment; and (3) a higher family income (P<.05). A motivational interviewing-style intervention shows promise to promote preventive behaviors in mothers of young children at high risk for caries.

Hybrid support vector regression and autoregressive integrated moving average models improved by particle swarm optimization for property crime rates forecasting with economic indicators.

PubMed

Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Sallehuddin, Roselina

2013-01-01

Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.

Linear regression analysis of survival data with missing censoring indicators.

PubMed

Wang, Qihua; Dinse, Gregg E

2011-04-01

Linear regression analysis has been studied extensively in a random censorship setting, but typically all of the censoring indicators are assumed to be observed. In this paper, we develop synthetic data methods for estimating regression parameters in a linear model when some censoring indicators are missing. We define estimators based on regression calibration, imputation, and inverse probability weighting techniques, and we prove all three estimators are asymptotically normal. The finite-sample performance of each estimator is evaluated via simulation. We illustrate our methods by assessing the effects of sex and age on the time to non-ambulatory progression for patients in a brain cancer clinical trial.

Associations between family and clinician ratings of child mental health: A study of UK CAMHS assessments and outcomes.

PubMed

Terrelonge, Dion N; Fugard, Andrew Jb

2017-10-01

The rated severity of child mental health problems depends on who is doing the rating, whether child, carer or clinician. It is important to know how these ratings relate to each other. To investigate to what extent clinicians' views are associated with carers' and young people's views in routine care in the United Kingdom. Ratings of clinician and parent/child viewpoints from a large Child and Adolescent Mental Health Services (CAMHS) sample ( ns 1773-47,299), as measured by the Children's Global Assessment Scale (CGAS) and Strengths and Difficulties Questionnaire (SDQ) respectively, were analysed. The parent SDQ added value score (AVS), which adjusts for regression to the mean and other non-treatment change, was also included in the analyses. Small-to-medium correlations were found between family and clinician ratings; however, ratings diverged for the lowest-function CGAS bands. Regression analyses showed that pro-social ratings from both child and parent contributed to clinician ratings. Knowing child-reported emotional problem severity made parent ratings of emotions irrelevant to clinician judgements. There was a positive association between SDQ AVS and CGAS; as hypothesised, CGAS showed more change than the SDQ AVS, suggesting that clinicians over-estimate change. This study shows the importance of multi-informant data gathering and the integration of multiple views by clinicians when monitoring outcomes.

Disability rates for cardiovascular and psychological disorders among autoworkers by job category, facility type, and facility overtime hours.

PubMed

Landsbergis, Paul A; Janevic, Teresa; Rothenberg, Laura; Adamu, Mohammed T; Johnson, Sylvia; Mirer, Franklin E

2013-07-01

We examined the association between long work hours, assembly line work and stress-related diseases utilizing objective health and employment data from an employer's administrative databases. A North American automobile manufacturing company provided data for claims for sickness, accident and disability insurance (work absence of at least 4 days) for cardiovascular disease (CVD), hypertension and psychological disorders, employee demographics, and facility hours worked per year for 1996-2001. Age-adjusted claim rates and age-adjusted rate ratios were calculated using Poisson regression, except for comparisons between production and skilled trades workers owing to lack of age denominator data by job category. Associations between overtime hours and claim rates by facility were examined by Poisson regression and multi-level Poisson regression. Claims for hypertension, coronary heart disease, CVD, and psychological disorders were associated with facility overtime hours. We estimate that a facility with 10 more overtime hours per week than another facility would have 4.36 more claims for psychological disorders, 2.33 more claims for CVD, and 3.29 more claims for hypertension per 1,000 employees per year. Assembly plants had the highest rates of claims for most conditions. Production workers tended to have higher rates of claims than skilled trades workers. Data from an auto manufacturer's administrative databases suggest that autoworkers working long hours, and assembly-line workers relative to skilled trades workers or workers in non-assembly facilities, have a higher risk of hypertension, CVD, and psychological disorders. Occupational disease surveillance and disease prevention programs need to fully utilize such administrative data. Copyright © 2013 Wiley Periodicals, Inc.

Neighborhood design and rates of walking and biking to elementary school in 34 California communities.

PubMed

Braza, Mark; Shoemaker, Wendy; Seeley, Anne

2004-01-01

This study evaluates the relationship between neighborhood design and rates of students walking and biking to elementary school. Pairwise correlations and multiple regression models were estimated based on a cross-sectional study of elementary schools and their surrounding neighborhoods. Setting and Subjects. Thirty-four (23%) of 150 California public elementary schools holding October 1999 Walk to School Day events participated in the study. Teachers asked fifth-grade students how they arrived to school 1 week before Walk to School Day. 1990 U.S. Census data measured population density and number of intersections per street mile, whereas 1998-1999 California Department of Education data measured school size, the percentage of students receiving public welfare, and the percentage of students of various ethnicities. Population density (p = .000) and school size (p = .053) were significantly associated with walking and biking rates in regression models controlling for number of intersections per street mile, the percentage of students receiving public welfare, and the percentage of students of various ethnicities. The number of intersections per street mile was associated with walking and biking rates in pairwise correlations (p = .003) but not in regression models. The results support the hypothesis that the walking and biking rates are higher in denser neighborhoods and to smaller schools but do not support the hypothesis that rates are higher in neighborhoods with a high number of intersections per street mile. We suggest that detailed data for a larger sample of students would allow statistical models to isolate the effect of specific design characteristics.

Use of empirical likelihood to calibrate auxiliary information in partly linear monotone regression models.

PubMed

Chen, Baojiang; Qin, Jing

2014-05-10

In statistical analysis, a regression model is needed if one is interested in finding the relationship between a response variable and covariates. When the response depends on the covariate, then it may also depend on the function of this covariate. If one has no knowledge of this functional form but expect for monotonic increasing or decreasing, then the isotonic regression model is preferable. Estimation of parameters for isotonic regression models is based on the pool-adjacent-violators algorithm (PAVA), where the monotonicity constraints are built in. With missing data, people often employ the augmented estimating method to improve estimation efficiency by incorporating auxiliary information through a working regression model. However, under the framework of the isotonic regression model, the PAVA does not work as the monotonicity constraints are violated. In this paper, we develop an empirical likelihood-based method for isotonic regression model to incorporate the auxiliary information. Because the monotonicity constraints still hold, the PAVA can be used for parameter estimation. Simulation studies demonstrate that the proposed method can yield more efficient estimates, and in some situations, the efficiency improvement is substantial. We apply this method to a dementia study. Copyright © 2013 John Wiley & Sons, Ltd.

Estimating an exchange rate between the EQ-5D-3L and ASCOT.

PubMed

Stevens, Katherine; Brazier, John; Rowen, Donna

2018-06-01

The aim was to estimate an exchange rate between EQ-5D-3L and the Adult Social Care Outcome Tool (ASCOT) using preference-based mapping via common time trade-off (TTO) valuations. EQ-5D and ASCOT are useful for examining cost-effectiveness within the health and social care sectors, respectively, but there is a policy need to understand overall benefits and compare across sectors to assess relative value for money. Standard statistical mapping is unsuitable since it relies on conceptual overlap of the measures but EQ-5D and ASCOT have different conceptualisations of quality of life. We use a preference-based mapping approach to estimate the exchange rate using common TTO valuations for both measures. A sample of health states from each measure was valued using TTO by 200 members of the UK adult general population. Regression analyses are used to generate separate equations between EQ-5D-3L and ASCOT values using their original value set and TTO values elicited here. These are solved as simultaneous equations to estimate the relationship between EQ-5D-3L and ASCOT. The relationship for moving from ASCOT to EQ-5D-3L is a linear transformation with an intercept of -0.0488 and gradient of 0.978. This enables QALY gains generated by ASCOT and EQ-5D to be compared across different interventions. This paper estimated an exchange rate between ASCOT and EQ-5D-3L using a preference-based mapping approach that does not compromise the descriptive systems of the two measures. This contributes to the development of preference-based mapping through the use of TTO as the common metric used to estimate the exchange rate between measures.

An Analysis of Advertising Effectiveness for U.S. Navy Recruiting

DTIC Science & Technology

1997-09-01

This thesis estimates the effect of Navy television advertising on enlistment rates of high quality male recruits (Armed Forces Qualification Test...Joint advertising is for all Armed Forces), Joint journal, and Joint direct mail advertising are explored. Enlistments are modeled as a function of...several factors including advertising , recruiters, and economic. Regression analyses (Ordinary Least Squares and Two Stage Least Squares) explore the

Machine learning and linear regression models to predict catchment-level base cation weathering rates across the southern Appalachian Mountain region, USA

Treesearch

Nicholas A. Povak; Paul F. Hessburg; Todd C. McDonnell; Keith M. Reynolds; Timothy J. Sullivan; R. Brion Salter; Bernard J. Crosby

2014-01-01

Accurate estimates of soil mineral weathering are required for regional critical load (CL) modeling to identify ecosystems at risk of the deleterious effects from acidification. Within a correlative modeling framework, we used modeled catchment-level base cation weathering (BCw) as the response variable to identify key environmental correlates and predict a continuous...

Efficient Regressions via Optimally Combining Quantile Information*

PubMed Central

Zhao, Zhibiao; Xiao, Zhijie

2014-01-01

We develop a generally applicable framework for constructing efficient estimators of regression models via quantile regressions. The proposed method is based on optimally combining information over multiple quantiles and can be applied to a broad range of parametric and nonparametric settings. When combining information over a fixed number of quantiles, we derive an upper bound on the distance between the efficiency of the proposed estimator and the Fisher information. As the number of quantiles increases, this upper bound decreases and the asymptotic variance of the proposed estimator approaches the Cramér-Rao lower bound under appropriate conditions. In the case of non-regular statistical estimation, the proposed estimator leads to super-efficient estimation. We illustrate the proposed method for several widely used regression models. Both asymptotic theory and Monte Carlo experiments show the superior performance over existing methods. PMID:25484481

Comparison of 3 estimation methods of mycophenolic acid AUC based on a limited sampling strategy in renal transplant patients.

PubMed

Hulin, Anne; Blanchet, Benoît; Audard, Vincent; Barau, Caroline; Furlan, Valérie; Durrbach, Antoine; Taïeb, Fabrice; Lang, Philippe; Grimbert, Philippe; Tod, Michel

2009-04-01

A significant relationship between mycophenolic acid (MPA) area under the plasma concentration-time curve (AUC) and the risk for rejection has been reported. Based on 3 concentration measurements, 3 approaches have been proposed for the estimation of MPA AUC, involving either a multilinear regression approach model (MLRA) or a Bayesian estimation using either gamma absorption or zero-order absorption population models. The aim of the study was to compare the 3 approaches for the estimation of MPA AUC in 150 renal transplant patients treated with mycophenolate mofetil and tacrolimus. The population parameters were determined in 77 patients (learning study). The AUC estimation methods were compared in the learning population and in 73 patients from another center (validation study). In the latter study, the reference AUCs were estimated by the trapezoidal rule on 8 measurements. MPA concentrations were measured by liquid chromatography. The gamma absorption model gave the best fit. In the learning study, the AUCs estimated by both Bayesian methods were very similar, whereas the multilinear approach was highly correlated but yielded estimates about 20% lower than Bayesian methods. This resulted in dosing recommendations differing by 250 mg/12 h or more in 27% of cases. In the validation study, AUC estimates based on the Bayesian method with gamma absorption model and multilinear regression approach model were, respectively, 12% higher and 7% lower than the reference values. To conclude, the bicompartmental model with gamma absorption rate gave the best fit. The 3 AUC estimation methods are highly correlated but not concordant. For a given patient, the same estimation method should always be used.

Associations of blood lead, cadmium, and mercury with estimated glomerular filtration rate in the Korean general population: Analysis of 2008-2010 Korean National Health and Nutrition Examination Survey data

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

Kim, Yangho; Lee, Byung-Kook, E-mail: bklee@sch.ac.kr

Introduction: The objective of this study was to evaluate associations between blood lead, cadmium, and mercury levels with estimated glomerular filtration rate in a general population of South Korean adults. Methods: This was a cross-sectional study based on data obtained in the Korean National Health and Nutrition Examination Survey (KNHANES) (2008-2010). The final analytical sample consisted of 5924 participants. Estimated glomerular filtration rate (eGFR) was calculated using the MDRD Study equation as an indicator of glomerular function. Results: In multiple linear regression analysis of log2-transformed blood lead as a continuous variable on eGFR, after adjusting for covariates including cadmium andmore » mercury, the difference in eGFR levels associated with doubling of blood lead were -2.624 mL/min per 1.73 m Superscript-Two (95% CI: -3.803 to -1.445). In multiple linear regression analysis using quartiles of blood lead as the independent variable, the difference in eGFR levels comparing participants in the highest versus the lowest quartiles of blood lead was -3.835 mL/min per 1.73 m Superscript-Two (95% CI: -5.730 to -1.939). In a multiple linear regression analysis using blood cadmium and mercury, as continuous or categorical variables, as independent variables, neither metal was a significant predictor of eGFR. Odds ratios (ORs) and 95% CI values for reduced eGFR calculated for log2-transformed blood metals and quartiles of the three metals showed similar trends after adjustment for covariates. Discussion: In this large, representative sample of South Korean adults, elevated blood lead level was consistently associated with lower eGFR levels and with the prevalence of reduced eGFR even in blood lead levels below 10 {mu}g/dL. In conclusion, elevated blood lead level was associated with lower eGFR in a Korean general population, supporting the role of lead as a risk factor for chronic kidney disease.« less

What is the cause of the decline in maternal mortality in India? Evidence from time series and cross-sectional analyses.

PubMed

Goli, Srinivas; Jaleel, Abdul C P

2014-05-01

Summary Studies on the causes of maternal mortality in India have focused on institutional deliveries, and the association of socioeconomic and demographic factors with the decline in maternal mortality has not been sufficiently investigated. By using both time series and cross-sectional data, this paper examines the factors associated with the decline in maternal mortality in India. Relative effects estimated by OLS regression analysis reveal that per capita state net domestic product (-1.49611, p<0.05), poverty ratio (0.02426, p<0.05), female literacy rate (-0.05905, p<0.10), infant mortality rate and total fertility rate (0.11755, p<0.05) show statistically significant association with the decline in the maternal mortality ratio in India. The Barro-regression estimate reveals that improvements in economic and demographic conditions such as growth in state income (β=0.35020, p<0.05) and reduction in poverty (β=0.01867, p<0.01) and fertility (β=0.02598, p<0.05) have a greater association with the decline in the maternal mortality ratio in India than institutional deliveries (β=0.00305). The negative β-coefficient (β=-0.69578, p<0.05), showing the effect of the initial maternal mortality ratio on change in maternal mortality ratio in the Barro-regression model, indicates a greater decline in maternal mortality ratio in laggard states compared with advanced states. Overall, comparing the estimates of relative effects, the socioeconomic and demographic factors have a stronger statistically significant association with the maternal mortality ratio than institutional deliveries. Interestingly, the weak association between 'increase in institutional deliveries' and 'decline in maternal mortality ratio' suggests that merely increasing deliveries alone will not help in ensuring maternal survival in India. Quality of services provided by the health facility, birth preparedness and avoiding delay in reaching health facility are also important. Deliveries in health facilities will not necessarily translate into increased survival chances of mothers unless women receive full antenatal care services and delays in reaching health facility are avoided.

Semiparametric modeling and estimation of the terminal behavior of recurrent marker processes before failure events.

PubMed

Chan, Kwun Chuen Gary; Wang, Mei-Cheng

2017-01-01

Recurrent event processes with marker measurements are mostly and largely studied with forward time models starting from an initial event. Interestingly, the processes could exhibit important terminal behavior during a time period before occurrence of the failure event. A natural and direct way to study recurrent events prior to a failure event is to align the processes using the failure event as the time origin and to examine the terminal behavior by a backward time model. This paper studies regression models for backward recurrent marker processes by counting time backward from the failure event. A three-level semiparametric regression model is proposed for jointly modeling the time to a failure event, the backward recurrent event process, and the marker observed at the time of each backward recurrent event. The first level is a proportional hazards model for the failure time, the second level is a proportional rate model for the recurrent events occurring before the failure event, and the third level is a proportional mean model for the marker given the occurrence of a recurrent event backward in time. By jointly modeling the three components, estimating equations can be constructed for marked counting processes to estimate the target parameters in the three-level regression models. Large sample properties of the proposed estimators are studied and established. The proposed models and methods are illustrated by a community-based AIDS clinical trial to examine the terminal behavior of frequencies and severities of opportunistic infections among HIV infected individuals in the last six months of life.

Indirectly estimated absolute lung cancer mortality rates by smoking status and histological type based on a systematic review

PubMed Central

2013-01-01

Background National smoking-specific lung cancer mortality rates are unavailable, and studies presenting estimates are limited, particularly by histology. This hinders interpretation. We attempted to rectify this by deriving estimates indirectly, combining data from national rates and epidemiological studies. Methods We estimated study-specific absolute mortality rates and variances by histology and smoking habit (never/ever/current/former) based on relative risk estimates derived from studies published in the 20th century, coupled with WHO mortality data for age 70–74 for the relevant country and period. Studies with populations grossly unrepresentative nationally were excluded. 70–74 was chosen based on analyses of large cohort studies presenting rates by smoking and age. Variations by sex, period and region were assessed by meta-analysis and meta-regression. Results 148 studies provided estimates (Europe 59, America 54, China 22, other Asia 13), 54 providing estimates by histology (squamous cell carcinoma, adenocarcinoma). For all smoking habits and lung cancer types, mortality rates were higher in males, the excess less evident for never smokers. Never smoker rates were clearly highest in China, and showed some increasing time trend, particularly for adenocarcinoma. Ever smoker rates were higher in parts of Europe and America than in China, with the time trend very clear, especially for adenocarcinoma. Variations by time trend and continent were clear for current smokers (rates being higher in Europe and America than Asia), but less clear for former smokers. Models involving continent and trend explained much variability, but non-linearity was sometimes seen (with rates lower in 1991–99 than 1981–90), and there was regional variation within continent (with rates in Europe often high in UK and low in Scandinavia, and higher in North than South America). Conclusions The indirect method may be questioned, because of variations in definition of smoking and lung cancer type in the epidemiological database, changes over time in diagnosis of lung cancer types, lack of national representativeness of some studies, and regional variation in smoking misclassification. However, the results seem consistent with the literature, and provide additional information on variability by time and region, including evidence of a rise in never smoker adenocarcinoma rates relative to squamous cell carcinoma rates. PMID:23570286

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.

A Wireless Electronic Nose System Using a Fe2O3 Gas Sensing Array and Least Squares Support Vector Regression

PubMed Central

Song, Kai; Wang, Qi; Liu, Qi; Zhang, Hongquan; Cheng, Yingguo

2011-01-01

This paper describes the design and implementation of a wireless electronic nose (WEN) system which can online detect the combustible gases methane and hydrogen (CH4/H2) and estimate their concentrations, either singly or in mixtures. The system is composed of two wireless sensor nodes—a slave node and a master node. The former comprises a Fe2O3 gas sensing array for the combustible gas detection, a digital signal processor (DSP) system for real-time sampling and processing the sensor array data and a wireless transceiver unit (WTU) by which the detection results can be transmitted to the master node connected with a computer. A type of Fe2O3 gas sensor insensitive to humidity is developed for resistance to environmental influences. A threshold-based least square support vector regression (LS-SVR)estimator is implemented on a DSP for classification and concentration measurements. Experimental results confirm that LS-SVR produces higher accuracy compared with artificial neural networks (ANNs) and a faster convergence rate than the standard support vector regression (SVR). The designed WEN system effectively achieves gas mixture analysis in a real-time process. PMID:22346587

Can a bank crisis break your heart?

PubMed Central

Stuckler, David; Meissner, Christopher M; King, Lawrence P

2008-01-01

Background To assess whether a banking system crisis increases short-term population cardiovascular mortality rates. Methods International, longitudinal multivariate regression analysis of cardiovascular disease mortality data from 1960 to 2002 Results A system-wide banking crisis increases population heart disease mortality rates by 6.4% (95% CI: 2.5% to 10.2%, p < 0.01) in high income countries, after controlling for economic change, macroeconomic instability, and population age and social distribution. The estimated effect is nearly four times as large in low income countries. Conclusion Banking crises are a significant determinant of short-term increases in heart disease mortality rates, and may have more severe consequences for developing countries. PMID:18197979

Generalized and synthetic regression estimators for randomized branch sampling

Treesearch

David L. R. Affleck; Timothy G. Gregoire

2015-01-01

In felled-tree studies, ratio and regression estimators are commonly used to convert more readily measured branch characteristics to dry crown mass estimates. In some cases, data from multiple trees are pooled to form these estimates. This research evaluates the utility of both tactics in the estimation of crown biomass following randomized branch sampling (...

Genome-wide analysis of adolescent psychotic-like experiences shows genetic overlap with psychiatric disorders.

PubMed

Pain, Oliver; Dudbridge, Frank; Cardno, Alastair G; Freeman, Daniel; Lu, Yi; Lundstrom, Sebastian; Lichtenstein, Paul; Ronald, Angelica

2018-03-31

This study aimed to test for overlap in genetic influences between psychotic-like experience traits shown by adolescents in the community, and clinically-recognized psychiatric disorders in adulthood, specifically schizophrenia, bipolar disorder, and major depression. The full spectra of psychotic-like experience domains, both in terms of their severity and type (positive, cognitive, and negative), were assessed using self- and parent-ratings in three European community samples aged 15-19 years (Final N incl. siblings = 6,297-10,098). A mega-genome-wide association study (mega-GWAS) for each psychotic-like experience domain was performed. Single nucleotide polymorphism (SNP)-heritability of each psychotic-like experience domain was estimated using genomic-relatedness-based restricted maximum-likelihood (GREML) and linkage disequilibrium- (LD-) score regression. Genetic overlap between specific psychotic-like experience domains and schizophrenia, bipolar disorder, and major depression was assessed using polygenic risk score (PRS) and LD-score regression. GREML returned SNP-heritability estimates of 3-9% for psychotic-like experience trait domains, with higher estimates for less skewed traits (Anhedonia, Cognitive Disorganization) than for more skewed traits (Paranoia and Hallucinations, Parent-rated Negative Symptoms). Mega-GWAS analysis identified one genome-wide significant association for Anhedonia within IDO2 but which did not replicate in an independent sample. PRS analysis revealed that the schizophrenia PRS significantly predicted all adolescent psychotic-like experience trait domains (Paranoia and Hallucinations only in non-zero scorers). The major depression PRS significantly predicted Anhedonia and Parent-rated Negative Symptoms in adolescence. Psychotic-like experiences during adolescence in the community show additive genetic effects and partly share genetic influences with clinically-recognized psychiatric disorders, specifically schizophrenia and major depression. © 2018 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.

A Revised Estimate of 20th Century Global Mean Sea Level

NASA Astrophysics Data System (ADS)

Hay, C.; Morrow, E.; Kopp, R. E., III; Mitrovica, J. X.

2014-12-01

One of the primary goals of paleo-sea level research is to assess the stability of ice sheets and glaciers in warming climates. In this context, the 20th century may be thought of as the most recent, recorded, and studied of all past episodes of warming. Over the past decade, a consensus has emerged in the literature that 20th century global mean sea level (GMSL), inferred from tide gauge records, rose at a mean rate of 1.6-1.9 mm/yr. This sea-level rise can be attributed to multiple sources, including thermal expansion of the oceans, ice sheet and glacier mass flux, and anthropogenic changes in land water storage. The Fifth Assessment Report of the IPCC summarized the estimated contributions of these sources over 1901-1990 and computed a total rate, using a bottom-up approach, of ~1.0 mm/yr, which falls significantly short of the rate inferred from tide gauge records. Using two independent probabilistic approaches that utilize models of glacial isostatic adjustment, ocean dynamics, and the sea-level fingerprints of rapid land-ice melt to analyze tide gauge records (Kalman smoothing and Gaussian process regression), we are able to close the 20th century sea-level budget and resolve the above enigma. Our revised estimate for the rate of GMSL rise during 1901-1990 is 1.1-1.3 mm/yr (90% credible interval). This value, which is ~20-30% less than previous estimates, suggests that the change in the GMSL rate from the 20th century to the last two decades (2.7 ± 0.4 mm/yr, consistent with past estimates) was greater than previous estimates. Moreover, since some forward projections of GMSL change into the next century are based in part on past estimates of GMSL change, our revised rate may impact projections of GMSL rise for the 21st century and beyond.

Neither fixed nor random: weighted least squares meta-regression.

PubMed

Stanley, T D; Doucouliagos, Hristos

2017-03-01

Our study revisits and challenges two core conventional meta-regression estimators: the prevalent use of 'mixed-effects' or random-effects meta-regression analysis and the correction of standard errors that defines fixed-effects meta-regression analysis (FE-MRA). We show how and explain why an unrestricted weighted least squares MRA (WLS-MRA) estimator is superior to conventional random-effects (or mixed-effects) meta-regression when there is publication (or small-sample) bias that is as good as FE-MRA in all cases and better than fixed effects in most practical applications. Simulations and statistical theory show that WLS-MRA provides satisfactory estimates of meta-regression coefficients that are practically equivalent to mixed effects or random effects when there is no publication bias. When there is publication selection bias, WLS-MRA always has smaller bias than mixed effects or random effects. In practical applications, an unrestricted WLS meta-regression is likely to give practically equivalent or superior estimates to fixed-effects, random-effects, and mixed-effects meta-regression approaches. However, random-effects meta-regression remains viable and perhaps somewhat preferable if selection for statistical significance (publication bias) can be ruled out and when random, additive normal heterogeneity is known to directly affect the 'true' regression coefficient. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Regional Regression Equations to Estimate Flow-Duration Statistics at Ungaged Stream Sites in Connecticut

USGS Publications Warehouse

Ahearn, Elizabeth A.

2010-01-01

Multiple linear regression equations for determining flow-duration statistics were developed to estimate select flow exceedances ranging from 25- to 99-percent for six 'bioperiods'-Salmonid Spawning (November), Overwinter (December-February), Habitat Forming (March-April), Clupeid Spawning (May), Resident Spawning (June), and Rearing and Growth (July-October)-in Connecticut. Regression equations also were developed to estimate the 25- and 99-percent flow exceedances without reference to a bioperiod. In total, 32 equations were developed. The predictive equations were based on regression analyses relating flow statistics from streamgages to GIS-determined basin and climatic characteristics for the drainage areas of those streamgages. Thirty-nine streamgages (and an additional 6 short-term streamgages and 28 partial-record sites for the non-bioperiod 99-percent exceedance) in Connecticut and adjacent areas of neighboring States were used in the regression analysis. Weighted least squares regression analysis was used to determine the predictive equations; weights were assigned based on record length. The basin characteristics-drainage area, percentage of area with coarse-grained stratified deposits, percentage of area with wetlands, mean monthly precipitation (November), mean seasonal precipitation (December, January, and February), and mean basin elevation-are used as explanatory variables in the equations. Standard errors of estimate of the 32 equations ranged from 10.7 to 156 percent with medians of 19.2 and 55.4 percent to predict the 25- and 99-percent exceedances, respectively. Regression equations to estimate high and median flows (25- to 75-percent exceedances) are better predictors (smaller variability of the residual values around the regression line) than the equations to estimate low flows (less than 75-percent exceedance). The Habitat Forming (March-April) bioperiod had the smallest standard errors of estimate, ranging from 10.7 to 20.9 percent. In contrast, the Rearing and Growth (July-October) bioperiod had the largest standard errors, ranging from 30.9 to 156 percent. The adjusted coefficient of determination of the equations ranged from 77.5 to 99.4 percent with medians of 98.5 and 90.6 percent to predict the 25- and 99-percent exceedances, respectively. Descriptive information on the streamgages used in the regression, measured basin and climatic characteristics, and estimated flow-duration statistics are provided in this report. Flow-duration statistics and the 32 regression equations for estimating flow-duration statistics in Connecticut are stored on the U.S. Geological Survey World Wide Web application ?StreamStats? (http://water.usgs.gov/osw/streamstats/index.html). The regression equations developed in this report can be used to produce unbiased estimates of select flow exceedances statewide.

Estimated Number of Preterm Births and Low Birth Weight Children Born in the United States Due to Maternal Binge Drinking

PubMed Central

Truong, Khoa D; Reifsnider, Odette S; Mayorga, Maria E; Spitler, Hugh

2013-01-01

Objective To estimate the aggregate burden of maternal binge drinking on preterm birth (PTB) and low birth weight (LBW) across American sociodemographic groups in 2008. Methods A simulation model was developed to estimate the number of PTB and LBW cases due to maternal binge drinking. Data inputs for the model included number of births and rates of preterm and LBW from the National Center for Health Statistics; female population by childbearing age groups from the U.S. Census; increased relative risks of preterm and LBW deliveries due to maternal binge drinking extracted from the literature; and adjusted prevalence of binge drinking among pregnant women estimated in a multivariate logistic regression model using Behavioral Risk Factor Surveillance System survey. Results The most conservative estimates attributed maternal binge drinking to 8,701 (95% CI: 7,804–9,598) PTBs (1.75% of all PTBs) and 5,627 (95% CI 5,121–6,133) LBW deliveries in 2008, with 3,708 (95% CI: 3,375–4,041) cases of both PTB and LBW. The estimated rate of PTB due to maternal binge drinking was 1.57% among all PTBs to White women, 0.69% among Black women, 3.31% among Hispanic women, and 2.35% among other races. Compared to other age groups, women ages 40–44 had the highest adjusted binge drinking rate and highest PTB rate due to maternal binge drinking (4.33%). Conclusion Maternal binge drinking contributed significantly to PTB and LBW differentially across sociodemographic groups. PMID:22711260

Estimating diabetes prevalence by small area in England.

PubMed

Congdon, Peter

2006-03-01

Diabetes risk is linked to both deprivation and ethnicity, and so prevalence will vary considerably between areas. Prevalence differences may partly account for geographic variation in health performance indicators for diabetes, which are based on age standardized hospitalization or operation rates. A positive correlation between prevalence and health outcomes indicates that the latter are not measuring only performance. A regression analysis of prevalence rates according to age, sex and ethnicity from the Health Survey for England (HSE) is undertaken and used (together with census data) to estimate diabetes prevalence for 354 English local authorities and 8000 smaller areas (electoral wards). An adjustment for social factors is based on a prevalence gradient over area-deprivation quintiles. A Bayesian estimation approach is used allowing simple inclusion of evidence on prevalence from other or historical sources. The estimated prevalent population in England is 1.5 million (188 000 type 1 and 1.341 million type 2). At strategic health authority (StHA) level, prevalence varies from 2.4 (Thames Valley) to 4 per cent (North East London). The prevalence estimates are used to assess variations between local authorities in adverse hospitalization indicators for diabetics and to assess the relationship between diabetes-related mortality and prevalence. In particular, rates of diabetic ketoacidosis (DKA) and coma are positively correlated with prevalence, while diabetic amputation rates are not. The methodology developed is applicable to developing small-area-prevalence estimates for a range of chronic diseases, when health surveys assess prevalence by demographic categories. In the application to diabetes prevalence, there is evidence that performance indicators as currently calculated are not corrected for prevalence.

Investigation of relative risk estimates from studies of the same population with contrasting response rates and designs.

PubMed

Mealing, Nicole M; Banks, Emily; Jorm, Louisa R; Steel, David G; Clements, Mark S; Rogers, Kris D

2010-04-01

There is little empirical evidence regarding the generalisability of relative risk estimates from studies which have relatively low response rates or are of limited representativeness. The aim of this study was to investigate variation in exposure-outcome relationships in studies of the same population with different response rates and designs by comparing estimates from the 45 and Up Study, a population-based cohort study (self-administered postal questionnaire, response rate 18%), and the New South Wales Population Health Survey (PHS) (computer-assisted telephone interview, response rate ~60%). Logistic regression analysis of questionnaire data from 45 and Up Study participants (n = 101,812) and 2006/2007 PHS participants (n = 14,796) was used to calculate prevalence estimates and odds ratios (ORs) for comparable variables, adjusting for age, sex and remoteness. ORs were compared using Wald tests modelling each study separately, with and without sampling weights. Prevalence of some outcomes (smoking, private health insurance, diabetes, hypertension, asthma) varied between the two studies. For highly comparable questionnaire items, exposure-outcome relationship patterns were almost identical between the studies and ORs for eight of the ten relationships examined did not differ significantly. For questionnaire items that were only moderately comparable, the nature of the observed relationships did not differ materially between the two studies, although many ORs differed significantly. These findings show that for a broad range of risk factors, two studies of the same population with varying response rate, sampling frame and mode of questionnaire administration yielded consistent estimates of exposure-outcome relationships. However, ORs varied between the studies where they did not use identical questionnaire items.

Evaluation of earthquake potential in China

NASA Astrophysics Data System (ADS)

Rong, Yufang

I present three earthquake potential estimates for magnitude 5.4 and larger earthquakes for China. The potential is expressed as the rate density (that is, the probability per unit area, magnitude and time). The three methods employ smoothed seismicity-, geologic slip rate-, and geodetic strain rate data. I test all three estimates, and another published estimate, against earthquake data. I constructed a special earthquake catalog which combines previous catalogs covering different times. I estimated moment magnitudes for some events using regression relationships that are derived in this study. I used the special catalog to construct the smoothed seismicity model and to test all models retrospectively. In all the models, I adopted a kind of Gutenberg-Richter magnitude distribution with modifications at higher magnitude. The assumed magnitude distribution depends on three parameters: a multiplicative " a-value," the slope or "b-value," and a "corner magnitude" marking a rapid decrease of earthquake rate with magnitude. I assumed the "b-value" to be constant for the whole study area and estimated the other parameters from regional or local geophysical data. The smoothed seismicity method assumes that the rate density is proportional to the magnitude of past earthquakes and declines as a negative power of the epicentral distance out to a few hundred kilometers. I derived the upper magnitude limit from the special catalog, and estimated local "a-values" from smoothed seismicity. I have begun a "prospective" test, and earthquakes since the beginning of 2000 are quite compatible with the model. For the geologic estimations, I adopted the seismic source zones that are used in the published Global Seismic Hazard Assessment Project (GSHAP) model. The zones are divided according to geological, geodetic and seismicity data. Corner magnitudes are estimated from fault length, while fault slip rates and an assumed locking depth determine earthquake rates. The geological model fits the earthquake data better than the GSHAP model. By smoothing geodetic strain rate, another potential model was constructed and tested. I derived the upper magnitude limit from the Special catalog, and assume local "a-values" proportional to geodetic strain rates. "Prospective" tests show that the geodetic strain rate model is quite compatible with earthquakes. By assuming the smoothed seismicity model as a null hypothesis, I tested every other model against it. Test results indicate that the smoothed seismicity model performs best.

Sampling system for wheat (Triticum aestivum L) area estimation using digital LANDSAT MSS data and aerial photographs. [Brazil

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Moreira, M. A.; Chen, S. C.; Batista, G. T.

1984-01-01

A procedure to estimate wheat (Triticum aestivum L) area using sampling technique based on aerial photographs and digital LANDSAT MSS data is developed. Aerial photographs covering 720 square km are visually analyzed. To estimate wheat area, a regression approach is applied using different sample sizes and various sampling units. As the size of sampling unit decreased, the percentage of sampled area required to obtain similar estimation performance also decreased. The lowest percentage of the area sampled for wheat estimation with relatively high precision and accuracy through regression estimation is 13.90% using 10 square km as the sampling unit. Wheat area estimation using only aerial photographs is less precise and accurate than those obtained by regression estimation.

Effect of racial and ethnic composition of neighborhoods in San Francisco on rates of mental health-related 911 calls.

PubMed

Kessell, Eric R; Alvidrez, Jennifer; McConnell, William A; Shumway, Martha

2009-10-01

This study investigated the association between the racial and ethnic residential composition of San Francisco neighborhoods and the rate of mental health-related 911 calls. A total of 1,341,608 emergency calls (28,197 calls related to mental health) to San Francisco's 911 system were made from January 2001 through June 2003. Police sector data in the call records were overlaid onto U.S. census tracts to estimate sector demographic and socioeconomic characteristics. Negative binomial regression was used to estimate the association between the percentage of black, Asian, Latino, and white residents and rates of mental health-related calls. A one-point increase in a sector's percentage of black residents was associated with a lower rate of mental health-related calls (incidence rate ratio=.99, p<.05). A sector's percentage of Asian and Latino residents had no significant effect. The observed relationship between the percentage of black residents and mental health-related calls is not consistent with known emergency mental health service utilization patterns.

A Bayesian approach to infer nitrogen loading rates from crop and land-use types surrounding private wells in the Central Valley, California

NASA Astrophysics Data System (ADS)

Ransom, Katherine M.; Bell, Andrew M.; Barber, Quinn E.; Kourakos, George; Harter, Thomas

2018-05-01

This study is focused on nitrogen loading from a wide variety of crop and land-use types in the Central Valley, California, USA, an intensively farmed region with high agricultural crop diversity. Nitrogen loading rates for several crop types have been measured based on field-scale experiments, and recent research has calculated nitrogen loading rates for crops throughout the Central Valley based on a mass balance approach. However, research is lacking to infer nitrogen loading rates for the broad diversity of crop and land-use types directly from groundwater nitrate measurements. Relating groundwater nitrate measurements to specific crops must account for the uncertainty about and multiplicity in contributing crops (and other land uses) to individual well measurements, and for the variability of nitrogen loading within farms and from farm to farm for the same crop type. In this study, we developed a Bayesian regression model that allowed us to estimate land-use-specific groundwater nitrogen loading rate probability distributions for 15 crop and land-use groups based on a database of recent nitrate measurements from 2149 private wells in the Central Valley. The water and natural, rice, and alfalfa and pasture groups had the lowest median estimated nitrogen loading rates, each with a median estimate below 5 kg N ha-1 yr-1. Confined animal feeding operations (dairies) and citrus and subtropical crops had the greatest median estimated nitrogen loading rates at approximately 269 and 65 kg N ha-1 yr-1, respectively. In general, our probability-based estimates compare favorably with previous direct measurements and with mass-balance-based estimates of nitrogen loading. Nitrogen mass-balance-based estimates are larger than our groundwater nitrate derived estimates for manured and nonmanured forage, nuts, cotton, tree fruit, and rice crops. These discrepancies are thought to be due to groundwater age mixing, dilution from infiltrating river water, or denitrification between the time when nitrogen leaves the root zone (point of reference for mass-balance-derived loading) and the time and location of groundwater measurement.

Suicide rates in China, 2004-2014: comparing data from two sample-based mortality surveillance systems.

PubMed

Sha, Feng; Chang, Qingsong; Law, Yik Wa; Hong, Qi; Yip, Paul S F

2018-02-13

The decreasing suicide rate in China has been regarded as a major contributor to the decline of global suicide rate in the past decade. However, previous estimations on China's suicide rates might not be accurate, since often they were based on the data from the Ministry of Health's Vital Registration ("MOH-VR") System, which is biased towards the better-off population. This study aims to compare suicide data extracted from the MOH-VR System with a more representative mortality surveillance system, namely the Center for Disease Control and Prevention's Disease Surveillance Points ("CDC-DSP") System, and update China's national and subnational suicide rates in the period of 2004-2014. The CDC-DSP data are obtained from the National Cause-of-Death Surveillance Dataset (2004-2014) and the MOH-VR data are from the Chinese Health Statistics Yearbooks (2005-2012) and the China Health and Family Planning Statistics Yearbooks (2013-2015). First, a negative binomial regression model was used to test the associations between the source of data (CDC-DSP/MOH-VR) and suicide rates in 2004-2014. Joinpoint regression analyses and Kitagawa's decomposition method are then applied to analyze the trends of the crude suicide rates. Both systems indicated China's suicide rates decreased over the study period. However, before the two systems merged in 2013, the CDC-DSP System reported significantly higher national suicide rates (IRR = 1.18, 95% Confidence Interval [CI]: 1.13-1.24) and rural suicide rates (IRR = 1.29, 95% CI: 1.21-1.38) than the MOH-VR System. The CDC-DSP System also showed significant reversing points in 2011 (95% CI: 2006-2012) and 2006 (95% CI: 2006-2008) on the rural and urban suicide trends. Moreover, the suicide rates in the east and central urban regions were reversed in 2011 and 2008. The biased MOH-VR System underestimated China's national and rural suicide rates. Although not widely appreciated in the field of suicide research, the CDC-DSP System provides more accurate estimations on China's suicide rates and is recommended for future studies to monitor the reversing trends of suicide rates in China's more developed areas.

Comparison of Maximum Likelihood Estimation Approach and Regression Approach in Detecting Quantitative Trait Lco Using RAPD Markers

Treesearch

Changren Weng; Thomas L. Kubisiak; C. Dana Nelson; James P. Geaghan; Michael Stine

1999-01-01

Single marker regression and single marker maximum likelihood estimation were tied to detect quantitative trait loci (QTLs) controlling the early height growth of longleaf pine and slash pine using a ((longleaf pine x slash pine) x slash pine) BC, population consisting of 83 progeny. Maximum likelihood estimation was found to be more power than regression and could...

Validation of a single-stage fixed-rate step test for the prediction of maximal oxygen uptake in healthy adults.

PubMed

Hansen, Dominique; Jacobs, Nele; Thijs, Herbert; Dendale, Paul; Claes, Neree

2016-09-01

Healthcare professionals with limited access to ergospirometry remain in need of valid and simple submaximal exercise tests to predict maximal oxygen uptake (VO2max ). Despite previous validation studies concerning fixed-rate step tests, accurate equations for the estimation of VO2max remain to be formulated from a large sample of healthy adults between age 18-75 years (n > 100). The aim of this study was to develop a valid equation to estimate VO2max from a fixed-rate step test in a larger sample of healthy adults. A maximal ergospirometry test, with assessment of cardiopulmonary parameters and VO2max , and a 5-min fixed-rate single-stage step test were executed in 112 healthy adults (age 18-75 years). During the step test and subsequent recovery, heart rate was monitored continuously. By linear regression analysis, an equation to predict VO2max from the step test was formulated. This equation was assessed for level of agreement by displaying Bland-Altman plots and calculation of intraclass correlations with measured VO2max . Validity further was assessed by employing a Jackknife procedure. The linear regression analysis generated the following equation to predict VO2max (l min(-1) ) from the step test: 0·054(BMI)+0·612(gender)+3·359(body height in m)+0·019(fitness index)-0·012(HRmax)-0·011(age)-3·475. This equation explained 78% of the variance in measured VO2max (F = 66·15, P<0·001). The level of agreement and intraclass correlation was high (ICC = 0·94, P<0·001) between measured and predicted VO2max . From this study, a valid fixed-rate single-stage step test equation has been developed to estimate VO2max in healthy adults. This tool could be employed by healthcare professionals with limited access to ergospirometry. © 2015 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.

Robust estimation for partially linear models with large-dimensional covariates

PubMed Central

Zhu, LiPing; Li, RunZe; Cui, HengJian

2014-01-01

We are concerned with robust estimation procedures to estimate the parameters in partially linear models with large-dimensional covariates. To enhance the interpretability, we suggest implementing a noncon-cave regularization method in the robust estimation procedure to select important covariates from the linear component. We establish the consistency for both the linear and the nonlinear components when the covariate dimension diverges at the rate of o(n), where n is the sample size. We show that the robust estimate of linear component performs asymptotically as well as its oracle counterpart which assumes the baseline function and the unimportant covariates were known a priori. With a consistent estimator of the linear component, we estimate the nonparametric component by a robust local linear regression. It is proved that the robust estimate of nonlinear component performs asymptotically as well as if the linear component were known in advance. Comprehensive simulation studies are carried out and an application is presented to examine the finite-sample performance of the proposed procedures. PMID:24955087

Robust estimation for partially linear models with large-dimensional covariates.

PubMed

Zhu, LiPing; Li, RunZe; Cui, HengJian

2013-10-01

We are concerned with robust estimation procedures to estimate the parameters in partially linear models with large-dimensional covariates. To enhance the interpretability, we suggest implementing a noncon-cave regularization method in the robust estimation procedure to select important covariates from the linear component. We establish the consistency for both the linear and the nonlinear components when the covariate dimension diverges at the rate of [Formula: see text], where n is the sample size. We show that the robust estimate of linear component performs asymptotically as well as its oracle counterpart which assumes the baseline function and the unimportant covariates were known a priori. With a consistent estimator of the linear component, we estimate the nonparametric component by a robust local linear regression. It is proved that the robust estimate of nonlinear component performs asymptotically as well as if the linear component were known in advance. Comprehensive simulation studies are carried out and an application is presented to examine the finite-sample performance of the proposed procedures.

Regional trends in short-duration precipitation extremes: a flexible multivariate monotone quantile regression approach

NASA Astrophysics Data System (ADS)

Cannon, Alex

2017-04-01

Estimating historical trends in short-duration rainfall extremes at regional and local scales is challenging due to low signal-to-noise ratios and the limited availability of homogenized observational data. In addition to being of scientific interest, trends in rainfall extremes are of practical importance, as their presence calls into question the stationarity assumptions that underpin traditional engineering and infrastructure design practice. Even with these fundamental challenges, increasingly complex questions are being asked about time series of extremes. For instance, users may not only want to know whether or not rainfall extremes have changed over time, they may also want information on the modulation of trends by large-scale climate modes or on the nonstationarity of trends (e.g., identifying hiatus periods or periods of accelerating positive trends). Efforts have thus been devoted to the development and application of more robust and powerful statistical estimators for regional and local scale trends. While a standard nonparametric method like the regional Mann-Kendall test, which tests for the presence of monotonic trends (i.e., strictly non-decreasing or non-increasing changes), makes fewer assumptions than parametric methods and pools information from stations within a region, it is not designed to visualize detected trends, include information from covariates, or answer questions about the rate of change in trends. As a remedy, monotone quantile regression (MQR) has been developed as a nonparametric alternative that can be used to estimate a common monotonic trend in extremes at multiple stations. Quantile regression makes efficient use of data by directly estimating conditional quantiles based on information from all rainfall data in a region, i.e., without having to precompute the sample quantiles. The MQR method is also flexible and can be used to visualize and analyze the nonlinearity of the detected trend. However, it is fundamentally a univariate technique, and cannot incorporate information from additional covariates, for example ENSO state or physiographic controls on extreme rainfall within a region. Here, the univariate MQR model is extended to allow the use of multiple covariates. Multivariate monotone quantile regression (MMQR) is based on a single hidden-layer feedforward network with the quantile regression error function and partial monotonicity constraints. The MMQR model is demonstrated via Monte Carlo simulations and the estimation and visualization of regional trends in moderate rainfall extremes based on homogenized sub-daily precipitation data at stations in Canada.

Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients

NASA Astrophysics Data System (ADS)

Gorgees, HazimMansoor; Mahdi, FatimahAssim

2018-05-01

This article concerns with comparing the performance of different types of ordinary ridge regression estimators that have been already proposed to estimate the regression parameters when the near exact linear relationships among the explanatory variables is presented. For this situations we employ the data obtained from tagi gas filling company during the period (2008-2010). The main result we reached is that the method based on the condition number performs better than other methods since it has smaller mean square error (MSE) than the other stated methods.

More green space is related to less antidepressant prescription rates in the Netherlands: A Bayesian geoadditive quantile regression approach.

PubMed

Helbich, Marco; Klein, Nadja; Roberts, Hannah; Hagedoorn, Paulien; Groenewegen, Peter P

2018-06-20

Exposure to green space seems to be beneficial for self-reported mental health. In this study we used an objective health indicator, namely antidepressant prescription rates. Current studies rely exclusively upon mean regression models assuming linear associations. It is, however, plausible that the presence of green space is non-linearly related with different quantiles of the outcome antidepressant prescription rates. These restrictions may contribute to inconsistent findings. Our aim was: a) to assess antidepressant prescription rates in relation to green space, and b) to analyze how the relationship varies non-linearly across different quantiles of antidepressant prescription rates. We used cross-sectional data for the year 2014 at a municipality level in the Netherlands. Ecological Bayesian geoadditive quantile regressions were fitted for the 15%, 50%, and 85% quantiles to estimate green space-prescription rate correlations, controlling for physical activity levels, socio-demographics, urbanicity, etc. RESULTS: The results suggested that green space was overall inversely and non-linearly associated with antidepressant prescription rates. More important, the associations differed across the quantiles, although the variation was modest. Significant non-linearities were apparent: The associations were slightly positive in the lower quantile and strongly negative in the upper one. Our findings imply that an increased availability of green space within a municipality may contribute to a reduction in the number of antidepressant prescriptions dispensed. Green space is thus a central health and community asset, whilst a minimum level of 28% needs to be established for health gains. The highest effectiveness occurred at a municipality surface percentage higher than 79%. This inverse dose-dependent relation has important implications for setting future community-level health and planning policies. Copyright © 2018 Elsevier Inc. All rights reserved.

Estimated number of preterm births and low birth weight children born in the United States due to maternal binge drinking.

PubMed

Truong, Khoa D; Reifsnider, Odette S; Mayorga, Maria E; Spitler, Hugh

2013-05-01

The objective of this study was to estimate the aggregate burden of maternal binge drinking on preterm birth (PTB) and low birth weight (LBW) across American sociodemographic groups in 2008. To estimate the aggregate burden of maternal binge drinking on preterm birth (PTB) and low birth weight (LBW) across American sociodemographic groups in 2008. A simulation model was developed to estimate the number of PTB and LBW cases due to maternal binge drinking. Data inputs for the model included number of births and rates of preterm and LBW from the National Center for Health Statistics; female population by childbearing age groups from the U.S. Census; increased relative risks of preterm and LBW deliveries due to maternal binge drinking extracted from the literature; and adjusted prevalence of binge drinking among pregnant women estimated in a multivariate logistic regression model using Behavioral Risk Factor Surveillance System survey. The most conservative estimates attributed maternal binge drinking to 8,701 (95% CI: 7,804-9,598) PTBs (1.75% of all PTBs) and 5,627 (95% CI 5,121-6,133) LBW deliveries in 2008, with 3,708 (95% CI: 3,375-4,041) cases of both PTB and LBW. The estimated rate of PTB due to maternal binge drinking was 1.57% among all PTBs to White women, 0.69% among Black women, 3.31% among Hispanic women, and 2.35% among other races. Compared to other age groups, women ages 40-44 had the highest adjusted binge drinking rate and highest PTB rate due to maternal binge drinking (4.33%). Maternal binge drinking contributed significantly to PTB and LBW differentially across sociodemographic groups.

Use of random regression to estimate genetic parameters of temperament across an age continuum in a crossbred cattle population.

PubMed

Littlejohn, B P; Riley, D G; Welsh, T H; Randel, R D; Willard, S T; Vann, R C

2018-05-12

The objective was to estimate genetic parameters of temperament in beef cattle across an age continuum. The population consisted predominantly of Brahman-British crossbred cattle. Temperament was quantified by: 1) pen score (PS), the reaction of a calf to a single experienced evaluator on a scale of 1 to 5 (1 = calm, 5 = excitable); 2) exit velocity (EV), the rate (m/sec) at which a calf traveled 1.83 m upon exiting a squeeze chute; and 3) temperament score (TS), the numerical average of PS and EV. Covariates included days of age and proportion of Bos indicus in the calf and dam. Random regression models included the fixed effects determined from the repeated measures models, except for calf age. Likelihood ratio tests were used to determine the most appropriate random structures. In repeated measures models, the proportion of Bos indicus in the calf was positively related with each calf temperament trait (0.41 ± 0.20, 0.85 ± 0.21, and 0.57 ± 0.18 for PS, EV, and TS, respectively; P < 0.01). There was an effect of contemporary group (combinations of season, year of birth, and management group) and dam age (P < 0.001) in all models. From repeated records analyses, estimates of heritability (h2) were 0.34 ± 0.04, 0.31 ± 0.04, and 0.39 ± 0.04, while estimates of permanent environmental variance as a proportion of the phenotypic variance (c2) were 0.30 ± 0.04, 0.31 ± 0.03, and 0.34 ± 0.04 for PS, EV, and TS, respectively. Quadratic additive genetic random regressions on Legendre polynomials of age were significant for all traits. Quadratic permanent environmental random regressions were significant for PS and TS, but linear permanent environmental random regressions were significant for EV. Random regression results suggested that these components change across the age dimension of these data. There appeared to be an increasing influence of permanent environmental effects and decreasing influence of additive genetic effects corresponding to increasing calf age for EV, and to a lesser extent for TS. Inherited temperament may be overcome by accumulating environmental stimuli with increases in age, especially after weaning.

Reasons for Persistently High Maternal and Perinatal Mortalities in Ethiopia: Part II-Socio-Economic and Cultural Factors

PubMed Central

Berhan, Yifru; Berhan, Asres

2014-01-01

Background The major causes of maternal and perinatal deaths are mostly pregnancy related. However, there are several predisposing factors for the increased risk of pregnancy related complications and deaths in developing countries. The objective of this review was to grossly estimate the effect of selected socioeconomic and cultural factors on maternal mortality, stillbirths and neonatal mortality in Ethiopia. Methods A comprehensive literature review was conducted focusing on the effect of total fertility rate (TFR), modern contraceptive use, harmful traditional practice, adult literacy rate and level of income on maternal and perinatal mortalities. For the majority of the data, regression analysis and Pearson correlation coefficient were used as a proxy indicator for the association of variables with maternal, fetal and neonatal mortality. Results Although there were variations in the methods for estimation, the TFR of women in Ethiopia declined from 5.9 to 4.8 in the last fifteen years, which was in the middle as compared with that of other African countries. The preference of injectable contraceptive method has increased by 7-fold, but the unmet contraceptive need was among the highest in Africa. About 50% reduction in female genital cutting (FGC) was reported although some women's attitude was positive towards the practice of FGC. The regression analysis demonstrated increased risk of stillbirths, neonatal and maternal mortality with increased TFR. The increased adult literacy rate was associated with increased antenatal care and skilled person attended delivery. Low adult literacy was also found to have a negative association with stillbirths and neonatal and maternal mortality. A similar trend was also observed with income. Conclusion Maternal mortality ratio, stillbirth rate and neonatal mortality rate had inverse relations with income and adult education. In Ethiopia, the high total fertility rate, low utilization of contraceptive methods, low adult literacy rate, low income and prevalent harmful traditional practices have probably contributed to the high maternal mortality ratio, stillbirth and neonatal mortality rates. PMID:25489187

The Association Between Rate and Severity of Exacerbations in Chronic Obstructive Pulmonary Disease: An Application of a Joint Frailty-Logistic Model

PubMed Central

Sadatsafavi, Mohsen; Sin, Don D.; Zafari, Zafar; Criner, Gerard; Connett, John E.; Lazarus, Stephen; Han, Meilan; Martinez, Fernando; Albert, Richard

2016-01-01

Exacerbations are a hallmark of chronic obstructive pulmonary disease (COPD). Evidence suggests the presence of substantial between-individual variability (heterogeneity) in exacerbation rates. The question of whether individuals vary in their tendency towards experiencing severe (versus mild) exacerbations, or whether there is an association between exacerbation rate and severity, has not yet been studied. We used data from the MACRO Study, a 1-year randomized trial of the use of azithromycin for prevention of COPD exacerbations (United States and Canada, 2006–2010; n = 1,107, mean age = 65.2 years, 59.1% male). A parametric frailty model was combined with a logistic regression model, with bivariate random effects capturing heterogeneity in rate and severity. The average rate of exacerbation was 1.53 episodes/year, with 95% of subjects having a model-estimated rate of 0.47–4.22 episodes/year. The overall ratio of severe exacerbations to total exacerbations was 0.22, with 95% of subjects having a model-estimated ratio of 0.04–0.60. We did not confirm an association between exacerbation rate and severity (P = 0.099). A unified model, implemented in standard software, could estimate joint heterogeneity in COPD exacerbation rate and severity and can have applications in similar contexts where inference on event time and intensity is considered. We provide SAS code (SAS Institute, Inc., Cary, North Carolina) and a simulated data set to facilitate further uses of this method. PMID:27737842

Probabilistic Estimates of Global Mean Sea Level and its Underlying Processes

NASA Astrophysics Data System (ADS)

Hay, C.; Morrow, E.; Kopp, R. E.; Mitrovica, J. X.

2015-12-01

Local sea level can vary significantly from the global mean value due to a suite of processes that includes ongoing sea-level changes due to the last ice age, land water storage, ocean circulation changes, and non-uniform sea-level changes that arise when modern-day land ice rapidly melts. Understanding these sources of spatial and temporal variability is critical to estimating past and present sea-level change and projecting future sea-level rise. Using two probabilistic techniques, a multi-model Kalman smoother and Gaussian process regression, we have reanalyzed 20th century tide gauge observations to produce a new estimate of global mean sea level (GMSL). Our methods allow us to extract global information from the sparse tide gauge field by taking advantage of the physics-based and model-derived geometry of the contributing processes. Both methods provide constraints on the sea-level contribution of glacial isostatic adjustment (GIA). The Kalman smoother tests multiple discrete models of glacial isostatic adjustment (GIA), probabilistically computing the most likely GIA model given the observations, while the Gaussian process regression characterizes the prior covariance structure of a suite of GIA models and then uses this structure to estimate the posterior distribution of local rates of GIA-induced sea-level change. We present the two methodologies, the model-derived geometries of the underlying processes, and our new probabilistic estimates of GMSL and GIA.

A Modeling Approach to Global Land Surface Monitoring with Low Resolution Satellite Imaging

NASA Technical Reports Server (NTRS)

Hlavka, Christine A.; Dungan, Jennifer; Livingston, Gerry P.; Gore, Warren J. (Technical Monitor)

1998-01-01

The effects of changing land use/land cover on global climate and ecosystems due to greenhouse gas emissions and changing energy and nutrient exchange rates are being addressed by federal programs such as NASA's Mission to Planet Earth (MTPE) and by international efforts such as the International Geosphere-Biosphere Program (IGBP). The quantification of these effects depends on accurate estimates of the global extent of critical land cover types such as fire scars in tropical savannas and ponds in Arctic tundra. To address the requirement for accurate areal estimates, methods for producing regional to global maps with satellite imagery are being developed. The only practical way to produce maps over large regions of the globe is with data of coarse spatial resolution, such as Advanced Very High Resolution Radiometer (AVHRR) weather satellite imagery at 1.1 km resolution or European Remote-Sensing Satellite (ERS) radar imagery at 100 m resolution. The accuracy of pixel counts as areal estimates is in doubt, especially for highly fragmented cover types such as fire scars and ponds. Efforts to improve areal estimates from coarse resolution maps have involved regression of apparent area from coarse data versus that from fine resolution in sample areas, but it has proven difficult to acquire sufficient fine scale data to develop the regression. A method for computing accurate estimates from coarse resolution maps using little or no fine data is therefore needed.

Determinants of preterm birth rates in Canada from 1981 through 1983 and from 1992 through 1994.

PubMed

Joseph, K S; Kramer, M S; Marcoux, S; Ohlsson, A; Wen, S W; Allen, A; Platt, R

1998-11-12

The rates of preterm birth have increased in many countries, including Canada, over the past 20 years. However, the factors underlying the increase are poorly understood. We used data from the Statistics Canada live-birth and stillbirth data bases to determine the effects of changes in the frequency of multiple births, registration of births occurring very early in gestation, patterns of obstetrical intervention, and use of ultrasonographic dating of gestational age on the rates of preterm birth in Canada from 1981 through 1983 and from 1992 through 1994. All births in 9 of the 12 provinces and territories of Canada were included. Logistic-regression analysis and Poisson regression analysis were used to estimate changes between the two three-year periods, after adjustment for the above-mentioned determinants of the likelihood of preterm births. Preterm births increased from 6.3 percent of live births in 1981 through 1983 to 6.8 percent in 1992 through 1994, a relative increase of 9 percent (95 percent confidence interval, 7 to 10 percent). Among singleton births, preterm births increased by 5 percent (95 percent confidence interval, 3 to 6 percent). Multiple births increased from 1.9 percent to 2.1 percent of all live births; the rates of preterm birth among live births resulting from multiple gestations increased by 25 percent (95 percent confidence interval, 21 to 28 percent). Adjustment for the determinants of the likelihood of preterm birth reduced the increase in the rate of preterm birth to 3 percent among all live births and 1 percent among singleton births. The recent increase in preterm births in Canada is largely attributable to changes in the frequency of multiple births, obstetrical intervention, and the use of ultrasound-based estimates of gestational age.

Ground penetrating radar: a case study for estimating root bulking rate in cassava (Manihot esculenta Crantz).

PubMed

Delgado, Alfredo; Hays, Dirk B; Bruton, Richard K; Ceballos, Hernán; Novo, Alexandre; Boi, Enrico; Selvaraj, Michael Gomez

2017-01-01

Understanding root traits is a necessary research front for selection of favorable genotypes or cultivation practices. Root and tuber crops having most of their economic potential stored below ground are favorable candidates for such studies. The ability to image and quantify subsurface root structure would allow breeders to classify root traits for rapid selection and allow agronomist the ability to derive effective cultivation practices. In spite of the huge role of Cassava ( Manihot esculenta Crantz), for food security and industrial uses, little progress has been made in understanding the onset and rate of the root-bulking process and the factors that influence it. The objective of this research was to determine the capability of ground penetrating radar (GPR) to predict root-bulking rates through the detection of total root biomass during its growth cycle. Our research provides the first application of GPR for detecting below ground biomass in cassava. Through an empirical study, linear regressions were derived to model cassava bulking rates. The linear equations derived suggest that GPR is a suitable measure of root biomass ( r  = .79). The regression analysis developed accounts for 63% of the variability in cassava biomass below ground. When modeling is performed at the variety level, it is evident that the variety models for SM 1219-9 and TMS 60444 outperform the HMC-1 variety model (r 2  = .77, .63 and .51 respectively). Using current modeling methods, it is possible to predict below ground biomass and estimate root bulking rates for selection of early root bulking in cassava. Results of this approach suggested that the general model was over predicting at early growth stages but became more precise in later root development.

Effect of Pedestrians on the Saturation Flow Rate of Right Turn Movements at Signalized Intersection - Case Study from Rasht City

NASA Astrophysics Data System (ADS)

Roshani, Mostafa; Bargegol, Iraj

2017-10-01

Saturation flow rate is one of the important items in the analysis of the capacity of signalized intersections that are affected by some factors. Pedestrian crossing on signalized intersection is one of the factors which influence the vehicles flow. In addition, the released researches determined that the greatest impact of pedestrian on the saturation flow occurred in the Conflict zone where the highest chance of the encounter of pedestrians and vehicles has in turning movements. The purpose of this paper is to estimate the saturation flow rate considering the effect of a pedestrian on right turn movements of the signalized intersections in Rasht city. For this goal, 6 signalized intersections with 90 cycles of reviews were selected for the estimation of saturation flow rate by the microscopic method and also 3 right turn lanes containing radius differences with 70 cycles of reviews were collected for the investigation of the pedestrians’ effects. Each phase of right turn lanes cycle was divided in the pieces of 10-second period which was totally 476 sample volumes of considered pedestrians and vehicles at that period. Only 101 samples of those were ranged as saturated conditions. Finally, using different regression models, the best relationship between pedestrian’s volume and right turning vehicles flow parameters was evaluated. The results indicate that there is a primarily linear relationship between pedestrian volume and right turning vehicles flow with R2=0.6261. According to this regression model with the increase in pedestrians, saturation flow rate will be reduced. In addition, by comparing the adjustment factor obtained in the present study and other studies, it was found that the effect of pedestrians on the right-turn movements in Rasht city is less than the rest of the world.

Homicide mortality rates in Canada, 2000-2009: Youth at increased risk.

PubMed

Basham, C Andrew; Snider, Carolyn

2016-10-20

To estimate and compare Canadian homicide mortality rates (HMRs) and trends in HMRs across age groups, with a focus on trends for youth. Data for the period of 2000 to 2009 were collected from Statistics Canada's CANSIM (Canadian Statistical Information Management) Table 102-0540 with the following ICD-10-CA coded external causes of death: X85 to Y09 (assault) and Y87.1 (sequelae of assault). Annual population counts from 2000 to 2009 were obtained from Statistics Canada's CANSIM Table 051-0001. Both death and population counts were organized into five-year age groups. A random effects negative binomial regression analysis was conducted to estimate age group-specific rates, rate ratios, and trends in homicide mortality. There were 9,878 homicide deaths in Canada during the study period. The increase in the overall homicide mortality rate (HMR) of 0.3% per year was not statistically significant (95% CI: -1.1% to +1.8%). Canadians aged 15-19 years and 20-24 years had the highest HMRs during the study period, and experienced statistically significant annual increases in their HMRs of 3% and 4% respectively (p < 0.05). A general, though not statistically significant, decrease in the HMR was observed for all age groups 50+ years. A fixed effects negative binomial regression model showed that the HMR for males was higher than for females over the study period [RRfemale/male = 0.473 (95% CI: 0.361, 0.621)], but no significant difference in sex-specific trends in the HMR was found. An increasing risk of homicide mortality was identified among Canadian youth, ages 15-24, over the 10-year study period. Research that seeks to understand the reasons for the increased homicide risk facing Canada's youth, and public policy responses to reduce this risk, are warranted.

Association between different measurements of blood pressure variability by ABP monitoring and ankle-brachial index.

PubMed

Wittke, Estefânia; Fuchs, Sandra C; Fuchs, Flávio D; Moreira, Leila B; Ferlin, Elton; Cichelero, Fábio T; Moreira, Carolina M; Neyeloff, Jeruza; Moreira, Marina B; Gus, Miguel

2010-11-05

Blood pressure (BP) variability has been associated with cardiovascular outcomes, but there is no consensus about the more effective method to measure it by ambulatory blood pressure monitoring (ABPM). We evaluated the association between three different methods to estimate BP variability by ABPM and the ankle brachial index (ABI). In a cross-sectional study of patients with hypertension, BP variability was estimated by the time rate index (the first derivative of SBP over time), standard deviation (SD) of 24-hour SBP; and coefficient of variability of 24-hour SBP. ABI was measured with a doppler probe. The sample included 425 patients with a mean age of 57 ± 12 years, being 69.2% women, 26.1% current smokers and 22.1% diabetics. Abnormal ABI (≤ 0.90 or ≥ 1.40) was present in 58 patients. The time rate index was 0.516 ± 0.146 mmHg/min in patients with abnormal ABI versus 0.476 ± 0.124 mmHg/min in patients with normal ABI (P = 0.007). In a logistic regression model the time rate index was associated with ABI, regardless of age (OR = 6.9, 95% CI = 1.1- 42.1; P = 0.04). In a multiple linear regression model, adjusting for age, SBP and diabetes, the time rate index was strongly associated with ABI (P < 0.01). None of the other indexes of BP variability were associated with ABI in univariate and multivariate analyses. Time rate index is a sensible method to measure BP variability by ABPM. Its performance for risk stratification of patients with hypertension should be explored in longitudinal studies.

Estimation of base temperatures for nine weed species.

PubMed

Steinmaus, S J; Prather, T S; Holt, J S

2000-02-01

Experiments were conducted to test several methods for estimating low temperature thresholds for seed germination. Temperature responses of nine weeds common in annual agroecosystems were assessed in temperature gradient experiments. Species included summer annuals (Amaranthus albus, A. palmeri, Digitaria sanguinalis, Echinochloa crus-galli, Portulaca oleracea, and Setaria glauca), winter annuals (Hirschfeldia incana and Sonchus oleraceus), and Conyza canadensis, which is classified as a summer or winter annual. The temperature below which development ceases (Tbase) was estimated as the x-intercept of four conventional germination rate indices regressed on temperature, by repeated probit analysis, and by a mathematical approach. An overall Tbase estimate for each species was the average across indices weighted by the reciprocal of the variance associated with the estimate. Germination rates increased linearly with temperature between 15 degrees C and 30 degrees C for all species. Consistent estimates of Tbase were obtained for most species using several indices. The most statistically robust and biologically relevant method was the reciprocal time to median germination, which can also be used to estimate other biologically meaningful parameters. The mean Tbase for summer annuals (13.8 degrees C) was higher than that for winter annuals (8.3 degrees C). The two germination response characteristics, Tbase and slope (rate), influence a species' germination behaviour in the field since the germination inhibiting effects of a high Tbase may be offset by the germination promoting effects of a rapid germination response to temperature. Estimates of Tbase may be incorporated into predictive thermal time models to assist weed control practitioners in making management decisions.

Quantification and regionalization of groundwater recharge in South-Central Kansas: Integrating field characterization, statistical analysis, and GIS

USGS Publications Warehouse

Sophocleous, M.

2000-01-01

A practical methodology for recharge characterization was developed based on several years of field-oriented research at 10 sites in the Great Bend Prairie of south-central Kansas. This methodology combines the soil-water budget on a storm-by-storm year-round basis with the resulting watertable rises. The estimated 1985-1992 average annual recharge was less than 50mm/year with a range from 15 mm/year (during the 1998 drought) to 178 mm/year (during the 1993 flood year). Most of this recharge occurs during the spring months. To regionalize these site-specific estimates, an additional methodology based on multiple (forward) regression analysis combined with classification and GIS overlay analyses was developed and implemented. The multiple regression analysis showed that the most influential variables were, in order of decreasing importance, total annual precipitation, average maximum springtime soil-profile water storage, average shallowest springtime depth to watertable, and average springtime precipitation rate. Therefore, four GIS (ARC/INFO) data "layers" or coverages were constructed for the study region based on these four variables, and each such coverage was classified into the same number of data classes to avoid biasing the results. The normalized regression coefficients were employed to weigh the class rankings of each recharge-affecting variable. This approach resulted in recharge zonations that agreed well with the site recharge estimates. During the "Great Flood of 1993," when rainfall totals exceeded normal levels by -200% in the northern portion of the study region, the developed regionalization methodology was tested against such extreme conditions, and proved to be both practical, based on readily available or easily measurable data, and robust. It was concluded that the combination of multiple regression and GIS overlay analyses is a powerful and practical approach to regionalizing small samples of recharge estimates.

Crop area estimation based on remotely-sensed data with an accurate but costly subsample

NASA Technical Reports Server (NTRS)

Gunst, R. F.

1983-01-01

Alternatives to sampling-theory stratified and regression estimators of crop production and timber biomass were examined. An alternative estimator which is viewed as especially promising is the errors-in-variable regression estimator. Investigations established the need for caution with this estimator when the ratio of two error variances is not precisely known.

An approach to using heart rate monitoring to estimate the ventilation and load of air pollution exposure.

PubMed

Cozza, Izabela Campos; Zanetta, Dirce Maria Trevisan; Fernandes, Frederico Leon Arrabal; da Rocha, Francisco Marcelo Monteiro; de Andre, Paulo Afonso; Garcia, Maria Lúcia Bueno; Paceli, Renato Batista; Prado, Gustavo Faibischew; Terra-Filho, Mario; do Nascimento Saldiva, Paulo Hilário; de Paula Santos, Ubiratan

2015-07-01

The effects of air pollution on health are associated with the amount of pollutants inhaled which depends on the environmental concentration and the inhaled air volume. It has not been clear whether statistical models of the relationship between heart rate and ventilation obtained using laboratory cardiopulmonary exercise test (CPET) can be applied to an external group to estimate ventilation. To develop and evaluate a model to estimate respiratory ventilation based on heart rate for inhaled load of pollutant assessment in field studies. Sixty non-smoking men; 43 public street workers (public street group) and 17 employees of the Forest Institute (park group) performed a maximum cardiopulmonary exercise test (CPET). Regression equation models were constructed with the heart rate and natural logarithmic of minute ventilation data obtained on CPET. Ten individuals were chosen randomly (public street group) and were used for external validation of the models (test group). All subjects also underwent heart rate register, and particulate matter (PM2.5) monitoring for a 24-hour period. For the public street group, the median difference between estimated and observed data was 0.5 (CI 95% -0.2 to 1.4) l/min and for the park group was 0.2 (CI 95% -0.2 to 1.2) l/min. In the test group, estimated values were smaller than the ones observed in the CPET, with a median difference of -2.4 (CI 95% -4.2 to -1.8) l/min. The mixed model estimated values suggest that this model is suitable for situations in which heart rate is around 120-140bpm. The mixed effect model is suitable for ventilation estimate, with good accuracy when applied to homogeneous groups, suggesting that, in this case, the model could be used in field studies to estimate ventilation. A small but significant difference in the median of external validation estimates was observed, suggesting that the applicability of the model to external groups needs further evaluation. Copyright © 2015 Elsevier B.V. All rights reserved.

Computation of nonlinear least squares estimator and maximum likelihood using principles in matrix calculus

NASA Astrophysics Data System (ADS)

Mahaboob, B.; Venkateswarlu, B.; Sankar, J. Ravi; Balasiddamuni, P.

2017-11-01

This paper uses matrix calculus techniques to obtain Nonlinear Least Squares Estimator (NLSE), Maximum Likelihood Estimator (MLE) and Linear Pseudo model for nonlinear regression model. David Pollard and Peter Radchenko [1] explained analytic techniques to compute the NLSE. However the present research paper introduces an innovative method to compute the NLSE using principles in multivariate calculus. This study is concerned with very new optimization techniques used to compute MLE and NLSE. Anh [2] derived NLSE and MLE of a heteroscedatistic regression model. Lemcoff [3] discussed a procedure to get linear pseudo model for nonlinear regression model. In this research article a new technique is developed to get the linear pseudo model for nonlinear regression model using multivariate calculus. The linear pseudo model of Edmond Malinvaud [4] has been explained in a very different way in this paper. David Pollard et.al used empirical process techniques to study the asymptotic of the LSE (Least-squares estimation) for the fitting of nonlinear regression function in 2006. In Jae Myung [13] provided a go conceptual for Maximum likelihood estimation in his work “Tutorial on maximum likelihood estimation

Measuring multi-joint stiffness during single movements: numerical validation of a novel time-frequency approach.

PubMed

Piovesan, Davide; Pierobon, Alberto; DiZio, Paul; Lackner, James R

2012-01-01

This study presents and validates a Time-Frequency technique for measuring 2-dimensional multijoint arm stiffness throughout a single planar movement as well as during static posture. It is proposed as an alternative to current regressive methods which require numerous repetitions to obtain average stiffness on a small segment of the hand trajectory. The method is based on the analysis of the reassigned spectrogram of the arm's response to impulsive perturbations and can estimate arm stiffness on a trial-by-trial basis. Analytic and empirical methods are first derived and tested through modal analysis on synthetic data. The technique's accuracy and robustness are assessed by modeling the estimation of stiffness time profiles changing at different rates and affected by different noise levels. Our method obtains results comparable with two well-known regressive techniques. We also test how the technique can identify the viscoelastic component of non-linear and higher than second order systems with a non-parametrical approach. The technique proposed here is very impervious to noise and can be used easily for both postural and movement tasks. Estimations of stiffness profiles are possible with only one perturbation, making our method a useful tool for estimating limb stiffness during motor learning and adaptation tasks, and for understanding the modulation of stiffness in individuals with neurodegenerative diseases.

Prediction of Maximal Aerobic Capacity in Severely Burned Children

PubMed Central

Porro, Laura; Rivero, Haidy G.; Gonzalez, Dante; Tan, Alai; Herndon, David N.; Suman, Oscar E.

2011-01-01

Introduction Maximal oxygen uptake (VO2 peak) is an indicator of cardiorespiratory fitness, but requires expensive equipment and a relatively high technical skill level. Purpose The aim of this study is to provide a formula for estimating VO2 peak in burned children, using information obtained without expensive equipment. Methods Children, with ≥40% total surface area burned (TBSA), underwent a modified Bruce treadmill test to asses VO2 peak at 6 months after injury. We recorded gender, age, %TBSA, %3rd degree burn, height, weight, treadmill time, maximal speed, maximal grade, and peak heart rate, and applied McHenry’s select algorithm to extract important independent variables and Robust multiple regression to establish prediction equations. Results 42 children; 7 to 17 years old were tested. Robust multiple regression model provided the equation: VO2=10.33 – 0.62 *Age (years) + 1.88 * Treadmill Time (min) + 2.3 (gender; Females = 0, Males = 1). The correlation between measured and estimated VO2 peak was R=0.80. We then validated the equation with a group of 33 burned children, which yielded a correlation between measured and estimated VO2 peak of R=0.79. Conclusions Using only a treadmill and easily gathered information, VO2 peak can be estimated in children with burns. PMID:21316155

Estimating design-flood discharges for streams in Iowa using drainage-basin and channel-geometry characteristics

USGS Publications Warehouse

Eash, D.A.

1993-01-01

Procedures provided for applying the drainage-basin and channel-geometry regression equations depend on whether the design-flood discharge estimate is for a site on an ungaged stream, an ungaged site on a gaged stream, or a gaged site. When both a drainage-basin and a channel-geometry regression-equation estimate are available for a stream site, a procedure is presented for determining a weighted average of the two flood estimates. The drainage-basin regression equations are applicable to unregulated rural drainage areas less than 1,060 square miles, and the channel-geometry regression equations are applicable to unregulated rural streams in Iowa with stabilized channels.

National scale biomass estimators for United States tree species

Treesearch

Jennifer C. Jenkins; David C. Chojnacky; Linda S. Heath; Richard A. Birdsey

2003-01-01

Estimates of national-scale forest carbon (C) stocks and fluxes are typically based on allometric regression equations developed using dimensional analysis techniques. However, the literature is inconsistent and incomplete with respect to large-scale forest C estimation. We compiled all available diameter-based allometric regression equations for estimating total...

Segmented regression analysis of interrupted time series data to assess outcomes of a South American road traffic alcohol policy change.

PubMed

Nistal-Nuño, Beatriz

2017-09-01

In Chile, a new law introduced in March 2012 decreased the legal blood alcohol concentration (BAC) limit for driving while impaired from 1 to 0.8 g/l and the legal BAC limit for driving under the influence of alcohol from 0.5 to 0.3 g/l. The goal is to assess the impact of this new law on mortality and morbidity outcomes in Chile. A review of national databases in Chile was conducted from January 2003 to December 2014. Segmented regression analysis of interrupted time series was used for analyzing the data. In a series of multivariable linear regression models, the change in intercept and slope in the monthly incidence rate of traffic deaths and injuries and association with alcohol per 100,000 inhabitants was estimated from pre-intervention to postintervention, while controlling for secular changes. In nested regression models, potential confounding seasonal effects were accounted for. All analyses were performed at a two-sided significance level of 0.05. Immediate level drops in all the monthly rates were observed after the law from the end of the prelaw period in the majority of models and in all the de-seasonalized models, although statistical significance was reached only in the model for injures related to alcohol. After the law, the estimated monthly rate dropped abruptly by -0.869 for injuries related to alcohol and by -0.859 adjusting for seasonality (P < 0.001). Regarding the postlaw long-term trends, it was evidenced a steeper decreasing trend after the law in the models for deaths related to alcohol, although these differences were not statistically significant. A strong evidence of a reduction in traffic injuries related to alcohol was found following the law in Chile. Although insufficient evidence was found of a statistically significant effect for the beneficial effects seen on deaths and overall injuries, potential clinically important effects cannot be ruled out. Copyright © 2017 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Poisson regression models outperform the geometrical model in estimating the peak-to-trough ratio of seasonal variation: a simulation study.

PubMed

Christensen, A L; Lundbye-Christensen, S; Dethlefsen, C

2011-12-01

Several statistical methods of assessing seasonal variation are available. Brookhart and Rothman [3] proposed a second-order moment-based estimator based on the geometrical model derived by Edwards [1], and reported that this estimator is superior in estimating the peak-to-trough ratio of seasonal variation compared with Edwards' estimator with respect to bias and mean squared error. Alternatively, seasonal variation may be modelled using a Poisson regression model, which provides flexibility in modelling the pattern of seasonal variation and adjustments for covariates. Based on a Monte Carlo simulation study three estimators, one based on the geometrical model, and two based on log-linear Poisson regression models, were evaluated in regards to bias and standard deviation (SD). We evaluated the estimators on data simulated according to schemes varying in seasonal variation and presence of a secular trend. All methods and analyses in this paper are available in the R package Peak2Trough[13]. Applying a Poisson regression model resulted in lower absolute bias and SD for data simulated according to the corresponding model assumptions. Poisson regression models had lower bias and SD for data simulated to deviate from the corresponding model assumptions than the geometrical model. This simulation study encourages the use of Poisson regression models in estimating the peak-to-trough ratio of seasonal variation as opposed to the geometrical model. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Group Additivity Determination for Oxygenates, Oxonium Ions, and Oxygen-Containing Carbenium Ions

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

Dellon, Lauren D.; Sung, Chun-Yi; Robichaud, David J.

Bio-oil produced from biomass fast pyrolysis often requires catalytic upgrading to remove oxygen and acidic species over zeolite catalysts. The elementary reactions in the mechanism for this process involve carbenium and oxonium ions. In order to develop a detailed kinetic model for the catalytic upgrading of biomass, rate constants are required for these elementary reactions. The parameters in the Arrhenius equation can be related to thermodynamic properties through structure-reactivity relationships, such as the Evans-Polanyi relationship. For this relationship, enthalpies of formation of each species are required, which can be reasonably estimated using group additivity. However, the literature previously lacked groupmore » additivity values for oxygenates, oxonium ions, and oxygen-containing carbenium ions. In this work, 71 group additivity values for these types of groups were regressed, 65 of which had not been reported previously and six of which were newly estimated based on regression in the context of the 65 new groups. Heats of formation based on atomization enthalpy calculations for a set of reference molecules and isodesmic reactions for a small set of larger species for which experimental data was available were used to demonstrate the accuracy of the Gaussian-4 quantum mechanical method in estimating enthalpies of formation for species involving the moieties of interest. Isodesmic reactions for a total of 195 species were constructed from the reference molecules to calculate enthalpies of formation that were used to regress the group additivity values. The results showed an average deviation of 1.95 kcal/mol between the values calculated from Gaussian-4 and isodesmic reactions versus those calculated from the group additivity values that were newly regressed. Importantly, the new groups enhance the database for group additivity values, especially those involving oxonium ions.« less

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.

Estimation of Standard Error of Regression Effects in Latent Regression Models Using Binder's Linearization. Research Report. ETS RR-07-09

ERIC Educational Resources Information Center

Li, Deping; Oranje, Andreas

2007-01-01

Two versions of a general method for approximating standard error of regression effect estimates within an IRT-based latent regression model are compared. The general method is based on Binder's (1983) approach, accounting for complex samples and finite populations by Taylor series linearization. In contrast, the current National Assessment of…

Propensity score estimation: machine learning and classification methods as alternatives to logistic regression

PubMed Central

Westreich, Daniel; Lessler, Justin; Funk, Michele Jonsson

2010-01-01

Summary Objective Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this Review was to assess machine learning alternatives to logistic regression which may accomplish the same goals but with fewer assumptions or greater accuracy. Study Design and Setting We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use. Results We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (CART), and meta-classifiers (in particular, boosting). Conclusion While the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and to a lesser extent decision trees (particularly CART) appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice. PMID:20630332

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.

Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression.

PubMed

Westreich, Daniel; Lessler, Justin; Funk, Michele Jonsson

2010-08-01

Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this review was to assess machine learning alternatives to logistic regression, which may accomplish the same goals but with fewer assumptions or greater accuracy. We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use. We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (classification and regression trees [CART]), and meta-classifiers (in particular, boosting). Although the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and, to a lesser extent, decision trees (particularly CART), appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice. Copyright (c) 2010 Elsevier Inc. All rights reserved.

A Solution to Separation and Multicollinearity in Multiple Logistic Regression

PubMed Central

Shen, Jianzhao; Gao, Sujuan

2010-01-01

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27–38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth’s penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study. PMID:20376286

A Solution to Separation and Multicollinearity in Multiple Logistic Regression.

PubMed

Shen, Jianzhao; Gao, Sujuan

2008-10-01

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.

How to estimate green house gas (GHG) emissions from an excavator by using CAT's performance chart

NASA Astrophysics Data System (ADS)

Hajji, Apif M.; Lewis, Michael P.

2017-09-01

Construction equipment activities are a major part of many infrastructure projects. This type of equipment typically releases large quantities of green house gas (GHG) emissions. GHG emissions may come from fuel consumption. Furthermore, equipment productivity affects the fuel consumption. Thus, an estimating tool based on the construction equipment productivity rate is able to accurately assess the GHG emissions resulted from the equipment activities. This paper proposes a methodology to estimate the environmental impact for a common construction activity. This paper delivers sensitivity analysis and a case study for an excavator based on trench excavation activity. The methodology delivered in this study can be applied to a stand-alone model, or a module that is integrated with other emissions estimators. The GHG emissions are highly correlated to diesel fuel use, which is approximately 10.15 kilograms (kg) of CO2 per gallon of diesel fuel. The results showed that the productivity rate model as the result from multiple regression analysis can be used as the basis for estimating GHG emissions, and also as the framework for developing emissions footprint and understanding the environmental impact from construction equipment activities introduction.

Simultaneous Estimation of Regression Functions for Marine Corps Technical Training Specialties.

DTIC Science & Technology

1985-01-03

Edmonton, Alberta CANADA 1 Dr. Frederic M. Lord Educational Testing Service 1 Dr. Earl Hunt Princeton, NJ 08541 Dept, of Psychology University of...111111-1.6 MICROCOPY RESOLUTION TEST CHART NATIONAL BUREAU OF STANDARDS-1963-A SIMIULTANEOUS ESTIMATION OF REGRESSION FUNCTIONS FOR MARINE CORPS...Bayesian techniques for simul- taneous estimation to the specification of regression weights for selection tests used in various technical training courses

Robust regression on noisy data for fusion scaling laws

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

Verdoolaege, Geert, E-mail: geert.verdoolaege@ugent.be; Laboratoire de Physique des Plasmas de l'ERM - Laboratorium voor Plasmafysica van de KMS

2014-11-15

We introduce the method of geodesic least squares (GLS) regression for estimating fusion scaling laws. Based on straightforward principles, the method is easily implemented, yet it clearly outperforms established regression techniques, particularly in cases of significant uncertainty on both the response and predictor variables. We apply GLS for estimating the scaling of the L-H power threshold, resulting in estimates for ITER that are somewhat higher than predicted earlier.

Smooth individual level covariates adjustment in disease mapping.

PubMed

Huque, Md Hamidul; Anderson, Craig; Walton, Richard; Woolford, Samuel; Ryan, Louise

2018-05-01

Spatial models for disease mapping should ideally account for covariates measured both at individual and area levels. The newly available "indiCAR" model fits the popular conditional autoregresssive (CAR) model by accommodating both individual and group level covariates while adjusting for spatial correlation in the disease rates. This algorithm has been shown to be effective but assumes log-linear associations between individual level covariates and outcome. In many studies, the relationship between individual level covariates and the outcome may be non-log-linear, and methods to track such nonlinearity between individual level covariate and outcome in spatial regression modeling are not well developed. In this paper, we propose a new algorithm, smooth-indiCAR, to fit an extension to the popular conditional autoregresssive model that can accommodate both linear and nonlinear individual level covariate effects while adjusting for group level covariates and spatial correlation in the disease rates. In this formulation, the effect of a continuous individual level covariate is accommodated via penalized splines. We describe a two-step estimation procedure to obtain reliable estimates of individual and group level covariate effects where both individual and group level covariate effects are estimated separately. This distributed computing framework enhances its application in the Big Data domain with a large number of individual/group level covariates. We evaluate the performance of smooth-indiCAR through simulation. Our results indicate that the smooth-indiCAR method provides reliable estimates of all regression and random effect parameters. We illustrate our proposed methodology with an analysis of data on neutropenia admissions in New South Wales (NSW), Australia. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Predicting the stage shift as a result of breast cancer screening in low- and middle-income countries: a proof of concept.

PubMed

Zelle, Sten G; Baltussen, Rob; Otten, Johannes D M; Heijnsdijk, Eveline A M; van Schoor, Guido; Broeders, Mireille J M

2015-03-01

To provide proof of concept for a simple model to estimate the stage shift as a result of breast cancer screening in low- and middle-income countries (LMICs). Stage shift is an essential early detection indicator and an important proxy for the performance and possible further impact of screening programmes. Our model could help LIMCs to choose appropriate control strategies. We assessed our model concept in three steps. First, we calculated the proportional performance rates (i.e. index number Z) based on 16 screening rounds of the Nijmegen Screening Program (384,884 screened women). Second, we used linear regression to assess the association between Z and the amount of stage shift observed in the programme. Third, we hypothesized how Z could be used to estimate the stage shift as a result of breast cancer screening in LMICs. Stage shifts can be estimated by the proportional performance rates (Zs) using linear regression. Zs calculated for each screening round are highly associated with the observed stage shifts in the Nijmegen Screening Program (Pearson's R: 0.798, R square: 0.637). Our model can predict the stage shifts in the Nijmegen Screening Program, and could be applied to settings with different characteristics, although it should not be straightforwardly used to estimate the impact on mortality. Further research should investigate the extrapolation of our model to other settings. As stage shift is an essential screening performance indicator, our model could provide important information on the performance of breast cancer screening programmes that LMICs consider implementing. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Mortality among 24,865 workers exposed to polychlorinated biphenyls (PCBs) in three electrical capacitor manufacturing plants: a ten-year update.

PubMed

Ruder, Avima M; Hein, Misty J; Hopf, Nancy B; Waters, Martha A

2014-03-01

The objective of this analysis was to evaluate mortality among a cohort of 24,865 capacitor-manufacturing workers exposed to polychlorinated biphenyls (PCBs) at plants in Indiana, Massachusetts, and New York and followed for mortality through 2008. Cumulative PCB exposure was estimated using plant-specific job-exposure matrices. External comparisons to US and state-specific populations used standardized mortality ratios, adjusted for gender, race, age and calendar year. Among long-term workers employed 3 months or longer, within-cohort comparisons used standardized rate ratios and multivariable Poisson regression modeling. Through 2008, more than one million person-years at risk and 8749 deaths were accrued. Among long-term employees, all-cause and all-cancer mortality were not elevated; of the a priori outcomes assessed only melanoma mortality was elevated. Mortality was elevated for some outcomes of a priori interest among subgroups of long-term workers: all cancer, intestinal cancer and amyotrophic lateral sclerosis (women); melanoma (men); melanoma and brain and nervous system cancer (Indiana plant); and melanoma and multiple myeloma (New York plant). Standardized rates of stomach and uterine cancer and multiple myeloma mortality increased with estimated cumulative PCB exposure. Poisson regression modeling showed significant associations with estimated cumulative PCB exposure for prostate and stomach cancer mortality. For other outcomes of a priori interest--rectal, liver, ovarian, breast, and thyroid cancer, non-Hodgkin lymphoma, Alzheimer disease, and Parkinson disease--neither elevated mortality nor positive associations with PCB exposure were observed. Associations between estimated cumulative PCB exposure and stomach, uterine, and prostate cancer and myeloma mortality confirmed our previous positive findings. Published by Elsevier GmbH.

Mortality among 24,865 workers exposed to polychlorinated biphenyls (PCBs) in three electrical capacitor manufacturing plants: A ten-year update

PubMed Central

Ruder, Avima M.; Hein, Misty J.; Hopf, Nancy B.; Waters, Martha A.

2015-01-01

The objective of this analysis was to evaluate mortality among a cohort of 24,865 capacitor-manufacturing workers exposed to polychlorinated biphenyls (PCBs) at plants in Indiana, Massachusetts, and New York and followed for mortality through 2008. Cumulative PCB exposure was estimated using plant-specific job-exposure matrices. External comparisons to US and state-specific populations used standardized mortality ratios, adjusted for gender, race, age and calendar year. Among long-term workers employed 3 months or longer, within-cohort comparisons used standardized rate ratios and multivariable Poisson regression modeling. Through 2008, more than one million person-years at risk and 8749 deaths were accrued. Among long-term employees, all-cause and all-cancer mortality were not elevated; of the a priori outcomes assessed only melanoma mortality was elevated. Mortality was elevated for some outcomes of a priori interest among subgroups of long-term workers: all cancer, intestinal cancer and amyotrophic lateral sclerosis (women); melanoma (men); melanoma and brain and nervous system cancer (Indiana plant); and melanoma and multiple myeloma (New York plant). Standardized rates of stomach and uterine cancer and multiple myeloma mortality increased with estimated cumulative PCB exposure. Poisson regression modeling showed significant associations with estimated cumulative PCB exposure for prostate and stomach cancer mortality. For other outcomes of a priori interest – rectal, liver, ovarian, breast, and thyroid cancer, non-Hodgkin lymphoma, Alzheimer disease, and Parkinson disease – neither elevated mortality nor positive associations with PCB exposure were observed. Associations between estimated cumulative PCB exposure and stomach, uterine, and prostate cancer and myeloma mortality confirmed our previous positive findings. PMID:23707056

An evaluation of agreement between pectoral spines and otoliths for estimating ages of catfishes

USGS Publications Warehouse

Olive, J.A.; Schramm, Harold; Gerard, Patrick D.; Irwin, E.

2011-01-01

Otoliths have been shown to provide more accurate ages than pectoral spine sections for several catfish populations; but sampling otoliths requires euthanizing the specimen, whereas spines can be sampled non-lethally. To evaluate whether, and under what conditions, spines provide the same or similar age estimates as otoliths, we examined data sets of individual fish aged from pectoral spines and otoliths for six blue catfish Ictalurus furcatus populations (n=420), 14 channel catfish Ictalurus punctatus populations (n=997), and 10 flathead catfish Pylodictus olivaris populations (n=947) from lotic and lentic waters throughout the central and eastern U.S. Logistic regression determined that agreement between ages estimated from otoliths and spines was consistently related to age, but inconsistently related to growth rate. When modeled at mean growth rate, we found at least 80% probability of no difference in spine- and otolith-assigned ages up to ages 4 and 5 for blue and channel catfish, respectively. For flathead catfish, an 80% probability of agreement between spine- and otolith-assigned ages did not occur at any age due to high incidence of differences in assigned ages even for age-1 fish. Logistic regression models predicted at least 80% probability that spine and otolith ages differed by ≤1 year up to ages 13, 16, and 9 for blue, channel, and flathead catfish, respectively. Age-bias assessment found mean spine-assigned age differed by less than 1 year from otolith-assigned age up to ages 19, 9, and 17 for blue catfish, channel catfish, and flathead catfish, respectively. These results can be used to help guide decisions about which structure is most appropriate for estimating catfish ages for particular populations and management objectives.

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.

Regression estimators for generic health-related quality of life and quality-adjusted life years.

PubMed

Basu, Anirban; Manca, Andrea

2012-01-01

To develop regression models for outcomes with truncated supports, such as health-related quality of life (HRQoL) data, and account for features typical of such data such as a skewed distribution, spikes at 1 or 0, and heteroskedasticity. Regression estimators based on features of the Beta distribution. First, both a single equation and a 2-part model are presented, along with estimation algorithms based on maximum-likelihood, quasi-likelihood, and Bayesian Markov-chain Monte Carlo methods. A novel Bayesian quasi-likelihood estimator is proposed. Second, a simulation exercise is presented to assess the performance of the proposed estimators against ordinary least squares (OLS) regression for a variety of HRQoL distributions that are encountered in practice. Finally, the performance of the proposed estimators is assessed by using them to quantify the treatment effect on QALYs in the EVALUATE hysterectomy trial. Overall model fit is studied using several goodness-of-fit tests such as Pearson's correlation test, link and reset tests, and a modified Hosmer-Lemeshow test. The simulation results indicate that the proposed methods are more robust in estimating covariate effects than OLS, especially when the effects are large or the HRQoL distribution has a large spike at 1. Quasi-likelihood techniques are more robust than maximum likelihood estimators. When applied to the EVALUATE trial, all but the maximum likelihood estimators produce unbiased estimates of the treatment effect. One and 2-part Beta regression models provide flexible approaches to regress the outcomes with truncated supports, such as HRQoL, on covariates, after accounting for many idiosyncratic features of the outcomes distribution. This work will provide applied researchers with a practical set of tools to model outcomes in cost-effectiveness analysis.

A new method for the production of social fragility functions and the result of its use in worldwide fatality loss estimation for earthquakes

NASA Astrophysics Data System (ADS)

Daniell, James; Wenzel, Friedemann

2014-05-01

A review of over 200 fatality models over the past 50 years for earthquake loss estimation from various authors has identified key parameters that influence fatality estimation in each of these models. These are often very specific and cannot be readily adapted globally. In the doctoral dissertation of the author, a new method is used for regression of fatalities to intensity using loss functions based not only on fatalities, but also using population models and other socioeconomic parameters created through time for every country worldwide for the period 1900-2013. A calibration of functions was undertaken from 1900-2008, and each individual quake analysed from 2009-2013 in real-time, in conjunction with www.earthquake-report.com. Using the CATDAT Damaging Earthquakes Database containing socioeconomic loss information for 7208 damaging earthquake events from 1900-2013 including disaggregation of secondary effects, fatality estimates for over 2035 events have been re-examined from 1900-2013. In addition, 99 of these events have detailed data for the individual cities and towns or have been reconstructed to create a death rate as a percentage of population. Many historical isoseismal maps and macroseismic intensity datapoint surveys collected globally, have been digitised and modelled covering around 1353 of these 2035 fatal events, to include an estimate of population, occupancy and socioeconomic climate at the time of the event at each intensity bracket. In addition, 1651 events without fatalities but causing damage have also been examined in this way. The production of socioeconomic and engineering indices such as HDI and building vulnerability has been undertaken on a country-level and state/province-level leading to a dataset allowing regressions not only using a static view of risk, but also allowing for the change in the socioeconomic climate between the earthquake events to be undertaken. This means that a year 1920 event in a country, will not simply be regressed against a year 2000 event, but normalised. A global human development index (HDI) (life expectancy, education and income) was developed and collected for the first time from 1900-2013 globally on a country and province level allowing for a very useful parameter in the regression. In addition, the occupancy rate from the time of day that the event occurred, as well as population density and individual earthquake attributes like the existence of a foreshock were also examined for the 3004 events in the regression analysis. Where an event has not occurred in a country previously, a regionalisation strategy based on building typologies, seismic code index, building practice, climate, earthquake history and socioeconomic climate is proposed. The result is a set of "social fragility functions" calculating fatalities for use in any country worldwide using the parameters of macroseismic intensity, population, HDI, time of day and occupancy, that provide a robust accurate method, which has not only been calibrated to country level data but to town and city data through time. The estimates will continue to be used in conjunction with Earthquake Report, a non-profit worldwide earthquake reporting website and has shown very promising results from 2010-2013 for rapid estimates of fatalities globally.

Estimation of Ordinary Differential Equation Parameters Using Constrained Local Polynomial Regression.

PubMed

Ding, A Adam; Wu, Hulin

2014-10-01

We propose a new method to use a constrained local polynomial regression to estimate the unknown parameters in ordinary differential equation models with a goal of improving the smoothing-based two-stage pseudo-least squares estimate. The equation constraints are derived from the differential equation model and are incorporated into the local polynomial regression in order to estimate the unknown parameters in the differential equation model. We also derive the asymptotic bias and variance of the proposed estimator. Our simulation studies show that our new estimator is clearly better than the pseudo-least squares estimator in estimation accuracy with a small price of computational cost. An application example on immune cell kinetics and trafficking for influenza infection further illustrates the benefits of the proposed new method.

Estimation of Ordinary Differential Equation Parameters Using Constrained Local Polynomial Regression

PubMed Central

Ding, A. Adam; Wu, Hulin

2015-01-01

We propose a new method to use a constrained local polynomial regression to estimate the unknown parameters in ordinary differential equation models with a goal of improving the smoothing-based two-stage pseudo-least squares estimate. The equation constraints are derived from the differential equation model and are incorporated into the local polynomial regression in order to estimate the unknown parameters in the differential equation model. We also derive the asymptotic bias and variance of the proposed estimator. Our simulation studies show that our new estimator is clearly better than the pseudo-least squares estimator in estimation accuracy with a small price of computational cost. An application example on immune cell kinetics and trafficking for influenza infection further illustrates the benefits of the proposed new method. PMID:26401093

Burden of norovirus gastroenteritis in the ambulatory setting--United States, 2001-2009.

PubMed

Gastañaduy, Paul A; Hall, Aron J; Curns, Aaron T; Parashar, Umesh D; Lopman, Benjamin A

2013-04-01

Gastroenteritis remains an important cause of morbidity in the United States. The burden of norovirus gastroenteritis in ambulatory US patients is not well understood. Cause-specified and cause-unspecified gastroenteritis emergency department (ED) and outpatient visits during July 2001-June 2009 were extracted from MarketScan insurance claim databases. By using cause-specified encounters, time-series regression models were fitted to predict the number of unspecified gastroenteritis visits due to specific pathogens other than norovirus. Model residuals were used to estimate norovirus visits. MarketScan rates were extrapolated to the US population to estimate national ambulatory visits. During 2001-2009, the estimated annual mean rates of norovirus-associated ED and outpatient visits were 14 and 57 cases per 10 000 persons, respectively, across all ages. Rates for ages 0-4, 5-17, 18-64, and ≥65 years were 38, 10, 12, and 15 ED visits per 10 000 persons, respectively, and 233, 85, 35, and 54 outpatient visits per 10 000 persons, respectively. Norovirus was estimated to cause 13% of all gastroenteritis-associated ambulatory visits, with ~50% of such visits occurring during November-February. Nationally, norovirus contributed to approximately 400 000 ED visits and 1.7 million office visits annually, resulting in $284 million in healthcare charges. Norovirus is a substantial cause of gastroenteritis in the ambulatory setting.

High-Order Model and Dynamic Filtering for Frame Rate Up-Conversion.

PubMed

Bao, Wenbo; Zhang, Xiaoyun; Chen, Li; Ding, Lianghui; Gao, Zhiyong

2018-08-01

This paper proposes a novel frame rate up-conversion method through high-order model and dynamic filtering (HOMDF) for video pixels. Unlike the constant brightness and linear motion assumptions in traditional methods, the intensity and position of the video pixels are both modeled with high-order polynomials in terms of time. Then, the key problem of our method is to estimate the polynomial coefficients that represent the pixel's intensity variation, velocity, and acceleration. We propose to solve it with two energy objectives: one minimizes the auto-regressive prediction error of intensity variation by its past samples, and the other minimizes video frame's reconstruction error along the motion trajectory. To efficiently address the optimization problem for these coefficients, we propose the dynamic filtering solution inspired by video's temporal coherence. The optimal estimation of these coefficients is reformulated into a dynamic fusion of the prior estimate from pixel's temporal predecessor and the maximum likelihood estimate from current new observation. Finally, frame rate up-conversion is implemented using motion-compensated interpolation by pixel-wise intensity variation and motion trajectory. Benefited from the advanced model and dynamic filtering, the interpolated frame has much better visual quality. Extensive experiments on the natural and synthesized videos demonstrate the superiority of HOMDF over the state-of-the-art methods in both subjective and objective comparisons.

Linear models: permutation methods

USGS Publications Warehouse

Cade, B.S.; Everitt, B.S.; Howell, D.C.

2005-01-01

Permutation tests (see Permutation Based Inference) for the linear model have applications in behavioral studies when traditional parametric assumptions about the error term in a linear model are not tenable. Improved validity of Type I error rates can be achieved with properly constructed permutation tests. Perhaps more importantly, increased statistical power, improved robustness to effects of outliers, and detection of alternative distributional differences can be achieved by coupling permutation inference with alternative linear model estimators. For example, it is well-known that estimates of the mean in linear model are extremely sensitive to even a single outlying value of the dependent variable compared to estimates of the median [7, 19]. Traditionally, linear modeling focused on estimating changes in the center of distributions (means or medians). However, quantile regression allows distributional changes to be estimated in all or any selected part of a distribution or responses, providing a more complete statistical picture that has relevance to many biological questions [6]...

Estimating Driving Performance Based on EEG Spectrum Analysis

NASA Astrophysics Data System (ADS)

Lin, Chin-Teng; Wu, Ruei-Cheng; Jung, Tzyy-Ping; Liang, Sheng-Fu; Huang, Teng-Yi

2005-12-01

The growing number of traffic accidents in recent years has become a serious concern to society. Accidents caused by driver's drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the driver's abilities of perception, recognition, and vehicle control abilities while sleepy. Preventing such accidents caused by drowsiness is highly desirable but requires techniques for continuously detecting, estimating, and predicting the level of alertness of drivers and delivering effective feedbacks to maintain their maximum performance. This paper proposes an EEG-based drowsiness estimation system that combines electroencephalogram (EEG) log subband power spectrum, correlation analysis, principal component analysis, and linear regression models to indirectly estimate driver's drowsiness level in a virtual-reality-based driving simulator. Our results demonstrated that it is feasible to accurately estimate quantitatively driving performance, expressed as deviation between the center of the vehicle and the center of the cruising lane, in a realistic driving simulator.

Economic and Health Predictors of National Postpartum Depression Prevalence: A Systematic Review, Meta-analysis, and Meta-Regression of 291 Studies from 56 Countries.

PubMed

Hahn-Holbrook, Jennifer; Cornwell-Hinrichs, Taylor; Anaya, Itzel

2017-01-01

Postpartum depression (PPD) poses a major global public health challenge. PPD is the most common complication associated with childbirth and exerts harmful effects on children. Although hundreds of PPD studies have been published, we lack accurate global or national PPD prevalence estimates and have no clear account of why PPD appears to vary so dramatically between nations. Accordingly, we conducted a meta-analysis to estimate the global and national prevalence of PPD and a meta-regression to identify economic, health, social, or policy factors associated with national PPD prevalence. We conducted a systematic review of all papers reporting PPD prevalence using the Edinburgh Postnatal Depression Scale. PPD prevalence and methods were extracted from each study. Random effects meta-analysis was used to estimate global and national PPD prevalence. To test for country level predictors, we drew on data from UNICEF, WHO, and the World Bank. Random effects meta-regression was used to test national predictors of PPD prevalence. 291 studies of 296284 women from 56 countries were identified. The global pooled prevalence of PPD was 17.7% (95% confidence interval: 16.6-18.8%), with significant heterogeneity across nations ( Q  = 16,823, p  = 0.000, I 2  = 98%), ranging from 3% (2-5%) in Singapore to 38% (35-41%) in Chile. Nations with significantly higher rates of income inequality ( R 2  = 41%), maternal mortality ( R 2  = 19%), infant mortality ( R 2  = 16%), or women of childbearing age working ≥40 h a week ( R 2  = 31%) have higher rates of PPD. Together, these factors explain 73% of the national variation in PPD prevalence. The global prevalence of PPD is greater than previously thought and varies dramatically by nation. Disparities in wealth inequality and maternal-child-health factors explain much of the national variation in PPD prevalence.

Modeling Heterogeneity in Relationships between Initial Status and Rates of Change: Latent Variable Regression in a Three-Level Hierarchical Model. CSE Report 647

ERIC Educational Resources Information Center

Choi, Kilchan; Seltzer, Michael

2005-01-01

In studies of change in education and numerous other fields, interest often centers on how differences in the status of individuals at the start of a time period of substantive interest relate to differences in subsequent change. This report presents a fully Bayesian approach to estimating three-level hierarchical models in which latent variable…

Challenges of Electronic Medical Surveillance Systems

DTIC Science & Technology

2004-06-01

More sophisticated approaches, such as regression models and classical autoregressive moving average ( ARIMA ) models that make estimates based on...with those predicted by a mathematical model . The primary benefit of ARIMA models is their ability to correct for local trends in the data so that...works well, for example, during a particularly severe flu season, where prolonged periods of high visit rates are adjusted to by the ARIMA model , thus

Fighting terrorism in Africa: Benchmarking policy harmonization

NASA Astrophysics Data System (ADS)

Asongu, Simplice A.; Tchamyou, Vanessa S.; Minkoua N., Jules R.; Asongu, Ndemaze; Tchamyou, Nina P.

2018-02-01

This study assesses the feasibility of policy harmonization in the fight against terrorism in 53 African countries with data for the period 1980-2012. Four terrorism variables are used, namely: domestic, transnational, unclear and total terrorism dynamics. The empirical evidence is based on absolute beta catch-up and sigma convergence estimation techniques. There is substantial absence of catch-up. The lowest rate of convergence in terrorism is in landlocked countries for regressions pertaining to unclear terrorism (3.43% per annum for 174.9 years) while the highest rate of convergence is in upper-middle-income countries in domestic terrorism regressions (15.33% per annum for 39.13 years). After comparing results from the two estimation techniques, it is apparent that in the contemporary era, countries with low levels of terrorism are not catching-up their counterparts with high levels of terrorism. As a policy implication, whereas some common policies may be feasibly adopted for the fight against terrorism, the findings based on the last periodic phase (2004-2012) are indicative that country-specific policies would better pay-off in the fight against terrorism than blanket common policies. Some suggestions of measures in fighting transnational terrorism have been discussed in the light of an anticipated surge in cross-national terrorism incidences in the coming years.

Accelerating Approximate Bayesian Computation with Quantile Regression: application to cosmological redshift distributions

NASA Astrophysics Data System (ADS)

Kacprzak, T.; Herbel, J.; Amara, A.; Réfrégier, A.

2018-02-01

Approximate Bayesian Computation (ABC) is a method to obtain a posterior distribution without a likelihood function, using simulations and a set of distance metrics. For that reason, it has recently been gaining popularity as an analysis tool in cosmology and astrophysics. Its drawback, however, is a slow convergence rate. We propose a novel method, which we call qABC, to accelerate ABC with Quantile Regression. In this method, we create a model of quantiles of distance measure as a function of input parameters. This model is trained on a small number of simulations and estimates which regions of the prior space are likely to be accepted into the posterior. Other regions are then immediately rejected. This procedure is then repeated as more simulations are available. We apply it to the practical problem of estimation of redshift distribution of cosmological samples, using forward modelling developed in previous work. The qABC method converges to nearly same posterior as the basic ABC. It uses, however, only 20% of the number of simulations compared to basic ABC, achieving a fivefold gain in execution time for our problem. For other problems the acceleration rate may vary; it depends on how close the prior is to the final posterior. We discuss possible improvements and extensions to this method.

Generalizability of Evidence-Based Assessment Recommendations for Pediatric Bipolar Disorder

PubMed Central

Jenkins, Melissa M.; Youngstrom, Eric A.; Youngstrom, Jennifer Kogos; Feeny, Norah C.; Findling, Robert L.

2013-01-01

Bipolar disorder is frequently clinically diagnosed in youths who do not actually satisfy DSM-IV criteria, yet cases that would satisfy full DSM-IV criteria are often undetected clinically. Evidence-based assessment methods that incorporate Bayesian reasoning have demonstrated improved diagnostic accuracy, and consistency; however, their clinical utility is largely unexplored. The present study examines the effectiveness of promising evidence-based decision-making compared to the clinical gold standard. Participants were 562 youth, ages 5-17 and predominantly African American, drawn from a community mental health clinic. Research diagnoses combined semi-structured interview with youths’ psychiatric, developmental, and family mental health histories. Independent Bayesian estimates relied on published risk estimates from other samples discriminated bipolar diagnoses, Area Under Curve=.75, p<.00005. The Bayes and confidence ratings correlated rs =.30. Agreement about an evidence-based assessment intervention “threshold model” (wait/assess/treat) had K=.24, p<.05. No potential moderators of agreement between the Bayesian estimates and confidence ratings, including type of bipolar illness, were significant. Bayesian risk estimates were highly correlated with logistic regression estimates using optimal sample weights, r=.81, p<.0005. Clinical and Bayesian approaches agree in terms of overall concordance and deciding next clinical action, even when Bayesian predictions are based on published estimates from clinically and demographically different samples. Evidence-based assessment methods may be useful in settings that cannot routinely employ gold standard assessments, and they may help decrease rates of overdiagnosis while promoting earlier identification of true cases. PMID:22004538

An Experimental Study in Determining Energy Expenditure from Treadmill Walking using Hip-Worn Inertial Sensors

PubMed Central

Vathsangam, Harshvardhan; Emken, Adar; Schroeder, E. Todd; Spruijt-Metz, Donna; Sukhatme, Gaurav S.

2011-01-01

This paper describes an experimental study in estimating energy expenditure from treadmill walking using a single hip-mounted triaxial inertial sensor comprised of a triaxial accelerometer and a triaxial gyroscope. Typical physical activity characterization using accelerometer generated counts suffers from two drawbacks - imprecison (due to proprietary counts) and incompleteness (due to incomplete movement description). We address these problems in the context of steady state walking by directly estimating energy expenditure with data from a hip-mounted inertial sensor. We represent the cyclic nature of walking with a Fourier transform of sensor streams and show how one can map this representation to energy expenditure (as measured by V O2 consumption, mL/min) using three regression techniques - Least Squares Regression (LSR), Bayesian Linear Regression (BLR) and Gaussian Process Regression (GPR). We perform a comparative analysis of the accuracy of sensor streams in predicting energy expenditure (measured by RMS prediction accuracy). Triaxial information is more accurate than uniaxial information. LSR based approaches are prone to outlier sensitivity and overfitting. Gyroscopic information showed equivalent if not better prediction accuracy as compared to accelerometers. Combining accelerometer and gyroscopic information provided better accuracy than using either sensor alone. We also analyze the best algorithmic approach among linear and nonlinear methods as measured by RMS prediction accuracy and run time. Nonlinear regression methods showed better prediction accuracy but required an order of magnitude of run time. This paper emphasizes the role of probabilistic techniques in conjunction with joint modeling of triaxial accelerations and rotational rates to improve energy expenditure prediction for steady-state treadmill walking. PMID:21690001

Analysis of the low-flow characteristics of streams in Louisiana

USGS Publications Warehouse

Lee, Fred N.

1985-01-01

The U.S. Geological Survey, in cooperation with the Louisiana Department of Transportation and Development, Office of Public Works, used geologic maps, soils maps, precipitation data, and low-flow data to define four hydrographic regions in Louisiana having distinct low-flow characteristics. Equations were derived, using regression analyses, to estimate the 7Q2, 7Q10, and 7Q20 flow rates for basically unaltered stream basins smaller than 525 square miles. Independent variables in the equations include drainage area (square miles), mean annual precipitation index (inches), and main channel slope (feet per mile). Average standard errors of regression ranged from +44 to +61 percent. Graphs are given for estimating the 7Q2, 7Q10, and 7Q20 for stream basins for which the drainage area of the most downstream data-collection site is larger than 525 square miles. Detailed examples are given in this report for the use of the equations and graphs.

Application of glas laser altimetry to detect elevation changes in East Antarctica

NASA Astrophysics Data System (ADS)

Scaioni, M.; Tong, X.; Li, R.

2013-10-01

In this paper the use of ICESat/GLAS laser altimeter for estimating multi-temporal elevation changes on polar ice sheets is afforded. Due to non-overlapping laser spots during repeat passes, interpolation methods are required to make comparisons. After reviewing the main methods described in the literature (crossover point analysis, cross-track DEM projection, space-temporal regressions), the last one has been chosen for its capability of providing more elevation change rate measurements. The standard implementation of the space-temporal linear regression technique has been revisited and improved to better cope with outliers and to check the estimability of model's parameters. GLAS data over the PANDA route in East Antarctica have been used for testing. Obtained results have been quite meaningful from a physical point of view, confirming the trend reported by the literature of a constant snow accumulation in the area during the two past decades, unlike the most part of the continent that has been losing mass.

Effects of practice on the Wechsler Adult Intelligence Scale-IV across 3- and 6-month intervals.

PubMed

Estevis, Eduardo; Basso, Michael R; Combs, Dennis

2012-01-01

A total of 54 participants (age M = 20.9; education M = 14.9; initial Full Scale IQ M = 111.6) were administered the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) at baseline and again either 3 or 6 months later. Scores on the Full Scale IQ, Verbal Comprehension, Working Memory, Perceptual Reasoning, Processing Speed, and General Ability Indices improved approximately 7, 5, 4, 5, 9, and 6 points, respectively, and increases were similar regardless of whether the re-examination occurred over 3- or 6-month intervals. Reliable change indices (RCI) were computed using the simple difference and bivariate regression methods, providing estimated base rates of change across time. The regression method provided more accurate estimates of reliable change than did the simple difference between baseline and follow-up scores. These findings suggest that prior exposure to the WAIS-IV results in significant score increments. These gains reflect practice effects instead of genuine intellectual changes, which may lead to errors in clinical judgment.

Recovering Galaxy Properties Using Gaussian Process SED Fitting

NASA Astrophysics Data System (ADS)

Iyer, Kartheik; Awan, Humna

2018-01-01

Information about physical quantities like the stellar mass, star formation rates, and ages for distant galaxies is contained in their spectral energy distributions (SEDs), obtained through photometric surveys like SDSS, CANDELS, LSST etc. However, noise in the photometric observations often is a problem, and using naive machine learning methods to estimate physical quantities can result in overfitting the noise, or converging on solutions that lie outside the physical regime of parameter space.We use Gaussian Process regression trained on a sample of SEDs corresponding to galaxies from a Semi-Analytic model (Somerville+15a) to estimate their stellar masses, and compare its performance to a variety of different methods, including simple linear regression, Random Forests, and k-Nearest Neighbours. We find that the Gaussian Process method is robust to noise and predicts not only stellar masses but also their uncertainties. The method is also robust in the cases where the distribution of the training data is not identical to the target data, which can be extremely useful when generalized to more subtle galaxy properties.

Local polynomial estimation of heteroscedasticity in a multivariate linear regression model and its applications in economics.

PubMed

Su, Liyun; Zhao, Yanyong; Yan, Tianshun; Li, Fenglan

2012-01-01

Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regression model. Firstly, the local polynomial fitting is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. One noteworthy feature of our approach is that we avoid the testing for heteroscedasticity by improving the traditional two-stage method. Due to non-parametric technique of local polynomial estimation, it is unnecessary to know the form of heteroscedastic function. Therefore, we can improve the estimation precision, when the heteroscedastic function is unknown. Furthermore, we verify that the regression coefficients is asymptotic normal based on numerical simulations and normal Q-Q plots of residuals. Finally, the simulation results and the local polynomial estimation of real data indicate that our approach is surely effective in finite-sample situations.

Regression-assisted deconvolution.

PubMed

McIntyre, Julie; Stefanski, Leonard A

2011-06-30

We present a semi-parametric deconvolution estimator for the density function of a random variable biX that is measured with error, a common challenge in many epidemiological studies. Traditional deconvolution estimators rely only on assumptions about the distribution of X and the error in its measurement, and ignore information available in auxiliary variables. Our method assumes the availability of a covariate vector statistically related to X by a mean-variance function regression model, where regression errors are normally distributed and independent of the measurement errors. Simulations suggest that the estimator achieves a much lower integrated squared error than the observed-data kernel density estimator when models are correctly specified and the assumption of normal regression errors is met. We illustrate the method using anthropometric measurements of newborns to estimate the density function of newborn length. Copyright © 2011 John Wiley & Sons, Ltd.

Fuzzy multinomial logistic regression analysis: A multi-objective programming approach

NASA Astrophysics Data System (ADS)

Abdalla, Hesham A.; El-Sayed, Amany A.; Hamed, Ramadan

2017-05-01

Parameter estimation for multinomial logistic regression is usually based on maximizing the likelihood function. For large well-balanced datasets, Maximum Likelihood (ML) estimation is a satisfactory approach. Unfortunately, ML can fail completely or at least produce poor results in terms of estimated probabilities and confidence intervals of parameters, specially for small datasets. In this study, a new approach based on fuzzy concepts is proposed to estimate parameters of the multinomial logistic regression. The study assumes that the parameters of multinomial logistic regression are fuzzy. Based on the extension principle stated by Zadeh and Bárdossy's proposition, a multi-objective programming approach is suggested to estimate these fuzzy parameters. A simulation study is used to evaluate the performance of the new approach versus Maximum likelihood (ML) approach. Results show that the new proposed model outperforms ML in cases of small datasets.

Restoration of Monotonicity Respecting in Dynamic Regression

PubMed Central

Huang, Yijian

2017-01-01

Dynamic regression models, including the quantile regression model and Aalen’s additive hazards model, are widely adopted to investigate evolving covariate effects. Yet lack of monotonicity respecting with standard estimation procedures remains an outstanding issue. Advances have recently been made, but none provides a complete resolution. In this article, we propose a novel adaptive interpolation method to restore monotonicity respecting, by successively identifying and then interpolating nearest monotonicity-respecting points of an original estimator. Under mild regularity conditions, the resulting regression coefficient estimator is shown to be asymptotically equivalent to the original. Our numerical studies have demonstrated that the proposed estimator is much more smooth and may have better finite-sample efficiency than the original as well as, when available as only in special cases, other competing monotonicity-respecting estimators. Illustration with a clinical study is provided. PMID:29430068

Modeling new production in upwelling centers - A case study of modeling new production from remotely sensed temperature and color

NASA Technical Reports Server (NTRS)

Dugdale, Richard C.; Wilkerson, Frances P.; Morel, Andre; Bricaud, Annick

1989-01-01

A method has been developed for estimating new production in upwelling systems from remotely sensed surface temperatures. A shift-up model predicts the rate of adaptation of nitrate uptake. The time base for the production cycle is obtained from a knowledge of surface heating rates and differences in temperature between the point of upwelling and each pixel. Nitrate concentrations are obtained from temperature-nitrate regression equations. The model was developed for the northwest Africa upwelling region, where shipboard measurements of new production were available. It can be employed in two modes, the first using only surface temperatures, and the second in which CZCS color data are incorporated. The major advance offered by this method is the capability to estimate new production on spatial and time scales inaccessible with shipboard approaches.

Estimation and Testing of Partial Covariances, Correlations, and Regression Weights Using Maximum Likelihood Factor Analysis.

ERIC Educational Resources Information Center

And Others; Werts, Charles E.

1979-01-01

It is shown how partial covariance, part and partial correlation, and regression weights can be estimated and tested for significance by means of a factor analytic model. Comparable partial covariance, correlations, and regression weights have identical significance tests. (Author)

Association between inaccurate estimation of body size and obesity in schoolchildren.

PubMed

Costa, Larissa da Cunha Feio; Silva, Diego Augusto Santos; Almeida, Sebastião de Sousa; de Vasconcelos, Francisco de Assis Guedes

2015-01-01

To investigate the prevalence of inaccurate estimation of own body size among Brazilian schoolchildren of both sexes aged 7-10 years, and to test whether overweight/obesity; excess body fat and central obesity are associated with inaccuracy. Accuracy of body size estimation was assessed using the Figure Rating Scale for Brazilian Children. Multinomial logistic regression was used to analyze associations. The overall prevalence of inaccurate body size estimation was 76%, with 34% of the children underestimating their body size and 42% overestimating their body size. Obesity measured by body mass index was associated with underestimation of body size in both sexes, while central obesity was only associated with overestimation of body size among girls. The results of this study suggest there is a high prevalence of inaccurate body size estimation and that inaccurate estimation is associated with obesity. Accurate estimation of own body size is important among obese schoolchildren because it may be the first step towards adopting healthy lifestyle behaviors.

Using Telephone and Informant Assessments to Estimate Missing Modified Mini-Mental State Exam Scores and Rates of Cognitive Decline

PubMed Central

Arnold, Alice M.; Newman, Anne B.; Dermond, Norma; Haan, Mary; Fitzpatrick, Annette

2009-01-01

Aim To estimate an equivalent to the Modified Mini-Mental State Exam (3MSE), and to compare changes in the 3MSE with and without the estimated scores. Methods Comparability study on a subset of 405 participants, aged ≥70 years, from the Cardiovascular Health Study (CHS), a longitudinal study in 4 United States communities. The 3MSE, the Telephone Interview for Cognitive Status (TICS) and the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) were administered within 30 days of one another. Regression models were developed to predict the 3MSE score from the TICS and/or IQCODE, and the predicted values were used to estimate missing 3MSE scores in longitudinal follow-up of 4,274 CHS participants. Results The TICS explained 67% of the variability in 3MSE scores, with a correlation of 0.82 between predicted and observed scores. The IQCODE alone was not a good estimate of 3MSE score, but improved the model fit when added to the TICS model. Using estimated 3MSE scores classified more participants with low cognition, and rates of decline were greater than when only the observed 3MSE scores were considered. Conclusions 3MSE scores can be reliably estimated from the TICS, with or without the IQCODE. Incorporating these estimates captured more cognitive decline in older adults. PMID:19407461

Comparison of SOC estimates and uncertainties from aerosol chemical composition and gas phase data in Atlanta

NASA Astrophysics Data System (ADS)

Pachon, Jorge E.; Balachandran, Sivaraman; Hu, Yongtao; Weber, Rodney J.; Mulholland, James A.; Russell, Armistead G.

2010-10-01

In the Southeastern US, organic carbon (OC) comprises about 30% of the PM 2.5 mass. A large fraction of OC is estimated to be of secondary origin. Long-term estimates of SOC and uncertainties are necessary in the evaluation of air quality policy effectiveness and epidemiologic studies. Four methods to estimate secondary organic carbon (SOC) and respective uncertainties are compared utilizing PM 2.5 chemical composition and gas phase data available in Atlanta from 1999 to 2007. The elemental carbon (EC) tracer and the regression methods, which rely on the use of tracer species of primary and secondary OC formation, provided intermediate estimates of SOC as 30% of OC. The other two methods, chemical mass balance (CMB) and positive matrix factorization (PMF) solve mass balance equations to estimate primary and secondary fractions based on source profiles and statistically-derived common factors, respectively. CMB had the highest estimate of SOC (46% of OC) while PMF led to the lowest (26% of OC). The comparison of SOC uncertainties, estimated based on propagation of errors, led to the regression method having the lowest uncertainty among the four methods. We compared the estimates with the water soluble fraction of the OC, which has been suggested as a surrogate of SOC when biomass burning is negligible, and found a similar trend with SOC estimates from the regression method. The regression method also showed the strongest correlation with daily SOC estimates from CMB using molecular markers. The regression method shows advantages over the other methods in the calculation of a long-term series of SOC estimates.

A regression-adjusted approach can estimate competing biomass

Treesearch

James H. Miller

1983-01-01

A method is presented for estimating above-ground herbaceous and woody biomass on competition research plots. On a set of destructively-sampled plots, an ocular estimate of biomass by vegetative component is first made, after which vegetation is clipped, dried, and weighed. Linear regressions are then calculated for each component between estimated and actual weights...

Regression sampling: some results for resource managers and researchers

Treesearch

William G. O' Regan; Robert W. Boyd

1974-01-01

Regression sampling is widely used in natural resources management and research to estimate quantities of resources per unit area. This note brings together results found in the statistical literature in the application of this sampling technique. Conditional and unconditional estimators are listed and for each estimator, exact variances and unbiased estimators for the...

Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting

PubMed Central

Chan, Kwun Chuen Gary; Yam, Sheung Chi Phillip; Zhang, Zheng

2015-01-01

Summary The estimation of average treatment effects based on observational data is extremely important in practice and has been studied by generations of statisticians under different frameworks. Existing globally efficient estimators require non-parametric estimation of a propensity score function, an outcome regression function or both, but their performance can be poor in practical sample sizes. Without explicitly estimating either functions, we consider a wide class calibration weights constructed to attain an exact three-way balance of the moments of observed covariates among the treated, the control, and the combined group. The wide class includes exponential tilting, empirical likelihood and generalized regression as important special cases, and extends survey calibration estimators to different statistical problems and with important distinctions. Global semiparametric efficiency for the estimation of average treatment effects is established for this general class of calibration estimators. The results show that efficiency can be achieved by solely balancing the covariate distributions without resorting to direct estimation of propensity score or outcome regression function. We also propose a consistent estimator for the efficient asymptotic variance, which does not involve additional functional estimation of either the propensity score or the outcome regression functions. The proposed variance estimator outperforms existing estimators that require a direct approximation of the efficient influence function. PMID:27346982

Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting.

PubMed

Chan, Kwun Chuen Gary; Yam, Sheung Chi Phillip; Zhang, Zheng

2016-06-01

The estimation of average treatment effects based on observational data is extremely important in practice and has been studied by generations of statisticians under different frameworks. Existing globally efficient estimators require non-parametric estimation of a propensity score function, an outcome regression function or both, but their performance can be poor in practical sample sizes. Without explicitly estimating either functions, we consider a wide class calibration weights constructed to attain an exact three-way balance of the moments of observed covariates among the treated, the control, and the combined group. The wide class includes exponential tilting, empirical likelihood and generalized regression as important special cases, and extends survey calibration estimators to different statistical problems and with important distinctions. Global semiparametric efficiency for the estimation of average treatment effects is established for this general class of calibration estimators. The results show that efficiency can be achieved by solely balancing the covariate distributions without resorting to direct estimation of propensity score or outcome regression function. We also propose a consistent estimator for the efficient asymptotic variance, which does not involve additional functional estimation of either the propensity score or the outcome regression functions. The proposed variance estimator outperforms existing estimators that require a direct approximation of the efficient influence function.

Implementations of geographically weighted lasso in spatial data with multicollinearity (Case study: Poverty modeling of Java Island)

NASA Astrophysics Data System (ADS)

Setiyorini, Anis; Suprijadi, Jadi; Handoko, Budhi

2017-03-01

Geographically Weighted Regression (GWR) is a regression model that takes into account the spatial heterogeneity effect. In the application of the GWR, inference on regression coefficients is often of interest, as is estimation and prediction of the response variable. Empirical research and studies have demonstrated that local correlation between explanatory variables can lead to estimated regression coefficients in GWR that are strongly correlated, a condition named multicollinearity. It later results on a large standard error on estimated regression coefficients, and, hence, problematic for inference on relationships between variables. Geographically Weighted Lasso (GWL) is a method which capable to deal with spatial heterogeneity and local multicollinearity in spatial data sets. GWL is a further development of GWR method, which adds a LASSO (Least Absolute Shrinkage and Selection Operator) constraint in parameter estimation. In this study, GWL will be applied by using fixed exponential kernel weights matrix to establish a poverty modeling of Java Island, Indonesia. The results of applying the GWL to poverty datasets show that this method stabilizes regression coefficients in the presence of multicollinearity and produces lower prediction and estimation error of the response variable than GWR does.

Estimating effects of limiting factors with regression quantiles

USGS Publications Warehouse

Cade, B.S.; Terrell, J.W.; Schroeder, R.L.

1999-01-01

In a recent Concepts paper in Ecology, Thomson et al. emphasized that assumptions of conventional correlation and regression analyses fundamentally conflict with the ecological concept of limiting factors, and they called for new statistical procedures to address this problem. The analytical issue is that unmeasured factors may be the active limiting constraint and may induce a pattern of unequal variation in the biological response variable through an interaction with the measured factors. Consequently, changes near the maxima, rather than at the center of response distributions, are better estimates of the effects expected when the observed factor is the active limiting constraint. Regression quantiles provide estimates for linear models fit to any part of a response distribution, including near the upper bounds, and require minimal assumptions about the form of the error distribution. Regression quantiles extend the concept of one-sample quantiles to the linear model by solving an optimization problem of minimizing an asymmetric function of absolute errors. Rank-score tests for regression quantiles provide tests of hypotheses and confidence intervals for parameters in linear models with heteroscedastic errors, conditions likely to occur in models of limiting ecological relations. We used selected regression quantiles (e.g., 5th, 10th, ..., 95th) and confidence intervals to test hypotheses that parameters equal zero for estimated changes in average annual acorn biomass due to forest canopy cover of oak (Quercus spp.) and oak species diversity. Regression quantiles also were used to estimate changes in glacier lily (Erythronium grandiflorum) seedling numbers as a function of lily flower numbers, rockiness, and pocket gopher (Thomomys talpoides fossor) activity, data that motivated the query by Thomson et al. for new statistical procedures. Both example applications showed that effects of limiting factors estimated by changes in some upper regression quantile (e.g., 90-95th) were greater than if effects were estimated by changes in the means from standard linear model procedures. Estimating a range of regression quantiles (e.g., 5-95th) provides a comprehensive description of biological response patterns for exploratory and inferential analyses in observational studies of limiting factors, especially when sampling large spatial and temporal scales.

Spatio-temporal water quality mapping from satellite images using geographically and temporally weighted regression

NASA Astrophysics Data System (ADS)

Chu, Hone-Jay; Kong, Shish-Jeng; Chang, Chih-Hua

2018-03-01

The turbidity (TB) of a water body varies with time and space. Water quality is traditionally estimated via linear regression based on satellite images. However, estimating and mapping water quality require a spatio-temporal nonstationary model, while TB mapping necessitates the use of geographically and temporally weighted regression (GTWR) and geographically weighted regression (GWR) models, both of which are more precise than linear regression. Given the temporal nonstationary models for mapping water quality, GTWR offers the best option for estimating regional water quality. Compared with GWR, GTWR provides highly reliable information for water quality mapping, boasts a relatively high goodness of fit, improves the explanation of variance from 44% to 87%, and shows a sufficient space-time explanatory power. The seasonal patterns of TB and the main spatial patterns of TB variability can be identified using the estimated TB maps from GTWR and by conducting an empirical orthogonal function (EOF) analysis.

Techniques for Estimating the Magnitude and Frequency of Peak Flows on Small Streams in Minnesota Based on Data through Water Year 2005

USGS Publications Warehouse

Lorenz, David L.; Sanocki, Chris A.; Kocian, Matthew J.

2010-01-01

Knowledge of the peak flow of floods of a given recurrence interval is essential for regulation and planning of water resources and for design of bridges, culverts, and dams along Minnesota's rivers and streams. Statistical techniques are needed to estimate peak flow at ungaged sites because long-term streamflow records are available at relatively few places. Because of the need to have up-to-date peak-flow frequency information in order to estimate peak flows at ungaged sites, the U.S. Geological Survey (USGS) conducted a peak-flow frequency study in cooperation with the Minnesota Department of Transportation and the Minnesota Pollution Control Agency. Estimates of peak-flow magnitudes for 1.5-, 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence intervals are presented for 330 streamflow-gaging stations in Minnesota and adjacent areas in Iowa and South Dakota based on data through water year 2005. The peak-flow frequency information was subsequently used in regression analyses to develop equations relating peak flows for selected recurrence intervals to various basin and climatic characteristics. Two statistically derived techniques-regional regression equation and region of influence regression-can be used to estimate peak flow on ungaged streams smaller than 3,000 square miles in Minnesota. Regional regression equations were developed for selected recurrence intervals in each of six regions in Minnesota: A (northwestern), B (north central and east central), C (northeastern), D (west central and south central), E (southwestern), and F (southeastern). The regression equations can be used to estimate peak flows at ungaged sites. The region of influence regression technique dynamically selects streamflow-gaging stations with characteristics similar to a site of interest. Thus, the region of influence regression technique allows use of a potentially unique set of gaging stations for estimating peak flow at each site of interest. Two methods of selecting streamflow-gaging stations, similarity and proximity, can be used for the region of influence regression technique. The regional regression equation technique is the preferred technique as an estimate of peak flow in all six regions for ungaged sites. The region of influence regression technique is not appropriate for regions C, E, and F because the interrelations of some characteristics of those regions do not agree with the interrelations throughout the rest of the State. Both the similarity and proximity methods for the region of influence technique can be used in the other regions (A, B, and D) to provide additional estimates of peak flow. The peak-flow-frequency estimates and basin characteristics for selected streamflow-gaging stations and regional peak-flow regression equations are included in this report.

[Regression on order statistics and its application in estimating nondetects for food exposure assessment].

PubMed

Yu, Xiaojin; Liu, Pei; Min, Jie; Chen, Qiguang

2009-01-01

To explore the application of regression on order statistics (ROS) in estimating nondetects for food exposure assessment. Regression on order statistics was adopted in analysis of cadmium residual data set from global food contaminant monitoring, the mean residual was estimated basing SAS programming and compared with the results from substitution methods. The results show that ROS method performs better obviously than substitution methods for being robust and convenient for posterior analysis. Regression on order statistics is worth to adopt,but more efforts should be make for details of application of this method.

Tornado Intensity Estimated from Damage Path Dimensions

PubMed Central

Elsner, James B.; Jagger, Thomas H.; Elsner, Ian J.

2014-01-01

The Newcastle/Moore and El Reno tornadoes of May 2013 are recent reminders of the destructive power of tornadoes. A direct estimate of a tornado's power is difficult and dangerous to get. An indirect estimate on a categorical scale is available from a post-storm survery of the damage. Wind speed bounds are attached to the scale, but the scale is not adequate for analyzing trends in tornado intensity separate from trends in tornado frequency. Here tornado intensity on a continuum is estimated from damage path length and width, which are measured on continuous scales and correlated to the EF rating. The wind speeds on the EF scale are treated as interval censored data and regressed onto the path dimensions and fatalities. The regression model indicates a 25% increase in expected intensity over a threshold intensity of 29 m s−1 for a 100 km increase in path length and a 17% increase in expected intensity for a one km increase in path width. The model shows a 43% increase in the expected intensity when fatalities are observed controlling for path dimensions. The estimated wind speeds correlate at a level of .77 (.34, .93) [95% confidence interval] with a small sample of wind speeds estimated independently from a doppler radar calibration. The estimated wind speeds allow analyses to be done on the tornado database that are not possible with the categorical scale. The modeled intensities can be used in climatology and in environmental and engineering applications. Research is needed to understand the upward trends in path length and width. PMID:25229242

Tornado intensity estimated from damage path dimensions.

PubMed

Elsner, James B; Jagger, Thomas H; Elsner, Ian J

2014-01-01

The Newcastle/Moore and El Reno tornadoes of May 2013 are recent reminders of the destructive power of tornadoes. A direct estimate of a tornado's power is difficult and dangerous to get. An indirect estimate on a categorical scale is available from a post-storm survery of the damage. Wind speed bounds are attached to the scale, but the scale is not adequate for analyzing trends in tornado intensity separate from trends in tornado frequency. Here tornado intensity on a continuum is estimated from damage path length and width, which are measured on continuous scales and correlated to the EF rating. The wind speeds on the EF scale are treated as interval censored data and regressed onto the path dimensions and fatalities. The regression model indicates a 25% increase in expected intensity over a threshold intensity of 29 m s(-1) for a 100 km increase in path length and a 17% increase in expected intensity for a one km increase in path width. The model shows a 43% increase in the expected intensity when fatalities are observed controlling for path dimensions. The estimated wind speeds correlate at a level of .77 (.34, .93) [95% confidence interval] with a small sample of wind speeds estimated independently from a doppler radar calibration. The estimated wind speeds allow analyses to be done on the tornado database that are not possible with the categorical scale. The modeled intensities can be used in climatology and in environmental and engineering applications. Research is needed to understand the upward trends in path length and width.

Remote sensing of biomass and annual net aerial primary productivity of a salt marsh

NASA Technical Reports Server (NTRS)

Hardisky, M. A.; Klemas, V.; Daiber, F. C.; Roman, C. T.

1984-01-01

Net aerial primary productivity is the rate of storage of organic matter in above-ground plant issues exceeding the respiratory use by the plants during the period of measurement. It is pointed out that this plant tissue represents the fixed carbon available for transfer to and consumption by the heterotrophic organisms in a salt marsh or the estuary. One method of estimating annual net aerial primary productivity (NAPP) required multiple harvesting of the marsh vegetation. A rapid nondestructive remote sensing technique for estimating biomass and NAPP would, therefore, be a significant asset. The present investigation was designed to employ simple regression models, equating spectral radiance indices with Spartina alterniflora biomass to nondestructively estimate salt marsh biomass. The results of the study showed that the considered approach can be successfully used to estimate salt marsh biomass.

Pseudo and conditional score approach to joint analysis of current count and current status data.

PubMed

Wen, Chi-Chung; Chen, Yi-Hau

2018-04-17

We develop a joint analysis approach for recurrent and nonrecurrent event processes subject to case I interval censorship, which are also known in literature as current count and current status data, respectively. We use a shared frailty to link the recurrent and nonrecurrent event processes, while leaving the distribution of the frailty fully unspecified. Conditional on the frailty, the recurrent event is assumed to follow a nonhomogeneous Poisson process, and the mean function of the recurrent event and the survival function of the nonrecurrent event are assumed to follow some general form of semiparametric transformation models. Estimation of the models is based on the pseudo-likelihood and the conditional score techniques. The resulting estimators for the regression parameters and the unspecified baseline functions are shown to be consistent with rates of square and cubic roots of the sample size, respectively. Asymptotic normality with closed-form asymptotic variance is derived for the estimator of the regression parameters. We apply the proposed method to a fracture-osteoporosis survey data to identify risk factors jointly for fracture and osteoporosis in elders, while accounting for association between the two events within a subject. © 2018, The International Biometric Society.

Low Survival Rates of Oral and Oropharyngeal Squamous Cell Carcinoma

PubMed Central

da Silva Júnior, Francisco Feliciano; dos Santos, Karine de Cássia Batista; Ferreira, Stefania Jeronimo

2017-01-01

Aim To assess the epidemiological and clinical factors that influence the prognosis of oral and oropharyngeal squamous cell carcinoma (SCC). Methods One hundred and twenty-one cases of oral and oropharyngeal SCC were selected. The survival curves for each variable were estimated using the Kaplan-Meier method. The Cox regression model was applied to assess the effect of the variables on survival. Results Cancers at an advanced stage were observed in 103 patients (85.1%). Cancers on the tongue were more frequent (23.1%). The survival analysis was 59.9% in one year, 40.7% in two years, and 27.8% in 5 years. There was a significant low survival rate linked to alcohol intake (p = 0.038), advanced cancer staging (p = 0.003), and procedures without surgery (p < 0.001). When these variables were included in the Cox regression model only surgery procedures (p = 0.005) demonstrated a significant effect on survival. Conclusion The findings suggest that patients who underwent surgery had a greater survival rate compared with those that did not. The low survival rates and the high percentage of patients diagnosed at advanced stages demonstrate that oral and oropharyngeal cancer patients should receive more attention. PMID:28638410

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.

A New Monte Carlo Method for Estimating Marginal Likelihoods.

PubMed

Wang, Yu-Bo; Chen, Ming-Hui; Kuo, Lynn; Lewis, Paul O

2018-06-01

Evaluating the marginal likelihood in Bayesian analysis is essential for model selection. Estimators based on a single Markov chain Monte Carlo sample from the posterior distribution include the harmonic mean estimator and the inflated density ratio estimator. We propose a new class of Monte Carlo estimators based on this single Markov chain Monte Carlo sample. This class can be thought of as a generalization of the harmonic mean and inflated density ratio estimators using a partition weighted kernel (likelihood times prior). We show that our estimator is consistent and has better theoretical properties than the harmonic mean and inflated density ratio estimators. In addition, we provide guidelines on choosing optimal weights. Simulation studies were conducted to examine the empirical performance of the proposed estimator. We further demonstrate the desirable features of the proposed estimator with two real data sets: one is from a prostate cancer study using an ordinal probit regression model with latent variables; the other is for the power prior construction from two Eastern Cooperative Oncology Group phase III clinical trials using the cure rate survival model with similar objectives.

Rates of return to sorghum and millet research investments: A meta-analysis.

PubMed

Zereyesus, Yacob A; Dalton, Timothy J

2017-01-01

Sorghum and millet grow in some of the most heterogeneous and austere agroecologies around the world. These crops are amongst the top five cereal sources of food and feed. Yet, few studies document the impact of sorghum and millet genetic enhancement. The Internal Rate of Return (ROR) is one of the most popular metrics used to measure the economic return on investment on agricultural research and development (R&D). This study conducted a meta-analysis of 59 sorghum and millet ROR estimates obtained from 25 sources published between 1958 and 2015. The average rate of return to sorghum and millet R&D investment is between 54-76 percent per year. All studies computed social rather than private RORs because the technologies were developed using public funds originating from host country National Agricultural Research Systems (NARS) and international organizations such as the INTSORMIL CRSP, ICRISAT and others. Nearly three quarter of the studies focused only on sorghum (72 percent) and around one tenth of the studies (8 percent) on millet. Regression models analyzed the determinants of variation in the reported RORs. Results show that ex-ante type and self-evaluated type of analyses are positively and significantly associated with the ROR estimates. Compared to estimates conducted by a university, results from international institutions and other mixed organizations provided significantly smaller estimates. Estimates conducted at national level also are significantly lower than those conducted at sub-national levels. The ROR is higher for studies conducted in the United States and for those conducted more recently. The study also reconstructed modified internal rate of return (MIRR) for a sub-sample of the reported RORs following recent methods from the literature. These results show that the MIRR estimates are significantly smaller than the reported ROR estimates. Both results indicate that investment in sorghum and millet research generates high social rates of return.

Rates of return to sorghum and millet research investments: A meta-analysis

PubMed Central

2017-01-01

Sorghum and millet grow in some of the most heterogeneous and austere agroecologies around the world. These crops are amongst the top five cereal sources of food and feed. Yet, few studies document the impact of sorghum and millet genetic enhancement. The Internal Rate of Return (ROR) is one of the most popular metrics used to measure the economic return on investment on agricultural research and development (R&D). This study conducted a meta-analysis of 59 sorghum and millet ROR estimates obtained from 25 sources published between 1958 and 2015. The average rate of return to sorghum and millet R&D investment is between 54–76 percent per year. All studies computed social rather than private RORs because the technologies were developed using public funds originating from host country National Agricultural Research Systems (NARS) and international organizations such as the INTSORMIL CRSP, ICRISAT and others. Nearly three quarter of the studies focused only on sorghum (72 percent) and around one tenth of the studies (8 percent) on millet. Regression models analyzed the determinants of variation in the reported RORs. Results show that ex-ante type and self-evaluated type of analyses are positively and significantly associated with the ROR estimates. Compared to estimates conducted by a university, results from international institutions and other mixed organizations provided significantly smaller estimates. Estimates conducted at national level also are significantly lower than those conducted at sub-national levels. The ROR is higher for studies conducted in the United States and for those conducted more recently. The study also reconstructed modified internal rate of return (MIRR) for a sub-sample of the reported RORs following recent methods from the literature. These results show that the MIRR estimates are significantly smaller than the reported ROR estimates. Both results indicate that investment in sorghum and millet research generates high social rates of return. PMID:28686700

Comparison of Mental Health Treatment Adequacy and Costs in Public Hospitals in Boston and Madrid.

PubMed

Carmona, Rodrigo; Cook, Benjamin Lê; Baca-García, Enrique; Chavez, Ligia; Alvarez, Kiara; Iza, Miren; Alegría, Margarita

2018-03-07

Analyses of healthcare expenditures and adequacy are needed to identify cost-effective policies and practices that improve mental healthcare quality. Data are from 2010 to 2012 electronic health records from three hospital psychiatry departments in Madrid (n = 29,944 person-years) and three in Boston (n = 14,109 person-years). Two-part multivariate generalized linear regression and logistic regression models were estimated to identify site differences in mental healthcare expenditures and quality of care. Annual total average treatment expenditures were $4442.14 in Boston and $2277.48 in Madrid. Boston patients used inpatient services more frequently and had higher 30-day re-admission rates (23.7 vs. 8.7%) despite higher rates of minimally adequate care (49.5 vs. 34.8%). Patients in Madrid were more likely to receive psychotropic medication, had fewer inpatient stays and readmissions, and had lower expenditures, but had lower rates of minimally adequate care. Differences in insurance and healthcare system policies and mental health professional roles may explain these dissimilarities.

Estimation of construction and demolition waste using waste generation rates in Chennai, India.

PubMed

Ram, V G; Kalidindi, Satyanarayana N

2017-06-01

A large amount of construction and demolition waste is being generated owing to rapid urbanisation in Indian cities. A reliable estimate of construction and demolition waste generation is essential to create awareness about this stream of solid waste among the government bodies in India. However, the required data to estimate construction and demolition waste generation in India are unavailable or not explicitly documented. This study proposed an approach to estimate construction and demolition waste generation using waste generation rates and demonstrated it by estimating construction and demolition waste generation in Chennai city. The demolition waste generation rates of primary materials were determined through regression analysis using waste generation data from 45 case studies. Materials, such as wood, electrical wires, doors, windows and reinforcement steel, were found to be salvaged and sold on the secondary market. Concrete and masonry debris were dumped in either landfills or unauthorised places. The total quantity of construction and demolition debris generated in Chennai city in 2013 was estimated to be 1.14 million tonnes. The proportion of masonry debris was found to be 76% of the total quantity of demolition debris. Construction and demolition debris forms about 36% of the total solid waste generated in Chennai city. A gross underestimation of construction and demolition waste generation in some earlier studies in India has also been shown. The methodology proposed could be utilised by government bodies, policymakers and researchers to generate reliable estimates of construction and demolition waste in other developing countries facing similar challenges of limited data availability.

Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic.

PubMed

Bowden, Jack; Del Greco M, Fabiola; Minelli, Cosetta; Davey Smith, George; Sheehan, Nuala A; Thompson, John R

2016-12-01

: MR-Egger regression has recently been proposed as a method for Mendelian randomization (MR) analyses incorporating summary data estimates of causal effect from multiple individual variants, which is robust to invalid instruments. It can be used to test for directional pleiotropy and provides an estimate of the causal effect adjusted for its presence. MR-Egger regression provides a useful additional sensitivity analysis to the standard inverse variance weighted (IVW) approach that assumes all variants are valid instruments. Both methods use weights that consider the single nucleotide polymorphism (SNP)-exposure associations to be known, rather than estimated. We call this the `NO Measurement Error' (NOME) assumption. Causal effect estimates from the IVW approach exhibit weak instrument bias whenever the genetic variants utilized violate the NOME assumption, which can be reliably measured using the F-statistic. The effect of NOME violation on MR-Egger regression has yet to be studied. An adaptation of the I2 statistic from the field of meta-analysis is proposed to quantify the strength of NOME violation for MR-Egger. It lies between 0 and 1, and indicates the expected relative bias (or dilution) of the MR-Egger causal estimate in the two-sample MR context. We call it IGX2 . The method of simulation extrapolation is also explored to counteract the dilution. Their joint utility is evaluated using simulated data and applied to a real MR example. In simulated two-sample MR analyses we show that, when a causal effect exists, the MR-Egger estimate of causal effect is biased towards the null when NOME is violated, and the stronger the violation (as indicated by lower values of IGX2 ), the stronger the dilution. When additionally all genetic variants are valid instruments, the type I error rate of the MR-Egger test for pleiotropy is inflated and the causal effect underestimated. Simulation extrapolation is shown to substantially mitigate these adverse effects. We demonstrate our proposed approach for a two-sample summary data MR analysis to estimate the causal effect of low-density lipoprotein on heart disease risk. A high value of IGX2 close to 1 indicates that dilution does not materially affect the standard MR-Egger analyses for these data. : Care must be taken to assess the NOME assumption via the IGX2 statistic before implementing standard MR-Egger regression in the two-sample summary data context. If IGX2 is sufficiently low (less than 90%), inferences from the method should be interpreted with caution and adjustment methods considered. © The Author 2016. Published by Oxford University Press on behalf of the International Epidemiological Association.

Predictive equations for the estimation of body size in seals and sea lions (Carnivora: Pinnipedia)

PubMed Central

Churchill, Morgan; Clementz, Mark T; Kohno, Naoki

2014-01-01

Body size plays an important role in pinniped ecology and life history. However, body size data is often absent for historical, archaeological, and fossil specimens. To estimate the body size of pinnipeds (seals, sea lions, and walruses) for today and the past, we used 14 commonly preserved cranial measurements to develop sets of single variable and multivariate predictive equations for pinniped body mass and total length. Principal components analysis (PCA) was used to test whether separate family specific regressions were more appropriate than single predictive equations for Pinnipedia. The influence of phylogeny was tested with phylogenetic independent contrasts (PIC). The accuracy of these regressions was then assessed using a combination of coefficient of determination, percent prediction error, and standard error of estimation. Three different methods of multivariate analysis were examined: bidirectional stepwise model selection using Akaike information criteria; all-subsets model selection using Bayesian information criteria (BIC); and partial least squares regression. The PCA showed clear discrimination between Otariidae (fur seals and sea lions) and Phocidae (earless seals) for the 14 measurements, indicating the need for family-specific regression equations. The PIC analysis found that phylogeny had a minor influence on relationship between morphological variables and body size. The regressions for total length were more accurate than those for body mass, and equations specific to Otariidae were more accurate than those for Phocidae. Of the three multivariate methods, the all-subsets approach required the fewest number of variables to estimate body size accurately. We then used the single variable predictive equations and the all-subsets approach to estimate the body size of two recently extinct pinniped taxa, the Caribbean monk seal (Monachus tropicalis) and the Japanese sea lion (Zalophus japonicus). Body size estimates using single variable regressions generally under or over-estimated body size; however, the all-subset regression produced body size estimates that were close to historically recorded body length for these two species. This indicates that the all-subset regression equations developed in this study can estimate body size accurately. PMID:24916814

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)

Estimated Probability of a Cervical Spine Injury During an ISS Mission

NASA Technical Reports Server (NTRS)

Brooker, John E.; Weaver, Aaron S.; Myers, Jerry G.

2013-01-01

Introduction: The Integrated Medical Model (IMM) utilizes historical data, cohort data, and external simulations as input factors to provide estimates of crew health, resource utilization and mission outcomes. The Cervical Spine Injury Module (CSIM) is an external simulation designed to provide the IMM with parameter estimates for 1) a probability distribution function (PDF) of the incidence rate, 2) the mean incidence rate, and 3) the standard deviation associated with the mean resulting from injury/trauma of the neck. Methods: An injury mechanism based on an idealized low-velocity blunt impact to the superior posterior thorax of an ISS crewmember was used as the simulated mission environment. As a result of this impact, the cervical spine is inertially loaded from the mass of the head producing an extension-flexion motion deforming the soft tissues of the neck. A multibody biomechanical model was developed to estimate the kinematic and dynamic response of the head-neck system from a prescribed acceleration profile. Logistic regression was performed on a dataset containing AIS1 soft tissue neck injuries from rear-end automobile collisions with published Neck Injury Criterion values producing an injury transfer function (ITF). An injury event scenario (IES) was constructed such that crew 1 is moving through a primary or standard translation path transferring large volume equipment impacting stationary crew 2. The incidence rate for this IES was estimated from in-flight data and used to calculate the probability of occurrence. The uncertainty in the model input factors were estimated from representative datasets and expressed in terms of probability distributions. A Monte Carlo Method utilizing simple random sampling was employed to propagate both aleatory and epistemic uncertain factors. Scatterplots and partial correlation coefficients (PCC) were generated to determine input factor sensitivity. CSIM was developed in the SimMechanics/Simulink environment with a Monte Carlo wrapper (MATLAB) used to integrate the components of the module. Results: The probability of generating an AIS1 soft tissue neck injury from the extension/flexion motion induced by a low-velocity blunt impact to the superior posterior thorax was fitted with a lognormal PDF with mean 0.26409, standard deviation 0.11353, standard error of mean 0.00114, and 95% confidence interval [0.26186, 0.26631]. Combining the probability of an AIS1 injury with the probability of IES occurrence was fitted with a Johnson SI PDF with mean 0.02772, standard deviation 0.02012, standard error of mean 0.00020, and 95% confidence interval [0.02733, 0.02812]. The input factor sensitivity analysis in descending order was IES incidence rate, ITF regression coefficient 1, impactor initial velocity, ITF regression coefficient 2, and all others (equipment mass, crew 1 body mass, crew 2 body mass) insignificant. Verification and Validation (V&V): The IMM V&V, based upon NASA STD 7009, was implemented which included an assessment of the data sets used to build CSIM. The documentation maintained includes source code comments and a technical report. The software code and documentation is under Subversion configuration management. Kinematic validation was performed by comparing the biomechanical model output to established corridors.

Using regression methods to estimate stream phosphorus loads at the Illinois River, Arkansas

USGS Publications Warehouse

Haggard, B.E.; Soerens, T.S.; Green, W.R.; Richards, R.P.

2003-01-01

The development of total maximum daily loads (TMDLs) requires evaluating existing constituent loads in streams. Accurate estimates of constituent loads are needed to calibrate watershed and reservoir models for TMDL development. The best approach to estimate constituent loads is high frequency sampling, particularly during storm events, and mass integration of constituents passing a point in a stream. Most often, resources are limited and discrete water quality samples are collected on fixed intervals and sometimes supplemented with directed sampling during storm events. When resources are limited, mass integration is not an accurate means to determine constituent loads and other load estimation techniques such as regression models are used. The objective of this work was to determine a minimum number of water-quality samples needed to provide constituent concentration data adequate to estimate constituent loads at a large stream. Twenty sets of water quality samples with and without supplemental storm samples were randomly selected at various fixed intervals from a database at the Illinois River, northwest Arkansas. The random sets were used to estimate total phosphorus (TP) loads using regression models. The regression-based annual TP loads were compared to the integrated annual TP load estimated using all the data. At a minimum, monthly sampling plus supplemental storm samples (six samples per year) was needed to produce a root mean square error of less than 15%. Water quality samples should be collected at least semi-monthly (every 15 days) in studies less than two years if seasonal time factors are to be used in the regression models. Annual TP loads estimated from independently collected discrete water quality samples further demonstrated the utility of using regression models to estimate annual TP loads in this stream system.