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.

[Application of SAS macro to evaluated multiplicative and additive interaction in logistic and Cox regression in clinical practices].

PubMed

Nie, Z Q; Ou, Y Q; Zhuang, J; Qu, Y J; Mai, J Z; Chen, J M; Liu, X Q

2016-05-01

Conditional logistic regression analysis and unconditional logistic regression analysis are commonly used in case control study, but Cox proportional hazard model is often used in survival data analysis. Most literature only refer to main effect model, however, generalized linear model differs from general linear model, and the interaction was composed of multiplicative interaction and additive interaction. The former is only statistical significant, but the latter has biological significance. In this paper, macros was written by using SAS 9.4 and the contrast ratio, attributable proportion due to interaction and synergy index were calculated while calculating the items of logistic and Cox regression interactions, and the confidence intervals of Wald, delta and profile likelihood were used to evaluate additive interaction for the reference in big data analysis in clinical epidemiology and in analysis of genetic multiplicative and additive interactions.

Maximum likelihood estimation of correction for dilution bias in simple linear regression using replicates from subjects with extreme first measurements.

PubMed

Berglund, Lars; Garmo, Hans; Lindbäck, Johan; Svärdsudd, Kurt; Zethelius, Björn

2008-09-30

The least-squares estimator of the slope in a simple linear regression model is biased towards zero when the predictor is measured with random error. A corrected slope may be estimated by adding data from a reliability study, which comprises a subset of subjects from the main study. The precision of this corrected slope depends on the design of the reliability study and estimator choice. Previous work has assumed that the reliability study constitutes a random sample from the main study. A more efficient design is to use subjects with extreme values on their first measurement. Previously, we published a variance formula for the corrected slope, when the correction factor is the slope in the regression of the second measurement on the first. In this paper we show that both designs improve by maximum likelihood estimation (MLE). The precision gain is explained by the inclusion of data from all subjects for estimation of the predictor's variance and by the use of the second measurement for estimation of the covariance between response and predictor. The gain of MLE enhances with stronger true relationship between response and predictor and with lower precision in the predictor measurements. We present a real data example on the relationship between fasting insulin, a surrogate marker, and true insulin sensitivity measured by a gold-standard euglycaemic insulin clamp, and simulations, where the behavior of profile-likelihood-based confidence intervals is examined. MLE was shown to be a robust estimator for non-normal distributions and efficient for small sample situations. Copyright (c) 2008 John Wiley & Sons, Ltd.

A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2013-01-01

Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between-study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta-regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example. PMID:23401213

Mixed effect Poisson log-linear models for clinical and epidemiological sleep hypnogram data

PubMed Central

Swihart, Bruce J.; Caffo, Brian S.; Crainiceanu, Ciprian; Punjabi, Naresh M.

2013-01-01

Bayesian Poisson log-linear multilevel models scalable to epidemiological studies are proposed to investigate population variability in sleep state transition rates. Hierarchical random effects are used to account for pairings of subjects and repeated measures within those subjects, as comparing diseased to non-diseased subjects while minimizing bias is of importance. Essentially, non-parametric piecewise constant hazards are estimated and smoothed, allowing for time-varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming exponentially distributed survival times. Such re-derivation allows synthesis of two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed. Supplementary material includes the analyzed data set as well as the code for a reproducible analysis. PMID:22241689

Human Language Technology: Opportunities and Challenges

DTIC Science & Technology

2005-01-01

because of the connections to and reliance on signal processing. Audio diarization critically includes indexing of speakers [12], since speaker ...to reduce inter- speaker variability in training. Standard techniques include vocal-tract length normalization, adaptation of acoustic models using...maximum likelihood linear regression (MLLR), and speaker -adaptive training based on MLLR. The acoustic models are mixtures of Gaussians, typically with

Survival Data and Regression Models

NASA Astrophysics Data System (ADS)

Grégoire, G.

2014-12-01

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

Adjusting for overdispersion in piecewise exponential regression models to estimate excess mortality rate in population-based research.

PubMed

Luque-Fernandez, Miguel Angel; Belot, Aurélien; Quaresma, Manuela; Maringe, Camille; Coleman, Michel P; Rachet, Bernard

2016-10-01

In population-based cancer research, piecewise exponential regression models are used to derive adjusted estimates of excess mortality due to cancer using the Poisson generalized linear modelling framework. However, the assumption that the conditional mean and variance of the rate parameter given the set of covariates x i are equal is strong and may fail to account for overdispersion given the variability of the rate parameter (the variance exceeds the mean). Using an empirical example, we aimed to describe simple methods to test and correct for overdispersion. We used a regression-based score test for overdispersion under the relative survival framework and proposed different approaches to correct for overdispersion including a quasi-likelihood, robust standard errors estimation, negative binomial regression and flexible piecewise modelling. All piecewise exponential regression models showed the presence of significant inherent overdispersion (p-value <0.001). However, the flexible piecewise exponential model showed the smallest overdispersion parameter (3.2 versus 21.3) for non-flexible piecewise exponential models. We showed that there were no major differences between methods. However, using a flexible piecewise regression modelling, with either a quasi-likelihood or robust standard errors, was the best approach as it deals with both, overdispersion due to model misspecification and true or inherent overdispersion.

Analysis of Sequence Data Under Multivariate Trait-Dependent Sampling.

PubMed

Tao, Ran; Zeng, Donglin; Franceschini, Nora; North, Kari E; Boerwinkle, Eric; Lin, Dan-Yu

2015-06-01

High-throughput DNA sequencing allows for the genotyping of common and rare variants for genetic association studies. At the present time and for the foreseeable future, it is not economically feasible to sequence all individuals in a large cohort. A cost-effective strategy is to sequence those individuals with extreme values of a quantitative trait. We consider the design under which the sampling depends on multiple quantitative traits. Under such trait-dependent sampling, standard linear regression analysis can result in bias of parameter estimation, inflation of type I error, and loss of power. We construct a likelihood function that properly reflects the sampling mechanism and utilizes all available data. We implement a computationally efficient EM algorithm and establish the theoretical properties of the resulting maximum likelihood estimators. Our methods can be used to perform separate inference on each trait or simultaneous inference on multiple traits. We pay special attention to gene-level association tests for rare variants. We demonstrate the superiority of the proposed methods over standard linear regression through extensive simulation studies. We provide applications to the Cohorts for Heart and Aging Research in Genomic Epidemiology Targeted Sequencing Study and the National Heart, Lung, and Blood Institute Exome Sequencing Project.

New method to incorporate Type B uncertainty into least-squares procedures in radionuclide metrology.

PubMed

Han, Jubong; Lee, K B; Lee, Jong-Man; Park, Tae Soon; Oh, J S; Oh, Pil-Jei

2016-03-01

We discuss a new method to incorporate Type B uncertainty into least-squares procedures. The new method is based on an extension of the likelihood function from which a conventional least-squares function is derived. The extended likelihood function is the product of the original likelihood function with additional PDFs (Probability Density Functions) that characterize the Type B uncertainties. The PDFs are considered to describe one's incomplete knowledge on correction factors being called nuisance parameters. We use the extended likelihood function to make point and interval estimations of parameters in the basically same way as the least-squares function used in the conventional least-squares method is derived. Since the nuisance parameters are not of interest and should be prevented from appearing in the final result, we eliminate such nuisance parameters by using the profile likelihood. As an example, we present a case study for a linear regression analysis with a common component of Type B uncertainty. In this example we compare the analysis results obtained from using our procedure with those from conventional methods. Copyright © 2015. Published by Elsevier Ltd.

Cumulative risk and AIDS-orphanhood: interactions of stigma, bullying and poverty on child mental health in South Africa.

PubMed

Cluver, Lucie; Orkin, Mark

2009-10-01

Research shows that AIDS-orphaned children are more likely to experience clinical-range psychological problems. Little is known about possible interactions between factors mediating these high distress levels. We assessed how food insecurity, bullying, and AIDS-related stigma interacted with each other and with likelihood of experiencing clinical-range disorder. In South Africa, 1025 adolescents completed standardised measures of depression, anxiety and post-traumatic stress. 52 potential mediators were measured, including AIDS-orphanhood status. Logistic regressions and hierarchical log-linear modelling were used to identify interactions among significant risk factors. Food insecurity, stigma and bullying all independently increased likelihood of disorder. Poverty and stigma were found to interact strongly, and with both present, likelihood of disorder rose from 19% to 83%. Similarly, bullying interacted with AIDS-orphanhood status, and with both present, likelihood of disorder rose from 12% to 76%. Approaches to alleviating psychological distress amongst AIDS-affected children must address cumulative risk effects.

Factors associated with single-vehicle and multi-vehicle road traffic collision injuries in Ireland.

PubMed

Donnelly-Swift, Erica; Kelly, Alan

2016-12-01

Generalised linear regression models were used to identify factors associated with fatal/serious road traffic collision injuries for single- and multi-vehicle collisions. Single-vehicle collisions and multi-vehicle collisions occurring during the hours of darkness or on a wet road surface had reduced likelihood of a fatal/serious injury. Single-vehicle 'driver with passengers' collisions occurring at junctions or on a hill/gradient were less likely to result in a fatal/serious injury. Multi-vehicle rear-end/angle collisions had reduced likelihood of a fatal/serious injury. Single-vehicle 'driver only' collisions and multi-vehicle collisions occurring on a public/bank holiday or on a hill/gradient were more likely to result in a fatal/serious injury. Single-vehicle collisions involving male drivers had increased likelihood of a fatal/serious injury and single-vehicle 'driver with passengers' collisions involving drivers under the age of 25 years also had increased likelihood of a fatal/serious injury. Findings can enlighten decision-makers to circumstances leading to fatal/serious injuries.

Non-linear auto-regressive models for cross-frequency coupling in neural time series

PubMed Central

Tallot, Lucille; Grabot, Laetitia; Doyère, Valérie; Grenier, Yves; Gramfort, Alexandre

2017-01-01

We address the issue of reliably detecting and quantifying cross-frequency coupling (CFC) in neural time series. Based on non-linear auto-regressive models, the proposed method provides a generative and parametric model of the time-varying spectral content of the signals. As this method models the entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model “goodness of fit” via the likelihood, enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to our model-based approach. Using three datasets obtained with invasive neurophysiological recordings in humans and rodents, we demonstrate that these models are able to replicate previous results obtained with other metrics, but also reveal new insights such as the influence of the amplitude of the slow oscillation. Using simulations, we demonstrate that our parametric method can reveal neural couplings with shorter signals than non-parametric methods. We also show how the likelihood can be used to find optimal filtering parameters, suggesting new properties on the spectrum of the driving signal, but also to estimate the optimal delay between the coupled signals, enabling a directionality estimation in the coupling. PMID:29227989

The application of parameter estimation to flight measurements to obtain lateral-directional stability derivatives of an augmented jet-flap STOL airplane

NASA Technical Reports Server (NTRS)

Stephenson, J. D.

1983-01-01

Flight experiments with an augmented jet flap STOL aircraft provided data from which the lateral directional stability and control derivatives were calculated by applying a linear regression parameter estimation procedure. The tests, which were conducted with the jet flaps set at a 65 deg deflection, covered a large range of angles of attack and engine power settings. The effect of changing the angle of the jet thrust vector was also investigated. Test results are compared with stability derivatives that had been predicted. The roll damping derived from the tests was significantly larger than had been predicted, whereas the other derivatives were generally in agreement with the predictions. Results obtained using a maximum likelihood estimation procedure are compared with those from the linear regression solutions.

Negotiating Multicollinearity with Spike-and-Slab Priors.

PubMed

Ročková, Veronika; George, Edward I

2014-08-01

In multiple regression under the normal linear model, the presence of multicollinearity is well known to lead to unreliable and unstable maximum likelihood estimates. This can be particularly troublesome for the problem of variable selection where it becomes more difficult to distinguish between subset models. Here we show how adding a spike-and-slab prior mitigates this difficulty by filtering the likelihood surface into a posterior distribution that allocates the relevant likelihood information to each of the subset model modes. For identification of promising high posterior models in this setting, we consider three EM algorithms, the fast closed form EMVS version of Rockova and George (2014) and two new versions designed for variants of the spike-and-slab formulation. For a multimodal posterior under multicollinearity, we compare the regions of convergence of these three algorithms. Deterministic annealing versions of the EMVS algorithm are seen to substantially mitigate this multimodality. A single simple running example is used for illustration throughout.

Avoiding overstating the strength of forensic evidence: Shrunk likelihood ratios/Bayes factors.

PubMed

Morrison, Geoffrey Stewart; Poh, Norman

2018-05-01

When strength of forensic evidence is quantified using sample data and statistical models, a concern may be raised as to whether the output of a model overestimates the strength of evidence. This is particularly the case when the amount of sample data is small, and hence sampling variability is high. This concern is related to concern about precision. This paper describes, explores, and tests three procedures which shrink the value of the likelihood ratio or Bayes factor toward the neutral value of one. The procedures are: (1) a Bayesian procedure with uninformative priors, (2) use of empirical lower and upper bounds (ELUB), and (3) a novel form of regularized logistic regression. As a benchmark, they are compared with linear discriminant analysis, and in some instances with non-regularized logistic regression. The behaviours of the procedures are explored using Monte Carlo simulated data, and tested on real data from comparisons of voice recordings, face images, and glass fragments. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

celerite: Scalable 1D Gaussian Processes in C++, Python, and Julia

NASA Astrophysics Data System (ADS)

Foreman-Mackey, Daniel; Agol, Eric; Ambikasaran, Sivaram; Angus, Ruth

2017-09-01

celerite provides fast and scalable Gaussian Process (GP) Regression in one dimension and is implemented in C++, Python, and Julia. The celerite API is designed to be familiar to users of george and, like george, celerite is designed to efficiently evaluate the marginalized likelihood of a dataset under a GP model. This is then be used alongside a non-linear optimization or posterior inference library for the best results.

Fast estimation of diffusion tensors under Rician noise by the EM algorithm.

PubMed

Liu, Jia; Gasbarra, Dario; Railavo, Juha

2016-01-15

Diffusion tensor imaging (DTI) is widely used to characterize, in vivo, the white matter of the central nerve system (CNS). This biological tissue contains much anatomic, structural and orientational information of fibers in human brain. Spectral data from the displacement distribution of water molecules located in the brain tissue are collected by a magnetic resonance scanner and acquired in the Fourier domain. After the Fourier inversion, the noise distribution is Gaussian in both real and imaginary parts and, as a consequence, the recorded magnitude data are corrupted by Rician noise. Statistical estimation of diffusion leads a non-linear regression problem. In this paper, we present a fast computational method for maximum likelihood estimation (MLE) of diffusivities under the Rician noise model based on the expectation maximization (EM) algorithm. By using data augmentation, we are able to transform a non-linear regression problem into the generalized linear modeling framework, reducing dramatically the computational cost. The Fisher-scoring method is used for achieving fast convergence of the tensor parameter. The new method is implemented and applied using both synthetic and real data in a wide range of b-amplitudes up to 14,000s/mm(2). Higher accuracy and precision of the Rician estimates are achieved compared with other log-normal based methods. In addition, we extend the maximum likelihood (ML) framework to the maximum a posteriori (MAP) estimation in DTI under the aforementioned scheme by specifying the priors. We will describe how close numerically are the estimators of model parameters obtained through MLE and MAP estimation. Copyright © 2015 Elsevier B.V. All rights reserved.

Synthesizing Regression Results: A Factored Likelihood Method

ERIC Educational Resources Information Center

Wu, Meng-Jia; Becker, Betsy Jane

2013-01-01

Regression methods are widely used by researchers in many fields, yet methods for synthesizing regression results are scarce. This study proposes using a factored likelihood method, originally developed to handle missing data, to appropriately synthesize regression models involving different predictors. This method uses the correlations reported…

Postmolar gestational trophoblastic neoplasia: beyond the traditional risk factors.

PubMed

Bakhtiyari, Mahmood; Mirzamoradi, Masoumeh; Kimyaiee, Parichehr; Aghaie, Abbas; Mansournia, Mohammd Ali; Ashrafi-Vand, Sepideh; Sarfjoo, Fatemeh Sadat

2015-09-01

To investigate the slope of linear regression of postevacuation serum hCG as an independent risk factor for postmolar gestational trophoblastic neoplasia (GTN). Multicenter retrospective cohort study. Academic referral health care centers. All subjects with confirmed hydatidiform mole and at least four measurements of β-hCG titer. None. Type and magnitude of the relationship between the slope of linear regression of β-hCG as a new risk factor and GTN using Bayesian logistic regression with penalized log-likelihood estimation. Among the high-risk and low-risk molar pregnancy cases, 11 (18.6%) and 19 cases (13.3%) had GTN, respectively. No significant relationship was found between the components of a high-risk pregnancy and GTN. The β-hCG return slope was higher in the spontaneous cure group. However, the initial level of this hormone in the first measurement was higher in the GTN group compared with in the spontaneous recovery group. The average time for diagnosing GTN in the high-risk molar pregnancy group was 2 weeks less than that of the low-risk molar pregnancy group. In addition to slope of linear regression of β-hCG (odds ratio [OR], 12.74, confidence interval [CI], 5.42-29.2), abortion history (OR, 2.53; 95% CI, 1.27-5.04) and large uterine height for gestational age (OR, 1.26; CI, 1.04-1.54) had the maximum effects on GTN outcome, respectively. The slope of linear regression of β-hCG was introduced as an independent risk factor, which could be used for clinical decision making based on records of β-hCG titer and subsequent prevention program. Copyright © 2015 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

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...

A review and comparison of Bayesian and likelihood-based inferences in beta regression and zero-or-one-inflated beta regression.

PubMed

Liu, Fang; Eugenio, Evercita C

2018-04-01

Beta regression is an increasingly popular statistical technique in medical research for modeling of outcomes that assume values in (0, 1), such as proportions and patient reported outcomes. When outcomes take values in the intervals [0,1), (0,1], or [0,1], zero-or-one-inflated beta (zoib) regression can be used. We provide a thorough review on beta regression and zoib regression in the modeling, inferential, and computational aspects via the likelihood-based and Bayesian approaches. We demonstrate the statistical and practical importance of correctly modeling the inflation at zero/one rather than ad hoc replacing them with values close to zero/one via simulation studies; the latter approach can lead to biased estimates and invalid inferences. We show via simulation studies that the likelihood-based approach is computationally faster in general than MCMC algorithms used in the Bayesian inferences, but runs the risk of non-convergence, large biases, and sensitivity to starting values in the optimization algorithm especially with clustered/correlated data, data with sparse inflation at zero and one, and data that warrant regularization of the likelihood. The disadvantages of the regular likelihood-based approach make the Bayesian approach an attractive alternative in these cases. Software packages and tools for fitting beta and zoib regressions in both the likelihood-based and Bayesian frameworks are also reviewed.

Negotiating Multicollinearity with Spike-and-Slab Priors

PubMed Central

Ročková, Veronika

2014-01-01

In multiple regression under the normal linear model, the presence of multicollinearity is well known to lead to unreliable and unstable maximum likelihood estimates. This can be particularly troublesome for the problem of variable selection where it becomes more difficult to distinguish between subset models. Here we show how adding a spike-and-slab prior mitigates this difficulty by filtering the likelihood surface into a posterior distribution that allocates the relevant likelihood information to each of the subset model modes. For identification of promising high posterior models in this setting, we consider three EM algorithms, the fast closed form EMVS version of Rockova and George (2014) and two new versions designed for variants of the spike-and-slab formulation. For a multimodal posterior under multicollinearity, we compare the regions of convergence of these three algorithms. Deterministic annealing versions of the EMVS algorithm are seen to substantially mitigate this multimodality. A single simple running example is used for illustration throughout. PMID:25419004

Economic dependency and divorce: implications for the private sphere.

PubMed

Clark, R

1990-01-01

"This paper asserts a connection between economic dependency and divorce. It argues that, because dependency deprives women of equal access to the public sphere and because it confines them, through normative definition, to the private sphere, it reduces their likelihood of seeking divorce. The paper also argues, contrary to recent findings, that socioeconomic development should be linearly and positively associated with divorce. Data from 51 nations are examined and multiple regression analysis [suggests] considerable support for these arguments." excerpt

Bivariate categorical data analysis using normal linear conditional multinomial probability model.

PubMed

Sun, Bingrui; Sutradhar, Brajendra

2015-02-10

Bivariate multinomial data such as the left and right eyes retinopathy status data are analyzed either by using a joint bivariate probability model or by exploiting certain odds ratio-based association models. However, the joint bivariate probability model yields marginal probabilities, which are complicated functions of marginal and association parameters for both variables, and the odds ratio-based association model treats the odds ratios involved in the joint probabilities as 'working' parameters, which are consequently estimated through certain arbitrary 'working' regression models. Also, this later odds ratio-based model does not provide any easy interpretations of the correlations between two categorical variables. On the basis of pre-specified marginal probabilities, in this paper, we develop a bivariate normal type linear conditional multinomial probability model to understand the correlations between two categorical variables. The parameters involved in the model are consistently estimated using the optimal likelihood and generalized quasi-likelihood approaches. The proposed model and the inferences are illustrated through an intensive simulation study as well as an analysis of the well-known Wisconsin Diabetic Retinopathy status data. Copyright © 2014 John Wiley & Sons, Ltd.

SCI Identification (SCIDNT) program user's guide. [maximum likelihood method for linear rotorcraft models

NASA Technical Reports Server (NTRS)

1979-01-01

The computer program Linear SCIDNT which evaluates rotorcraft stability and control coefficients from flight or wind tunnel test data is described. It implements the maximum likelihood method to maximize the likelihood function of the parameters based on measured input/output time histories. Linear SCIDNT may be applied to systems modeled by linear constant-coefficient differential equations. This restriction in scope allows the application of several analytical results which simplify the computation and improve its efficiency over the general nonlinear case.

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

Reanalysis of cancer mortality in Japanese A-bomb survivors exposed to low doses of radiation: bootstrap and simulation methods

PubMed Central

2009-01-01

Background The International Commission on Radiological Protection (ICRP) recommended annual occupational dose limit is 20 mSv. Cancer mortality in Japanese A-bomb survivors exposed to less than 20 mSv external radiation in 1945 was analysed previously, using a latency model with non-linear dose response. Questions were raised regarding statistical inference with this model. Methods Cancers with over 100 deaths in the 0 - 20 mSv subcohort of the 1950-1990 Life Span Study are analysed with Poisson regression models incorporating latency, allowing linear and non-linear dose response. Bootstrap percentile and Bias-corrected accelerated (BCa) methods and simulation of the Likelihood Ratio Test lead to Confidence Intervals for Excess Relative Risk (ERR) and tests against the linear model. Results The linear model shows significant large, positive values of ERR for liver and urinary cancers at latencies from 37 - 43 years. Dose response below 20 mSv is strongly non-linear at the optimal latencies for the stomach (11.89 years), liver (36.9), lung (13.6), leukaemia (23.66), and pancreas (11.86) and across broad latency ranges. Confidence Intervals for ERR are comparable using Bootstrap and Likelihood Ratio Test methods and BCa 95% Confidence Intervals are strictly positive across latency ranges for all 5 cancers. Similar risk estimates for 10 mSv (lagged dose) are obtained from the 0 - 20 mSv and 5 - 500 mSv data for the stomach, liver, lung and leukaemia. Dose response for the latter 3 cancers is significantly non-linear in the 5 - 500 mSv range. Conclusion Liver and urinary cancer mortality risk is significantly raised using a latency model with linear dose response. A non-linear model is strongly superior for the stomach, liver, lung, pancreas and leukaemia. Bootstrap and Likelihood-based confidence intervals are broadly comparable and ERR is strictly positive by bootstrap methods for all 5 cancers. Except for the pancreas, similar estimates of latency and risk from 10 mSv are obtained from the 0 - 20 mSv and 5 - 500 mSv subcohorts. Large and significant cancer risks for Japanese survivors exposed to less than 20 mSv external radiation from the atomic bombs in 1945 cast doubt on the ICRP recommended annual occupational dose limit. PMID:20003238

Majorization Minimization by Coordinate Descent for Concave Penalized Generalized Linear Models

PubMed Central

Jiang, Dingfeng; Huang, Jian

2013-01-01

Recent studies have demonstrated theoretical attractiveness of a class of concave penalties in variable selection, including the smoothly clipped absolute deviation and minimax concave penalties. The computation of the concave penalized solutions in high-dimensional models, however, is a difficult task. We propose a majorization minimization by coordinate descent (MMCD) algorithm for computing the concave penalized solutions in generalized linear models. In contrast to the existing algorithms that use local quadratic or local linear approximation to the penalty function, the MMCD seeks to majorize the negative log-likelihood by a quadratic loss, but does not use any approximation to the penalty. This strategy makes it possible to avoid the computation of a scaling factor in each update of the solutions, which improves the efficiency of coordinate descent. Under certain regularity conditions, we establish theoretical convergence property of the MMCD. We implement this algorithm for a penalized logistic regression model using the SCAD and MCP penalties. Simulation studies and a data example demonstrate that the MMCD works sufficiently fast for the penalized logistic regression in high-dimensional settings where the number of covariates is much larger than the sample size. PMID:25309048

Log-normal frailty models fitted as Poisson generalized linear mixed models.

PubMed

Hirsch, Katharina; Wienke, Andreas; Kuss, Oliver

2016-12-01

The equivalence of a survival model with a piecewise constant baseline hazard function and a Poisson regression model has been known since decades. As shown in recent studies, this equivalence carries over to clustered survival data: A frailty model with a log-normal frailty term can be interpreted and estimated as a generalized linear mixed model with a binary response, a Poisson likelihood, and a specific offset. Proceeding this way, statistical theory and software for generalized linear mixed models are readily available for fitting frailty models. This gain in flexibility comes at the small price of (1) having to fix the number of pieces for the baseline hazard in advance and (2) having to "explode" the data set by the number of pieces. In this paper we extend the simulations of former studies by using a more realistic baseline hazard (Gompertz) and by comparing the model under consideration with competing models. Furthermore, the SAS macro %PCFrailty is introduced to apply the Poisson generalized linear mixed approach to frailty models. The simulations show good results for the shared frailty model. Our new %PCFrailty macro provides proper estimates, especially in case of 4 events per piece. The suggested Poisson generalized linear mixed approach for log-normal frailty models based on the %PCFrailty macro provides several advantages in the analysis of clustered survival data with respect to more flexible modelling of fixed and random effects, exact (in the sense of non-approximate) maximum likelihood estimation, and standard errors and different types of confidence intervals for all variance parameters. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Local Intrinsic Dimension Estimation by Generalized Linear Modeling.

PubMed

Hino, Hideitsu; Fujiki, Jun; Akaho, Shotaro; Murata, Noboru

2017-07-01

We propose a method for intrinsic dimension estimation. By fitting the power of distance from an inspection point and the number of samples included inside a ball with a radius equal to the distance, to a regression model, we estimate the goodness of fit. Then, by using the maximum likelihood method, we estimate the local intrinsic dimension around the inspection point. The proposed method is shown to be comparable to conventional methods in global intrinsic dimension estimation experiments. Furthermore, we experimentally show that the proposed method outperforms a conventional local dimension estimation method.

A regularization corrected score method for nonlinear regression models with covariate error.

PubMed

Zucker, David M; Gorfine, Malka; Li, Yi; Tadesse, Mahlet G; Spiegelman, Donna

2013-03-01

Many regression analyses involve explanatory variables that are measured with error, and failing to account for this error is well known to lead to biased point and interval estimates of the regression coefficients. We present here a new general method for adjusting for covariate error. Our method consists of an approximate version of the Stefanski-Nakamura corrected score approach, using the method of regularization to obtain an approximate solution of the relevant integral equation. We develop the theory in the setting of classical likelihood models; this setting covers, for example, linear regression, nonlinear regression, logistic regression, and Poisson regression. The method is extremely general in terms of the types of measurement error models covered, and is a functional method in the sense of not involving assumptions on the distribution of the true covariate. We discuss the theoretical properties of the method and present simulation results in the logistic regression setting (univariate and multivariate). For illustration, we apply the method to data from the Harvard Nurses' Health Study concerning the relationship between physical activity and breast cancer mortality in the period following a diagnosis of breast cancer. Copyright © 2013, The International Biometric Society.

Power and Sample Size Calculations for Logistic Regression Tests for Differential Item Functioning

ERIC Educational Resources Information Center

Li, Zhushan

2014-01-01

Logistic regression is a popular method for detecting uniform and nonuniform differential item functioning (DIF) effects. Theoretical formulas for the power and sample size calculations are derived for likelihood ratio tests and Wald tests based on the asymptotic distribution of the maximum likelihood estimators for the logistic regression model.…

Comparison of standard maximum likelihood classification and polytomous logistic regression used in remote sensing

Treesearch

John Hogland; Nedret Billor; Nathaniel Anderson

2013-01-01

Discriminant analysis, referred to as maximum likelihood classification within popular remote sensing software packages, is a common supervised technique used by analysts. Polytomous logistic regression (PLR), also referred to as multinomial logistic regression, is an alternative classification approach that is less restrictive, more flexible, and easy to interpret. To...

SMURC: High-Dimension Small-Sample Multivariate Regression With Covariance Estimation.

PubMed

Bayar, Belhassen; Bouaynaya, Nidhal; Shterenberg, Roman

2017-03-01

We consider a high-dimension low sample-size multivariate regression problem that accounts for correlation of the response variables. The system is underdetermined as there are more parameters than samples. We show that the maximum likelihood approach with covariance estimation is senseless because the likelihood diverges. We subsequently propose a normalization of the likelihood function that guarantees convergence. We call this method small-sample multivariate regression with covariance (SMURC) estimation. We derive an optimization problem and its convex approximation to compute SMURC. Simulation results show that the proposed algorithm outperforms the regularized likelihood estimator with known covariance matrix and the sparse conditional Gaussian graphical model. We also apply SMURC to the inference of the wing-muscle gene network of the Drosophila melanogaster (fruit fly).

A LANDSAT study of ephemeral and perennial rangeland vegetation and soils

NASA Technical Reports Server (NTRS)

Bentley, R. G., Jr. (Principal Investigator); Salmon-Drexler, B. C.; Bonner, W. J.; Vincent, R. K.

1976-01-01

The author has identified the following significant results. Several methods of computer processing were applied to LANDSAT data for mapping vegetation characteristics of perennial rangeland in Montana and ephemeral rangeland in Arizona. The choice of optimal processing technique was dependent on prescribed mapping and site condition. Single channel level slicing and ratioing of channels were used for simple enhancement. Predictive models for mapping percent vegetation cover based on data from field spectra and LANDSAT data were generated by multiple linear regression of six unique LANDSAT spectral ratios. Ratio gating logic and maximum likelihood classification were applied successfully to recognize plant communities in Montana. Maximum likelihood classification did little to improve recognition of terrain features when compared to a single channel density slice in sparsely vegetated Arizona. LANDSAT was found to be more sensitive to differences between plant communities based on percentages of vigorous vegetation than to actual physical or spectral differences among plant species.

Assessment of parametric uncertainty for groundwater reactive transport modeling,

USGS Publications Warehouse

Shi, Xiaoqing; Ye, Ming; Curtis, Gary P.; Miller, Geoffery L.; Meyer, Philip D.; Kohler, Matthias; Yabusaki, Steve; Wu, Jichun

2014-01-01

The validity of using Gaussian assumptions for model residuals in uncertainty quantification of a groundwater reactive transport model was evaluated in this study. Least squares regression methods explicitly assume Gaussian residuals, and the assumption leads to Gaussian likelihood functions, model parameters, and model predictions. While the Bayesian methods do not explicitly require the Gaussian assumption, Gaussian residuals are widely used. This paper shows that the residuals of the reactive transport model are non-Gaussian, heteroscedastic, and correlated in time; characterizing them requires using a generalized likelihood function such as the formal generalized likelihood function developed by Schoups and Vrugt (2010). For the surface complexation model considered in this study for simulating uranium reactive transport in groundwater, parametric uncertainty is quantified using the least squares regression methods and Bayesian methods with both Gaussian and formal generalized likelihood functions. While the least squares methods and Bayesian methods with Gaussian likelihood function produce similar Gaussian parameter distributions, the parameter distributions of Bayesian uncertainty quantification using the formal generalized likelihood function are non-Gaussian. In addition, predictive performance of formal generalized likelihood function is superior to that of least squares regression and Bayesian methods with Gaussian likelihood function. The Bayesian uncertainty quantification is conducted using the differential evolution adaptive metropolis (DREAM(zs)) algorithm; as a Markov chain Monte Carlo (MCMC) method, it is a robust tool for quantifying uncertainty in groundwater reactive transport models. For the surface complexation model, the regression-based local sensitivity analysis and Morris- and DREAM(ZS)-based global sensitivity analysis yield almost identical ranking of parameter importance. The uncertainty analysis may help select appropriate likelihood functions, improve model calibration, and reduce predictive uncertainty in other groundwater reactive transport and environmental modeling.

An iteratively reweighted least-squares approach to adaptive robust adjustment of parameters in linear regression models with autoregressive and t-distributed deviations

NASA Astrophysics Data System (ADS)

Kargoll, Boris; Omidalizarandi, Mohammad; Loth, Ina; Paffenholz, Jens-André; Alkhatib, Hamza

2018-03-01

In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student's) t-distribution. This error model allows for the stochastic modeling of multiple outliers and for an adaptive robust maximum likelihood (ML) estimation of the unknown regression and AR coefficients, the scale parameter, and the degree of freedom of the t-distribution. This approach is meant to be an extension of known estimators, which tend to focus only on the regression model, or on the AR error model, or on normally distributed errors. For the purpose of ML estimation, we derive an expectation conditional maximization either algorithm, which leads to an easy-to-implement version of iteratively reweighted least squares. The estimation performance of the algorithm is evaluated via Monte Carlo simulations for a Fourier as well as a spline model in connection with AR colored noise models of different orders and with three different sampling distributions generating the white noise components. We apply the algorithm to a vibration dataset recorded by a high-accuracy, single-axis accelerometer, focusing on the evaluation of the estimated AR colored noise model.

The allometry of coarse root biomass: log-transformed linear regression or nonlinear regression?

PubMed

Lai, Jiangshan; Yang, Bo; Lin, Dunmei; Kerkhoff, Andrew J; Ma, Keping

2013-01-01

Precise estimation of root biomass is important for understanding carbon stocks and dynamics in forests. Traditionally, biomass estimates are based on allometric scaling relationships between stem diameter and coarse root biomass calculated using linear regression (LR) on log-transformed data. Recently, it has been suggested that nonlinear regression (NLR) is a preferable fitting method for scaling relationships. But while this claim has been contested on both theoretical and empirical grounds, and statistical methods have been developed to aid in choosing between the two methods in particular cases, few studies have examined the ramifications of erroneously applying NLR. Here, we use direct measurements of 159 trees belonging to three locally dominant species in east China to compare the LR and NLR models of diameter-root biomass allometry. We then contrast model predictions by estimating stand coarse root biomass based on census data from the nearby 24-ha Gutianshan forest plot and by testing the ability of the models to predict known root biomass values measured on multiple tropical species at the Pasoh Forest Reserve in Malaysia. Based on likelihood estimates for model error distributions, as well as the accuracy of extrapolative predictions, we find that LR on log-transformed data is superior to NLR for fitting diameter-root biomass scaling models. More importantly, inappropriately using NLR leads to grossly inaccurate stand biomass estimates, especially for stands dominated by smaller trees.

A FORTRAN program for multivariate survival analysis on the personal computer.

PubMed

Mulder, P G

1988-01-01

In this paper a FORTRAN program is presented for multivariate survival or life table regression analysis in a competing risks' situation. The relevant failure rate (for example, a particular disease or mortality rate) is modelled as a log-linear function of a vector of (possibly time-dependent) explanatory variables. The explanatory variables may also include the variable time itself, which is useful for parameterizing piecewise exponential time-to-failure distributions in a Gompertz-like or Weibull-like way as a more efficient alternative to Cox's proportional hazards model. Maximum likelihood estimates of the coefficients of the log-linear relationship are obtained from the iterative Newton-Raphson method. The program runs on a personal computer under DOS; running time is quite acceptable, even for large samples.

Weibull Modulus Estimated by the Non-linear Least Squares Method: A Solution to Deviation Occurring in Traditional Weibull Estimation

NASA Astrophysics Data System (ADS)

Li, T.; Griffiths, W. D.; Chen, J.

2017-11-01

The Maximum Likelihood method and the Linear Least Squares (LLS) method have been widely used to estimate Weibull parameters for reliability of brittle and metal materials. In the last 30 years, many researchers focused on the bias of Weibull modulus estimation, and some improvements have been achieved, especially in the case of the LLS method. However, there is a shortcoming in these methods for a specific type of data, where the lower tail deviates dramatically from the well-known linear fit in a classic LLS Weibull analysis. This deviation can be commonly found from the measured properties of materials, and previous applications of the LLS method on this kind of dataset present an unreliable linear regression. This deviation was previously thought to be due to physical flaws ( i.e., defects) contained in materials. However, this paper demonstrates that this deviation can also be caused by the linear transformation of the Weibull function, occurring in the traditional LLS method. Accordingly, it may not be appropriate to carry out a Weibull analysis according to the linearized Weibull function, and the Non-linear Least Squares method (Non-LS) is instead recommended for the Weibull modulus estimation of casting properties.

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.

Epidemiologic programs for computers and calculators. A microcomputer program for multiple logistic regression by unconditional and conditional maximum likelihood methods.

PubMed

Campos-Filho, N; Franco, E L

1989-02-01

A frequent procedure in matched case-control studies is to report results from the multivariate unmatched analyses if they do not differ substantially from the ones obtained after conditioning on the matching variables. Although conceptually simple, this rule requires that an extensive series of logistic regression models be evaluated by both the conditional and unconditional maximum likelihood methods. Most computer programs for logistic regression employ only one maximum likelihood method, which requires that the analyses be performed in separate steps. This paper describes a Pascal microcomputer (IBM PC) program that performs multiple logistic regression by both maximum likelihood estimation methods, which obviates the need for switching between programs to obtain relative risk estimates from both matched and unmatched analyses. The program calculates most standard statistics and allows factoring of categorical or continuous variables by two distinct methods of contrast. A built-in, descriptive statistics option allows the user to inspect the distribution of cases and controls across categories of any given variable.

Graft Diameter as a Predictor for Revision Anterior Cruciate Ligament Reconstruction and KOOS and EQ-5D Values: A Cohort Study From the Swedish National Knee Ligament Register Based on 2240 Patients.

PubMed

Snaebjörnsson, Thorkell; Hamrin Senorski, Eric; Ayeni, Olufemi R; Alentorn-Geli, Eduard; Krupic, Ferid; Norberg, Fredrik; Karlsson, Jón; Samuelsson, Kristian

2017-07-01

Anterior cruciate ligament (ACL) reconstruction (ACLR) using a hamstring tendon (HT) autograft is an effective and widespread method. Recent studies have identified a relationship between the graft diameter and revision ACLR. To evaluate the influence of the graft diameter on revision ACLR and patient-reported outcomes in patients undergoing primary ACLR using HT autografts. Cohort study; Level of evidence, 2. A prospective cohort study was conducted using the Swedish National Knee Ligament Register (SNKLR) involving all patients undergoing primary ACLR using HT autografts. Patients with graft failure who needed revision surgery (cases) were compared with patients not undergoing revision surgery (controls). The control group was matched for sex, age, and graft fixation method in a 3:1 ratio. Conditional logistic regression was performed to produce odds ratios and 95% CIs. Univariate linear regression analyses were performed for patient-related outcomes. The Knee injury and Osteoarthritis Outcome Score (KOOS) and EuroQol 5 dimensions questionnaire (EQ-5D) values were obtained. A total of 2240 patients were included in which there were 560 cases and 1680 controls. No significant differences between the cases and controls were found for sex (52.9% male), mean age (21.7 years), and femoral and tibial fixation. The mean graft diameter for the cases was 8.0 ± 0.74 mm and for the controls was 8.1 ± 0.76 mm. In the present cohort, the likelihood of revision surgery for every 0.5-mm increase in the HT autograft diameter between 7.0 and 10.0 mm was 0.86 (95% CI, 0.75-0.99; P = .03). Univariate linear regression analysis found no significant regression coefficient for the change in KOOS or EQ-5D values. In a large cohort of patients after primary ACLR with HT autografts, an increase in the graft diameter between 7.0 and 10.0 mm resulted in a 0.86 times lower likelihood of revision surgery with every 0.5-mm increase. This study provides further evidence of the importance of the HT autograft size in intraoperative decision making.

Multiplicative Forests for Continuous-Time Processes

PubMed Central

Weiss, Jeremy C.; Natarajan, Sriraam; Page, David

2013-01-01

Learning temporal dependencies between variables over continuous time is an important and challenging task. Continuous-time Bayesian networks effectively model such processes but are limited by the number of conditional intensity matrices, which grows exponentially in the number of parents per variable. We develop a partition-based representation using regression trees and forests whose parameter spaces grow linearly in the number of node splits. Using a multiplicative assumption we show how to update the forest likelihood in closed form, producing efficient model updates. Our results show multiplicative forests can be learned from few temporal trajectories with large gains in performance and scalability. PMID:25284967

Multiplicative Forests for Continuous-Time Processes.

PubMed

Weiss, Jeremy C; Natarajan, Sriraam; Page, David

2012-01-01

Learning temporal dependencies between variables over continuous time is an important and challenging task. Continuous-time Bayesian networks effectively model such processes but are limited by the number of conditional intensity matrices, which grows exponentially in the number of parents per variable. We develop a partition-based representation using regression trees and forests whose parameter spaces grow linearly in the number of node splits. Using a multiplicative assumption we show how to update the forest likelihood in closed form, producing efficient model updates. Our results show multiplicative forests can be learned from few temporal trajectories with large gains in performance and scalability.

Global estimation of long-term persistence in annual river runoff

NASA Astrophysics Data System (ADS)

Markonis, Y.; Moustakis, Y.; Nasika, C.; Sychova, P.; Dimitriadis, P.; Hanel, M.; Máca, P.; Papalexiou, S. M.

2018-03-01

Long-term persistence (LTP) of annual river runoff is a topic of ongoing hydrological research, due to its implications to water resources management. Here, we estimate its strength, measured by the Hurst coefficient H, in 696 annual, globally distributed, streamflow records with at least 80 years of data. We use three estimation methods (maximum likelihood estimator, Whittle estimator and least squares variance) resulting in similar mean values of H close to 0.65. Subsequently, we explore potential factors influencing H by two linear (Spearman's rank correlation, multiple linear regression) and two non-linear (self-organizing maps, random forests) techniques. Catchment area is found to be crucial for medium to larger watersheds, while climatic controls, such as aridity index, have higher impact to smaller ones. Our findings indicate that long-term persistence is weaker than found in other studies, suggesting that enhanced LTP is encountered in large-catchment rivers, were the effect of spatial aggregation is more intense. However, we also show that the estimated values of H can be reproduced by a short-term persistence stochastic model such as an auto-regressive AR(1) process. A direct consequence is that some of the most common methods for the estimation of H coefficient, might not be suitable for discriminating short- and long-term persistence even in long observational records.

Updated logistic regression equations for the calculation of post-fire debris-flow likelihood in the western United States

USGS Publications Warehouse

Staley, Dennis M.; Negri, Jacquelyn A.; Kean, Jason W.; Laber, Jayme L.; Tillery, Anne C.; Youberg, Ann M.

2016-06-30

Wildfire can significantly alter the hydrologic response of a watershed to the extent that even modest rainstorms can generate dangerous flash floods and debris flows. To reduce public exposure to hazard, the U.S. Geological Survey produces post-fire debris-flow hazard assessments for select fires in the western United States. We use publicly available geospatial data describing basin morphology, burn severity, soil properties, and rainfall characteristics to estimate the statistical likelihood that debris flows will occur in response to a storm of a given rainfall intensity. Using an empirical database and refined geospatial analysis methods, we defined new equations for the prediction of debris-flow likelihood using logistic regression methods. We showed that the new logistic regression model outperformed previous models used to predict debris-flow likelihood.

Revisiting tests for neglected nonlinearity using artificial neural networks.

PubMed

Cho, Jin Seo; Ishida, Isao; White, Halbert

2011-05-01

Tests for regression neglected nonlinearity based on artificial neural networks (ANNs) have so far been studied by separately analyzing the two ways in which the null of regression linearity can hold. This implies that the asymptotic behavior of general ANN-based tests for neglected nonlinearity is still an open question. Here we analyze a convenient ANN-based quasi-likelihood ratio statistic for testing neglected nonlinearity, paying careful attention to both components of the null. We derive the asymptotic null distribution under each component separately and analyze their interaction. Somewhat remarkably, it turns out that the previously known asymptotic null distribution for the type 1 case still applies, but under somewhat stronger conditions than previously recognized. We present Monte Carlo experiments corroborating our theoretical results and showing that standard methods can yield misleading inference when our new, stronger regularity conditions are violated.

Restricted maximum likelihood estimation of genetic principal components and smoothed covariance matrices

PubMed Central

Meyer, Karin; Kirkpatrick, Mark

2005-01-01

Principal component analysis is a widely used 'dimension reduction' technique, albeit generally at a phenotypic level. It is shown that we can estimate genetic principal components directly through a simple reparameterisation of the usual linear, mixed model. This is applicable to any analysis fitting multiple, correlated genetic effects, whether effects for individual traits or sets of random regression coefficients to model trajectories. Depending on the magnitude of genetic correlation, a subset of the principal component generally suffices to capture the bulk of genetic variation. Corresponding estimates of genetic covariance matrices are more parsimonious, have reduced rank and are smoothed, with the number of parameters required to model the dispersion structure reduced from k(k + 1)/2 to m(2k - m + 1)/2 for k effects and m principal components. Estimation of these parameters, the largest eigenvalues and pertaining eigenvectors of the genetic covariance matrix, via restricted maximum likelihood using derivatives of the likelihood, is described. It is shown that reduced rank estimation can reduce computational requirements of multivariate analyses substantially. An application to the analysis of eight traits recorded via live ultrasound scanning of beef cattle is given. PMID:15588566

Normal Theory Two-Stage ML Estimator When Data Are Missing at the Item Level

PubMed Central

Savalei, Victoria; Rhemtulla, Mijke

2017-01-01

In many modeling contexts, the variables in the model are linear composites of the raw items measured for each participant; for instance, regression and path analysis models rely on scale scores, and structural equation models often use parcels as indicators of latent constructs. Currently, no analytic estimation method exists to appropriately handle missing data at the item level. Item-level multiple imputation (MI), however, can handle such missing data straightforwardly. In this article, we develop an analytic approach for dealing with item-level missing data—that is, one that obtains a unique set of parameter estimates directly from the incomplete data set and does not require imputations. The proposed approach is a variant of the two-stage maximum likelihood (TSML) methodology, and it is the analytic equivalent of item-level MI. We compare the new TSML approach to three existing alternatives for handling item-level missing data: scale-level full information maximum likelihood, available-case maximum likelihood, and item-level MI. We find that the TSML approach is the best analytic approach, and its performance is similar to item-level MI. We recommend its implementation in popular software and its further study. PMID:29276371

Normal Theory Two-Stage ML Estimator When Data Are Missing at the Item Level.

PubMed

Savalei, Victoria; Rhemtulla, Mijke

2017-08-01

In many modeling contexts, the variables in the model are linear composites of the raw items measured for each participant; for instance, regression and path analysis models rely on scale scores, and structural equation models often use parcels as indicators of latent constructs. Currently, no analytic estimation method exists to appropriately handle missing data at the item level. Item-level multiple imputation (MI), however, can handle such missing data straightforwardly. In this article, we develop an analytic approach for dealing with item-level missing data-that is, one that obtains a unique set of parameter estimates directly from the incomplete data set and does not require imputations. The proposed approach is a variant of the two-stage maximum likelihood (TSML) methodology, and it is the analytic equivalent of item-level MI. We compare the new TSML approach to three existing alternatives for handling item-level missing data: scale-level full information maximum likelihood, available-case maximum likelihood, and item-level MI. We find that the TSML approach is the best analytic approach, and its performance is similar to item-level MI. We recommend its implementation in popular software and its further study.

An overall strategy based on regression models to estimate relative survival and model the effects of prognostic factors in cancer survival studies.

PubMed

Remontet, L; Bossard, N; Belot, A; Estève, J

2007-05-10

Relative survival provides a measure of the proportion of patients dying from the disease under study without requiring the knowledge of the cause of death. We propose an overall strategy based on regression models to estimate the relative survival and model the effects of potential prognostic factors. The baseline hazard was modelled until 10 years follow-up using parametric continuous functions. Six models including cubic regression splines were considered and the Akaike Information Criterion was used to select the final model. This approach yielded smooth and reliable estimates of mortality hazard and allowed us to deal with sparse data taking into account all the available information. Splines were also used to model simultaneously non-linear effects of continuous covariates and time-dependent hazard ratios. This led to a graphical representation of the hazard ratio that can be useful for clinical interpretation. Estimates of these models were obtained by likelihood maximization. We showed that these estimates could be also obtained using standard algorithms for Poisson regression. Copyright 2006 John Wiley & Sons, Ltd.

Occupational outcomes of adult childhood cancer survivors: A report from the Childhood Cancer Survivor Study

PubMed Central

Kirchhoff, Anne C.; Krull, Kevin R.; Ness, Kirsten K.; Park, Elyse R.; Oeffinger, Kevin C.; Hudson, Melissa M.; Stovall, Marilyn; Robison, Leslie L.; Wickizer, Thomas; Leisenring, Wendy

2010-01-01

Background We examined whether survivors from the Childhood Cancer Survivor Study were less likely to be in higher skill occupations than a sibling comparison and whether certain survivors were at higher risk. Methods We created three mutually-exclusive occupational categories for participants aged ≥25 years: Managerial/Professional and Non-Physical and Physical Service/Blue Collar. We examined currently employed survivors (N=4845) and siblings (N=1727) in multivariable generalized linear models to evaluate the likelihood of being in the three occupational categories. Among all participants, we used multinomial logistic regression to examine the likelihood of these outcomes in comparison to being unemployed (survivors N=6671; siblings N=2129). Multivariable linear models were used to assess survivor occupational differences by cancer and treatment variables. Personal income was compared by occupation. Results Employed survivors were less often in higher skilled Managerial/Professional occupations (Relative Risk=0.93, 95% Confidence Interval 0.89–0.98) than siblings. Survivors who were Black, were diagnosed at a younger age, or had high-dose cranial radiation were less likely to hold Professional occupations than other survivors. In multinomial models, female survivors’ likelihood of being in full-time Professional occupations (27%) was lower than male survivors (42%) and female (41%) and male (50%) siblings. Survivors’ personal income was lower than siblings within each of the three occupational categories in models adjusted for sociodemographic variables. Conclusions Adult childhood cancer survivors are employed in lower skill jobs than siblings. Survivors with certain treatment histories are at higher risk and may require vocational assistance throughout adulthood. PMID:21246530

Power calculations for likelihood ratio tests for offspring genotype risks, maternal effects, and parent-of-origin (POO) effects in the presence of missing parental genotypes when unaffected siblings are available.

PubMed

Rampersaud, E; Morris, R W; Weinberg, C R; Speer, M C; Martin, E R

2007-01-01

Genotype-based likelihood-ratio tests (LRT) of association that examine maternal and parent-of-origin effects have been previously developed in the framework of log-linear and conditional logistic regression models. In the situation where parental genotypes are missing, the expectation-maximization (EM) algorithm has been incorporated in the log-linear approach to allow incomplete triads to contribute to the LRT. We present an extension to this model which we call the Combined_LRT that incorporates additional information from the genotypes of unaffected siblings to improve assignment of incompletely typed families to mating type categories, thereby improving inference of missing parental data. Using simulations involving a realistic array of family structures, we demonstrate the validity of the Combined_LRT under the null hypothesis of no association and provide power comparisons under varying levels of missing data and using sibling genotype data. We demonstrate the improved power of the Combined_LRT compared with the family-based association test (FBAT), another widely used association test. Lastly, we apply the Combined_LRT to a candidate gene analysis in Autism families, some of which have missing parental genotypes. We conclude that the proposed log-linear model will be an important tool for future candidate gene studies, for many complex diseases where unaffected siblings can often be ascertained and where epigenetic factors such as imprinting may play a role in disease etiology.

Factors associated with persons with disability employment in India: a cross-sectional study.

PubMed

Naraharisetti, Ramya; Castro, Marcia C

2016-10-07

Over twenty million persons with disability in India are increasingly being offered poverty alleviation strategies, including employment programs. This study employs a spatial analytic approach to identify correlates of employment among persons with disability in India, considering sight, speech, hearing, movement, and mental disabilities. Based on 2001 Census data, this study utilizes linear regression and spatial autoregressive models to identify factors associated with the proportion employed among persons with disability at the district level. Models stratified by rural and urban areas were also considered. Spatial autoregressive models revealed that different factors contribute to employment of persons with disability in rural and urban areas. In rural areas, having mental disability decreased the likelihood of employment, while being female and having movement, or sight impairment (compared to other disabilities) increased the likelihood of employment. In urban areas, being female and illiterate decreased the likelihood of employment but having sight, mental and movement impairment (compared to other disabilities) increased the likelihood of employment. Poverty alleviation programs designed for persons with disability in India should account for differences in employment by disability types and should be spatially targeted. Since persons with disability in rural and urban areas have different factors contributing to their employment, it is vital that government and service-planning organizations account for these differences when creating programs aimed at livelihood development.

pLARmEB: integration of least angle regression with empirical Bayes for multilocus genome-wide association studies.

PubMed

Zhang, J; Feng, J-Y; Ni, Y-L; Wen, Y-J; Niu, Y; Tamba, C L; Yue, C; Song, Q; Zhang, Y-M

2017-06-01

Multilocus genome-wide association studies (GWAS) have become the state-of-the-art procedure to identify quantitative trait nucleotides (QTNs) associated with complex traits. However, implementation of multilocus model in GWAS is still difficult. In this study, we integrated least angle regression with empirical Bayes to perform multilocus GWAS under polygenic background control. We used an algorithm of model transformation that whitened the covariance matrix of the polygenic matrix K and environmental noise. Markers on one chromosome were included simultaneously in a multilocus model and least angle regression was used to select the most potentially associated single-nucleotide polymorphisms (SNPs), whereas the markers on the other chromosomes were used to calculate kinship matrix as polygenic background control. The selected SNPs in multilocus model were further detected for their association with the trait by empirical Bayes and likelihood ratio test. We herein refer to this method as the pLARmEB (polygenic-background-control-based least angle regression plus empirical Bayes). Results from simulation studies showed that pLARmEB was more powerful in QTN detection and more accurate in QTN effect estimation, had less false positive rate and required less computing time than Bayesian hierarchical generalized linear model, efficient mixed model association (EMMA) and least angle regression plus empirical Bayes. pLARmEB, multilocus random-SNP-effect mixed linear model and fast multilocus random-SNP-effect EMMA methods had almost equal power of QTN detection in simulation experiments. However, only pLARmEB identified 48 previously reported genes for 7 flowering time-related traits in Arabidopsis thaliana.

Three methods to construct predictive models using logistic regression and likelihood ratios to facilitate adjustment for pretest probability give similar results.

PubMed

Chan, Siew Foong; Deeks, Jonathan J; Macaskill, Petra; Irwig, Les

2008-01-01

To compare three predictive models based on logistic regression to estimate adjusted likelihood ratios allowing for interdependency between diagnostic variables (tests). This study was a review of the theoretical basis, assumptions, and limitations of published models; and a statistical extension of methods and application to a case study of the diagnosis of obstructive airways disease based on history and clinical examination. Albert's method includes an offset term to estimate an adjusted likelihood ratio for combinations of tests. Spiegelhalter and Knill-Jones method uses the unadjusted likelihood ratio for each test as a predictor and computes shrinkage factors to allow for interdependence. Knottnerus' method differs from the other methods because it requires sequencing of tests, which limits its application to situations where there are few tests and substantial data. Although parameter estimates differed between the models, predicted "posttest" probabilities were generally similar. Construction of predictive models using logistic regression is preferred to the independence Bayes' approach when it is important to adjust for dependency of tests errors. Methods to estimate adjusted likelihood ratios from predictive models should be considered in preference to a standard logistic regression model to facilitate ease of interpretation and application. Albert's method provides the most straightforward approach.

Quasi- and pseudo-maximum likelihood estimators for discretely observed continuous-time Markov branching processes

PubMed Central

Chen, Rui; Hyrien, Ollivier

2011-01-01

This article deals with quasi- and pseudo-likelihood estimation in a class of continuous-time multi-type Markov branching processes observed at discrete points in time. “Conventional” and conditional estimation are discussed for both approaches. We compare their properties and identify situations where they lead to asymptotically equivalent estimators. Both approaches possess robustness properties, and coincide with maximum likelihood estimation in some cases. Quasi-likelihood functions involving only linear combinations of the data may be unable to estimate all model parameters. Remedial measures exist, including the resort either to non-linear functions of the data or to conditioning the moments on appropriate sigma-algebras. The method of pseudo-likelihood may also resolve this issue. We investigate the properties of these approaches in three examples: the pure birth process, the linear birth-and-death process, and a two-type process that generalizes the previous two examples. Simulations studies are conducted to evaluate performance in finite samples. PMID:21552356

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.

A Note on Three Statistical Tests in the Logistic Regression DIF Procedure

ERIC Educational Resources Information Center

Paek, Insu

2012-01-01

Although logistic regression became one of the well-known methods in detecting differential item functioning (DIF), its three statistical tests, the Wald, likelihood ratio (LR), and score tests, which are readily available under the maximum likelihood, do not seem to be consistently distinguished in DIF literature. This paper provides a clarifying…

Comparison of IRT Likelihood Ratio Test and Logistic Regression DIF Detection Procedures

ERIC Educational Resources Information Center

Atar, Burcu; Kamata, Akihito

2011-01-01

The Type I error rates and the power of IRT likelihood ratio test and cumulative logit ordinal logistic regression procedures in detecting differential item functioning (DIF) for polytomously scored items were investigated in this Monte Carlo simulation study. For this purpose, 54 simulation conditions (combinations of 3 sample sizes, 2 sample…

Identification of internal properties of fibers and micro-swimmers

NASA Astrophysics Data System (ADS)

Plouraboue, Franck; Thiam, Ibrahima; Delmotte, Blaise; Climent, Eric; PSC Collaboration

2016-11-01

In this presentation we discuss the identifiability of constitutive parameters of passive or active micro-swimmers. We first present a general framework for describing fibers or micro-swimmers using a bead-model description. Using a kinematic constraint formulation to describe fibers, flagellum or cilia, we find explicit linear relationship between elastic constitutive parameters and generalised velocities from computing contact forces. This linear formulation then permits to address explicitly identifiability conditions and solve for parameter identification. We show that both active forcing and passive parameters are both identifiable independently but not simultaneously. We also provide unbiased estimators for elastic parameters as well as active ones in the presence of Langevin-like forcing with Gaussian noise using normal linear regression models and maximum likelihood method. These theoretical results are illustrated in various configurations of relaxed or actuated passives fibers, and active filament of known passive properties, showing the efficiency of the proposed approach for direct parameter identification. The convergence of the proposed estimators is successfully tested numerically.

Estimating the variance for heterogeneity in arm-based network meta-analysis.

PubMed

Piepho, Hans-Peter; Madden, Laurence V; Roger, James; Payne, Roger; Williams, Emlyn R

2018-04-19

Network meta-analysis can be implemented by using arm-based or contrast-based models. Here we focus on arm-based models and fit them using generalized linear mixed model procedures. Full maximum likelihood (ML) estimation leads to biased trial-by-treatment interaction variance estimates for heterogeneity. Thus, our objective is to investigate alternative approaches to variance estimation that reduce bias compared with full ML. Specifically, we use penalized quasi-likelihood/pseudo-likelihood and hierarchical (h) likelihood approaches. In addition, we consider a novel model modification that yields estimators akin to the residual maximum likelihood estimator for linear mixed models. The proposed methods are compared by simulation, and 2 real datasets are used for illustration. Simulations show that penalized quasi-likelihood/pseudo-likelihood and h-likelihood reduce bias and yield satisfactory coverage rates. Sum-to-zero restriction and baseline contrasts for random trial-by-treatment interaction effects, as well as a residual ML-like adjustment, also reduce bias compared with an unconstrained model when ML is used, but coverage rates are not quite as good. Penalized quasi-likelihood/pseudo-likelihood and h-likelihood are therefore recommended. Copyright © 2018 John Wiley & Sons, Ltd.

Influence of weather, rank, and home advantage on football outcomes in the Gulf region.

PubMed

Brocherie, Franck; Girard, Olivier; Farooq, Abdulaziz; Millet, Grégoire P

2015-02-01

The objective of this study was to investigate the effects of weather, rank, and home advantage on international football match results and scores in the Gulf Cooperation Council (GCC) region. Football matches (n = 2008) in six GCC countries were analyzed. To determine the weather influence on the likelihood of favorable outcome and goal difference, generalized linear model with a logit link function and multiple regression analysis were performed. In the GCC region, home teams tend to have greater likelihood of a favorable outcome (P < 0.001) and higher goal difference (P < 0.001). Temperature difference was identified as a significant explanatory variable when used independently (P < 0.001) or after adjustment for home advantage and team ranking (P < 0.001). The likelihood of favorable outcome for GCC teams increases by 3% for every 1-unit increase in temperature difference. After inclusion of interaction with opposition, this advantage remains significant only when playing against non-GCC opponents. While home advantage increased the odds of favorable outcome (P < 0.001) and goal difference (P < 0.001) after inclusion of interaction term, the likelihood of favorable outcome for a GCC team decreased (P < 0.001) when playing against a stronger opponent. Finally, the temperature and wet bulb globe temperature approximation were found as better indicators of the effect of environmental conditions than absolute and relative humidity or heat index on match outcomes. In GCC region, higher temperature increased the likelihood of a favorable outcome when playing against non-GCC teams. However, international ranking should be considered because an opponent with a higher rank reduced, but did not eliminate, the likelihood of a favorable outcome.

Comparison of stability and control parameters for a light, single-engine, high-winged aircraft using different flight test and parameter estimation techniques

NASA Technical Reports Server (NTRS)

Suit, W. T.; Cannaday, R. L.

1979-01-01

The longitudinal and lateral stability and control parameters for a high wing, general aviation, airplane are examined. Estimations using flight data obtained at various flight conditions within the normal range of the aircraft are presented. The estimations techniques, an output error technique (maximum likelihood) and an equation error technique (linear regression), are presented. The longitudinal static parameters are estimated from climbing, descending, and quasi steady state flight data. The lateral excitations involve a combination of rudder and ailerons. The sensitivity of the aircraft modes of motion to variations in the parameter estimates are discussed.

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.

A flexible count data regression model for risk analysis.

PubMed

Guikema, Seth D; Coffelt, Jeremy P; Goffelt, Jeremy P

2008-02-01

In many cases, risk and reliability analyses involve estimating the probabilities of discrete events such as hardware failures and occurrences of disease or death. There is often additional information in the form of explanatory variables that can be used to help estimate the likelihood of different numbers of events in the future through the use of an appropriate regression model, such as a generalized linear model. However, existing generalized linear models (GLM) are limited in their ability to handle the types of variance structures often encountered in using count data in risk and reliability analysis. In particular, standard models cannot handle both underdispersed data (variance less than the mean) and overdispersed data (variance greater than the mean) in a single coherent modeling framework. This article presents a new GLM based on a reformulation of the Conway-Maxwell Poisson (COM) distribution that is useful for both underdispersed and overdispersed count data and demonstrates this model by applying it to the assessment of electric power system reliability. The results show that the proposed COM GLM can provide as good of fits to data as the commonly used existing models for overdispered data sets while outperforming these commonly used models for underdispersed data sets.

Estimation of Complex Generalized Linear Mixed Models for Measurement and Growth

ERIC Educational Resources Information Center

Jeon, Minjeong

2012-01-01

Maximum likelihood (ML) estimation of generalized linear mixed models (GLMMs) is technically challenging because of the intractable likelihoods that involve high dimensional integrations over random effects. The problem is magnified when the random effects have a crossed design and thus the data cannot be reduced to small independent clusters. A…

Additive hazards regression and partial likelihood estimation for ecological monitoring data across space.

PubMed

Lin, Feng-Chang; Zhu, Jun

2012-01-01

We develop continuous-time models for the analysis of environmental or ecological monitoring data such that subjects are observed at multiple monitoring time points across space. Of particular interest are additive hazards regression models where the baseline hazard function can take on flexible forms. We consider time-varying covariates and take into account spatial dependence via autoregression in space and time. We develop statistical inference for the regression coefficients via partial likelihood. Asymptotic properties, including consistency and asymptotic normality, are established for parameter estimates under suitable regularity conditions. Feasible algorithms utilizing existing statistical software packages are developed for computation. We also consider a simpler additive hazards model with homogeneous baseline hazard and develop hypothesis testing for homogeneity. A simulation study demonstrates that the statistical inference using partial likelihood has sound finite-sample properties and offers a viable alternative to maximum likelihood estimation. For illustration, we analyze data from an ecological study that monitors bark beetle colonization of red pines in a plantation of Wisconsin.

Primary health care providers' advice for a dental checkup and dental use in children.

PubMed

Beil, Heather A; Rozier, R Gary

2010-08-01

In this study we estimated factors associated with children being advised to see the dentist by a doctor or other health provider; tested for an association between the advisement on the likelihood that the child would visit the dentist; and estimated the effect of the advisement on dental costs. We identified a sample of 5268 children aged 2 to 11 years in the 2004 Medical Expenditures Panel Survey. A cross-sectional analysis with logistic regression models was conducted to estimate the likelihood of the child receiving a recommendation for a dental checkup, and to determine its effect on the likelihood of having a dental visit. Differences in cost for children who received a recommendation were assessed by using a linear regression model. All analyses were conducted separately on children aged 2 to 5 (n = 2031) and aged 6 to 11 (n = 3237) years. Forty-seven percent of 2- to 5-year-olds and 37% of 6- to 11-year-olds had been advised to see the dentist. Children aged 2 to 5 who received a recommendation were more likely to have a dental visit (odds ratio: 2.89 [95% confidence interval: 2.16-3.87]), but no difference was observed among older children. Advice had no effect on dental costs in either age group. Health providers' recommendation that pediatric patients visit the dentist was associated with an increase in dental visits among young children. Providers have the potential to play an important role in establishing a dental home for children at an early age. Future research should examine potential interventions to increase effective dental referrals by health providers.

Bayesian Inference for the Stereotype Regression Model: Application to a Case-control Study of Prostate Cancer

PubMed Central

Ahn, Jaeil; Mukherjee, Bhramar; Banerjee, Mousumi; Cooney, Kathleen A.

2011-01-01

Summary The stereotype regression model for categorical outcomes, proposed by Anderson (1984) is nested between the baseline category logits and adjacent category logits model with proportional odds structure. The stereotype model is more parsimonious than the ordinary baseline-category (or multinomial logistic) model due to a product representation of the log odds-ratios in terms of a common parameter corresponding to each predictor and category specific scores. The model could be used for both ordered and unordered outcomes. For ordered outcomes, the stereotype model allows more flexibility than the popular proportional odds model in capturing highly subjective ordinal scaling which does not result from categorization of a single latent variable, but are inherently multidimensional in nature. As pointed out by Greenland (1994), an additional advantage of the stereotype model is that it provides unbiased and valid inference under outcome-stratified sampling as in case-control studies. In addition, for matched case-control studies, the stereotype model is amenable to classical conditional likelihood principle, whereas there is no reduction due to sufficiency under the proportional odds model. In spite of these attractive features, the model has been applied less, as there are issues with maximum likelihood estimation and likelihood based testing approaches due to non-linearity and lack of identifiability of the parameters. We present comprehensive Bayesian inference and model comparison procedure for this class of models as an alternative to the classical frequentist approach. We illustrate our methodology by analyzing data from The Flint Men’s Health Study, a case-control study of prostate cancer in African-American men aged 40 to 79 years. We use clinical staging of prostate cancer in terms of Tumors, Nodes and Metastatsis (TNM) as the categorical response of interest. PMID:19731262

Hospitalization event notifications and reductions in readmissions of Medicare fee-for-service beneficiaries in the Bronx, New York.

PubMed

Unruh, Mark Aaron; Jung, Hye-Young; Kaushal, Rainu; Vest, Joshua R

2017-04-01

Follow-up with a primary care provider after hospital discharge has been associated with a reduced likelihood of readmission. However, primary care providers are frequently unaware of their patients' hospitalizations. Event notification may be an effective tool for reducing readmissions by notifying primary care providers when their patients have been admitted to and discharged from a hospital. We examined the effect of an event notification system on 30-day readmissions in the Bronx, New York. The Bronx has among the highest readmission rates in the country and is a particularly challenging setting to improve care due to the low socioeconomic status of the county and high rates of poor health behaviors among its residents. The study cohort included 2559 Medicare fee-for-service beneficiaries associated with 14 141 hospital admissions over the period January 2010 through June 2014. Linear regression models with beneficiary-level fixed-effects were used to estimate the impact of event notifications on readmissions by comparing the likelihood of rehospitalization for a beneficiary before and after event notifications were active. The unadjusted 30-day readmission rate when event notifications were not active was 29.5% compared to 26.5% when alerts were active. Regression estimates indicated that active hospitalization alert services were associated with a 2.9 percentage point reduction in the likelihood of readmission (95% confidence interval: -5.5, -0.4). Alerting providers through event notifications may be an effective tool for improving the quality and efficiency of care among high-risk populations. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Using artificial intelligence to predict the risk for posterior capsule opacification after phacoemulsification.

PubMed

Mohammadi, Seyed-Farzad; Sabbaghi, Mostafa; Z-Mehrjardi, Hadi; Hashemi, Hassan; Alizadeh, Somayeh; Majdi, Mercede; Taee, Farough

2012-03-01

To apply artificial intelligence models to predict the occurrence of posterior capsule opacification (PCO) after phacoemulsification. Farabi Eye Hospital, Tehran, Iran. Clinical-based cross-sectional study. The posterior capsule status of eyes operated on for age-related cataract and the need for laser capsulotomy were determined. After a literature review, data polishing, and expert consultation, 10 input variables were selected. The QUEST algorithm was used to develop a decision tree. Three back-propagation artificial neural networks were constructed with 4, 20, and 40 neurons in 2 hidden layers and trained with the same transfer functions (log-sigmoid and linear transfer) and training protocol with randomly selected eyes. They were then tested on the remaining eyes and the networks compared for their performance. Performance indices were used to compare resultant models with the results of logistic regression analysis. The models were trained using 282 randomly selected eyes and then tested using 70 eyes. Laser capsulotomy for clinically significant PCO was indicated or had been performed 2 years postoperatively in 40 eyes. A sample decision tree was produced with accuracy of 50% (likelihood ratio 0.8). The best artificial neural network, which showed 87% accuracy and a positive likelihood ratio of 8, was achieved with 40 neurons. The area under the receiver-operating-characteristic curve was 0.71. In comparison, logistic regression reached accuracy of 80%; however, the likelihood ratio was not measurable because the sensitivity was zero. A prototype artificial neural network was developed that predicted posterior capsule status (requiring capsulotomy) with reasonable accuracy. No author has a financial or proprietary interest in any material or method mentioned. Copyright © 2012 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

Robust inference in the negative binomial regression model with an application to falls data.

PubMed

Aeberhard, William H; Cantoni, Eva; Heritier, Stephane

2014-12-01

A popular way to model overdispersed count data, such as the number of falls reported during intervention studies, is by means of the negative binomial (NB) distribution. Classical estimating methods are well-known to be sensitive to model misspecifications, taking the form of patients falling much more than expected in such intervention studies where the NB regression model is used. We extend in this article two approaches for building robust M-estimators of the regression parameters in the class of generalized linear models to the NB distribution. The first approach achieves robustness in the response by applying a bounded function on the Pearson residuals arising in the maximum likelihood estimating equations, while the second approach achieves robustness by bounding the unscaled deviance components. For both approaches, we explore different choices for the bounding functions. Through a unified notation, we show how close these approaches may actually be as long as the bounding functions are chosen and tuned appropriately, and provide the asymptotic distributions of the resulting estimators. Moreover, we introduce a robust weighted maximum likelihood estimator for the overdispersion parameter, specific to the NB distribution. Simulations under various settings show that redescending bounding functions yield estimates with smaller biases under contamination while keeping high efficiency at the assumed model, and this for both approaches. We present an application to a recent randomized controlled trial measuring the effectiveness of an exercise program at reducing the number of falls among people suffering from Parkinsons disease to illustrate the diagnostic use of such robust procedures and their need for reliable inference. © 2014, The International Biometric Society.

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.

Occupational outcomes of adult childhood cancer survivors: A report from the childhood cancer survivor study.

PubMed

Kirchhoff, Anne C; Krull, Kevin R; Ness, Kirsten K; Park, Elyse R; Oeffinger, Kevin C; Hudson, Melissa M; Stovall, Marilyn; Robison, Leslie L; Wickizer, Thomas; Leisenring, Wendy

2011-07-01

The authors examined whether survivors from the Childhood Cancer Survivor Study were less likely to be in higher-skill occupations than a sibling comparison and whether certain survivors were at higher risk for lower-skill jobs. The authors created 3 mutually exclusive occupational categories for participants aged ≥ 25 years: Managerial/Professional, Nonphysical Service/Blue Collar, and Physical Service/Blue Collar. The authors examined currently employed survivors (4845) and their siblings (1727) in multivariable generalized linear models to evaluate the likelihood of being in 1 of the 3 occupational categories. Multinomial logistic regression was used among all participants to examine the likelihood of these outcomes compared to being unemployed (survivors, 6671; siblings, 2129). Multivariable linear models were used to assess survivor occupational differences by cancer-  and treatment-related variables. Personal income was compared by occupation. Employed survivors were less often in higher-skilled Managerial/Professional occupations (relative risk, 0.93; 95% confidence interval 0.89-0.98) than their siblings. Survivors who were black, were diagnosed at a younger age, or had high-dose cranial radiation were less likely to hold Managerial/Professional occupations than other survivors. In multinomial models, female survivors' likelihood of being in full-time Managerial/Professional occupations (27%) was lower than male survivors (42%) and female (41%) and male (50%) siblings. Survivors' personal income was lower than siblings within each of the 3 occupational categories in models adjusted for sociodemographic variables. Adult childhood cancer survivors are employed in lower-skill jobs than siblings. Survivors with certain treatment histories are at higher risk for lower-skill jobs and may require vocational assistance throughout adulthood. Copyright © 2011 American Cancer Society.

Hospital participation in Meaningful Use and racial disparities in readmissions.

PubMed

Unruh, Mark Aaron; Jung, Hye Young; Kaushal, Rainu; Vest, Joshua R

2018-01-01

To measure the impact of hospital participation in Meaningful Use (MU) on disparities in 30-day readmissions associated with race. A retrospective cohort study that compared the likelihood of 30-day readmission for Medicare beneficiaries discharged from hospitals participating in Stage 1 of MU with the likelihood of readmission for beneficiaries concurrently discharged from hospitals that were not participating in the initiative. Inpatient claims for 2,414,205 Medicare beneficiaries from Florida, New York, and Washington State were used as the primary data source. The study period (2009-2013) included at least 2 years of baseline data prior to each hospital initiating participation in MU. Estimates were derived with linear regression models that included hospital and time fixed effects. By including both hospital and time fixed effects, estimates were based on discharges from the same hospital in the same time period. MU participation among hospitals was not associated with a statistically significant change in readmissions for the broader Medicare population (percentage points [PP], 0.6; 95% CI, -0.2 to 1.4), but hospitals' participation in the initiative was associated with a lower likelihood of readmission for African American beneficiaries (PP, -0.9; 95% CI, -1.5 to -0.4). Hospital participation in MU reduced disparities in 30-day readmissions for African American Medicare beneficiaries.

Critical consciousness, racial and gender discrimination, and HIV disease markers in African American women with HIV.

PubMed

Kelso, Gwendolyn A; Cohen, Mardge H; Weber, Kathleen M; Dale, Sannisha K; Cruise, Ruth C; Brody, Leslie R

2014-07-01

Critical consciousness, the awareness of social oppression, is important to investigate as a buffer against HIV disease progression in HIV-infected African American women in the context of experiences with discrimination. Critical consciousness comprises several dimensions, including social group identification, discontent with distribution of social power, rejection of social system legitimacy, and a collective action orientation. The current study investigated self-reported critical consciousness as a moderator of perceived gender and racial discrimination on HIV viral load and CD4+ cell count in 67 African American HIV-infected women. Higher critical consciousness was found to be related to higher likelihood of having CD4+ counts over 350 and lower likelihood of detectable viral load when perceived racial discrimination was high, as revealed by multiple logistic regressions that controlled for highly active antiretroviral therapy (HAART) adherence. Multiple linear regressions showed that at higher levels of perceived gender and racial discrimination, women endorsing high critical consciousness had a larger positive difference between nadir CD4+ (lowest pre-HAART) and current CD4+ count than women endorsing low critical consciousness. These findings suggest that raising awareness of social oppression to promote joining with others to enact social change may be an important intervention strategy to improve HIV outcomes in African American HIV-infected women who report experiencing high levels of gender and racial discrimination.

Critical Consciousness, Racial and Gender Discrimination, and HIV Disease Markers in African American Women with HIV

PubMed Central

Kelso, Gwendolyn A.; Cohen, Mardge H.; Weber, Kathleen M.; Dale, Sannisha K.; Cruise, Ruth C.; Brody, Leslie R.

2014-01-01

Critical consciousness, the awareness of social oppression, is important to investigate as a buffer against HIV disease progression in HIV-infected African American women in the context of experiences with discrimination. Critical consciousness comprises several dimensions, including social group identification, discontent with distribution of social power, rejection of social system legitimacy, and a collective action orientation. The current study investigated self-reported critical consciousness as a moderator of perceived gender and racial discrimination on HIV viral load and CD4+ cell count in 67 African American HIV-infected women. Higher critical consciousness was found to be related to higher likelihood of having CD4+ counts over 350 and lower likelihood of detectable viral load when perceived racial discrimination was high, as revealed by multiple logistic regressions that controlled for highly active antiretroviral therapy (HAART) adherence. Multiple linear regressions showed that at higher levels of perceived gender and racial discrimination, women endorsing high critical consciousness had a larger positive difference between nadir CD4+ (lowest pre-HAART) and current CD4+ count than women endorsing low critical consciousness. These findings suggest that raising awareness of social oppression to promote joining with others to enact social change may be an important intervention strategy to improve HIV outcomes in African American HIV-infected women who report experiencing high levels of gender and racial discrimination. PMID:24077930

A comparison of model-based imputation methods for handling missing predictor values in a linear regression model: A simulation study

NASA Astrophysics Data System (ADS)

Hasan, Haliza; Ahmad, Sanizah; Osman, Balkish Mohd; Sapri, Shamsiah; Othman, Nadirah

2017-08-01

In regression analysis, missing covariate data has been a common problem. Many researchers use ad hoc methods to overcome this problem due to the ease of implementation. However, these methods require assumptions about the data that rarely hold in practice. Model-based methods such as Maximum Likelihood (ML) using the expectation maximization (EM) algorithm and Multiple Imputation (MI) are more promising when dealing with difficulties caused by missing data. Then again, inappropriate methods of missing value imputation can lead to serious bias that severely affects the parameter estimates. The main objective of this study is to provide a better understanding regarding missing data concept that can assist the researcher to select the appropriate missing data imputation methods. A simulation study was performed to assess the effects of different missing data techniques on the performance of a regression model. The covariate data were generated using an underlying multivariate normal distribution and the dependent variable was generated as a combination of explanatory variables. Missing values in covariate were simulated using a mechanism called missing at random (MAR). Four levels of missingness (10%, 20%, 30% and 40%) were imposed. ML and MI techniques available within SAS software were investigated. A linear regression analysis was fitted and the model performance measures; MSE, and R-Squared were obtained. Results of the analysis showed that MI is superior in handling missing data with highest R-Squared and lowest MSE when percent of missingness is less than 30%. Both methods are unable to handle larger than 30% level of missingness.

Measurement of pediatric regional cerebral blood flow from 6 months to 15 years of age in a clinical population.

PubMed

Carsin-Vu, Aline; Corouge, Isabelle; Commowick, Olivier; Bouzillé, Guillaume; Barillot, Christian; Ferré, Jean-Christophe; Proisy, Maia

2018-04-01

To investigate changes in cerebral blood flow (CBF) in gray matter (GM) between 6 months and 15 years of age and to provide CBF values for the brain, GM, white matter (WM), hemispheres and lobes. Between 2013 and 2016, we retrospectively included all clinical MRI examinations with arterial spin labeling (ASL). We excluded subjects with a condition potentially affecting brain perfusion. For each subject, mean values of CBF in the brain, GM, WM, hemispheres and lobes were calculated. GM CBF was fitted using linear, quadratic and cubic polynomial regression against age. Regression models were compared with Akaike's information criterion (AIC), and Likelihood Ratio tests. 84 children were included (44 females/40 males). Mean CBF values were 64.2 ± 13.8 mL/100 g/min in GM, and 29.3 ± 10.0 mL/100 g/min in WM. The best-fit model of brain perfusion was the cubic polynomial function (AIC = 672.7, versus respectively AIC = 673.9 and AIC = 674.1 with the linear negative function and the quadratic polynomial function). A statistically significant difference between the tested models demonstrating the superiority of the quadratic (p = 0.18) or cubic polynomial model (p = 0.06), over the negative linear regression model was not found. No effect of general anesthesia (p = 0.34) or of gender (p = 0.16) was found. we provided values for ASL CBF in the brain, GM, WM, hemispheres, and lobes over a wide pediatric age range, approximately showing inverted U-shaped changes in GM perfusion over the course of childhood. Copyright © 2018 Elsevier B.V. All rights reserved.

Sparse Logistic Regression for Diagnosis of Liver Fibrosis in Rat by Using SCAD-Penalized Likelihood

PubMed Central

Yan, Fang-Rong; Lin, Jin-Guan; Liu, Yu

2011-01-01

The objective of the present study is to find out the quantitative relationship between progression of liver fibrosis and the levels of certain serum markers using mathematic model. We provide the sparse logistic regression by using smoothly clipped absolute deviation (SCAD) penalized function to diagnose the liver fibrosis in rats. Not only does it give a sparse solution with high accuracy, it also provides the users with the precise probabilities of classification with the class information. In the simulative case and the experiment case, the proposed method is comparable to the stepwise linear discriminant analysis (SLDA) and the sparse logistic regression with least absolute shrinkage and selection operator (LASSO) penalty, by using receiver operating characteristic (ROC) with bayesian bootstrap estimating area under the curve (AUC) diagnostic sensitivity for selected variable. Results show that the new approach provides a good correlation between the serum marker levels and the liver fibrosis induced by thioacetamide (TAA) in rats. Meanwhile, this approach might also be used in predicting the development of liver cirrhosis. PMID:21716672

Empirical Likelihood in Nonignorable Covariate-Missing Data Problems.

PubMed

Xie, Yanmei; Zhang, Biao

2017-04-20

Missing covariate data occurs often in regression analysis, which frequently arises in the health and social sciences as well as in survey sampling. We study methods for the analysis of a nonignorable covariate-missing data problem in an assumed conditional mean function when some covariates are completely observed but other covariates are missing for some subjects. We adopt the semiparametric perspective of Bartlett et al. (Improving upon the efficiency of complete case analysis when covariates are MNAR. Biostatistics 2014;15:719-30) on regression analyses with nonignorable missing covariates, in which they have introduced the use of two working models, the working probability model of missingness and the working conditional score model. In this paper, we study an empirical likelihood approach to nonignorable covariate-missing data problems with the objective of effectively utilizing the two working models in the analysis of covariate-missing data. We propose a unified approach to constructing a system of unbiased estimating equations, where there are more equations than unknown parameters of interest. One useful feature of these unbiased estimating equations is that they naturally incorporate the incomplete data into the data analysis, making it possible to seek efficient estimation of the parameter of interest even when the working regression function is not specified to be the optimal regression function. We apply the general methodology of empirical likelihood to optimally combine these unbiased estimating equations. We propose three maximum empirical likelihood estimators of the underlying regression parameters and compare their efficiencies with other existing competitors. We present a simulation study to compare the finite-sample performance of various methods with respect to bias, efficiency, and robustness to model misspecification. The proposed empirical likelihood method is also illustrated by an analysis of a data set from the US National Health and Nutrition Examination Survey (NHANES).

A geometric approach to non-linear correlations with intrinsic scatter

NASA Astrophysics Data System (ADS)

Pihajoki, Pauli

2017-12-01

We propose a new mathematical model for n - k-dimensional non-linear correlations with intrinsic scatter in n-dimensional data. The model is based on Riemannian geometry and is naturally symmetric with respect to the measured variables and invariant under coordinate transformations. We combine the model with a Bayesian approach for estimating the parameters of the correlation relation and the intrinsic scatter. A side benefit of the approach is that censored and truncated data sets and independent, arbitrary measurement errors can be incorporated. We also derive analytic likelihoods for the typical astrophysical use case of linear relations in n-dimensional Euclidean space. We pay particular attention to the case of linear regression in two dimensions and compare our results to existing methods. Finally, we apply our methodology to the well-known MBH-σ correlation between the mass of a supermassive black hole in the centre of a galactic bulge and the corresponding bulge velocity dispersion. The main result of our analysis is that the most likely slope of this correlation is ∼6 for the data sets used, rather than the values in the range of ∼4-5 typically quoted in the literature for these data.

Constructing a consumption model of fine dining from the perspective of behavioral economics

PubMed Central

Tsai, Sang-Bing

2018-01-01

Numerous factors affect how people choose a fine dining restaurant, including food quality, service quality, food safety, and hedonic value. A conceptual framework for evaluating restaurant selection behavior has not yet been developed. This study surveyed 150 individuals with fine dining experience and proposed the use of mental accounting and axiomatic design to construct a consumer economic behavior model. Linear and logistic regressions were employed to determine model correlations and the probability of each factor affecting behavior. The most crucial factor was food quality, followed by service and dining motivation, particularly regarding family dining. Safe ingredients, high cooking standards, and menu innovation all increased the likelihood of consumers choosing fine dining restaurants. PMID:29641554

Constructing a consumption model of fine dining from the perspective of behavioral economics.

PubMed

Hsu, Sheng-Hsun; Hsiao, Cheng-Fu; Tsai, Sang-Bing

2018-01-01

Numerous factors affect how people choose a fine dining restaurant, including food quality, service quality, food safety, and hedonic value. A conceptual framework for evaluating restaurant selection behavior has not yet been developed. This study surveyed 150 individuals with fine dining experience and proposed the use of mental accounting and axiomatic design to construct a consumer economic behavior model. Linear and logistic regressions were employed to determine model correlations and the probability of each factor affecting behavior. The most crucial factor was food quality, followed by service and dining motivation, particularly regarding family dining. Safe ingredients, high cooking standards, and menu innovation all increased the likelihood of consumers choosing fine dining restaurants.

Comparing methods of analysing datasets with small clusters: case studies using four paediatric datasets.

PubMed

Marston, Louise; Peacock, Janet L; Yu, Keming; Brocklehurst, Peter; Calvert, Sandra A; Greenough, Anne; Marlow, Neil

2009-07-01

Studies of prematurely born infants contain a relatively large percentage of multiple births, so the resulting data have a hierarchical structure with small clusters of size 1, 2 or 3. Ignoring the clustering may lead to incorrect inferences. The aim of this study was to compare statistical methods which can be used to analyse such data: generalised estimating equations, multilevel models, multiple linear regression and logistic regression. Four datasets which differed in total size and in percentage of multiple births (n = 254, multiple 18%; n = 176, multiple 9%; n = 10 098, multiple 3%; n = 1585, multiple 8%) were analysed. With the continuous outcome, two-level models produced similar results in the larger dataset, while generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) produced divergent estimates using the smaller dataset. For the dichotomous outcome, most methods, except generalised least squares multilevel modelling (ML GH 'xtlogit' in Stata) gave similar odds ratios and 95% confidence intervals within datasets. For the continuous outcome, our results suggest using multilevel modelling. We conclude that generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) should be used with caution when the dataset is small. Where the outcome is dichotomous and there is a relatively large percentage of non-independent data, it is recommended that these are accounted for in analyses using logistic regression with adjusted standard errors or multilevel modelling. If, however, the dataset has a small percentage of clusters greater than size 1 (e.g. a population dataset of children where there are few multiples) there appears to be less need to adjust for clustering.

Parafunctional habits are associated cumulatively to painful temporomandibular disorders in adolescents.

PubMed

Fernandes, Giovana; Franco-Micheloni, Ana Lúcia; Siqueira, José Tadeu Tesseroli; Gonçalves, Daniela Aparecida Godói; Camparis, Cinara Maria

2016-01-01

This cross-sectional study was designed to evaluate the effect of sleep bruxism, awake bruxism and parafunctional habits, both separately and cumulatively, on the likelihood of adolescents to present painful TMD. The study was conducted on a sample of 1,094 adolescents (aged 12-14). The presence of painful TMD was assessed using the Research Diagnostic Criteria for Temporomandibular Disorders, Axis I. Data on sleep bruxism, awake bruxism and parafunctional habits (nail/pen/pencil/lip/cheek biting, resting one's head on one's hand, and gum chewing) were researched by self-report. After adjusting for potential demographic confounders using logistic regression, each of the predictor variables (sleep bruxism, awake bruxism and parafunctional habits) was significantly associated with painful TMD. In addition, the odds for painful TMD were higher in the concomitant presence of two (OR=4.6, [95%CI=2.06, 10.37]) or three predictor (OR=13.7, [95%CI=5.72, 32.96]) variables. These findings indicate that the presence of concomitant muscle activities during sleep and awake bruxism and parafunctional habits increases the likelihood almost linearly of adolescents to present painful TMD.

Modeling gene expression measurement error: a quasi-likelihood approach

PubMed Central

Strimmer, Korbinian

2003-01-01

Background Using suitable error models for gene expression measurements is essential in the statistical analysis of microarray data. However, the true probabilistic model underlying gene expression intensity readings is generally not known. Instead, in currently used approaches some simple parametric model is assumed (usually a transformed normal distribution) or the empirical distribution is estimated. However, both these strategies may not be optimal for gene expression data, as the non-parametric approach ignores known structural information whereas the fully parametric models run the risk of misspecification. A further related problem is the choice of a suitable scale for the model (e.g. observed vs. log-scale). Results Here a simple semi-parametric model for gene expression measurement error is presented. In this approach inference is based an approximate likelihood function (the extended quasi-likelihood). Only partial knowledge about the unknown true distribution is required to construct this function. In case of gene expression this information is available in the form of the postulated (e.g. quadratic) variance structure of the data. As the quasi-likelihood behaves (almost) like a proper likelihood, it allows for the estimation of calibration and variance parameters, and it is also straightforward to obtain corresponding approximate confidence intervals. Unlike most other frameworks, it also allows analysis on any preferred scale, i.e. both on the original linear scale as well as on a transformed scale. It can also be employed in regression approaches to model systematic (e.g. array or dye) effects. Conclusions The quasi-likelihood framework provides a simple and versatile approach to analyze gene expression data that does not make any strong distributional assumptions about the underlying error model. For several simulated as well as real data sets it provides a better fit to the data than competing models. In an example it also improved the power of tests to identify differential expression. PMID:12659637

A computational approach to compare regression modelling strategies in prediction research.

PubMed

Pajouheshnia, Romin; Pestman, Wiebe R; Teerenstra, Steven; Groenwold, Rolf H H

2016-08-25

It is often unclear which approach to fit, assess and adjust a model will yield the most accurate prediction model. We present an extension of an approach for comparing modelling strategies in linear regression to the setting of logistic regression and demonstrate its application in clinical prediction research. A framework for comparing logistic regression modelling strategies by their likelihoods was formulated using a wrapper approach. Five different strategies for modelling, including simple shrinkage methods, were compared in four empirical data sets to illustrate the concept of a priori strategy comparison. Simulations were performed in both randomly generated data and empirical data to investigate the influence of data characteristics on strategy performance. We applied the comparison framework in a case study setting. Optimal strategies were selected based on the results of a priori comparisons in a clinical data set and the performance of models built according to each strategy was assessed using the Brier score and calibration plots. The performance of modelling strategies was highly dependent on the characteristics of the development data in both linear and logistic regression settings. A priori comparisons in four empirical data sets found that no strategy consistently outperformed the others. The percentage of times that a model adjustment strategy outperformed a logistic model ranged from 3.9 to 94.9 %, depending on the strategy and data set. However, in our case study setting the a priori selection of optimal methods did not result in detectable improvement in model performance when assessed in an external data set. The performance of prediction modelling strategies is a data-dependent process and can be highly variable between data sets within the same clinical domain. A priori strategy comparison can be used to determine an optimal logistic regression modelling strategy for a given data set before selecting a final modelling approach.

Factors associated with adverse clinical outcomes among obstetric trainees

PubMed Central

Aiken PhD, Catherine E.; Aiken, Abigail; Park, Hannah; Brockelsby, Jeremy C.; Prentice, Andrew

2016-01-01

Objective To determine whether UK obstetric trainees transitioning from directly to indirectly-supervised practice have a higher likelihood of adverse patient outcomes from operative deliveries compared to other indirectly supervised trainees and to examine whether performing more procedures under direct supervision is associated with fewer adverse outcomes in initial indirect practice. Methods We examined all deliveries (13,861) conducted by obstetricians at a single centre over 5 years (2008-2013). Mixed-effects logistic regression models were used to compare estimated blood loss, maternal trauma, umbilical arterial pH, delayed neonatal respiration, failed instrumental delivery, and critical incidents for trainees in their first indirectly-supervised year with trainees in all other years of indirect practice. Outcomes for trainees in their first indirectly-supervised 3 months were compared to their outcomes for the remainder of the year. Linear regression was used to examine the relationship between number of procedures performed under direct supervision and initial outcomes under indirect supervision. Results Trainees in their first indirectly-supervised year had a higher likelihood of >2 litres estimated blood loss at any delivery (OR 1.32;CI(1.01-1.64) p<0.05) and of failed instrumental delivery (OR 2.33;CI(1.37-3.29) p<0.05) compared with other indirectly-supervised trainees. Other measured outcomes showed no significant differences. Within the first three months of indirect supervision, the likelihood of operative vaginal deliveries with >1litre estimated blood loss (OR 2.54;CI(1.88-3.20) p<0.05) was higher compared to the remainder of the first year. Performing more deliveries under direct supervision prior to beginning indirectly-supervised training was associated with decreased risk of >1litre estimated blood loss (p<0.05). Conclusions Obstetric trainees in their first year of indirectly-supervised practice have a higher likelihood of immediate adverse delivery outcomes, which are primarily maternal rather than neonatal. Undertaking more directly supervised procedures prior to transitioning to indirectly-supervised practice may reduce adverse outcomes, suggesting that experience is a key consideration in obstetric training programme design. PMID:26077215

Coping Styles in Heart Failure Patients with Depressive Symptoms

PubMed Central

Trivedi, Ranak B.; Blumenthal, James A.; O'Connor, Christopher; Adams, Kirkwood; Hinderliter, Alan; Sueta-Dupree, Carla; Johnson, Kristy; Sherwood, Andrew

2009-01-01

Objective Elevated depressive symptoms have been linked to poorer prognosis in heart failure (HF) patients. Our objective was to identify coping styles associated with depressive symptoms in HF patients. Methods 222 stable HF patients (32.75% female, 45.4% non-Hispanic Black) completed multiple questionnaires. Beck Depression Inventory (BDI) assessed depressive symptoms, Life Orientation Test (LOT-R) assessed optimism, ENRICHD Social Support Inventory (ESSI) and Perceived Social Support Scale (PSSS) assessed social support, and COPE assessed coping styles. Linear regression analyses were employed to assess the association of coping styles with continuous BDI scores. Logistic regression analyses were performed using BDI scores dichotomized into BDI<10 versus BDI≥10, to identify coping styles accompanying clinically significant depressive symptoms. Results In linear regression models, higher BDI scores were associated with lower scores on the acceptance (β=-.14), humor (β=-.15), planning (β=-.15), and emotional support (β=-.14) subscales of the COPE, and higher scores on the behavioral disengagement (β=.41), denial (β=.33), venting (β=.25), and mental disengagement (β=.22) subscales. Higher PSSS and ESSI scores were associated with lower BDI scores (β=-.32 and -.25, respectively). Higher LOT-R scores were associated with higher BDI scores (β=.39, p<.001). In logistical regression models, BDI≥10 was associated with greater likelihood of behavioral disengagement (OR=1.3), denial (OR=1.2), mental disengagement (OR=1.3), venting (OR=1.2), and pessimism (OR=1.2), and lower perceived social support measured by PSSS (OR=.92) and ESSI (OR=.92). Conclusion Depressive symptoms in HF patients are associated with avoidant coping, lower perceived social support, and pessimism. Results raise the possibility that interventions designed to improve coping may reduce depressive symptoms. PMID:19773027

A new statistical approach to climate change detection and attribution

NASA Astrophysics Data System (ADS)

Ribes, Aurélien; Zwiers, Francis W.; Azaïs, Jean-Marc; Naveau, Philippe

2017-01-01

We propose here a new statistical approach to climate change detection and attribution that is based on additive decomposition and simple hypothesis testing. Most current statistical methods for detection and attribution rely on linear regression models where the observations are regressed onto expected response patterns to different external forcings. These methods do not use physical information provided by climate models regarding the expected response magnitudes to constrain the estimated responses to the forcings. Climate modelling uncertainty is difficult to take into account with regression based methods and is almost never treated explicitly. As an alternative to this approach, our statistical model is only based on the additivity assumption; the proposed method does not regress observations onto expected response patterns. We introduce estimation and testing procedures based on likelihood maximization, and show that climate modelling uncertainty can easily be accounted for. Some discussion is provided on how to practically estimate the climate modelling uncertainty based on an ensemble of opportunity. Our approach is based on the " models are statistically indistinguishable from the truth" paradigm, where the difference between any given model and the truth has the same distribution as the difference between any pair of models, but other choices might also be considered. The properties of this approach are illustrated and discussed based on synthetic data. Lastly, the method is applied to the linear trend in global mean temperature over the period 1951-2010. Consistent with the last IPCC assessment report, we find that most of the observed warming over this period (+0.65 K) is attributable to anthropogenic forcings (+0.67 ± 0.12 K, 90 % confidence range), with a very limited contribution from natural forcings (-0.01± 0.02 K).

Coping styles in heart failure patients with depressive symptoms.

PubMed

Trivedi, Ranak B; Blumenthal, James A; O'Connor, Christopher; Adams, Kirkwood; Hinderliter, Alan; Dupree, Carla; Johnson, Kristy; Sherwood, Andrew

2009-10-01

Elevated depressive symptoms have been linked to poorer prognosis in heart failure (HF) patients. Our objective was to identify coping styles associated with depressive symptoms in HF patients. A total of 222 stable HF patients (32.75% female, 45.4% non-Hispanic black) completed multiple questionnaires. Beck Depression Inventory (BDI) assessed depressive symptoms, Life Orientation Test (LOT-R) assessed optimism, ENRICHD Social Support Inventory (ESSI) and Perceived Social Support Scale (PSSS) assessed social support, and COPE assessed coping styles. Linear regression analyses were employed to assess the association of coping styles with continuous BDI scores. Logistic regression analyses were performed using BDI scores dichotomized into BDI<10 vs. BDI> or =10, to identify coping styles accompanying clinically significant depressive symptoms. In linear regression models, higher BDI scores were associated with lower scores on the acceptance (beta=-.14), humor (beta=-.15), planning (beta=-.15), and emotional support (beta=-.14) subscales of the COPE, and higher scores on the behavioral disengagement (beta=.41), denial (beta=.33), venting (beta=.25), and mental disengagement (beta=.22) subscales. Higher PSSS and ESSI scores were associated with lower BDI scores (beta=-.32 and -.25, respectively). Higher LOT-R scores were associated with higher BDI scores (beta=.39, P<.001). In logistical regression models, BDI> or =10 was associated with greater likelihood of behavioral disengagement (OR=1.3), denial (OR=1.2), mental disengagement (OR=1.3), venting (OR=1.2), and pessimism (OR=1.2), and lower perceived social support measured by PSSS (OR=.92) and ESSI (OR=.92). Depressive symptoms in HF patients are associated with avoidant coping, lower perceived social support, and pessimism. Results raise the possibility that interventions designed to improve coping may reduce depressive symptoms.

Estimating Function Approaches for Spatial Point Processes

NASA Astrophysics Data System (ADS)

Deng, Chong

Spatial point pattern data consist of locations of events that are often of interest in biological and ecological studies. Such data are commonly viewed as a realization from a stochastic process called spatial point process. To fit a parametric spatial point process model to such data, likelihood-based methods have been widely studied. However, while maximum likelihood estimation is often too computationally intensive for Cox and cluster processes, pairwise likelihood methods such as composite likelihood, Palm likelihood usually suffer from the loss of information due to the ignorance of correlation among pairs. For many types of correlated data other than spatial point processes, when likelihood-based approaches are not desirable, estimating functions have been widely used for model fitting. In this dissertation, we explore the estimating function approaches for fitting spatial point process models. These approaches, which are based on the asymptotic optimal estimating function theories, can be used to incorporate the correlation among data and yield more efficient estimators. We conducted a series of studies to demonstrate that these estmating function approaches are good alternatives to balance the trade-off between computation complexity and estimating efficiency. First, we propose a new estimating procedure that improves the efficiency of pairwise composite likelihood method in estimating clustering parameters. Our approach combines estimating functions derived from pairwise composite likeli-hood estimation and estimating functions that account for correlations among the pairwise contributions. Our method can be used to fit a variety of parametric spatial point process models and can yield more efficient estimators for the clustering parameters than pairwise composite likelihood estimation. We demonstrate its efficacy through a simulation study and an application to the longleaf pine data. Second, we further explore the quasi-likelihood approach on fitting second-order intensity function of spatial point processes. However, the original second-order quasi-likelihood is barely feasible due to the intense computation and high memory requirement needed to solve a large linear system. Motivated by the existence of geometric regular patterns in the stationary point processes, we find a lower dimension representation of the optimal weight function and propose a reduced second-order quasi-likelihood approach. Through a simulation study, we show that the proposed method not only demonstrates superior performance in fitting the clustering parameter but also merits in the relaxation of the constraint of the tuning parameter, H. Third, we studied the quasi-likelihood type estimating funciton that is optimal in a certain class of first-order estimating functions for estimating the regression parameter in spatial point process models. Then, by using a novel spectral representation, we construct an implementation that is computationally much more efficient and can be applied to more general setup than the original quasi-likelihood method.

The relationship between apical root resorption and orthodontic tooth movement in growing subjects.

PubMed

Xu, Tianmin; Baumrind, S

2002-07-01

To investigate the relationship between apical root resorption and orthodontic tooth movement in growing subjects. 58 growing subjects were collected randomly into the study sample and another 40 non-treated cases were used as control. The apical resoption of the upper central incisors was measured on periapical film and the incisor displacement was measured on lateral cephalogram. Using multiple linear regression analysis to examine the relationship between root resoption and the displacement of the upper incisor apex in each of four direction (retraction, advancement, intrusion and extrusion). The statistically significant negative association were found between resorption and both intrusion (P < 0.001) and extrusion (P < 0.05), but no significant association was found between resorption and both retraction and advancement. The regression analysis implied an average of 2.29 mm resorption in the absence of apical displacement. The likelihood that the magnitude of displacement of the incisor root is positively associated with root resoption in the population of treated growing subjects is very small.

Radar modulation classification using time-frequency representation and nonlinear regression

NASA Astrophysics Data System (ADS)

De Luigi, Christophe; Arques, Pierre-Yves; Lopez, Jean-Marc; Moreau, Eric

1999-09-01

In naval electronic environment, pulses emitted by radars are collected by ESM receivers. For most of them the intrapulse signal is modulated by a particular law. To help the classical identification process, a classification and estimation of this modulation law is applied on the intrapulse signal measurements. To estimate with a good accuracy the time-varying frequency of a signal corrupted by an additive noise, one method has been chosen. This method consists on the Wigner distribution calculation, the instantaneous frequency is then estimated by the peak location of the distribution. Bias and variance of the estimator are performed by computed simulations. In a estimated sequence of frequencies, we assume the presence of false and good estimated ones, the hypothesis of Gaussian distribution is made on the errors. A robust non linear regression method, based on the Levenberg-Marquardt algorithm, is thus applied on these estimated frequencies using a Maximum Likelihood Estimator. The performances of the method are tested by using varied modulation laws and different signal to noise ratios.

Varicocele is associated with hypogonadism and impaired erectile function: a prospective comparative study.

PubMed

Ji, B; Jin, X-B

2017-08-01

We conducted this prospective comparative study to examine the hypothesis that varicocele was associated with hypogonadism and impaired erectile function as reflected in International Index of Erectile Function-5 (IIEF-5) scores as well as nocturnal penile tumescence and rigidity (NPTR) parameters. From December 2014 to December 2015, a total of 130 males with varicocele complaining of infertility or scrotal discomfort and 130 age-matched healthy males chosen from volunteer healthy hospital staff as controls were recruited in this study. Serum testosterone (TT) levels and IIEF-5 scores as well as NPTR parameters were evaluated and compared between varicocele and control subjects. All participants were further grouped into hypogonadism based on the cut-off value 300 ng/dL. A total of 45 of 130 patients were identified as hypogonadism, while it was not found in control subjects. A multivariate logistic regression with likelihood ratio test revealed that TT levels as well as grade III and II varicocele posed significant indicators for hypogonadism occurrence (chi-square of likelihood ratio = 12.40, df = 3, p < .01). Furthermore, TT levels and infertility duration were associated with IIEF-5 scores in a multivariate linear regression analysis (adjusted R 2  = 0.545). In conclusion, the correlation of grade III and II varicocele with an increased risk of hypogonadism was confirmed in this study and an impaired erectile function correlated with TT levels and infertility duration was also observed. © 2016 Blackwell Verlag GmbH.

Profile-Likelihood Approach for Estimating Generalized Linear Mixed Models with Factor Structures

ERIC Educational Resources Information Center

Jeon, Minjeong; Rabe-Hesketh, Sophia

2012-01-01

In this article, the authors suggest a profile-likelihood approach for estimating complex models by maximum likelihood (ML) using standard software and minimal programming. The method works whenever setting some of the parameters of the model to known constants turns the model into a standard model. An important class of models that can be…

The use of auxiliary variables in capture-recapture and removal experiments

USGS Publications Warehouse

Pollock, K.H.; Hines, J.E.; Nichols, J.D.

1984-01-01

The dependence of animal capture probabilities on auxiliary variables is an important practical problem which has not been considered in the development of estimation procedures for capture-recapture and removal experiments. In this paper the linear logistic binary regression model is used to relate the probability of capture to continuous auxiliary variables. The auxiliary variables could be environmental quantities such as air or water temperature, or characteristics of individual animals, such as body length or weight. Maximum likelihood estimators of the population parameters are considered for a variety of models which all assume a closed population. Testing between models is also considered. The models can also be used when one auxiliary variable is a measure of the effort expended in obtaining the sample.

Valuing a Lifestyle Intervention for Middle Eastern Immigrants at Risk of Diabetes.

PubMed

Saha, Sanjib; Gerdtham, Ulf-G; Siddiqui, Faiza; Bennet, Louise

2018-02-27

Willingness-to-pay (WTP) techniques are increasingly being used in the healthcare sector for assessing the value of interventions. The objective of this study was to estimate WTP and its predictors in a randomized controlled trial of a lifestyle intervention exclusively targeting Middle Eastern immigrants living in Malmö, Sweden, who are at high risk of type 2 diabetes. We used the contingent valuation method to evaluate WTP. The questionnaire was designed following the payment-scale approach, and administered at the end of the trial, giving an ex-post perspective. We performed logistic regression and linear regression techniques to identify the factors associated with zero WTP value and positive WTP values. The intervention group had significantly higher average WTP than the control group (216 SEK vs. 127 SEK; p = 0.035; 1 U.S.$ = 8.52 SEK, 2015 price year) per month. The regression models demonstrated that being in the intervention group, acculturation, and self-employment were significant factors associated with positive WTP values. Male participants and lower-educated participants had a significantly higher likelihood of zero WTP. In this era of increased migration, our findings can help policy makers to take informed decisions to implement lifestyle interventions for immigrant populations.

Valuing a Lifestyle Intervention for Middle Eastern Immigrants at Risk of Diabetes

PubMed Central

Siddiqui, Faiza

2018-01-01

Willingness-to-pay (WTP) techniques are increasingly being used in the healthcare sector for assessing the value of interventions. The objective of this study was to estimate WTP and its predictors in a randomized controlled trial of a lifestyle intervention exclusively targeting Middle Eastern immigrants living in Malmö, Sweden, who are at high risk of type 2 diabetes. We used the contingent valuation method to evaluate WTP. The questionnaire was designed following the payment-scale approach, and administered at the end of the trial, giving an ex-post perspective. We performed logistic regression and linear regression techniques to identify the factors associated with zero WTP value and positive WTP values. The intervention group had significantly higher average WTP than the control group (216 SEK vs. 127 SEK; p = 0.035; 1 U.S.$ = 8.52 SEK, 2015 price year) per month. The regression models demonstrated that being in the intervention group, acculturation, and self-employment were significant factors associated with positive WTP values. Male participants and lower-educated participants had a significantly higher likelihood of zero WTP. In this era of increased migration, our findings can help policy makers to take informed decisions to implement lifestyle interventions for immigrant populations. PMID:29495529

Predicting the occurrence of wildfires with binary structured additive regression models.

PubMed

Ríos-Pena, Laura; Kneib, Thomas; Cadarso-Suárez, Carmen; Marey-Pérez, Manuel

2017-02-01

Wildfires are one of the main environmental problems facing societies today, and in the case of Galicia (north-west Spain), they are the main cause of forest destruction. This paper used binary structured additive regression (STAR) for modelling the occurrence of wildfires in Galicia. Binary STAR models are a recent contribution to the classical logistic regression and binary generalized additive models. Their main advantage lies in their flexibility for modelling non-linear effects, while simultaneously incorporating spatial and temporal variables directly, thereby making it possible to reveal possible relationships among the variables considered. The results showed that the occurrence of wildfires depends on many covariates which display variable behaviour across space and time, and which largely determine the likelihood of ignition of a fire. The joint possibility of working on spatial scales with a resolution of 1 × 1 km cells and mapping predictions in a colour range makes STAR models a useful tool for plotting and predicting wildfire occurrence. Lastly, it will facilitate the development of fire behaviour models, which can be invaluable when it comes to drawing up fire-prevention and firefighting plans. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Continuous Threshold Expectile Model.

PubMed

Zhang, Feipeng; Li, Qunhua

2017-12-01

Expectile regression is a useful tool for exploring the relation between the response and the explanatory variables beyond the conditional mean. A continuous threshold expectile regression is developed for modeling data in which the effect of a covariate on the response variable is linear but varies below and above an unknown threshold in a continuous way. The estimators for the threshold and the regression coefficients are obtained using a grid search approach. The asymptotic properties for all the estimators are derived, and the estimator for the threshold is shown to achieve root-n consistency. A weighted CUSUM type test statistic is proposed for the existence of a threshold at a given expectile, and its asymptotic properties are derived under both the null and the local alternative models. This test only requires fitting the model under the null hypothesis in the absence of a threshold, thus it is computationally more efficient than the likelihood-ratio type tests. Simulation studies show that the proposed estimators and test have desirable finite sample performance in both homoscedastic and heteroscedastic cases. The application of the proposed method on a Dutch growth data and a baseball pitcher salary data reveals interesting insights. The proposed method is implemented in the R package cthreshER .

Effects of vehicle power on passenger vehicle speeds.

PubMed

McCartt, Anne T; Hu, Wen

2017-07-04

During the past 2 decades, there have been large increases in mean horsepower and the mean horsepower-to-vehicle weight ratio for all types of new passenger vehicles in the United States. This study examined the relationship between travel speeds and vehicle power, defined as horsepower per 100 pounds of vehicle weight. Speed cameras measured travel speeds and photographed license plates and drivers of passenger vehicles traveling on roadways in Northern Virginia during daytime off-peak hours in spring 2013. The driver licensing agencies in the District of Columbia, Maryland, and Virginia provided vehicle information numbers (VINs) by matching license plate numbers with vehicle registration records and provided the age, gender, and ZIP code of the registered owner(s). VINs were decoded to obtain the curb weight and horsepower of vehicles. The study focused on 26,659 observed vehicles for which information on horsepower was available and the observed age and gender of drivers matched vehicle registration records. Log-linear regression estimated the effects of vehicle power on mean travel speeds, and logistic regression estimated the effects of vehicle power on the likelihood of a vehicle traveling over the speed limit and more than 10 mph over the limit. After controlling for driver characteristics, speed limit, vehicle type, and traffic volume, a 1-unit increase in vehicle power was associated with a 0.7% increase in mean speed, a 2.7% increase in the likelihood of a vehicle exceeding the speed limit by any amount, and an 11.6% increase in the likelihood of a vehicle exceeding the limit by 10 mph. All of these increases were highly significant. Speeding persists as a major factor in crashes in the United States. There are indications that travel speeds have increased in recent years. The current findings suggest the trend toward substantially more powerful vehicles may be contributing to higher speeds. Given the strong association between travel speed and crash risk and crash severity, this is cause for concern.

Do depression and anxiety reduce the likelihood of remission in rheumatoid arthritis and psoriatic arthritis? Data from the prospective multicentre NOR-DMARD study.

PubMed

Michelsen, Brigitte; Kristianslund, Eirik Klami; Sexton, Joseph; Hammer, Hilde Berner; Fagerli, Karen Minde; Lie, Elisabeth; Wierød, Ada; Kalstad, Synøve; Rødevand, Erik; Krøll, Frode; Haugeberg, Glenn; Kvien, Tore K

2017-11-01

To investigate the predictive value of baseline depression/anxiety on the likelihood of achieving joint remission in rheumatoid arthritis (RA) and psoriatic arthritis (PsA) as well as the associations between baseline depression/anxiety and the components of the remission criteria at follow-up. We included 1326 patients with RA and 728 patients with PsA from the prospective observational NOR-DMARD study starting first-time tumour necrosis factor inhibitors or methotrexate. The predictive value of depression/anxiety on remission was explored in prespecified logistic regression models and the associations between baseline depression/anxiety and the components of the remission criteria in prespecified multiple linear regression models. Baseline depression/anxiety according to EuroQoL-5D-3L, Short Form-36 (SF-36) Mental Health subscale ≤56 and SF-36 Mental Component Summary ≤38 negatively predicted 28-joint Disease Activity Score <2.6, Simplified Disease Activity Index ≤3.3, Clinical Disease Activity Index ≤2.8, ACR/EULAR Boolean and Disease Activity Index for Psoriatic Arthritis ≤4 remission after 3 and 6 months treatment in RA (p≤0.008) and partly in PsA (p from 0.001 to 0.73). Baseline depression/anxiety was associated with increased patient's and evaluator's global assessment, tender joint count and joint pain in RA at follow-up, but not with swollen joint count and acute phase reactants. Depression and anxiety may reduce likelihood of joint remission based on composite scores in RA and PsA and should be taken into account in individual patients when making a shared decision on a treatment target. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Posterior propriety for hierarchical models with log-likelihoods that have norm bounds

DOE PAGES

Michalak, Sarah E.; Morris, Carl N.

2015-07-17

Statisticians often use improper priors to express ignorance or to provide good frequency properties, requiring that posterior propriety be verified. Our paper addresses generalized linear mixed models, GLMMs, when Level I parameters have Normal distributions, with many commonly-used hyperpriors. It provides easy-to-verify sufficient posterior propriety conditions based on dimensions, matrix ranks, and exponentiated norm bounds, ENBs, for the Level I likelihood. Since many familiar likelihoods have ENBs, which is often verifiable via log-concavity and MLE finiteness, our novel use of ENBs permits unification of posterior propriety results and posterior MGF/moment results for many useful Level I distributions, including those commonlymore » used with multilevel generalized linear models, e.g., GLMMs and hierarchical generalized linear models, HGLMs. Furthermore, those who need to verify existence of posterior distributions or of posterior MGFs/moments for a multilevel generalized linear model given a proper or improper multivariate F prior as in Section 1 should find the required results in Sections 1 and 2 and Theorem 3 (GLMMs), Theorem 4 (HGLMs), or Theorem 5 (posterior MGFs/moments).« less

Empirical likelihood inference in randomized clinical trials.

PubMed

Zhang, Biao

2017-01-01

In individually randomized controlled trials, in addition to the primary outcome, information is often available on a number of covariates prior to randomization. This information is frequently utilized to undertake adjustment for baseline characteristics in order to increase precision of the estimation of average treatment effects; such adjustment is usually performed via covariate adjustment in outcome regression models. Although the use of covariate adjustment is widely seen as desirable for making treatment effect estimates more precise and the corresponding hypothesis tests more powerful, there are considerable concerns that objective inference in randomized clinical trials can potentially be compromised. In this paper, we study an empirical likelihood approach to covariate adjustment and propose two unbiased estimating functions that automatically decouple evaluation of average treatment effects from regression modeling of covariate-outcome relationships. The resulting empirical likelihood estimator of the average treatment effect is as efficient as the existing efficient adjusted estimators 1 when separate treatment-specific working regression models are correctly specified, yet are at least as efficient as the existing efficient adjusted estimators 1 for any given treatment-specific working regression models whether or not they coincide with the true treatment-specific covariate-outcome relationships. We present a simulation study to compare the finite sample performance of various methods along with some results on analysis of a data set from an HIV clinical trial. The simulation results indicate that the proposed empirical likelihood approach is more efficient and powerful than its competitors when the working covariate-outcome relationships by treatment status are misspecified.

A simulation study on Bayesian Ridge regression models for several collinearity levels

NASA Astrophysics Data System (ADS)

Efendi, Achmad; Effrihan

2017-12-01

When analyzing data with multiple regression model if there are collinearities, then one or several predictor variables are usually omitted from the model. However, there sometimes some reasons, for instance medical or economic reasons, the predictors are all important and should be included in the model. Ridge regression model is not uncommon in some researches to use to cope with collinearity. Through this modeling, weights for predictor variables are used for estimating parameters. The next estimation process could follow the concept of likelihood. Furthermore, for the estimation nowadays the Bayesian version could be an alternative. This estimation method does not match likelihood one in terms of popularity due to some difficulties; computation and so forth. Nevertheless, with the growing improvement of computational methodology recently, this caveat should not at the moment become a problem. This paper discusses about simulation process for evaluating the characteristic of Bayesian Ridge regression parameter estimates. There are several simulation settings based on variety of collinearity levels and sample sizes. The results show that Bayesian method gives better performance for relatively small sample sizes, and for other settings the method does perform relatively similar to the likelihood method.

Linear mixed-effects models to describe individual tree crown width for China-fir in Fujian Province, southeast China.

PubMed

Hao, Xu; Yujun, Sun; Xinjie, Wang; Jin, Wang; Yao, Fu

2015-01-01

A multiple linear model was developed for individual tree crown width of Cunninghamia lanceolata (Lamb.) Hook in Fujian province, southeast China. Data were obtained from 55 sample plots of pure China-fir plantation stands. An Ordinary Linear Least Squares (OLS) regression was used to establish the crown width model. To adjust for correlations between observations from the same sample plots, we developed one level linear mixed-effects (LME) models based on the multiple linear model, which take into account the random effects of plots. The best random effects combinations for the LME models were determined by the Akaike's information criterion, the Bayesian information criterion and the -2logarithm likelihood. Heteroscedasticity was reduced by three residual variance functions: the power function, the exponential function and the constant plus power function. The spatial correlation was modeled by three correlation structures: the first-order autoregressive structure [AR(1)], a combination of first-order autoregressive and moving average structures [ARMA(1,1)], and the compound symmetry structure (CS). Then, the LME model was compared to the multiple linear model using the absolute mean residual (AMR), the root mean square error (RMSE), and the adjusted coefficient of determination (adj-R2). For individual tree crown width models, the one level LME model showed the best performance. An independent dataset was used to test the performance of the models and to demonstrate the advantage of calibrating LME models.

Income and Subjective Well-Being: New Insights from Relatively Healthy American Women, Ages 49-79.

PubMed

Wyshak, Grace

2016-01-01

The interests of economists, psychologists, social scientists and others on the relations of income, demographics, religion and subjective well-being, have generated a vast global literature. It is apparent that biomedical research has focused on white with men. The Women's Health Initiative and Observational Study (WHI OS) was initiated in 1992. The OS represents the scientific need for social priorities to improve the health and welfare of women; it includes 93.676 relatively healthy postmenopausal women, 49 to 79, from diverse backgrounds. The objective of this study is to examine how lifestyle and other factors influence women's health. Data from the WHI OS questionnaire were analyzed. Statistical methods included descriptive statistics square, correlations, linear regression and analyses of covariance (GLM). New findings and insights relate primarily to general health, religion, club attendance, and likelihood of depression. The most important predictor of excellent or very good health is quality of life and general health is a major predictor of quality of life. A great deal of strength and comfort from religion was reported by 62.98% of the women, with little variation by denomination. More from religion related to poorer health, and less likelihood of depression. Religion and lower income are in accord with of across country studies. Attendance at clubs was associated with religion and with all factors associated with religion, except income. Though general health and likelihood of depression are highly correlated, better health is associated with higher income; however, likelihood of depression is not associated with income--contrary to conventional wisdom about socioeconomic disparities and mental health. Subjective well-being variables, with the exception of quality of life, were not associated with income. Social networks--religion and clubs--among a diverse population, warrant further attention from economists, psychologists, sociologists, and others.

Advanced statistics: linear regression, part I: simple linear regression.

PubMed

Marill, Keith A

2004-01-01

Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.

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)

Explaining Match Outcome During The Men’s Basketball Tournament at The Olympic Games

PubMed Central

Leicht, Anthony S.; Gómez, Miguel A.; Woods, Carl T.

2017-01-01

In preparation for the Olympics, there is a limited opportunity for coaches and athletes to interact regularly with team performance indicators providing important guidance to coaches for enhanced match success at the elite level. This study examined the relationship between match outcome and team performance indicators during men’s basketball tournaments at the Olympic Games. Twelve team performance indicators were collated from all men’s teams and matches during the basketball tournament of the 2004-2016 Olympic Games (n = 156). Linear and non-linear analyses examined the relationship between match outcome and team performance indicator characteristics; namely, binary logistic regression and a conditional interference (CI) classification tree. The most parsimonious logistic regression model retained ‘assists’, ‘defensive rebounds’, ‘field-goal percentage’, ‘fouls’, ‘fouls against’, ‘steals’ and ‘turnovers’ (delta AIC <0.01; Akaike weight = 0.28) with a classification accuracy of 85.5%. Conversely, four performance indicators were retained with the CI classification tree with an average classification accuracy of 81.4%. However, it was the combination of ‘field-goal percentage’ and ‘defensive rebounds’ that provided the greatest probability of winning (93.2%). Match outcome during the men’s basketball tournaments at the Olympic Games was identified by a unique combination of performance indicators. Despite the average model accuracy being marginally higher for the logistic regression analysis, the CI classification tree offered a greater practical utility for coaches through its resolution of non-linear phenomena to guide team success. Key points A unique combination of team performance indicators explained 93.2% of winning observations in men’s basketball at the Olympics. Monitoring of these team performance indicators may provide coaches with the capability to devise multiple game plans or strategies to enhance their likelihood of winning. Incorporation of machine learning techniques with team performance indicators may provide a valuable and strategic approach to explain patterns within multivariate datasets in sport science. PMID:29238245

Spectral likelihood expansions for Bayesian inference

NASA Astrophysics Data System (ADS)

Nagel, Joseph B.; Sudret, Bruno

2016-03-01

A spectral approach to Bayesian inference is presented. It pursues the emulation of the posterior probability density. The starting point is a series expansion of the likelihood function in terms of orthogonal polynomials. From this spectral likelihood expansion all statistical quantities of interest can be calculated semi-analytically. The posterior is formally represented as the product of a reference density and a linear combination of polynomial basis functions. Both the model evidence and the posterior moments are related to the expansion coefficients. This formulation avoids Markov chain Monte Carlo simulation and allows one to make use of linear least squares instead. The pros and cons of spectral Bayesian inference are discussed and demonstrated on the basis of simple applications from classical statistics and inverse modeling.

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

PubMed Central

Zhu, Xiang; Stephens, Matthew

2017-01-01

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

Evaluation of weighted regression and sample size in developing a taper model for loblolly pine

Treesearch

Kenneth L. Cormier; Robin M. Reich; Raymond L. Czaplewski; William A. Bechtold

1992-01-01

A stem profile model, fit using pseudo-likelihood weighted regression, was used to estimate merchantable volume of loblolly pine (Pinus taeda L.) in the southeast. The weighted regression increased model fit marginally, but did not substantially increase model performance. In all cases, the unweighted regression models performed as well as the...

Place over traits? Purchasing edibles from medical marijuana dispensaries in Los Angeles, CA.

PubMed

Kepple, Nancy Jo; Freisthler, Bridget

2017-10-01

To examine discrete purchasing behaviors of marijuana-infused edibles from medical marijuana dispensaries with the aim to identify potential venue- and individual-level targets for prevention. Two-stage, venue-based sampling approach was used to randomly select patrons exiting 16 medical marijuana dispensaries in Los Angeles, California during Spring 2013. Hierarchical generalized linear modeling was used to examine the likelihood of purchasing edibles among 524 patrons reporting a discrete purchase regressed on characteristics of the sampled dispensaries and their patrons. At a venue level, patrons were more likely to purchase edibles from dispensaries located within Census tracts with higher median incomes or in close proximity to a higher number of dispensaries. At an individual level, patrons who identified as Black or Hispanic were associated with a lower likelihood of purchasing edibles when compared to patrons who identified as other non-White, non-Hispanic race/ethnicity. Place-based policies focused on regulating edible sales through dispensaries may be fruitful in influencing access to edibles. Additionally, social marketing campaigns may benefit from targeting both locations where edible purchases are more likely and populations who are more likely to purchase edibles. Copyright © 2017 Elsevier Ltd. All rights reserved.

Bayesian logistic regression approaches to predict incorrect DRG assignment.

PubMed

Suleiman, Mani; Demirhan, Haydar; Boyd, Leanne; Girosi, Federico; Aksakalli, Vural

2018-05-07

Episodes of care involving similar diagnoses and treatments and requiring similar levels of resource utilisation are grouped to the same Diagnosis-Related Group (DRG). In jurisdictions which implement DRG based payment systems, DRGs are a major determinant of funding for inpatient care. Hence, service providers often dedicate auditing staff to the task of checking that episodes have been coded to the correct DRG. The use of statistical models to estimate an episode's probability of DRG error can significantly improve the efficiency of clinical coding audits. This study implements Bayesian logistic regression models with weakly informative prior distributions to estimate the likelihood that episodes require a DRG revision, comparing these models with each other and to classical maximum likelihood estimates. All Bayesian approaches had more stable model parameters than maximum likelihood. The best performing Bayesian model improved overall classification per- formance by 6% compared to maximum likelihood, with a 34% gain compared to random classification, respectively. We found that the original DRG, coder and the day of coding all have a significant effect on the likelihood of DRG error. Use of Bayesian approaches has improved model parameter stability and classification accuracy. This method has already lead to improved audit efficiency in an operational capacity.

IMNN: Information Maximizing Neural Networks

NASA Astrophysics Data System (ADS)

Charnock, Tom; Lavaux, Guilhem; Wandelt, Benjamin D.

2018-04-01

This software trains artificial neural networks to find non-linear functionals of data that maximize Fisher information: information maximizing neural networks (IMNNs). As compressing large data sets vastly simplifies both frequentist and Bayesian inference, important information may be inadvertently missed. Likelihood-free inference based on automatically derived IMNN summaries produces summaries that are good approximations to sufficient statistics. IMNNs are robustly capable of automatically finding optimal, non-linear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise, inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra, and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima.

X-31 aerodynamic characteristics determined from flight data

NASA Technical Reports Server (NTRS)

Kokolios, Alex

1993-01-01

The lateral aerodynamic characteristics of the X-31 were determined at angles of attack ranging from 20 to 45 deg. Estimates of the lateral stability and control parameters were obtained by applying two parameter estimation techniques, linear regression, and the extended Kalman filter to flight test data. An attempt to apply maximum likelihood to extract parameters from the flight data was also made but failed for the reasons presented. An overview of the System Identification process is given. The overview includes a listing of the more important properties of all three estimation techniques that were applied to the data. A comparison is given of results obtained from flight test data and wind tunnel data for four important lateral parameters. Finally, future research to be conducted in this area is discussed.

Piecewise exponential survival times and analysis of case-cohort data.

PubMed

Li, Yan; Gail, Mitchell H; Preston, Dale L; Graubard, Barry I; Lubin, Jay H

2012-06-15

Case-cohort designs select a random sample of a cohort to be used as control with cases arising from the follow-up of the cohort. Analyses of case-cohort studies with time-varying exposures that use Cox partial likelihood methods can be computer intensive. We propose a piecewise-exponential approach where Poisson regression model parameters are estimated from a pseudolikelihood and the corresponding variances are derived by applying Taylor linearization methods that are used in survey research. The proposed approach is evaluated using Monte Carlo simulations. An illustration is provided using data from the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study of male smokers in Finland, where a case-cohort study of serum glucose level and pancreatic cancer was analyzed. Copyright © 2012 John Wiley & Sons, Ltd.

The associations between religion, bereavement and depression among Hong Kong nurses.

PubMed

Cheung, Teris; Lee, Paul H; Yip, Paul S F

2017-07-04

This paper is to examine the associations between religion, bereavement and depression among nursing professionals using a cross-sectional survey design. There is little empirical evidence in Asia suggesting that religion may either increase or lower the likelihood of nursing professionals being depressed. We analyzed the results of a Mental Health Survey soliciting data from 850 Hong Kong nurses (aged 21-59, 178 males) regarding their mental well-being and associated factors, including participants' socio-economic profile and recent life-events. Multiple linear regression analyses examined associations between religion, bereavement and depression. Religious faith is weakly associated with lower self-reported depression in bereavement. Our findings confirm those studies suggesting that religion positively affects mental health and yet healthcare providers have yet to assimilate this insight.

Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs

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

Zhang, Baichuan; Choudhury, Sutanay; Al-Hasan, Mohammad

2016-02-01

Estimating the confidence for a link is a critical task for Knowledge Graph construction. Link prediction, or predicting the likelihood of a link in a knowledge graph based on prior state is a key research direction within this area. We propose a Latent Feature Embedding based link recommendation model for prediction task and utilize Bayesian Personalized Ranking based optimization technique for learning models for each predicate. Experimental results on large-scale knowledge bases such as YAGO2 show that our approach achieves substantially higher performance than several state-of-art approaches. Furthermore, we also study the performance of the link prediction algorithm in termsmore » of topological properties of the Knowledge Graph and present a linear regression model to reason about its expected level of accuracy.« less

Exploring the association between exposure to suicide and suicide risk among military service members and veterans.

PubMed

Hom, Melanie A; Stanley, Ian H; Gutierrez, Peter M; Joiner, Thomas E

2017-01-01

Past research suggests that suicide has a profound impact on surviving family members and friends; yet, little is known about experiences with suicide bereavement among military populations. This study aimed to characterize experiences with suicide exposure and their associations with lifetime and current psychiatric symptoms among military service members and veterans. A sample of 1753 United States military service members and veterans completed self-report questionnaires assessing experiences with suicide exposure, lifetime history of suicidal thoughts and behaviors, current suicidal symptoms, and perceived likelihood of making a future suicide attempt. The majority of participants (57.3%) reported knowing someone who had died by suicide, and of these individuals, most (53.1%) reported having lost a friend to suicide. Chi-square tests, one-way ANOVAs, and logistic regression analyses revealed that those who reported knowing a suicide decedent were more likely to report more severe current suicidal symptoms and a history of suicidal thoughts and behaviors compared to those who did not know a suicide decedent. Hierarchical linear regression analyses indicated that greater self-reported interpersonal closeness to a suicide decedent predicted greater self-reported likelihood of a future suicide attempt, even after controlling for current suicidal symptoms and prior suicidal thoughts and behaviors. This study utilized cross-sectional data, and information regarding degree of exposure to suicide was not collected. Military personnel and veterans who have been bereaved by suicide may themselves be at elevated risk for suicidal thoughts and behaviors. Additional work is needed to delineate the relationship between these experiences. Copyright © 2016 Elsevier B.V. All rights reserved.

Neighbourhood fast food outlets and obesity in children and adults: the CLAN Study.

PubMed

Crawford, David A; Timperio, Anna F; Salmon, Jo A; Baur, Louise; Giles-Corti, Billie; Roberts, Rebecca J; Jackson, Michelle L; Andrianopoulos, Nick; Ball, Kylie

2008-01-01

We examined associations between density of and proximity to fast food outlets and body weight in a sample of children (137 aged 8-9 years and 243 aged 13-15 years) and their parents (322 fathers and 362 mothers). Children's measured and parents' self-reported heights and weights were used to calculate body mass index (BMI). Locations of major fast food outlets were geocoded. Bivariate linear regression analyses examined associations between the presence of any fast food outlet within a 2 km buffer around participants' homes, fast food outlet density within the 2 km buffer, and distance to the nearest outlet and BMI. Each independent variable was also entered into separate bivariate logistic regression analyses to predict the odds of being overweight or obese. Among older children, those with at least one outlet within 2 km had lower BMI z-scores. The further that fathers lived from an outlet, the higher their BMI. Among 13-15-year-old girls and their fathers, the likelihood of overweight/obesity was reduced by 80% and 50%, respectively, if they had at least one fast food outlet within 2 km of their home. Among older girls, the likelihood of being overweight/obese was reduced by 14% with each additional outlet within 2 km. Fathers' odds of being overweight/obese increased by 13% for each additional kilometre to the nearest outlet. While consumption of fast food has been shown to be associated with obesity, this study provides little support for the concept that exposure to fast food outlets in the local neighbourhood increases risk of obesity.

Examining the Matthew effect on the motivation and ability to stay at work after heart disease.

PubMed

Meland, Eivind; Grønhaug, Siri; Oystese, Kristin; Mildestvedt, Thomas

2011-07-01

Cardiac rehabilitation should safeguard that socioeconomic factors or other differences that affect people's cardiovascular health are not further aggravated after healthcare treatment. The study examines whether socioeconomic status, emotional problems, or the severity of disease affect people's ability to continue to work after heart disease. We also examined if these effects can be explained by differences in motivational factors. 217 patients (41 women) from the Krokeide Rehabilitation Centre in Bergen participated. Multiple linear regression analysis was used to examine motivational differences, and logistic regression analysis was used to examine whether socioeconomic factors or other differences affected people's ability to continue to work after heart disease. Self-efficacy for future work strongly impacted the likelihood of being incapacitated for work during the 2-year follow-up. The household's total income and emotional problems were statistically significant related to patients dropping out from work in the course of the observation. The association between emotional problems and future work was mediated by motivational problems. The relation between income and future incapacity for work could not be explained by motivational factors. The study shows a clear Matthew effect on people's ability to continue to work after heart disease as low-income groups and people with emotional problems are more at risk of dropping out of work. This Matthew effect was, however, only explained by the motivational difficulties for the association between emotional distress and dropping out of work and not for the impact of household income on the likelihood of leaving work.

Predictors and overestimation of recalled mobile phone use among children and adolescents.

PubMed

Aydin, Denis; Feychting, Maria; Schüz, Joachim; Andersen, Tina Veje; Poulsen, Aslak Harbo; Prochazka, Michaela; Klæboe, Lars; Kuehni, Claudia E; Tynes, Tore; Röösli, Martin

2011-12-01

A growing body of literature addresses possible health effects of mobile phone use in children and adolescents by relying on the study participants' retrospective reconstruction of mobile phone use. In this study, we used data from the international case-control study CEFALO to compare self-reported with objectively operator-recorded mobile phone use. The aim of the study was to assess predictors of level of mobile phone use as well as factors that are associated with overestimating own mobile phone use. For cumulative number and duration of calls as well as for time since first subscription we calculated the ratio of self-reported to operator-recorded mobile phone use. We used multiple linear regression models to assess possible predictors of the average number and duration of calls per day and logistic regression models to assess possible predictors of overestimation. The cumulative number and duration of calls as well as the time since first subscription of mobile phones were overestimated on average by the study participants. Likelihood to overestimate number and duration of calls was not significantly different for controls compared to cases (OR=1.1, 95%-CI: 0.5 to 2.5 and OR=1.9, 95%-CI: 0.85 to 4.3, respectively). However, likelihood to overestimate was associated with other health related factors such as age and sex. As a consequence, such factors act as confounders in studies relying solely on self-reported mobile phone use and have to be considered in the analysis. Copyright © 2011 Elsevier Ltd. All rights reserved.

Personality and adolescent pregnancy outcomes

PubMed Central

Harville, Emily W.; Madkour, Aubrey Spriggs; Xie, Yiqiong

2014-01-01

Aims To examine the relationship between personality, pregnancy and birth outcomes in adolescents Background Personality has been shown to be a strong predictor of many health outcomes. Adolescents who become pregnant have worse birth outcomes than adults. Design Cross-sectional study using data from the National Longitudinal Study of Adolescent Health (baseline, 1994-1995; follow-up, 2007-2008). Methods The study sample was 6529 girls, 820 of whom reported on pregnancy outcomes for a teenage birth. Personality data was taken from the Mini International Personality Item Pool personality tool, which measures the five-factor personality traits of neuroticism, conscientiousness, intellect/imagination, extraversion and agreeableness. Logistic regression was used to predict teen pregnancy and linear regression was used to predict birth weight and gestational age with adjustment for confounders and stratification by race. Results Agreeableness and intellect/imagination were associated with a reduced likelihood of becoming pregnant as an adolescent, while neuroticism, conscientiousness and extraversion were all associated with an increased likelihood of becoming pregnant. Higher neuroticism was associated with lower birth weight and gestational age among Black girls, but not non-Black. Conscientiousness was associated with lower gestational age among non-Black girls. No relationships were found with extraversion or agreeableness and birth outcomes. Receiving late or no prenatal care was associated with higher intellect/imagination. Conclusions Personality is understudied with respect to pregnancy and birth outcomes compared with other health outcomes. Such research could help professionals and clinicians design and target programs that best fit the characteristics of the population most likely to need them, such as those with high neuroticism. PMID:25040691

Estimating a Logistic Discrimination Functions When One of the Training Samples Is Subject to Misclassification: A Maximum Likelihood Approach.

PubMed

Nagelkerke, Nico; Fidler, Vaclav

2015-01-01

The problem of discrimination and classification is central to much of epidemiology. Here we consider the estimation of a logistic regression/discrimination function from training samples, when one of the training samples is subject to misclassification or mislabeling, e.g. diseased individuals are incorrectly classified/labeled as healthy controls. We show that this leads to zero-inflated binomial model with a defective logistic regression or discrimination function, whose parameters can be estimated using standard statistical methods such as maximum likelihood. These parameters can be used to estimate the probability of true group membership among those, possibly erroneously, classified as controls. Two examples are analyzed and discussed. A simulation study explores properties of the maximum likelihood parameter estimates and the estimates of the number of mislabeled observations.

An evaluation of supervised classifiers for indirectly detecting salt-affected areas at irrigation scheme level

NASA Astrophysics Data System (ADS)

Muller, Sybrand Jacobus; van Niekerk, Adriaan

2016-07-01

Soil salinity often leads to reduced crop yield and quality and can render soils barren. Irrigated areas are particularly at risk due to intensive cultivation and secondary salinization caused by waterlogging. Regular monitoring of salt accumulation in irrigation schemes is needed to keep its negative effects under control. The dynamic spatial and temporal characteristics of remote sensing can provide a cost-effective solution for monitoring salt accumulation at irrigation scheme level. This study evaluated a range of pan-fused SPOT-5 derived features (spectral bands, vegetation indices, image textures and image transformations) for classifying salt-affected areas in two distinctly different irrigation schemes in South Africa, namely Vaalharts and Breede River. The relationship between the input features and electro conductivity measurements were investigated using regression modelling (stepwise linear regression, partial least squares regression, curve fit regression modelling) and supervised classification (maximum likelihood, nearest neighbour, decision tree analysis, support vector machine and random forests). Classification and regression trees and random forest were used to select the most important features for differentiating salt-affected and unaffected areas. The results showed that the regression analyses produced weak models (<0.4 R squared). Better results were achieved using the supervised classifiers, but the algorithms tend to over-estimate salt-affected areas. A key finding was that none of the feature sets or classification algorithms stood out as being superior for monitoring salt accumulation at irrigation scheme level. This was attributed to the large variations in the spectral responses of different crops types at different growing stages, coupled with their individual tolerances to saline conditions.

Comparison of two weighted integration models for the cueing task: linear and likelihood

NASA Technical Reports Server (NTRS)

Shimozaki, Steven S.; Eckstein, Miguel P.; Abbey, Craig K.

2003-01-01

In a task in which the observer must detect a signal at two locations, presenting a precue that predicts the location of a signal leads to improved performance with a valid cue (signal location matches the cue), compared to an invalid cue (signal location does not match the cue). The cue validity effect has often been explained with a limited capacity attentional mechanism improving the perceptual quality at the cued location. Alternatively, the cueing effect can also be explained by unlimited capacity models that assume a weighted combination of noisy responses across the two locations. We compare two weighted integration models, a linear model and a sum of weighted likelihoods model based on a Bayesian observer. While qualitatively these models are similar, quantitatively they predict different cue validity effects as the signal-to-noise ratios (SNR) increase. To test these models, 3 observers performed in a cued discrimination task of Gaussian targets with an 80% valid precue across a broad range of SNR's. Analysis of a limited capacity attentional switching model was also included and rejected. The sum of weighted likelihoods model best described the psychophysical results, suggesting that human observers approximate a weighted combination of likelihoods, and not a weighted linear combination.

Revision of laser-induced damage threshold evaluation from damage probability data

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

Bataviciute, Gintare; Grigas, Povilas; Smalakys, Linas

2013-04-15

In this study, the applicability of commonly used Damage Frequency Method (DFM) is addressed in the context of Laser-Induced Damage Threshold (LIDT) testing with pulsed lasers. A simplified computer model representing the statistical interaction between laser irradiation and randomly distributed damage precursors is applied for Monte Carlo experiments. The reproducibility of LIDT predicted from DFM is examined under both idealized and realistic laser irradiation conditions by performing numerical 1-on-1 tests. A widely accepted linear fitting resulted in systematic errors when estimating LIDT and its error bars. For the same purpose, a Bayesian approach was proposed. A novel concept of parametricmore » regression based on varying kernel and maximum likelihood fitting technique is introduced and studied. Such approach exhibited clear advantages over conventional linear fitting and led to more reproducible LIDT evaluation. Furthermore, LIDT error bars are obtained as a natural outcome of parametric fitting which exhibit realistic values. The proposed technique has been validated on two conventionally polished fused silica samples (355 nm, 5.7 ns).« less

Impact of Delayed Time to Advanced Imaging on Missed Appointments Across Different Demographic and Socioeconomic Factors.

PubMed

Daye, Dania; Carrodeguas, Emmanuel; Glover, McKinley; Guerrier, Claude Emmanuel; Harvey, H Benjamin; Flores, Efrén J

2018-05-01

The aim of this study was to investigate the impact of wait days (WDs) on missed outpatient MRI appointments across different demographic and socioeconomic factors. An institutional review board-approved retrospective study was conducted among adult patients scheduled for outpatient MRI during a 12-month period. Scheduling data and demographic information were obtained. Imaging missed appointments were defined as missed scheduled imaging encounters. WDs were defined as the number of days from study order to appointment. Multivariate logistic regression was applied to assess the contribution of race and socioeconomic factors to missed appointments. Linear regression was performed to assess the relationship between missed appointment rates and WDs stratified by race, income, and patient insurance groups with analysis of covariance statistics. A total of 42,727 patients met the inclusion criteria. Mean WDs were 7.95 days. Multivariate regression showed increased odds ratio for missed appointments for patients with increased WDs (7-21 days: odds ratio [OR], 1.39; >21 days: OR, 1.77), African American patients (OR, 1.71), Hispanic patients (OR, 1.30), patients with noncommercial insurance (OR, 2.00-2.55), and those with imaging performed at the main hospital campus (OR, 1.51). Missed appointment rate linearly increased with WDs, with analysis of covariance revealing underrepresented minorities and Medicaid insurance as significant effect modifiers. Increased WDs for advanced imaging significantly increases the likelihood of missed appointments. This effect is most pronounced among underrepresented minorities and patients with lower socioeconomic status. Efforts to reduce WDs may improve equity in access to and utilization of advanced diagnostic imaging for all patients. Copyright © 2018. Published by Elsevier Inc.

Correlation and simple linear regression.

PubMed

Eberly, Lynn E

2007-01-01

This chapter highlights important steps in using correlation and simple linear regression to address scientific questions about the association of two continuous variables with each other. These steps include estimation and inference, assessing model fit, the connection between regression and ANOVA, and study design. Examples in microbiology are used throughout. This chapter provides a framework that is helpful in understanding more complex statistical techniques, such as multiple linear regression, linear mixed effects models, logistic regression, and proportional hazards regression.

Large signal-to-noise ratio quantification in MLE for ARARMAX models

NASA Astrophysics Data System (ADS)

Zou, Yiqun; Tang, Xiafei

2014-06-01

It has been shown that closed-loop linear system identification by indirect method can be generally transferred to open-loop ARARMAX (AutoRegressive AutoRegressive Moving Average with eXogenous input) estimation. For such models, the gradient-related optimisation with large enough signal-to-noise ratio (SNR) can avoid the potential local convergence in maximum likelihood estimation. To ease the application of this condition, the threshold SNR needs to be quantified. In this paper, we build the amplitude coefficient which is an equivalence to the SNR and prove the finiteness of the threshold amplitude coefficient within the stability region. The quantification of threshold is achieved by the minimisation of an elaborately designed multi-variable cost function which unifies all the restrictions on the amplitude coefficient. The corresponding algorithm based on two sets of physically realisable system input-output data details the minimisation and also points out how to use the gradient-related method to estimate ARARMAX parameters when local minimum is present as the SNR is small. Then, the algorithm is tested on a theoretical AutoRegressive Moving Average with eXogenous input model for the derivation of the threshold and a gas turbine engine real system for model identification, respectively. Finally, the graphical validation of threshold on a two-dimensional plot is discussed.

HYPOTHESIS TESTING FOR HIGH-DIMENSIONAL SPARSE BINARY REGRESSION

PubMed Central

Mukherjee, Rajarshi; Pillai, Natesh S.; Lin, Xihong

2015-01-01

In this paper, we study the detection boundary for minimax hypothesis testing in the context of high-dimensional, sparse binary regression models. Motivated by genetic sequencing association studies for rare variant effects, we investigate the complexity of the hypothesis testing problem when the design matrix is sparse. We observe a new phenomenon in the behavior of detection boundary which does not occur in the case of Gaussian linear regression. We derive the detection boundary as a function of two components: a design matrix sparsity index and signal strength, each of which is a function of the sparsity of the alternative. For any alternative, if the design matrix sparsity index is too high, any test is asymptotically powerless irrespective of the magnitude of signal strength. For binary design matrices with the sparsity index that is not too high, our results are parallel to those in the Gaussian case. In this context, we derive detection boundaries for both dense and sparse regimes. For the dense regime, we show that the generalized likelihood ratio is rate optimal; for the sparse regime, we propose an extended Higher Criticism Test and show it is rate optimal and sharp. We illustrate the finite sample properties of the theoretical results using simulation studies. PMID:26246645

Methods of estimating or accounting for neighborhood associations with health using complex survey data.

PubMed

Brumback, Babette A; Cai, Zhuangyu; Dailey, Amy B

2014-05-15

Reasons for health disparities may include neighborhood-level factors, such as availability of health services, social norms, and environmental determinants, as well as individual-level factors. Investigating health inequalities using nationally or locally representative data often requires an approach that can accommodate a complex sampling design, in which individuals have unequal probabilities of selection into the study. The goal of the present article is to review and compare methods of estimating or accounting for neighborhood influences with complex survey data. We considered 3 types of methods, each generalized for use with complex survey data: ordinary regression, conditional likelihood regression, and generalized linear mixed-model regression. The relative strengths and weaknesses of each method differ from one study to another; we provide an overview of the advantages and disadvantages of each method theoretically, in terms of the nature of the estimable associations and the plausibility of the assumptions required for validity, and also practically, via a simulation study and 2 epidemiologic data analyses. The first analysis addresses determinants of repeat mammography screening use using data from the 2005 National Health Interview Survey. The second analysis addresses disparities in preventive oral health care using data from the 2008 Florida Behavioral Risk Factor Surveillance System Survey.

City-Level Adult Stroke Prevalence in Relation to Remote Sensing Derived PM2.5 Adjusting for Unhealthy Behaviors and Medical Risk Factors

NASA Astrophysics Data System (ADS)

Hu, Z.

2018-04-01

This research explores the use of PM2.5 gird derived from remote sensing for assessing the effect of long-term exposure to PM2.5 (ambient air pollution of particulate matter with an aerodynamic diameter of 2.5 μm or less) on stroke, adjusting for unhealthy behaviors and medical risk factors. Health data was obtained from the newly published CDC "500 Cities Project" which provides city- and census tract-level small area estimates for chronic disease risk factors, and clinical preventive service use for the largest 500 cities in the United States. PM2.5 data was acquired from the "The Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD), V1 (1998-2012)" datasets. Average PM2.5 were calculated for each city using a GIS zonal statistics function. Map data visualization and pattern comparison, univariate linear regression, and a multivariate linear regression model fitted using a generalized linear model via penalized maximum likelihood found that long-term exposure to ambient PM2.5 may increase the risk of stroke. Increasing physical activity, reducing smoking and body weight, enough sleeping, controlling diseases such as blood pressure, coronary heart disease, diabetes, and cholesterol, may mitigate the effect. PM2.5 grids derived from moderate resolution satellite remote sensing imagery may offer a unique opportunity to fill the data gap due to limited ground monitoring at broader scales. The evidence of raised stroke prevalence risk in high PM2.5 areas would support targeting of policy interventions on such areas to reduce pollution levels and protect human health.

Immunohistochemically detectable metallothionein expression in malignant pleural mesotheliomas is strongly associated with early failure to platin-based chemotherapy.

PubMed

Mairinger, Fabian D; Schmeller, Jan; Borchert, Sabrina; Wessolly, Michael; Mairinger, Elena; Kollmeier, Jens; Hager, Thomas; Mairinger, Thomas; Christoph, Daniel C; Walter, Robert F H; Eberhardt, Wilfried E E; Plönes, Till; Wohlschlaeger, Jeremias; Jasani, Bharat; Schmid, Kurt Werner; Bankfalvi, Agnes

2018-04-27

Malignant pleural mesothelioma (MPM) is a biologically highly aggressive tumor arising from the pleura with a dismal prognosis. Cisplatin is the drug of choice for the treatment of MPM, and carboplatin seems to have comparable efficacy. Nevertheless, cisplatin treatment results in a response rate of merely 14% and a median survival of less than seven months. Due to their role in many cellular processes, methallothioneins (MTs) have been widely studied in various cancers. The known heavy metal detoxifying effect of MT-I and MT-II may be the reason for heavy metal drug resistance of various cancers including MPM. 105 patients were retrospectively analyzed immunohistochemically for their MT expression levels. Survival analysis was done by Cox-regression, and statistical significance determined using likelihood ratio, Wald test and Score (logrank) tests. Cox-regression analyses were done in a linear and logarithmic scale revealing a significant association between expression of MT and shortened overall survival (OS) in a linear (p=0.0009) and logarithmic scale (p=0.0003). Reduced progression free survival (PFS) was also observed for MT expressing tumors (linear: p=0.0134, log: p=0.0152). Since both, overall survival and progression-free survival are negatively correlated with detectable MT expression in MPM, our results indicate a possible resistance to platin-based chemotherapy associated with MT expression upregulation, found exclusively in progressive MPM samples. Initial cell culture studies suggest promoter DNA hypomethylation and expression of miRNA-566 a direct regulator of copper transporter SLC31A1 and a putative regulator of MT1A and MT2A gene expression, to be responsible for the drug resistance.

Immunohistochemically detectable metallothionein expression in malignant pleural mesotheliomas is strongly associated with early failure to platin-based chemotherapy

PubMed Central

Borchert, Sabrina; Wessolly, Michael; Mairinger, Elena; Kollmeier, Jens; Hager, Thomas; Mairinger, Thomas; Christoph, Daniel C.; Walter, Robert F.H.; Eberhardt, Wilfried E.E.; Plönes, Till; Wohlschlaeger, Jeremias; Jasani, Bharat; Schmid, Kurt Werner; Bankfalvi, Agnes

2018-01-01

Background Malignant pleural mesothelioma (MPM) is a biologically highly aggressive tumor arising from the pleura with a dismal prognosis. Cisplatin is the drug of choice for the treatment of MPM, and carboplatin seems to have comparable efficacy. Nevertheless, cisplatin treatment results in a response rate of merely 14% and a median survival of less than seven months. Due to their role in many cellular processes, methallothioneins (MTs) have been widely studied in various cancers. The known heavy metal detoxifying effect of MT-I and MT-II may be the reason for heavy metal drug resistance of various cancers including MPM. Methods 105 patients were retrospectively analyzed immunohistochemically for their MT expression levels. Survival analysis was done by Cox-regression, and statistical significance determined using likelihood ratio, Wald test and Score (logrank) tests. Results Cox-regression analyses were done in a linear and logarithmic scale revealing a significant association between expression of MT and shortened overall survival (OS) in a linear (p=0.0009) and logarithmic scale (p=0.0003). Reduced progression free survival (PFS) was also observed for MT expressing tumors (linear: p=0.0134, log: p=0.0152). Conclusion Since both, overall survival and progression-free survival are negatively correlated with detectable MT expression in MPM, our results indicate a possible resistance to platin-based chemotherapy associated with MT expression upregulation, found exclusively in progressive MPM samples. Initial cell culture studies suggest promoter DNA hypomethylation and expression of miRNA-566 a direct regulator of copper transporter SLC31A1 and a putative regulator of MT1A and MT2A gene expression, to be responsible for the drug resistance. PMID:29854276

Improving mass-univariate analysis of neuroimaging data by modelling important unknown covariates: Application to Epigenome-Wide Association Studies.

PubMed

Guillaume, Bryan; Wang, Changqing; Poh, Joann; Shen, Mo Jun; Ong, Mei Lyn; Tan, Pei Fang; Karnani, Neerja; Meaney, Michael; Qiu, Anqi

2018-06-01

Statistical inference on neuroimaging data is often conducted using a mass-univariate model, equivalent to fitting a linear model at every voxel with a known set of covariates. Due to the large number of linear models, it is challenging to check if the selection of covariates is appropriate and to modify this selection adequately. The use of standard diagnostics, such as residual plotting, is clearly not practical for neuroimaging data. However, the selection of covariates is crucial for linear regression to ensure valid statistical inference. In particular, the mean model of regression needs to be reasonably well specified. Unfortunately, this issue is often overlooked in the field of neuroimaging. This study aims to adopt the existing Confounder Adjusted Testing and Estimation (CATE) approach and to extend it for use with neuroimaging data. We propose a modification of CATE that can yield valid statistical inferences using Principal Component Analysis (PCA) estimators instead of Maximum Likelihood (ML) estimators. We then propose a non-parametric hypothesis testing procedure that can improve upon parametric testing. Monte Carlo simulations show that the modification of CATE allows for more accurate modelling of neuroimaging data and can in turn yield a better control of False Positive Rate (FPR) and Family-Wise Error Rate (FWER). We demonstrate its application to an Epigenome-Wide Association Study (EWAS) on neonatal brain imaging and umbilical cord DNA methylation data obtained as part of a longitudinal cohort study. Software for this CATE study is freely available at http://www.bioeng.nus.edu.sg/cfa/Imaging_Genetics2.html. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

The biomechanics of concussion in unhelmeted football players in Australia: a case–control study

PubMed Central

McIntosh, Andrew S; Patton, Declan A; Fréchède, Bertrand; Pierré, Paul-André; Ferry, Edouard; Barthels, Tobias

2014-01-01

Objective Concussion is a prevalent brain injury in sport and the wider community. Despite this, little research has been conducted investigating the dynamics of impacts to the unprotected human head and injury causation in vivo, in particular the roles of linear and angular head acceleration. Setting Professional contact football in Australia. Participants Adult male professional Australian rules football players participating in 30 games randomly selected from 103 games. Cases selected based on an observable head impact, no observable symptoms (eg, loss-of-consciousness and convulsions), no on-field medical management and no injury recorded at the time. Primary and secondary outcome measures A data set for no-injury head impact cases comprising head impact locations and head impact dynamic parameters estimated through rigid body simulations using the MAthematical DYnamic MOdels (MADYMO) human facet model. This data set was compared to previously reported concussion case data. Results Qualitative analysis showed that the head was more vulnerable to lateral impacts. Logistic regression analyses of head acceleration and velocity components revealed that angular acceleration of the head in the coronal plane had the strongest association with concussion; tentative tolerance levels of 1747 rad/s2 and 2296 rad/s2 were reported for a 50% and 75% likelihood of concussion, respectively. The mean maximum resultant angular accelerations for the concussion and no-injury cases were 7951 rad/s2 (SD 3562 rad/s2) and 4300 rad/s2 (SD 3657 rad/s2), respectively. Linear acceleration is currently used in the assessment of helmets and padded headgear. The 50% and 75% likelihood of concussion values for resultant linear head acceleration in this study were 65.1 and 88.5 g, respectively. Conclusions As hypothesised by Holbourn over 70 years ago, angular acceleration plays an important role in the pathomechanics of concussion, which has major ramifications in terms of helmet design and other efforts to prevent and manage concussion. PMID:24844272

Maximum Likelihood Estimation of Nonlinear Structural Equation Models with Ignorable Missing Data

ERIC Educational Resources Information Center

Lee, Sik-Yum; Song, Xin-Yuan; Lee, John C. K.

2003-01-01

The existing maximum likelihood theory and its computer software in structural equation modeling are established on the basis of linear relationships among latent variables with fully observed data. However, in social and behavioral sciences, nonlinear relationships among the latent variables are important for establishing more meaningful models…

Short communication: Genetic variation of saturated fatty acids in Holsteins in the Walloon region of Belgium.

PubMed

Arnould, V M-R; Hammami, H; Soyeurt, H; Gengler, N

2010-09-01

Random regression test-day models using Legendre polynomials are commonly used for the estimation of genetic parameters and genetic evaluation for test-day milk production traits. However, some researchers have reported that these models present some undesirable properties such as the overestimation of variances at the edges of lactation. Describing genetic variation of saturated fatty acids expressed in milk fat might require the testing of different models. Therefore, 3 different functions were used and compared to take into account the lactation curve: (1) Legendre polynomials with the same order as currently applied for genetic model for production traits; 2) linear splines with 10 knots; and 3) linear splines with the same 10 knots reduced to 3 parameters. The criteria used were Akaike's information and Bayesian information criteria, percentage square biases, and log-likelihood function. These criteria indentified Legendre polynomials and linear splines with 10 knots reduced to 3 parameters models as the most useful. Reducing more complex models using eigenvalues seemed appealing because the resulting models are less time demanding and can reduce convergence difficulties, because convergence properties also seemed to be improved. Finally, the results showed that the reduced spline model was very similar to the Legendre polynomials model. Copyright (c) 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Accounting for informatively missing data in logistic regression by means of reassessment sampling.

PubMed

Lin, Ji; Lyles, Robert H

2015-05-20

We explore the 'reassessment' design in a logistic regression setting, where a second wave of sampling is applied to recover a portion of the missing data on a binary exposure and/or outcome variable. We construct a joint likelihood function based on the original model of interest and a model for the missing data mechanism, with emphasis on non-ignorable missingness. The estimation is carried out by numerical maximization of the joint likelihood function with close approximation of the accompanying Hessian matrix, using sharable programs that take advantage of general optimization routines in standard software. We show how likelihood ratio tests can be used for model selection and how they facilitate direct hypothesis testing for whether missingness is at random. Examples and simulations are presented to demonstrate the performance of the proposed method. Copyright © 2015 John Wiley & Sons, Ltd.

The logistic model for predicting the non-gonoactive Aedes aegypti females.

PubMed

Reyes-Villanueva, Filiberto; Rodríguez-Pérez, Mario A

2004-01-01

To estimate, using logistic regression, the likelihood of occurrence of a non-gonoactive Aedes aegypti female, previously fed human blood, with relation to body size and collection method. This study was conducted in Monterrey, Mexico, between 1994 and 1996. Ten samplings of 60 mosquitoes of Ae. aegypti females were carried out in three dengue endemic areas: six of biting females, two of emerging mosquitoes, and two of indoor resting females. Gravid females, as well as those with blood in the gut were removed. Mosquitoes were taken to the laboratory and engorged on human blood. After 48 hours, ovaries were dissected to register whether they were gonoactive or non-gonoactive. Wing-length in mm was an indicator for body size. The logistic regression model was used to assess the likelihood of non-gonoactivity, as a binary variable, in relation to wing-length and collection method. Of the 600 females, 164 (27%) remained non-gonoactive, with a wing-length range of 1.9-3.2 mm, almost equal to that of all females (1.8-3.3 mm). The logistic regression model showed a significant likelihood of a female remaining non-gonoactive (Y=1). The collection method did not influence the binary response, but there was an inverse relationship between non-gonoactivity and wing-length. Dengue vector populations from Monterrey, Mexico display a wide-range body size. Logistic regression was a useful tool to estimate the likelihood for an engorged female to remain non-gonoactive. The necessity for a second blood meal is present in any female, but small mosquitoes are more likely to bite again within a 2-day interval, in order to attain egg maturation. The English version of this paper is available too at: http://www.insp.mx/salud/index.html.

Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat

PubMed Central

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-01-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models. PMID:23275882

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

PubMed

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-12-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

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…

Characterizing the performance of the Conway-Maxwell Poisson generalized linear model.

PubMed

Francis, Royce A; Geedipally, Srinivas Reddy; Guikema, Seth D; Dhavala, Soma Sekhar; Lord, Dominique; LaRocca, Sarah

2012-01-01

Count data are pervasive in many areas of risk analysis; deaths, adverse health outcomes, infrastructure system failures, and traffic accidents are all recorded as count events, for example. Risk analysts often wish to estimate the probability distribution for the number of discrete events as part of doing a risk assessment. Traditional count data regression models of the type often used in risk assessment for this problem suffer from limitations due to the assumed variance structure. A more flexible model based on the Conway-Maxwell Poisson (COM-Poisson) distribution was recently proposed, a model that has the potential to overcome the limitations of the traditional model. However, the statistical performance of this new model has not yet been fully characterized. This article assesses the performance of a maximum likelihood estimation method for fitting the COM-Poisson generalized linear model (GLM). The objectives of this article are to (1) characterize the parameter estimation accuracy of the MLE implementation of the COM-Poisson GLM, and (2) estimate the prediction accuracy of the COM-Poisson GLM using simulated data sets. The results of the study indicate that the COM-Poisson GLM is flexible enough to model under-, equi-, and overdispersed data sets with different sample mean values. The results also show that the COM-Poisson GLM yields accurate parameter estimates. The COM-Poisson GLM provides a promising and flexible approach for performing count data regression. © 2011 Society for Risk Analysis.

An Evaluation of Statistical Strategies for Making Equating Function Selections. Research Report. ETS RR-08-60

ERIC Educational Resources Information Center

Moses, Tim

2008-01-01

Nine statistical strategies for selecting equating functions in an equivalent groups design were evaluated. The strategies of interest were likelihood ratio chi-square tests, regression tests, Kolmogorov-Smirnov tests, and significance tests for equated score differences. The most accurate strategies in the study were the likelihood ratio tests…

Effects of mental demands during dispensing on perceived medication safety and employee well being: A study of workload in pediatric hospital pharmacies

PubMed Central

Holden, Richard J.; Patel, Neal R.; Scanlon, Matthew C.; Shalaby, Theresa M.; Arnold, Judi M.; Karsh, Ben-Tzion

2009-01-01

Background Pharmacy workload is a modifiable work system factor believed to affect both medication safety outcomes and employee outcomes such as job satisfaction. Objectives This study sought to measure the effect of workload on safety and employee outcomes in two pediatric hospitals and to do so using a novel approach to pharmacy workload measurement. Methods Rather than measuring prescription volume or other similar indicators, this study measured the type and intensity of mental demands experienced during the medication dispensing tasks. The effects of external (interruptions, divided attention, rushing) and internal (concentration, effort) task demands on perceived medication error likelihood, adverse drug event likelihood, job dissatisfaction, and burnout were statistically estimated using multiple linear and logistic regression. Results Pharmacists and pharmacy technicians reported high levels of external and internal mental demands during dispensing. The study supported the hypothesis that external demands (interruptions, divided attention, rushing) negatively impacted medication safety and employee well being outcomes. However, as hypothesized, increasing levels of internal demands (concentration and effort) were not associated with greater perceived likelihood of error, adverse drug events, or burnout, and even had a positive effect on job satisfaction. Conclusion Replicating a prior study in nursing, this study shows that new conceptualizations and measures of workload can generate important new findings about both detrimental and beneficial effects of workload on patient safety and employee well being. This study discusses what those findings imply for policy, management, and design concerning automation, cognition, and staffing. PMID:21111387

Effects of mental demands during dispensing on perceived medication safety and employee well-being: a study of workload in pediatric hospital pharmacies.

PubMed

Holden, Richard J; Patel, Neal R; Scanlon, Matthew C; Shalaby, Theresa M; Arnold, Judi M; Karsh, Ben-Tzion

2010-12-01

Pharmacy workload is a modifiable work system factor believed to affect both medication safety outcomes and employee outcomes, such as job satisfaction. This study sought to measure the effect of workload on safety and employee outcomes in 2 pediatric hospitals and to do so using a novel approach to pharmacy workload measurement. Rather than measuring prescription volume or other similar indicators, this study measured the type and intensity of mental demands experienced during the medication dispensing tasks. The effects of external (interruptions, divided attention, and rushing) and internal (concentration and effort) task demands on perceived medication error likelihood, adverse drug event likelihood, job dissatisfaction, and burnout were statistically estimated using multiple linear and logistic regression. Pharmacists and pharmacy technicians reported high levels of external and internal mental demands during dispensing. The study supported the hypothesis that external demands (interruptions, divided attention, and rushing) negatively impacted medication safety and employee well-being outcomes. However, as hypothesized, increasing levels of internal demands (concentration and effort) were not associated with greater perceived likelihood of error, adverse drug events, or burnout and even had a positive effect on job satisfaction. Replicating a prior study in nursing, this study shows that new conceptualizations and measures of workload can generate important new findings about both detrimental and beneficial effects of workload on patient safety and employee well-being. This study discusses what those findings imply for policy, management, and design concerning automation, cognition, and staffing. Copyright © 2010 Elsevier Inc. All rights reserved.

Estimating multilevel logistic regression models when the number of clusters is low: a comparison of different statistical software procedures.

PubMed

Austin, Peter C

2010-04-22

Multilevel logistic regression models are increasingly being used to analyze clustered data in medical, public health, epidemiological, and educational research. Procedures for estimating the parameters of such models are available in many statistical software packages. There is currently little evidence on the minimum number of clusters necessary to reliably fit multilevel regression models. We conducted a Monte Carlo study to compare the performance of different statistical software procedures for estimating multilevel logistic regression models when the number of clusters was low. We examined procedures available in BUGS, HLM, R, SAS, and Stata. We found that there were qualitative differences in the performance of different software procedures for estimating multilevel logistic models when the number of clusters was low. Among the likelihood-based procedures, estimation methods based on adaptive Gauss-Hermite approximations to the likelihood (glmer in R and xtlogit in Stata) or adaptive Gaussian quadrature (Proc NLMIXED in SAS) tended to have superior performance for estimating variance components when the number of clusters was small, compared to software procedures based on penalized quasi-likelihood. However, only Bayesian estimation with BUGS allowed for accurate estimation of variance components when there were fewer than 10 clusters. For all statistical software procedures, estimation of variance components tended to be poor when there were only five subjects per cluster, regardless of the number of clusters.

The emotional cost of distance: Geographic social network dispersion and post-traumatic stress among survivors of Hurricane Katrina.

PubMed

Morris, Katherine Ann; Deterding, Nicole M

2016-09-01

Social networks offer important emotional and instrumental support following natural disasters. However, displacement may geographically disperse network members, making it difficult to provide and receive support necessary for psychological recovery after trauma. We examine the association between distance to network members and post-traumatic stress using survey data, and identify potential mechanisms underlying this association using in-depth qualitative interviews. We use longitudinal, mixed-methods data from the Resilience in Survivors of Katrina (RISK) Project to capture the long-term effects of Hurricane Katrina on low-income mothers from New Orleans. Baseline surveys occurred approximately one year before the storm and follow-up surveys and in-depth interviews were conducted five years later. We use a sequential explanatory analytic design. With logistic regression, we estimate the association of geographic network dispersion with the likelihood of post-traumatic stress. With linear regressions, we estimate the association of network dispersion with the three post-traumatic stress sub-scales. Using maximal variation sampling, we use qualitative interview data to elaborate identified statistical associations. We find network dispersion is positively associated with the likelihood of post-traumatic stress, controlling for individual-level socio-demographic characteristics, exposure to hurricane-related trauma, perceived social support, and New Orleans residency. We identify two social-psychological mechanisms present in qualitative data: respondents with distant network members report a lack of deep belonging and a lack of mattering as they are unable to fulfill obligations to important distant ties. Results indicate the importance of physical proximity to emotionally-intimate network ties for long-term psychological recovery. Copyright © 2016 Elsevier Ltd. All rights reserved.

Analysis of age as a factor in NASA astronaut selection and career landmarks.

PubMed

Kovacs, Gregory T A; Shadden, Mark

2017-01-01

NASA's periodic selection of astronauts is a highly selective process accepting applications from the general population, wherein the mechanics of selection are not made public. This research was an effort to determine if biases (specifically age) exist in the process and, if so, at which points they might manifest. Two sets of analyses were conducted. The first utilized data requested via the Freedom of Information Act (FOIA) on NASA astronaut applicants for the 2009 and 2013 selection years. Using a series of multinomial and logistic regressions, the data were analyzed to uncover whether age of the applicants linearly or nonlinearly affected their likelihood of receiving an invitation, as well as their likelihood of being selected into the astronaut program. The second used public data on age at selection and age at other career milestones for every astronaut selected from 1959 to 2013 to analyze trends in age over time using ordinary least-squares (OLS) regression and Pearson's correlation. The results for the FOIA data revealed a nonlinear relationship between age and receiving an interview, as well as age and selection into the astronaut program, but the most striking observation was the loss of age diversity at each stage of selection. Applicants younger or older than approximately 40 years were significantly less likely to receive invitations for interviews and were significantly less likely to be selected as an astronaut. Analysis of the public-source data for all selections since the beginning of the astronaut program revealed significant age trends over time including a gradual increase in selectee age and decreased tenure at NASA after last flight, with average age at retirement steady over the entire history of the astronaut program at approximately 48 years.

Analysis of age as a factor in NASA astronaut selection and career landmarks

PubMed Central

Shadden, Mark

2017-01-01

NASA’s periodic selection of astronauts is a highly selective process accepting applications from the general population, wherein the mechanics of selection are not made public. This research was an effort to determine if biases (specifically age) exist in the process and, if so, at which points they might manifest. Two sets of analyses were conducted. The first utilized data requested via the Freedom of Information Act (FOIA) on NASA astronaut applicants for the 2009 and 2013 selection years. Using a series of multinomial and logistic regressions, the data were analyzed to uncover whether age of the applicants linearly or nonlinearly affected their likelihood of receiving an invitation, as well as their likelihood of being selected into the astronaut program. The second used public data on age at selection and age at other career milestones for every astronaut selected from 1959 to 2013 to analyze trends in age over time using ordinary least-squares (OLS) regression and Pearson’s correlation. The results for the FOIA data revealed a nonlinear relationship between age and receiving an interview, as well as age and selection into the astronaut program, but the most striking observation was the loss of age diversity at each stage of selection. Applicants younger or older than approximately 40 years were significantly less likely to receive invitations for interviews and were significantly less likely to be selected as an astronaut. Analysis of the public-source data for all selections since the beginning of the astronaut program revealed significant age trends over time including a gradual increase in selectee age and decreased tenure at NASA after last flight, with average age at retirement steady over the entire history of the astronaut program at approximately 48 years. PMID:28749968

Periodontal status and high-sensitivity C-reactive protein levels in polycystic ovary syndrome with and without medical treatment.

PubMed

Porwal, Surya; Tewari, Shikha; Sharma, Rajinder K; Singhal, Savita Rani; Narula, Satish C

2014-10-01

Recently, some studies have revealed the effect of polycystic ovary syndrome (PCOS) on gingival inflammation. This cross-sectional study attempts to assess the periodontal status and systemic inflammation of women receiving medical treatment for PCOS and women newly diagnosed with PCOS. A total of 126 participants comprising 41 newly diagnosed patients with PCOS (PCOS-N), 45 patients with PCOS on medical treatment (PCOS-MT), and 40 systemically healthy controls (control group [CG]) were examined. Periodontal parameters, anthropometric parameters, and serum levels of high-sensitivity C-reactive protein (hsCRP) were recorded. Women with newly diagnosed PCOS had increased sites with bleeding on probing (BOP), probing depth, clinical attachment level (CAL), waist circumference (WC), hsCRP, and prevalence of periodontitis compared with control and PCOS-MT groups (P ≤0.05). On partial correlation analysis after controlling for confounders, BOP and CAL correlated positively and significantly with hsCRP (P = 0.01 and P = 0.005). Multivariate linear regression analysis revealed that BOP and CAL (dependent variable) (P = 0.009/R(2) = 0.05 and P = 0.005/R(2) = 0.07, respectively) had significant association with hsCRP. Furthermore, hsCRP, when considered as outcome, also exhibited association with CAL and WC (P = 0.002/R(2) = 0.07 and P = 0.04/R(2) = 0.106). Logistic regression analysis demonstrated that the PCOS-N group had 2.88 times increased likelihood of having moderate periodontitis (adjusted odds ratio 2.88, 95% confidence interval 1.18 to 6.98). Women with newly diagnosed PCOS may have increased prevalence and likelihood for periodontitis, with higher measures of periodontal inflammation and breakdown than those on medical treatment for PCOS and systemically healthy females. Furthermore, periodontal breakdown might depend on systemic inflammation and vice versa.

A pseudo-penalized quasi-likelihood approach to the spatial misalignment problem with non-normal data.

PubMed

Lopiano, Kenneth K; Young, Linda J; Gotway, Carol A

2014-09-01

Spatially referenced datasets arising from multiple sources are routinely combined to assess relationships among various outcomes and covariates. The geographical units associated with the data, such as the geographical coordinates or areal-level administrative units, are often spatially misaligned, that is, observed at different locations or aggregated over different geographical units. As a result, the covariate is often predicted at the locations where the response is observed. The method used to align disparate datasets must be accounted for when subsequently modeling the aligned data. Here we consider the case where kriging is used to align datasets in point-to-point and point-to-areal misalignment problems when the response variable is non-normally distributed. If the relationship is modeled using generalized linear models, the additional uncertainty induced from using the kriging mean as a covariate introduces a Berkson error structure. In this article, we develop a pseudo-penalized quasi-likelihood algorithm to account for the additional uncertainty when estimating regression parameters and associated measures of uncertainty. The method is applied to a point-to-point example assessing the relationship between low-birth weights and PM2.5 levels after the onset of the largest wildfire in Florida history, the Bugaboo scrub fire. A point-to-areal misalignment problem is presented where the relationship between asthma events in Florida's counties and PM2.5 levels after the onset of the fire is assessed. Finally, the method is evaluated using a simulation study. Our results indicate the method performs well in terms of coverage for 95% confidence intervals and naive methods that ignore the additional uncertainty tend to underestimate the variability associated with parameter estimates. The underestimation is most profound in Poisson regression models. © 2014, The International Biometric Society.

Inferring epidemiological parameters from phylogenies using regression-ABC: A comparative study

PubMed Central

Gascuel, Olivier

2017-01-01

Inferring epidemiological parameters such as the R0 from time-scaled phylogenies is a timely challenge. Most current approaches rely on likelihood functions, which raise specific issues that range from computing these functions to finding their maxima numerically. Here, we present a new regression-based Approximate Bayesian Computation (ABC) approach, which we base on a large variety of summary statistics intended to capture the information contained in the phylogeny and its corresponding lineage-through-time plot. The regression step involves the Least Absolute Shrinkage and Selection Operator (LASSO) method, which is a robust machine learning technique. It allows us to readily deal with the large number of summary statistics, while avoiding resorting to Markov Chain Monte Carlo (MCMC) techniques. To compare our approach to existing ones, we simulated target trees under a variety of epidemiological models and settings, and inferred parameters of interest using the same priors. We found that, for large phylogenies, the accuracy of our regression-ABC is comparable to that of likelihood-based approaches involving birth-death processes implemented in BEAST2. Our approach even outperformed these when inferring the host population size with a Susceptible-Infected-Removed epidemiological model. It also clearly outperformed a recent kernel-ABC approach when assuming a Susceptible-Infected epidemiological model with two host types. Lastly, by re-analyzing data from the early stages of the recent Ebola epidemic in Sierra Leone, we showed that regression-ABC provides more realistic estimates for the duration parameters (latency and infectiousness) than the likelihood-based method. Overall, ABC based on a large variety of summary statistics and a regression method able to perform variable selection and avoid overfitting is a promising approach to analyze large phylogenies. PMID:28263987

Nutritional and developmental status among 6- to 8-month-old children in southwestern Uganda: a cross-sectional study.

PubMed

Muhoozi, Grace K M; Atukunda, Prudence; Mwadime, Robert; Iversen, Per Ole; Westerberg, Ane C

2016-01-01

Undernutrition continues to pose challenges to Uganda's children, but there is limited knowledge on its association with physical and intellectual development. In this cross-sectional study, we assessed the nutritional status and milestone development of 6- to 8-month-old children and associated factors in two districts of southwestern Uganda. Five hundred and twelve households with mother-infant (6-8 months) pairs were randomly sampled. Data about background variables (e.g. household characteristics, poverty likelihood, and child dietary diversity scores (CDDS)) were collected using questionnaires. Bayley Scales of Infant and Toddler Development (BSID III) and Ages and Stages questionnaires (ASQ) were used to collect data on child development. Anthropometric measures were used to determine z-scores for weight-for-age (WAZ), length-for-age (LAZ), weight-for-length (WLZ), head circumference (HCZ), and mid-upper arm circumference. Chi-square tests, correlation coefficients, and linear regression analyses were used to relate background variables, nutritional status indicators, and infant development. The prevalence of underweight, stunting, and wasting was 12.1, 24.6, and 4.7%, respectively. Household head education, gender, sanitation, household size, maternal age and education, birth order, poverty likelihood, and CDDS were associated (p<0.05) with WAZ, LAZ, and WLZ. Regression analysis showed that gender, sanitation, CDDS, and likelihood to be below the poverty line were predictors (p<0.05) of undernutrition. BSID III indicated development delay of 1.3% in cognitive and language, and 1.6% in motor development. The ASQ indicated delayed development of 24, 9.1, 25.2, 12.2, and 15.1% in communication, fine motor, gross motor, problem solving, and personal social ability, respectively. All nutritional status indicators except HCZ were positively and significantly associated with development domains. WAZ was the main predictor for all development domains. Undernutrition among infants living in impoverished rural Uganda was associated with household sanitation, poverty, and low dietary diversity. Development domains were positively and significantly associated with nutritional status. Nutritional interventions might add value to improvement of child growth and development.

Nutritional and developmental status among 6- to 8-month-old children in southwestern Uganda: a cross-sectional study

PubMed Central

Muhoozi, Grace K. M.; Atukunda, Prudence; Mwadime, Robert; Iversen, Per Ole; Westerberg, Ane C.

2016-01-01

Background Undernutrition continues to pose challenges to Uganda's children, but there is limited knowledge on its association with physical and intellectual development. Objective In this cross-sectional study, we assessed the nutritional status and milestone development of 6- to 8-month-old children and associated factors in two districts of southwestern Uganda. Design Five hundred and twelve households with mother–infant (6–8 months) pairs were randomly sampled. Data about background variables (e.g. household characteristics, poverty likelihood, and child dietary diversity scores (CDDS)) were collected using questionnaires. Bayley Scales of Infant and Toddler Development (BSID III) and Ages and Stages questionnaires (ASQ) were used to collect data on child development. Anthropometric measures were used to determine z-scores for weight-for-age (WAZ), length-for-age (LAZ), weight-for-length (WLZ), head circumference (HCZ), and mid-upper arm circumference. Chi-square tests, correlation coefficients, and linear regression analyses were used to relate background variables, nutritional status indicators, and infant development. Results The prevalence of underweight, stunting, and wasting was 12.1, 24.6, and 4.7%, respectively. Household head education, gender, sanitation, household size, maternal age and education, birth order, poverty likelihood, and CDDS were associated (p<0.05) with WAZ, LAZ, and WLZ. Regression analysis showed that gender, sanitation, CDDS, and likelihood to be below the poverty line were predictors (p<0.05) of undernutrition. BSID III indicated development delay of 1.3% in cognitive and language, and 1.6% in motor development. The ASQ indicated delayed development of 24, 9.1, 25.2, 12.2, and 15.1% in communication, fine motor, gross motor, problem solving, and personal social ability, respectively. All nutritional status indicators except HCZ were positively and significantly associated with development domains. WAZ was the main predictor for all development domains. Conclusion Undernutrition among infants living in impoverished rural Uganda was associated with household sanitation, poverty, and low dietary diversity. Development domains were positively and significantly associated with nutritional status. Nutritional interventions might add value to improvement of child growth and development. PMID:27238555

Smoothed Residual Plots for Generalized Linear Models. Technical Report #450.

ERIC Educational Resources Information Center

Brant, Rollin

Methods for examining the viability of assumptions underlying generalized linear models are considered. By appealing to the likelihood, a natural generalization of the raw residual plot for normal theory models is derived and is applied to investigating potential misspecification of the linear predictor. A smooth version of the plot is also…

Socio-economic patterning of tobacco use in Indian states

PubMed Central

Karan, A.; Selvaraj, S.; Bhan, N.; Subramanian, S. V.; Millett, C.

2013-01-01

BACKGROUND: Studies in India have identified marked variations in overall tobacco use between socio-economic groups. We examined whether associations between socio-economic status (SES) and tobacco use varied across individual Indian states by tobacco type. METHODS: Cross-sectional survey of 100 855 households in 24 Indian states and Union Territories conducted in 2009–2010. Outcome measures were household tobacco consumption by type. Logistic and linear regression models were used to examine associations at the household level between education, income and use and volume of tobacco consumed. RESULTS: Overall, 52% of households used any form of tobacco product; the predominant form was smokeless tobacco (22%), followed by bidi (17%) and cigarettes (4%). Increasing household income and higher education level were associated with a higher likelihood of cigarette use but a lower likelihood of bidi and smokeless tobacco use in some Indian states. Increasing household income was associated with higher volumes of cigarette and bidi use among consuming households; however, association between educational level and volume of tobacco consumption was inconsistent. CONCLUSION: SES has a varying impact on different types of tobacco use in Indian states. Policy makers should consider socio-economic patterning of tobacco use when designing, implementing and evaluating tobacco control interventions in different states of India. PMID:23827038

Pseudo-second order models for the adsorption of safranin onto activated carbon: comparison of linear and non-linear regression methods.

PubMed

Kumar, K Vasanth

2007-04-02

Kinetic experiments were carried out for the sorption of safranin onto activated carbon particles. The kinetic data were fitted to pseudo-second order model of Ho, Sobkowsk and Czerwinski, Blanchard et al. and Ritchie by linear and non-linear regression methods. Non-linear method was found to be a better way of obtaining the parameters involved in the second order rate kinetic expressions. Both linear and non-linear regression showed that the Sobkowsk and Czerwinski and Ritchie's pseudo-second order models were the same. Non-linear regression analysis showed that both Blanchard et al. and Ho have similar ideas on the pseudo-second order model but with different assumptions. The best fit of experimental data in Ho's pseudo-second order expression by linear and non-linear regression method showed that Ho pseudo-second order model was a better kinetic expression when compared to other pseudo-second order kinetic expressions.

Planning an Innovative School: How to Reduce the Likelihood of Regression toward the Mean

ERIC Educational Resources Information Center

Tubin, Dorit

2009-01-01

Establishing an innovative school requires a great deal of planning effort, human power and resources. Nevertheless, many innovative schools suffer a process of regression toward the mean and lose their innovative zeal. Based on the life cycle approach, which claims that part of this trend of regression is embodied in the planning phase, and on…

E-cigarettes and National Adolescent Cigarette Use: 2004-2014.

PubMed

Dutra, Lauren M; Glantz, Stanton A

2017-02-01

E-cigarette use is rapidly increasing among adolescents in the United States, with some suggesting that e-cigarettes are the cause of declining youth cigarette smoking. We hypothesized that the decline in youth smoking changed after e-cigarettes arrived on the US market in 2007. Data were collected by using cross-sectional, nationally representative school-based samples of sixth- through 12th-graders from 2004-2014 National Youth Tobacco Surveys (samples ranged from 16 614 in 2013 to 25 324 in 2004). Analyses were conducted by using interrupted time series of ever (≥1 puff) and current (last 30 days) cigarette smoking. Logistic regression was used to identify psychosocial risk factors associated with cigarette smoking in the 2004-2009 samples; this model was then applied to estimate the probability of cigarette smoking among cigarette smokers and e-cigarette users in the 2011-2014 samples. Youth cigarette smoking decreased linearly between 2004 and 2014 (P = .009 for ever smoking and P = .05 for current smoking), with no significant change in this trend after 2009 (P = .57 and .23). Based on the psychosocial model of smoking, including demographic characteristics, willingness to wear clothing with a tobacco logo, living with a smoker, likelihood of smoking in the next year, likelihood of smoking cigarettes from a friend, and use of tobacco products other than cigarettes or e-cigarettes, the model categorized <25% of current e-cigarette-only users (between 11.0% in 2012 and 23.1% in 2013) as current smokers. The introduction of e-cigarettes was not associated with a change in the linear decline in cigarette smoking among youth. E-cigarette-only users would be unlikely to have initiated tobacco product use with cigarettes. Copyright © 2017 by the American Academy of Pediatrics.

E-cigarettes and National Adolescent Cigarette Use: 2004–2014

PubMed Central

Dutra, Lauren M.

2017-01-01

BACKGROUND: E-cigarette use is rapidly increasing among adolescents in the United States, with some suggesting that e-cigarettes are the cause of declining youth cigarette smoking. We hypothesized that the decline in youth smoking changed after e-cigarettes arrived on the US market in 2007. METHODS: Data were collected by using cross-sectional, nationally representative school-based samples of sixth- through 12th-graders from 2004–2014 National Youth Tobacco Surveys (samples ranged from 16 614 in 2013 to 25 324 in 2004). Analyses were conducted by using interrupted time series of ever (≥1 puff) and current (last 30 days) cigarette smoking. Logistic regression was used to identify psychosocial risk factors associated with cigarette smoking in the 2004–2009 samples; this model was then applied to estimate the probability of cigarette smoking among cigarette smokers and e-cigarette users in the 2011–2014 samples. RESULTS: Youth cigarette smoking decreased linearly between 2004 and 2014 (P = .009 for ever smoking and P = .05 for current smoking), with no significant change in this trend after 2009 (P = .57 and .23). Based on the psychosocial model of smoking, including demographic characteristics, willingness to wear clothing with a tobacco logo, living with a smoker, likelihood of smoking in the next year, likelihood of smoking cigarettes from a friend, and use of tobacco products other than cigarettes or e-cigarettes, the model categorized <25% of current e-cigarette–only users (between 11.0% in 2012 and 23.1% in 2013) as current smokers. CONCLUSIONS: The introduction of e-cigarettes was not associated with a change in the linear decline in cigarette smoking among youth. E-cigarette–only users would be unlikely to have initiated tobacco product use with cigarettes. PMID:28115540

Prediction of spatially explicit rainfall intensity-duration thresholds for post-fire debris-flow generation in the western United States

NASA Astrophysics Data System (ADS)

Staley, Dennis; Negri, Jacquelyn; Kean, Jason

2016-04-01

Population expansion into fire-prone steeplands has resulted in an increase in post-fire debris-flow risk in the western United States. Logistic regression methods for determining debris-flow likelihood and the calculation of empirical rainfall intensity-duration thresholds for debris-flow initiation represent two common approaches for characterizing hazard and reducing risk. Logistic regression models are currently being used to rapidly assess debris-flow hazard in response to design storms of known intensities (e.g. a 10-year recurrence interval rainstorm). Empirical rainfall intensity-duration thresholds comprise a major component of the United States Geological Survey (USGS) and the National Weather Service (NWS) debris-flow early warning system at a regional scale in southern California. However, these two modeling approaches remain independent, with each approach having limitations that do not allow for synergistic local-scale (e.g. drainage-basin scale) characterization of debris-flow hazard during intense rainfall. The current logistic regression equations consider rainfall a unique independent variable, which prevents the direct calculation of the relation between rainfall intensity and debris-flow likelihood. Regional (e.g. mountain range or physiographic province scale) rainfall intensity-duration thresholds fail to provide insight into the basin-scale variability of post-fire debris-flow hazard and require an extensive database of historical debris-flow occurrence and rainfall characteristics. Here, we present a new approach that combines traditional logistic regression and intensity-duration threshold methodologies. This method allows for local characterization of both the likelihood that a debris-flow will occur at a given rainfall intensity, the direct calculation of the rainfall rates that will result in a given likelihood, and the ability to calculate spatially explicit rainfall intensity-duration thresholds for debris-flow generation in recently burned areas. Our approach synthesizes the two methods by incorporating measured rainfall intensity into each model variable (based on measures of topographic steepness, burn severity and surface properties) within the logistic regression equation. This approach provides a more realistic representation of the relation between rainfall intensity and debris-flow likelihood, as likelihood values asymptotically approach zero when rainfall intensity approaches 0 mm/h, and increase with more intense rainfall. Model performance was evaluated by comparing predictions to several existing regional thresholds. The model, based upon training data collected in southern California, USA, has proven to accurately predict rainfall intensity-duration thresholds for other areas in the western United States not included in the original training dataset. In addition, the improved logistic regression model shows promise for emergency planning purposes and real-time, site-specific early warning. With further validation, this model may permit the prediction of spatially-explicit intensity-duration thresholds for debris-flow generation in areas where empirically derived regional thresholds do not exist. This improvement would permit the expansion of the early-warning system into other regions susceptible to post-fire debris flow.

Lung nodule malignancy prediction using multi-task convolutional neural network

NASA Astrophysics Data System (ADS)

Li, Xiuli; Kao, Yueying; Shen, Wei; Li, Xiang; Xie, Guotong

2017-03-01

In this paper, we investigated the problem of diagnostic lung nodule malignancy prediction using thoracic Computed Tomography (CT) screening. Unlike most existing studies classify the nodules into two types benign and malignancy, we interpreted the nodule malignancy prediction as a regression problem to predict continuous malignancy level. We proposed a joint multi-task learning algorithm using Convolutional Neural Network (CNN) to capture nodule heterogeneity by extracting discriminative features from alternatingly stacked layers. We trained a CNN regression model to predict the nodule malignancy, and designed a multi-task learning mechanism to simultaneously share knowledge among 9 different nodule characteristics (Subtlety, Calcification, Sphericity, Margin, Lobulation, Spiculation, Texture, Diameter and Malignancy), and improved the final prediction result. Each CNN would generate characteristic-specific feature representations, and then we applied multi-task learning on the features to predict the corresponding likelihood for that characteristic. We evaluated the proposed method on 2620 nodules CT scans from LIDC-IDRI dataset with the 5-fold cross validation strategy. The multitask CNN regression result for regression RMSE and mapped classification ACC were 0.830 and 83.03%, while the results for single task regression RMSE 0.894 and mapped classification ACC 74.9%. Experiments show that the proposed method could predict the lung nodule malignancy likelihood effectively and outperforms the state-of-the-art methods. The learning framework could easily be applied in other anomaly likelihood prediction problem, such as skin cancer and breast cancer. It demonstrated the possibility of our method facilitating the radiologists for nodule staging assessment and individual therapeutic planning.

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.

A simple linear regression method for quantitative trait loci linkage analysis with censored observations.

PubMed

Anderson, Carl A; McRae, Allan F; Visscher, Peter M

2006-07-01

Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using simulation we compare this method to both the Cox and Weibull proportional hazards models and a standard linear regression method that ignores censoring. The grouped linear regression method is of equivalent power to both the Cox and Weibull proportional hazards methods and is significantly better than the standard linear regression method when censored observations are present. The method is also robust to the proportion of censored individuals and the underlying distribution of the trait. On the basis of linear regression methodology, the grouped linear regression model is computationally simple and fast and can be implemented readily in freely available statistical software.

Income and Subjective Well-Being: New Insights from Relatively Healthy American Women, Ages 49-79

PubMed Central

Wyshak, Grace

2016-01-01

The interests of economists, psychologists, social scientists and others on the relations of income, demographics, religion and subjective well-being, have generated a vast global literature. It is apparent that biomedical research has focused on white with men. The Women’s Health Initiative and Observational Study (WHI OS) was initiated in 1992. The OS represents the scientific need for social priorities to improve the health and welfare of women; it includes 93.676 relatively healthy postmenopausal women, 49 to 79, from diverse backgrounds. The objective of this study is to examine how lifestyle and other factors influence women’s health. Data from the WHI OS questionnaire were analyzed. Statistical methods included descriptive statistics square, correlations, linear regression and analyses of covariance (GLM). New findings and insights relate primarily to general health, religion, club attendance, and likelihood of depression. The most important predictor of excellent or very good health is quality of life and general health is a major predictor of quality of life. A great deal of strength and comfort from religion was reported by 62.98% of the women, with little variation by denomination. More from religion related to poorer health, and less likelihood of depression. Religion and lower income are in accord with of across country studies. Attendance at clubs was associated with religion and with all factors associated with religion, except income. Though general health and likelihood of depression are highly correlated, better health is associated with higher income; however, likelihood of depression is not associated with income—contrary to conventional wisdom about socioeconomic disparities and mental health. Subjective well-being variables, with the exception of quality of life, were not associated with income. Social networks—religion and clubs—among a diverse population, warrant further attention from economists, psychologists, sociologists, and others. PMID:26828901

Assessing reproductive choices of women and the likelihood of oocyte cryopreservation in the era of elective oocyte freezing.

PubMed

Milman, Lauren W; Senapati, Suneeta; Sammel, Mary D; Cameron, Katherine D; Gracia, Clarisa

2017-05-01

To assess reproductive choices of reproductive-age women in the United States and factors that influence consideration of elective egg freezing (EF). Cross-sectional internet-based survey. Not applicable. One thousand women aged 21-45 years. An anonymous 63-item self-administered questionnaire was distributed to a representative cross-section of women aged 21-45 years, stratified by age <35 years. One-half of the sample had at least one child, and the other one-half did not. All of the participants were interested in having children. Ordinal logistic regression was performed to characterize the association of population characteristics and reproductive knowledge with likelihood to consider EF. Willingness to pay was assessed with the use of a linear prediction model that calculated dollar amounts at varying success rates. Overall, 87.2% of the sample reported awareness of EF for fertility preservation and 25% would consider this option, yet only 29.8% knew what the EF process entails. Once informed of the process, 30% of women changed their level of consideration. In a multivariable model, Asian race, single status, and infertility increased the likelihood of considering EF. Women likely to consider egg freezing would be willing to pay $3,811.55 (95% confidence interval $2,862.66-$4,760.44). If the total cost were $10,000, 91% of the cohort would accept at minimum a 50% chance of successful delivery. This study is one of the largest cohorts of reproductive-age women in the United States addressing reproductive choices and factors associated with the importance of having a biologically related child and the likelihood of considering EF to preserve fertility. This study provides important insight into the willingness to pay for this elective endeavor. Copyright © 2017 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Linear regression crash prediction models : issues and proposed solutions.

DOT National Transportation Integrated Search

2010-05-01

The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...

Likelihood Methods for Adaptive Filtering and Smoothing. Technical Report #455.

ERIC Educational Resources Information Center

Butler, Ronald W.

The dynamic linear model or Kalman filtering model provides a useful methodology for predicting the past, present, and future states of a dynamic system, such as an object in motion or an economic or social indicator that is changing systematically with time. Recursive likelihood methods for adaptive Kalman filtering and smoothing are developed.…

Constrained Maximum Likelihood Estimation for Two-Level Mean and Covariance Structure Models

ERIC Educational Resources Information Center

Bentler, Peter M.; Liang, Jiajuan; Tang, Man-Lai; Yuan, Ke-Hai

2011-01-01

Maximum likelihood is commonly used for the estimation of model parameters in the analysis of two-level structural equation models. Constraints on model parameters could be encountered in some situations such as equal factor loadings for different factors. Linear constraints are the most common ones and they are relatively easy to handle in…

Comparison between Linear and Nonlinear Regression in a Laboratory Heat Transfer Experiment

ERIC Educational Resources Information Center

Gonçalves, Carine Messias; Schwaab, Marcio; Pinto, José Carlos

2013-01-01

In order to interpret laboratory experimental data, undergraduate students are used to perform linear regression through linearized versions of nonlinear models. However, the use of linearized models can lead to statistically biased parameter estimates. Even so, it is not an easy task to introduce nonlinear regression and show for the students…

Composite Linear Models | Division of Cancer Prevention

Cancer.gov

By Stuart G. Baker The composite linear models software is a matrix approach to compute maximum likelihood estimates and asymptotic standard errors for models for incomplete multinomial data. It implements the method described in Baker SG. Composite linear models for incomplete multinomial data. Statistics in Medicine 1994;13:609-622. The software includes a library of thirty

Modeling drought impact occurrence based on climatological drought indices for four European countries

NASA Astrophysics Data System (ADS)

Stagge, James H.; Kohn, Irene; Tallaksen, Lena M.; Stahl, Kerstin

2014-05-01

The relationship between atmospheric conditions and the likelihood of a significant drought impact has, in the past, been difficult to quantify, particularly in Europe where political boundaries and language have made acquiring comprehensive drought impact information difficult. As such, the majority of studies linking meteorological drought with the occurrence or severity of drought impacts have previously focused on specific regions, very detailed impact types, or both. This study describes a new methodology to link the likelihood of drought impact occurrence with climatological drought indices across different European climatic regions and impact sectors using the newly developed European Drought Impact report Inventory (EDII), a collaborative database of drought impact information (www.geo.uio.no/edc/droughtdb/). The Standardized Precipitation Index (SPI) and Standardized Precipitation-Evapotranspiration Index (SPEI) are used as predictor variables to quantify meteorological drought severity over prior time periods (here 1, 2, 3, 6, 9, 12, and 24 months are used). The indices are derived using the gridded WATCH Forcing Datasets, covering the period 1958-2012. Analysis was performed using logistic regression to identify the climatological drought index and accumulation period, or linear combination of drought indices, that best predicts the likelihood of a documented drought impact, defined by monthly presence/absence. The analysis was carried out for a subset of four European countries (Germany, UK, Norway, Slovenia) and four of the best documented impact sectors: Public Water Supply, Agriculture and Livestock Farming, Energy and Industry, and Environmental Quality. Preliminary results show that drought impacts in these countries occur most frequently due to a combination of short-term (2-6 month) precipitation deficits and long-term (12-24 month) potential evapotranspiration anomaly, likely associated with increased temperatures. Agricultural drought impacts were explained best by shorter, seasonal indices (2-6 months), while impacts to the Energy sector were best explained by long-duration (12-24 month) anomalies, related to hydropower reservoir storage. Notably, drought impacts in the UK were not affected by short (< 6 month) anomalies, which may point to successful management strategies or underlying geoclimatic differences. By identifying the climatological drought indices most strongly linked to drought impact occurrence and generating regression equations that can predict the likelihood of a drought event, this research is a valuable step towards measuring and predicting drought risk. This work provides a methodological example using only a subset of European countries and impact types, but the accuracy and scope of these results will improve as the EDII grows with further contributions and collaboration.

The Application of the Cumulative Logistic Regression Model to Automated Essay Scoring

ERIC Educational Resources Information Center

Haberman, Shelby J.; Sinharay, Sandip

2010-01-01

Most automated essay scoring programs use a linear regression model to predict an essay score from several essay features. This article applied a cumulative logit model instead of the linear regression model to automated essay scoring. Comparison of the performances of the linear regression model and the cumulative logit model was performed on a…

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

Methods for estimating drought streamflow probabilities for Virginia streams

USGS Publications Warehouse

Austin, Samuel H.

2014-01-01

Maximum likelihood logistic regression model equations used to estimate drought flow probabilities for Virginia streams are presented for 259 hydrologic basins in Virginia. Winter streamflows were used to estimate the likelihood of streamflows during the subsequent drought-prone summer months. The maximum likelihood logistic regression models identify probable streamflows from 5 to 8 months in advance. More than 5 million streamflow daily values collected over the period of record (January 1, 1900 through May 16, 2012) were compiled and analyzed over a minimum 10-year (maximum 112-year) period of record. The analysis yielded the 46,704 equations with statistically significant fit statistics and parameter ranges published in two tables in this report. These model equations produce summer month (July, August, and September) drought flow threshold probabilities as a function of streamflows during the previous winter months (November, December, January, and February). Example calculations are provided, demonstrating how to use the equations to estimate probable streamflows as much as 8 months in advance.

Correlates of motivation to change in pathological gamblers completing cognitive-behavioral group therapy.

PubMed

Gómez-Peña, Mónica; Penelo, Eva; Granero, Roser; Fernández-Aranda, Fernando; Alvarez-Moya, Eva; Santamaría, Juan José; Moragas, Laura; Neus Aymamí, Maria; Gunnard, Katarina; Menchón, José M; Jimenez-Murcia, Susana

2012-07-01

The present study analyzes the association between the motivation to change and the cognitive-behavioral group intervention, in terms of dropouts and relapses, in a sample of male pathological gamblers. The specific objectives were as follows: (a) to estimate the predictive value of baseline University of Rhode Island Change Assessment scale (URICA) scores (i.e., at the start of the study) as regards the risk of relapse and dropout during treatment and (b) to assess the incremental predictive ability of URICA scores, as regards the mean change produced in the clinical status of patients between the start and finish of treatment. The relationship between the URICA and the response to treatment was analyzed by means of a pre-post design applied to a sample of 191 patients who were consecutively receiving cognitive-behavioral group therapy. The statistical analysis included logistic regression models and hierarchical multiple linear regression models. The discriminative ability of the models including the four URICA scores regarding the likelihood of relapse and dropout was acceptable (area under the receiver operating haracteristic curve: .73 and .71, respectively). No significant predictive ability was found as regards the differences between baseline and posttreatment scores (changes in R(2) below 5% in the multiple regression models). The availability of useful measures of motivation to change would enable treatment outcomes to be optimized through the application of specific therapeutic interventions. © 2012 Wiley Periodicals, Inc.

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

Priors in Whole-Genome Regression: The Bayesian Alphabet Returns

PubMed Central

Gianola, Daniel

2013-01-01

Whole-genome enabled prediction of complex traits has received enormous attention in animal and plant breeding and is making inroads into human and even Drosophila genetics. The term “Bayesian alphabet” denotes a growing number of letters of the alphabet used to denote various Bayesian linear regressions that differ in the priors adopted, while sharing the same sampling model. We explore the role of the prior distribution in whole-genome regression models for dissecting complex traits in what is now a standard situation with genomic data where the number of unknown parameters (p) typically exceeds sample size (n). Members of the alphabet aim to confront this overparameterization in various manners, but it is shown here that the prior is always influential, unless n ≫ p. This happens because parameters are not likelihood identified, so Bayesian learning is imperfect. Since inferences are not devoid of the influence of the prior, claims about genetic architecture from these methods should be taken with caution. However, all such procedures may deliver reasonable predictions of complex traits, provided that some parameters (“tuning knobs”) are assessed via a properly conducted cross-validation. It is concluded that members of the alphabet have a room in whole-genome prediction of phenotypes, but have somewhat doubtful inferential value, at least when sample size is such that n ≪ p. PMID:23636739

(Where) Is Functional Decline Isolating? Disordered Environments and the Onset of Disability.

PubMed

Schafer, Markus H

2018-03-01

The onset of disability is believed to undermine social connectedness and raise the risk of social isolation, yet spatial environments are seldom considered in this process. This study examines whether unruly home and neighborhood conditions intensify the association between disability onset and several dimensions of social connectedness. I incorporate longitudinal data from the National Social Life, Health, and Aging Project, which contains environmental evaluations conducted by trained observers ( N = 1,558). Results from Poisson, ordinal logistic, and linear regression models reveal heterogeneous consequences of disablement: disability onset was associated with reduced core network size, fewer friends, lower likelihood of social interaction, and less overall social connectedness-though mainly when accompanied by higher levels of household disorder. There was limited evidence that neighborhood disorder moderated consequences of disability. Findings point to the importance of the home as an environmental resource and underscore important contextual contingencies in the isolating consequences of disability.

Depression screening and management among adolescents in primary care: factors associated with best practice.

PubMed

Taliaferro, Lindsay A; Hetler, Joel; Edwall, Glenace; Wright, Catherine; Edwards, Anne R; Borowsky, Iris W

2013-06-01

To compare depression identification and management perceptions and practices between professions and disciplines in primary care and examine factors that increase the likelihood of administering a standardized depression screening instrument, asking about patients' depressive symptoms, and using best practice when managing depressed adolescents. Data came from an online survey of clinicians in Minnesota (20% response rate). Analyses involved bivariate tests and linear regressions. The analytic sample comprised 260 family medicine physicians, 127 pediatricians, 96 family nurse practitioners, and 54 pediatric nurse practitioners. Overall, few differences emerged between physicians and nurse practitioners or family and pediatric clinicians regarding addressing depression among adolescents. Two factors associated with administering a standardized instrument included having clear protocols for follow-up after depression screening and feeling better prepared to address depression among adolescents. Enhancing clinicians' competence to address depression and developing postscreening protocols could help providers implement universal screening in primary care.

Inverse sequential procedures for the monitoring of time series

NASA Technical Reports Server (NTRS)

Radok, Uwe; Brown, Timothy J.

1995-01-01

When one or more new values are added to a developing time series, they change its descriptive parameters (mean, variance, trend, coherence). A 'change index (CI)' is developed as a quantitative indicator that the changed parameters remain compatible with the existing 'base' data. CI formulate are derived, in terms of normalized likelihood ratios, for small samples from Poisson, Gaussian, and Chi-Square distributions, and for regression coefficients measuring linear or exponential trends. A substantial parameter change creates a rapid or abrupt CI decrease which persists when the length of the bases is changed. Except for a special Gaussian case, the CI has no simple explicit regions for tests of hypotheses. However, its design ensures that the series sampled need not conform strictly to the distribution form assumed for the parameter estimates. The use of the CI is illustrated with both constructed and observed data samples, processed with a Fortran code 'Sequitor'.

Resolving the percentage of component terrains within single resolution elements

NASA Technical Reports Server (NTRS)

Marsh, S. E.; Switzer, P.; Kowalik, W. S.; Lyon, R. J. P.

1980-01-01

An approximate maximum likelihood technique employing a widely available discriminant analysis program is discussed that has been developed for resolving the percentage of component terrains within single resolution elements. The method uses all four channels of Landsat data simultaneously and does not require prior knowledge of the percentage of components in mixed pixels. It was tested in five cases that were chosen to represent mixtures of outcrop, soil and vegetation which would typically be encountered in geologic studies with Landsat data. For all five cases, the method proved to be superior to single band weighted average and linear regression techniques and permitted an estimate of the total area occupied by component terrains to within plus or minus 6% of the true area covered. Its major drawback is a consistent overestimation of the pixel component percent of the darker materials (vegetation) and an underestimation of the pixel component percent of the brighter materials (sand).

A Two-Step Method to Identify Positive Deviant Physician Organizations of Accountable Care Organizations with Robust Performance Management Systems.

PubMed

Pimperl, Alexander F; Rodriguez, Hector P; Schmittdiel, Julie A; Shortell, Stephen M

2018-06-01

To identify positive deviant (PD) physician organizations of Accountable Care Organizations (ACOs) with robust performance management systems (PMSYS). Third National Survey of Physician Organizations (NSPO3, n = 1,398). Organizational and external factors from NSPO3 were analyzed. Linear regression estimated the association of internal and contextual factors on PMSYS. Two cutpoints (75th/90th percentiles) identified PDs with the largest residuals and highest PMSYS scores. A total of 65 and 41 PDs were identified using 75th and 90th percentiles cutpoints, respectively. The 90th percentile more strongly differentiated PDs from non-PDs. Having a high proportion of vulnerable patients appears to constrain PMSYS development. Our PD identification method increases the likelihood that PD organizations selected for in-depth inquiry are high-performing organizations that exceed expectations. © Health Research and Educational Trust.

Transmission of linear regression patterns between time series: From relationship in time series to complex networks

NASA Astrophysics Data System (ADS)

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

PubMed

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

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.

Analysis of threats to research validity introduced by audio recording clinic visits: Selection bias, Hawthorne effect, both, or neither?

PubMed Central

Henry, Stephen G.; Jerant, Anthony; Iosif, Ana-Maria; Feldman, Mitchell D.; Cipri, Camille; Kravitz, Richard L.

2015-01-01

Objective To identify factors associated with participant consent to record visits; to estimate effects of recording on patient-clinician interactions Methods Secondary analysis of data from a randomized trial studying communication about depression; participants were asked for optional consent to audio record study visits. Multiple logistic regression was used to model likelihood of patient and clinician consent. Multivariable regression and propensity score analyses were used to estimate effects of audio recording on 6 dependent variables: discussion of depressive symptoms, preventive health, and depression diagnosis; depression treatment recommendations; visit length; visit difficulty. Results Of 867 visits involving 135 primary care clinicians, 39% were recorded. For clinicians, only working in academic settings (P=0.003) and having worked longer at their current practice (P=0.02) were associated with increased likelihood of consent. For patients, white race (P=0.002) and diabetes (P=0.03) were associated with increased likelihood of consent. Neither multivariable regression nor propensity score analyses revealed any significant effects of recording on the variables examined. Conclusion Few clinician or patient characteristics were significantly associated with consent. Audio recording had no significant effect on any dependent variables. Practice Implications Benefits of recording clinic visits likely outweigh the risks of bias in this setting. PMID:25837372

Functional linear models for zero-inflated count data with application to modeling hospitalizations in patients on dialysis.

PubMed

Sentürk, Damla; Dalrymple, Lorien S; Nguyen, Danh V

2014-11-30

We propose functional linear models for zero-inflated count data with a focus on the functional hurdle and functional zero-inflated Poisson (ZIP) models. Although the hurdle model assumes the counts come from a mixture of a degenerate distribution at zero and a zero-truncated Poisson distribution, the ZIP model considers a mixture of a degenerate distribution at zero and a standard Poisson distribution. We extend the generalized functional linear model framework with a functional predictor and multiple cross-sectional predictors to model counts generated by a mixture distribution. We propose an estimation procedure for functional hurdle and ZIP models, called penalized reconstruction, geared towards error-prone and sparsely observed longitudinal functional predictors. The approach relies on dimension reduction and pooling of information across subjects involving basis expansions and penalized maximum likelihood techniques. The developed functional hurdle model is applied to modeling hospitalizations within the first 2 years from initiation of dialysis, with a high percentage of zeros, in the Comprehensive Dialysis Study participants. Hospitalization counts are modeled as a function of sparse longitudinal measurements of serum albumin concentrations, patient demographics, and comorbidities. Simulation studies are used to study finite sample properties of the proposed method and include comparisons with an adaptation of standard principal components regression. Copyright © 2014 John Wiley & Sons, Ltd.

Least median of squares and iteratively re-weighted least squares as robust linear regression methods for fluorimetric determination of α-lipoic acid in capsules in ideal and non-ideal cases of linearity.

PubMed

Korany, Mohamed A; Gazy, Azza A; Khamis, Essam F; Ragab, Marwa A A; Kamal, Miranda F

2018-06-01

This study outlines two robust regression approaches, namely least median of squares (LMS) and iteratively re-weighted least squares (IRLS) to investigate their application in instrument analysis of nutraceuticals (that is, fluorescence quenching of merbromin reagent upon lipoic acid addition). These robust regression methods were used to calculate calibration data from the fluorescence quenching reaction (∆F and F-ratio) under ideal or non-ideal linearity conditions. For each condition, data were treated using three regression fittings: Ordinary Least Squares (OLS), LMS and IRLS. Assessment of linearity, limits of detection (LOD) and quantitation (LOQ), accuracy and precision were carefully studied for each condition. LMS and IRLS regression line fittings showed significant improvement in correlation coefficients and all regression parameters for both methods and both conditions. In the ideal linearity condition, the intercept and slope changed insignificantly, but a dramatic change was observed for the non-ideal condition and linearity intercept. Under both linearity conditions, LOD and LOQ values after the robust regression line fitting of data were lower than those obtained before data treatment. The results obtained after statistical treatment indicated that the linearity ranges for drug determination could be expanded to lower limits of quantitation by enhancing the regression equation parameters after data treatment. Analysis results for lipoic acid in capsules, using both fluorimetric methods, treated by parametric OLS and after treatment by robust LMS and IRLS were compared for both linearity conditions. Copyright © 2018 John Wiley & Sons, Ltd.

Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields

PubMed Central

Weller, Daniel; Shiwakoti, Suvash; Bergholz, Peter; Grohn, Yrjo; Wiedmann, Martin

2015-01-01

Technological advancements, particularly in the field of geographic information systems (GIS), have made it possible to predict the likelihood of foodborne pathogen contamination in produce production environments using geospatial models. Yet, few studies have examined the validity and robustness of such models. This study was performed to test and refine the rules associated with a previously developed geospatial model that predicts the prevalence of Listeria monocytogenes in produce farms in New York State (NYS). Produce fields for each of four enrolled produce farms were categorized into areas of high or low predicted L. monocytogenes prevalence using rules based on a field's available water storage (AWS) and its proximity to water, impervious cover, and pastures. Drag swabs (n = 1,056) were collected from plots assigned to each risk category. Logistic regression, which tested the ability of each rule to accurately predict the prevalence of L. monocytogenes, validated the rules based on water and pasture. Samples collected near water (odds ratio [OR], 3.0) and pasture (OR, 2.9) showed a significantly increased likelihood of L. monocytogenes isolation compared to that for samples collected far from water and pasture. Generalized linear mixed models identified additional land cover factors associated with an increased likelihood of L. monocytogenes isolation, such as proximity to wetlands. These findings validated a subset of previously developed rules that predict L. monocytogenes prevalence in produce production environments. This suggests that GIS and geospatial models can be used to accurately predict L. monocytogenes prevalence on farms and can be used prospectively to minimize the risk of preharvest contamination of produce. PMID:26590280

Separation in Logistic Regression: Causes, Consequences, and Control.

PubMed

Mansournia, Mohammad Ali; Geroldinger, Angelika; Greenland, Sander; Heinze, Georg

2018-04-01

Separation is encountered in regression models with a discrete outcome (such as logistic regression) where the covariates perfectly predict the outcome. It is most frequent under the same conditions that lead to small-sample and sparse-data bias, such as presence of a rare outcome, rare exposures, highly correlated covariates, or covariates with strong effects. In theory, separation will produce infinite estimates for some coefficients. In practice, however, separation may be unnoticed or mishandled because of software limits in recognizing and handling the problem and in notifying the user. We discuss causes of separation in logistic regression and describe how common software packages deal with it. We then describe methods that remove separation, focusing on the same penalized-likelihood techniques used to address more general sparse-data problems. These methods improve accuracy, avoid software problems, and allow interpretation as Bayesian analyses with weakly informative priors. We discuss likelihood penalties, including some that can be implemented easily with any software package, and their relative advantages and disadvantages. We provide an illustration of ideas and methods using data from a case-control study of contraceptive practices and urinary tract infection.

Digital Image Restoration Under a Regression Model - The Unconstrained, Linear Equality and Inequality Constrained Approaches

DTIC Science & Technology

1974-01-01

REGRESSION MODEL - THE UNCONSTRAINED, LINEAR EQUALITY AND INEQUALITY CONSTRAINED APPROACHES January 1974 Nelson Delfino d’Avila Mascarenha;? Image...Report 520 DIGITAL IMAGE RESTORATION UNDER A REGRESSION MODEL THE UNCONSTRAINED, LINEAR EQUALITY AND INEQUALITY CONSTRAINED APPROACHES January...a two- dimensional form adequately describes the linear model . A dis- cretization is performed by using quadrature methods. By trans

Element enrichment factor calculation using grain-size distribution and functional data regression.

PubMed

Sierra, C; Ordóñez, C; Saavedra, A; Gallego, J R

2015-01-01

In environmental geochemistry studies it is common practice to normalize element concentrations in order to remove the effect of grain size. Linear regression with respect to a particular grain size or conservative element is a widely used method of normalization. In this paper, the utility of functional linear regression, in which the grain-size curve is the independent variable and the concentration of pollutant the dependent variable, is analyzed and applied to detrital sediment. After implementing functional linear regression and classical linear regression models to normalize and calculate enrichment factors, we concluded that the former regression technique has some advantages over the latter. First, functional linear regression directly considers the grain-size distribution of the samples as the explanatory variable. Second, as the regression coefficients are not constant values but functions depending on the grain size, it is easier to comprehend the relationship between grain size and pollutant concentration. Third, regularization can be introduced into the model in order to establish equilibrium between reliability of the data and smoothness of the solutions. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

Secondhand smoke exposure in the workplace.

PubMed

Skeer, Margie; Cheng, Debbie M; Rigotti, Nancy A; Siegel, Michael

2005-05-01

Currently, there is little understanding of the relationship between the strength of workplace smoking policies and the likelihood and duration, not just the likelihood, of exposure to secondhand smoke at work. This study assessed self-reported exposure to secondhand smoke at work in hours per week among a cross-sectional sample of 3650 Massachusetts adults who were employed primarily at a single worksite outside the home that was not mainly outdoors. The sample data were from a larger longitudinal study designed to examine the effect of community-based tobacco control interventions on adult and youth smoking behavior. Participants were identified through a random-digit-dialing telephone survey. Multiple logistic regression and zero-inflated negative binomial regression models were used to estimate the independent effect of workplace smoking policies on the likelihood and duration of exposure to secondhand smoke. Compared to employees whose workplace banned smoking completely, those whose workplace provided designated smoking areas had 2.9 times the odds of being exposed to secondhand smoke and 1.74 times the duration of exposure, while those with no restrictions had 10.27 times the odds of being exposed and 6.34 times the duration of exposure. Workplace smoking policies substantially reduce the likelihood of self-reported secondhand smoke exposure among employees in the workplace and also greatly affect the duration of exposure.

Incorporating real-time traffic and weather data to explore road accident likelihood and severity in urban arterials.

PubMed

Theofilatos, Athanasios

2017-06-01

The effective treatment of road accidents and thus the enhancement of road safety is a major concern to societies due to the losses in human lives and the economic and social costs. The investigation of road accident likelihood and severity by utilizing real-time traffic and weather data has recently received significant attention by researchers. However, collected data mainly stem from freeways and expressways. Consequently, the aim of the present paper is to add to the current knowledge by investigating accident likelihood and severity by exploiting real-time traffic and weather data collected from urban arterials in Athens, Greece. Random Forests (RF) are firstly applied for preliminary analysis purposes. More specifically, it is aimed to rank candidate variables according to their relevant importance and provide a first insight on the potential significant variables. Then, Bayesian logistic regression as well finite mixture and mixed effects logit models are applied to further explore factors associated with accident likelihood and severity respectively. Regarding accident likelihood, the Bayesian logistic regression showed that variations in traffic significantly influence accident occurrence. On the other hand, accident severity analysis revealed a generally mixed influence of traffic variations on accident severity, although international literature states that traffic variations increase severity. Lastly, weather parameters did not find to have a direct influence on accident likelihood or severity. The study added to the current knowledge by incorporating real-time traffic and weather data from urban arterials to investigate accident occurrence and accident severity mechanisms. The identification of risk factors can lead to the development of effective traffic management strategies to reduce accident occurrence and severity of injuries in urban arterials. Copyright © 2017 Elsevier Ltd and National Safety Council. All rights reserved.

Who Will Win?: Predicting the Presidential Election Using Linear Regression

ERIC Educational Resources Information Center

Lamb, John H.

2007-01-01

This article outlines a linear regression activity that engages learners, uses technology, and fosters cooperation. Students generated least-squares linear regression equations using TI-83 Plus[TM] graphing calculators, Microsoft[C] Excel, and paper-and-pencil calculations using derived normal equations to predict the 2004 presidential election.…

A quasi-likelihood approach to non-negative matrix factorization

PubMed Central

Devarajan, Karthik; Cheung, Vincent C.K.

2017-01-01

A unified approach to non-negative matrix factorization based on the theory of generalized linear models is proposed. This approach embeds a variety of statistical models, including the exponential family, within a single theoretical framework and provides a unified view of such factorizations from the perspective of quasi-likelihood. Using this framework, a family of algorithms for handling signal-dependent noise is developed and its convergence proven using the Expectation-Maximization algorithm. In addition, a measure to evaluate the goodness-of-fit of the resulting factorization is described. The proposed methods allow modeling of non-linear effects via appropriate link functions and are illustrated using an application in biomedical signal processing. PMID:27348511

What are hierarchical models and how do we analyze them?

USGS Publications Warehouse

Royle, Andy

2016-01-01

In this chapter we provide a basic definition of hierarchical models and introduce the two canonical hierarchical models in this book: site occupancy and N-mixture models. The former is a hierarchical extension of logistic regression and the latter is a hierarchical extension of Poisson regression. We introduce basic concepts of probability modeling and statistical inference including likelihood and Bayesian perspectives. We go through the mechanics of maximizing the likelihood and characterizing the posterior distribution by Markov chain Monte Carlo (MCMC) methods. We give a general perspective on topics such as model selection and assessment of model fit, although we demonstrate these topics in practice in later chapters (especially Chapters 5, 6, 7, and 10 Chapter 5 Chapter 6 Chapter 7 Chapter 10)

Cox Regression Models with Functional Covariates for Survival Data.

PubMed

Gellar, Jonathan E; Colantuoni, Elizabeth; Needham, Dale M; Crainiceanu, Ciprian M

2015-06-01

We extend the Cox proportional hazards model to cases when the exposure is a densely sampled functional process, measured at baseline. The fundamental idea is to combine penalized signal regression with methods developed for mixed effects proportional hazards models. The model is fit by maximizing the penalized partial likelihood, with smoothing parameters estimated by a likelihood-based criterion such as AIC or EPIC. The model may be extended to allow for multiple functional predictors, time varying coefficients, and missing or unequally-spaced data. Methods were inspired by and applied to a study of the association between time to death after hospital discharge and daily measures of disease severity collected in the intensive care unit, among survivors of acute respiratory distress syndrome.

The microcomputer scientific software series 2: general linear model--regression.

Treesearch

Harold M. Rauscher

1983-01-01

The general linear model regression (GLMR) program provides the microcomputer user with a sophisticated regression analysis capability. The output provides a regression ANOVA table, estimators of the regression model coefficients, their confidence intervals, confidence intervals around the predicted Y-values, residuals for plotting, a check for multicollinearity, a...

An evaluation of several different classification schemes - Their parameters and performance. [maximum likelihood decision for crop identification

NASA Technical Reports Server (NTRS)

Scholz, D.; Fuhs, N.; Hixson, M.

1979-01-01

The overall objective of this study was to apply and evaluate several of the currently available classification schemes for crop identification. The approaches examined were: (1) a per point Gaussian maximum likelihood classifier, (2) a per point sum of normal densities classifier, (3) a per point linear classifier, (4) a per point Gaussian maximum likelihood decision tree classifier, and (5) a texture sensitive per field Gaussian maximum likelihood classifier. Three agricultural data sets were used in the study: areas from Fayette County, Illinois, and Pottawattamie and Shelby Counties in Iowa. The segments were located in two distinct regions of the Corn Belt to sample variability in soils, climate, and agricultural practices.

A Statistical Approach to Passive Target Tracking.

DTIC Science & Technology

1981-04-01

a fixed heading of 90 degrees. For 7F. A. Graybill , An Introduction to Linear Statistical Models , Vol. 1, New York: John Wiley&-Sons -Inc. (1961). 13...likelihood estimators. 12 NCSC TM 311-81 The adjustment for a changing error variance is easy using the linear model approach; i.e., use weighted

Impact of Lifestyle and Metformin Interventions on the Risk of Progression to Diabetes and Regression to Normal Glucose Regulation in Overweight or Obese People With Impaired Glucose Regulation.

PubMed

Herman, William H; Pan, Qing; Edelstein, Sharon L; Mather, Kieren J; Perreault, Leigh; Barrett-Connor, Elizabeth; Dabelea, Dana M; Horton, Edward; Kahn, Steven E; Knowler, William C; Lorenzo, Carlos; Pi-Sunyer, Xavier; Venditti, Elizabeth; Ye, Wen

2017-12-01

Both lifestyle and metformin interventions can delay or prevent progression to type 2 diabetes mellitus (DM) in people with impaired glucose regulation, but there is considerable interindividual variation in the likelihood of receiving benefit. Understanding an individual's 3-year risk of progressing to DM and regressing to normal glucose regulation (NGR) might facilitate benefit-based tailored treatment. We used the values of 19 clinical variables measured at the Diabetes Prevention Program (DPP) baseline evaluation and Cox proportional hazards models to assess the 3-year risk of progression to DM and regression to NGR separately for DPP lifestyle, metformin, and placebo participants who were adherent to the interventions. Lifestyle participants who lost ≥5% of their initial body weight at 6 months and metformin and placebo participants who reported taking ≥80% of their prescribed medication at the 6-month follow-up were defined as adherent. Eleven of 19 clinical variables measured at baseline predicted progression to DM, and 6 of 19 predicted regression to NGR. Compared with adherent placebo participants at lowest risk of developing diabetes, participants at lowest risk of developing diabetes who adhered to a lifestyle intervention had an 8% absolute risk reduction (ARR) of developing diabetes and a 35% greater absolute likelihood of reverting to NGR. Participants at lowest risk of developing diabetes who adhered to a metformin intervention had no reduction in their risk of developing diabetes and a 17% greater absolute likelihood of reverting to NGR. Participants at highest risk of developing DM who adhered to a lifestyle intervention had a 39% ARR of developing diabetes and a 24% greater absolute likelihood of reverting to NGR, whereas those who adhered to the metformin intervention had a 25% ARR of developing diabetes and an 11% greater absolute likelihood of reverting to NGR. Unlike our previous analyses that sought to explain population risk, these analyses evaluate individual risk. The models can be used by overweight and obese adults with fasting hyperglycemia and impaired glucose tolerance to facilitate personalized decision-making by allowing them to explicitly weigh the benefits and feasibility of the lifestyle and metformin interventions. © 2017 by the American Diabetes Association.

Racial/ethnic disparities in self-reported short sleep duration among US-born and foreign-born adults.

PubMed

Cunningham, Timothy J; Wheaton, Anne G; Ford, Earl S; Croft, Janet B

2016-12-01

Racial/ethnic health disparities are infrequently considered by nativity status in the United States, although the immigrant population has practically doubled since 1990. We investigated the modifying role of nativity status (US- vs. foreign-born) on racial/ethnic disparities in short sleep duration (<7 h), which has serious health consequences. Cross-sectional data from 23,505 US-born and 4,326 foreign-born adults aged ≥ 18 years from the 2012 National Health Interview Survey and multivariable log-linear regression were used to estimate prevalence ratios (PR) for reporting short sleep duration and their corresponding 95% confidence intervals (CI). After controlling for sociodemographic covariates, short sleep was more prevalent among blacks (PR 1.29, 95% CI: 1.21-1.37), Hispanics (PR 1.18, 95% CI: 1.08, 1.29), and Asians (PR 1.37, 95% CI: 1.16-1.61) than whites among US-born adults. Short sleep was more prevalent among blacks (PR 1.71, 95% CI: 1.38, 2.13) and Asians (PR 1.23, 95% CI: 1.02, 1.47) than whites among the foreign-born. Among both US- and foreign-born adults, blacks and Asians had a higher likelihood of short sleep compared to whites. US-born Hispanics, but not foreign-born Hispanics, had a higher likelihood than their white counterparts. Future research should aim to uncover mechanisms underlying these disparities.

An alternative method to measure the likelihood of a financial crisis in an emerging market

NASA Astrophysics Data System (ADS)

Özlale, Ümit; Metin-Özcan, Kıvılcım

2007-07-01

This paper utilizes an early warning system in order to measure the likelihood of a financial crisis in an emerging market economy. We introduce a methodology, where we can both obtain a likelihood series and analyze the time-varying effects of several macroeconomic variables on this likelihood. Since the issue is analyzed in a non-linear state space framework, the extended Kalman filter emerges as the optimal estimation algorithm. Taking the Turkish economy as our laboratory, the results indicate that both the derived likelihood measure and the estimated time-varying parameters are meaningful and can successfully explain the path that the Turkish economy had followed between 2000 and 2006. The estimated parameters also suggest that overvalued domestic currency, current account deficit and the increase in the default risk increase the likelihood of having an economic crisis in the economy. Overall, the findings in this paper suggest that the estimation methodology introduced in this paper can also be applied to other emerging market economies as well.

The biomechanics of concussion in unhelmeted football players in Australia: a case-control study.

PubMed

McIntosh, Andrew S; Patton, Declan A; Fréchède, Bertrand; Pierré, Paul-André; Ferry, Edouard; Barthels, Tobias

2014-05-20

Concussion is a prevalent brain injury in sport and the wider community. Despite this, little research has been conducted investigating the dynamics of impacts to the unprotected human head and injury causation in vivo, in particular the roles of linear and angular head acceleration. Professional contact football in Australia. Adult male professional Australian rules football players participating in 30 games randomly selected from 103 games. Cases selected based on an observable head impact, no observable symptoms (eg, loss-of-consciousness and convulsions), no on-field medical management and no injury recorded at the time. A data set for no-injury head impact cases comprising head impact locations and head impact dynamic parameters estimated through rigid body simulations using the MAthematical DYnamic MOdels (MADYMO) human facet model. This data set was compared to previously reported concussion case data. Qualitative analysis showed that the head was more vulnerable to lateral impacts. Logistic regression analyses of head acceleration and velocity components revealed that angular acceleration of the head in the coronal plane had the strongest association with concussion; tentative tolerance levels of 1747 rad/s(2) and 2296 rad/s(2) were reported for a 50% and 75% likelihood of concussion, respectively. The mean maximum resultant angular accelerations for the concussion and no-injury cases were 7951 rad/s(2) (SD 3562 rad/s(2)) and 4300 rad/s(2) (SD 3657 rad/s(2)), respectively. Linear acceleration is currently used in the assessment of helmets and padded headgear. The 50% and 75% likelihood of concussion values for resultant linear head acceleration in this study were 65.1 and 88.5 g, respectively. As hypothesised by Holbourn over 70 years ago, angular acceleration plays an important role in the pathomechanics of concussion, which has major ramifications in terms of helmet design and other efforts to prevent and manage concussion. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

[Comparison of application of Cochran-Armitage trend test and linear regression analysis for rate trend analysis in epidemiology study].

PubMed

Wang, D Z; Wang, C; Shen, C F; Zhang, Y; Zhang, H; Song, G D; Xue, X D; Xu, Z L; Zhang, S; Jiang, G H

2017-05-10

We described the time trend of acute myocardial infarction (AMI) from 1999 to 2013 in Tianjin incidence rate with Cochran-Armitage trend (CAT) test and linear regression analysis, and the results were compared. Based on actual population, CAT test had much stronger statistical power than linear regression analysis for both overall incidence trend and age specific incidence trend (Cochran-Armitage trend P value

[Ultrasonic measurements of fetal thalamus, caudate nucleus and lenticular nucleus in prenatal diagnosis].

PubMed

Yang, Ruiqi; Wang, Fei; Zhang, Jialing; Zhu, Chonglei; Fan, Limei

2015-05-19

To establish the reference values of thalamus, caudate nucleus and lenticular nucleus diameters through fetal thalamic transverse section. A total of 265 fetuses at our hospital were randomly selected from November 2012 to August 2014. And the transverse and length diameters of thalamus, caudate nucleus and lenticular nucleus were measured. SPSS 19.0 statistical software was used to calculate the regression curve of fetal diameter changes and gestational weeks of pregnancy. P < 0.05 was considered as having statistical significance. The linear regression equation of fetal thalamic length diameter and gestational week was: Y = 0.051X+0.201, R = 0.876, linear regression equation of thalamic transverse diameter and fetal gestational week was: Y = 0.031X+0.229, R = 0.817, linear regression equation of fetal head of caudate nucleus length diameter and gestational age was: Y = 0.033X+0.101, R = 0.722, linear regression equation of fetal head of caudate nucleus transverse diameter and gestational week was: R = 0.025 - 0.046, R = 0.711, linear regression equation of fetal lentiform nucleus length diameter and gestational week was: Y = 0.046+0.229, R = 0.765, linear regression equation of fetal lentiform nucleus diameter and gestational week was: Y = 0.025 - 0.05, R = 0.772. Ultrasonic measurement of diameter of fetal thalamus caudate nucleus, and lenticular nucleus through thalamic transverse section is simple and convenient. And measurements increase with fetal gestational weeks and there is linear regression relationship between them.

Local Linear Regression for Data with AR Errors.

PubMed

Li, Runze; Li, Yan

2009-07-01

In many statistical applications, data are collected over time, and they are likely correlated. In this paper, we investigate how to incorporate the correlation information into the local linear regression. Under the assumption that the error process is an auto-regressive process, a new estimation procedure is proposed for the nonparametric regression by using local linear regression method and the profile least squares techniques. We further propose the SCAD penalized profile least squares method to determine the order of auto-regressive process. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed procedure, and to compare the performance of the proposed procedures with the existing one. From our empirical studies, the newly proposed procedures can dramatically improve the accuracy of naive local linear regression with working-independent error structure. We illustrate the proposed methodology by an analysis of real data set.

Orthogonal Regression: A Teaching Perspective

ERIC Educational Resources Information Center

Carr, James R.

2012-01-01

A well-known approach to linear least squares regression is that which involves minimizing the sum of squared orthogonal projections of data points onto the best fit line. This form of regression is known as orthogonal regression, and the linear model that it yields is known as the major axis. A similar method, reduced major axis regression, is…

Improvement of dose calculation in radiation therapy due to metal artifact correction using the augmented likelihood image reconstruction.

PubMed

Ziemann, Christian; Stille, Maik; Cremers, Florian; Buzug, Thorsten M; Rades, Dirk

2018-04-17

Metal artifacts caused by high-density implants lead to incorrectly reconstructed Hounsfield units in computed tomography images. This can result in a loss of accuracy in dose calculation in radiation therapy. This study investigates the potential of the metal artifact reduction algorithms, Augmented Likelihood Image Reconstruction and linear interpolation, in improving dose calculation in the presence of metal artifacts. In order to simulate a pelvis with a double-sided total endoprosthesis, a polymethylmethacrylate phantom was equipped with two steel bars. Artifacts were reduced by applying the Augmented Likelihood Image Reconstruction, a linear interpolation, and a manual correction approach. Using the treatment planning system Eclipse™, identical planning target volumes for an idealized prostate as well as structures for bladder and rectum were defined in corrected and noncorrected images. Volumetric modulated arc therapy plans have been created with double arc rotations with and without avoidance sectors that mask out the prosthesis. The irradiation plans were analyzed for variations in the dose distribution and their homogeneity. Dosimetric measurements were performed using isocentric positioned ionization chambers. Irradiation plans based on images containing artifacts lead to a dose error in the isocenter of up to 8.4%. Corrections with the Augmented Likelihood Image Reconstruction reduce this dose error to 2.7%, corrections with linear interpolation to 3.2%, and manual artifact correction to 4.1%. When applying artifact correction, the dose homogeneity was slightly improved for all investigated methods. Furthermore, the calculated mean doses are higher for rectum and bladder if avoidance sectors are applied. Streaking artifacts cause an imprecise dose calculation within irradiation plans. Using a metal artifact correction algorithm, the planning accuracy can be significantly improved. Best results were accomplished using the Augmented Likelihood Image Reconstruction algorithm. © 2018 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

A Sequential Ensemble Prediction System at Convection Permitting Scales

NASA Astrophysics Data System (ADS)

Milan, M.; Simmer, C.

2012-04-01

A Sequential Assimilation Method (SAM) following some aspects of particle filtering with resampling, also called SIR (Sequential Importance Resampling), is introduced and applied in the framework of an Ensemble Prediction System (EPS) for weather forecasting on convection permitting scales, with focus to precipitation forecast. At this scale and beyond, the atmosphere increasingly exhibits chaotic behaviour and non linear state space evolution due to convectively driven processes. One way to take full account of non linear state developments are particle filter methods, their basic idea is the representation of the model probability density function by a number of ensemble members weighted by their likelihood with the observations. In particular particle filter with resampling abandons ensemble members (particles) with low weights restoring the original number of particles adding multiple copies of the members with high weights. In our SIR-like implementation we substitute the likelihood way to define weights and introduce a metric which quantifies the "distance" between the observed atmospheric state and the states simulated by the ensemble members. We also introduce a methodology to counteract filter degeneracy, i.e. the collapse of the simulated state space. To this goal we propose a combination of resampling taking account of simulated state space clustering and nudging. By keeping cluster representatives during resampling and filtering, the method maintains the potential for non linear system state development. We assume that a particle cluster with initially low likelihood may evolve in a state space with higher likelihood in a subsequent filter time thus mimicking non linear system state developments (e.g. sudden convection initiation) and remedies timing errors for convection due to model errors and/or imperfect initial condition. We apply a simplified version of the resampling, the particles with highest weights in each cluster are duplicated; for the model evolution for each particle pair one particle evolves using the forward model; the second particle, however, is nudged to the radar and satellite observation during its evolution based on the forward model.

Practical Session: Simple Linear Regression

NASA Astrophysics Data System (ADS)

Clausel, M.; Grégoire, G.

2014-12-01

Two exercises are proposed to illustrate the simple linear regression. The first one is based on the famous Galton's data set on heredity. We use the lm R command and get coefficients estimates, standard error of the error, R2, residuals …In the second example, devoted to data related to the vapor tension of mercury, we fit a simple linear regression, predict values, and anticipate on multiple linear regression. This pratical session is an excerpt from practical exercises proposed by A. Dalalyan at EPNC (see Exercises 1 and 2 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_4.pdf).

Morse Code, Scrabble, and the Alphabet

ERIC Educational Resources Information Center

Richardson, Mary; Gabrosek, John; Reischman, Diann; Curtiss, Phyliss

2004-01-01

In this paper we describe an interactive activity that illustrates simple linear regression. Students collect data and analyze it using simple linear regression techniques taught in an introductory applied statistics course. The activity is extended to illustrate checks for regression assumptions and regression diagnostics taught in an…

Load estimator (LOADEST): a FORTRAN program for estimating constituent loads in streams and rivers

USGS Publications Warehouse

Runkel, Robert L.; Crawford, Charles G.; Cohn, Timothy A.

2004-01-01

LOAD ESTimator (LOADEST) is a FORTRAN program for estimating constituent loads in streams and rivers. Given a time series of streamflow, additional data variables, and constituent concentration, LOADEST assists the user in developing a regression model for the estimation of constituent load (calibration). Explanatory variables within the regression model include various functions of streamflow, decimal time, and additional user-specified data variables. The formulated regression model then is used to estimate loads over a user-specified time interval (estimation). Mean load estimates, standard errors, and 95 percent confidence intervals are developed on a monthly and(or) seasonal basis. The calibration and estimation procedures within LOADEST are based on three statistical estimation methods. The first two methods, Adjusted Maximum Likelihood Estimation (AMLE) and Maximum Likelihood Estimation (MLE), are appropriate when the calibration model errors (residuals) are normally distributed. Of the two, AMLE is the method of choice when the calibration data set (time series of streamflow, additional data variables, and concentration) contains censored data. The third method, Least Absolute Deviation (LAD), is an alternative to maximum likelihood estimation when the residuals are not normally distributed. LOADEST output includes diagnostic tests and warnings to assist the user in determining the appropriate estimation method and in interpreting the estimated loads. This report describes the development and application of LOADEST. Sections of the report describe estimation theory, input/output specifications, sample applications, and installation instructions.

Computational tools for exact conditional logistic regression.

PubMed

Corcoran, C; Mehta, C; Patel, N; Senchaudhuri, P

Logistic regression analyses are often challenged by the inability of unconditional likelihood-based approximations to yield consistent, valid estimates and p-values for model parameters. This can be due to sparseness or separability in the data. Conditional logistic regression, though useful in such situations, can also be computationally unfeasible when the sample size or number of explanatory covariates is large. We review recent developments that allow efficient approximate conditional inference, including Monte Carlo sampling and saddlepoint approximations. We demonstrate through real examples that these methods enable the analysis of significantly larger and more complex data sets. We find in this investigation that for these moderately large data sets Monte Carlo seems a better alternative, as it provides unbiased estimates of the exact results and can be executed in less CPU time than can the single saddlepoint approximation. Moreover, the double saddlepoint approximation, while computationally the easiest to obtain, offers little practical advantage. It produces unreliable results and cannot be computed when a maximum likelihood solution does not exist. Copyright 2001 John Wiley & Sons, Ltd.

A baseline-free procedure for transformation models under interval censorship.

PubMed

Gu, Ming Gao; Sun, Liuquan; Zuo, Guoxin

2005-12-01

An important property of Cox regression model is that the estimation of regression parameters using the partial likelihood procedure does not depend on its baseline survival function. We call such a procedure baseline-free. Using marginal likelihood, we show that an baseline-free procedure can be derived for a class of general transformation models under interval censoring framework. The baseline-free procedure results a simplified and stable computation algorithm for some complicated and important semiparametric models, such as frailty models and heteroscedastic hazard/rank regression models, where the estimation procedures so far available involve estimation of the infinite dimensional baseline function. A detailed computational algorithm using Markov Chain Monte Carlo stochastic approximation is presented. The proposed procedure is demonstrated through extensive simulation studies, showing the validity of asymptotic consistency and normality. We also illustrate the procedure with a real data set from a study of breast cancer. A heuristic argument showing that the score function is a mean zero martingale is provided.

Parameter estimation of history-dependent leaky integrate-and-fire neurons using maximum-likelihood methods

PubMed Central

Dong, Yi; Mihalas, Stefan; Russell, Alexander; Etienne-Cummings, Ralph; Niebur, Ernst

2012-01-01

When a neuronal spike train is observed, what can we say about the properties of the neuron that generated it? A natural way to answer this question is to make an assumption about the type of neuron, select an appropriate model for this type, and then to choose the model parameters as those that are most likely to generate the observed spike train. This is the maximum likelihood method. If the neuron obeys simple integrate and fire dynamics, Paninski, Pillow, and Simoncelli (2004) showed that its negative log-likelihood function is convex and that its unique global minimum can thus be found by gradient descent techniques. The global minimum property requires independence of spike time intervals. Lack of history dependence is, however, an important constraint that is not fulfilled in many biological neurons which are known to generate a rich repertoire of spiking behaviors that are incompatible with history independence. Therefore, we expanded the integrate and fire model by including one additional variable, a variable threshold (Mihalas & Niebur, 2009) allowing for history-dependent firing patterns. This neuronal model produces a large number of spiking behaviors while still being linear. Linearity is important as it maintains the distribution of the random variables and still allows for maximum likelihood methods to be used. In this study we show that, although convexity of the negative log-likelihood is not guaranteed for this model, the minimum of the negative log-likelihood function yields a good estimate for the model parameters, in particular if the noise level is treated as a free parameter. Furthermore, we show that a nonlinear function minimization method (r-algorithm with space dilation) frequently reaches the global minimum. PMID:21851282

Advanced statistics: linear regression, part II: multiple linear regression.

PubMed

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Reversed inverse regression for the univariate linear calibration and its statistical properties derived using a new methodology

NASA Astrophysics Data System (ADS)

Kang, Pilsang; Koo, Changhoi; Roh, Hokyu

2017-11-01

Since simple linear regression theory was established at the beginning of the 1900s, it has been used in a variety of fields. Unfortunately, it cannot be used directly for calibration. In practical calibrations, the observed measurements (the inputs) are subject to errors, and hence they vary, thus violating the assumption that the inputs are fixed. Therefore, in the case of calibration, the regression line fitted using the method of least squares is not consistent with the statistical properties of simple linear regression as already established based on this assumption. To resolve this problem, "classical regression" and "inverse regression" have been proposed. However, they do not completely resolve the problem. As a fundamental solution, we introduce "reversed inverse regression" along with a new methodology for deriving its statistical properties. In this study, the statistical properties of this regression are derived using the "error propagation rule" and the "method of simultaneous error equations" and are compared with those of the existing regression approaches. The accuracy of the statistical properties thus derived is investigated in a simulation study. We conclude that the newly proposed regression and methodology constitute the complete regression approach for univariate linear calibrations.

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

PubMed

Ferrari, Alberto; Comelli, Mario

2016-12-01

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

On the Relation between the Linear Factor Model and the Latent Profile Model

ERIC Educational Resources Information Center

Halpin, Peter F.; Dolan, Conor V.; Grasman, Raoul P. P. P.; De Boeck, Paul

2011-01-01

The relationship between linear factor models and latent profile models is addressed within the context of maximum likelihood estimation based on the joint distribution of the manifest variables. Although the two models are well known to imply equivalent covariance decompositions, in general they do not yield equivalent estimates of the…

Postincident Support for Healthcare Workers Experiencing Occupational Violence and Aggression.

PubMed

Shea, Tracey; Cooper, Brian; De Cieri, Helen; Sheehan, Cathy; Donohue, Ross; Lindsay, Sarah

2018-05-10

To investigate the relative contributions of workplace type, occupational violence and aggression (OVA) strategies and interventions along with perceptions of the occupational health and safety (OHS) environment on the likelihood of receiving postincident support following the experience of OVA. We used a cross-sectional study design with an online survey to collect data from employees in nursing and midwifery in Victoria, Australia. Survey data collected from 3,072 members of the Australian Nursing and Midwifery Federation (Victorian branch) were analyzed using logistic regression. Of the 3,072 respondents who had experienced OVA in the preceding 12 months, 1,287 (42%) reported that they had received postincident support. Hierarchical logistic regression revealed that the OHS environment was the dominant factor that predicted the likelihood of workers receiving postincident support. Working in a positive OHS environment characterized by higher levels of leading indicators of OHS, prioritization of OHS, supervisor support for safety, and team psychological safety was the stronger predictor of postincident support. Being employed in a workplace that offered training in the management and prevention of OVA also increased the likelihood of receiving postincident support. While training in the management and prevention of OVA contributed to the likelihood of receiving postincident support, a greater emphasis on the OHS environment was more important in predicting the likelihood that workers received support. This study identifies workplace practices that facilitate the provision of postincident support for healthcare workers. Facilitating effective postincident support could improve outcomes for workers, their patients and workplaces, and society in general. © 2018 Sigma Theta Tau International.

OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis.

PubMed

Vajargah, Kianoush Fathi; Sadeghi-Bazargani, Homayoun; Mehdizadeh-Esfanjani, Robab; Savadi-Oskouei, Daryoush; Farhoudi, Mehdi

2012-01-01

The objective of the present study was to assess the comparable applicability of orthogonal projections to latent structures (OPLS) statistical model vs traditional linear regression in order to investigate the role of trans cranial doppler (TCD) sonography in predicting ischemic stroke prognosis. The study was conducted on 116 ischemic stroke patients admitted to a specialty neurology ward. The Unified Neurological Stroke Scale was used once for clinical evaluation on the first week of admission and again six months later. All data was primarily analyzed using simple linear regression and later considered for multivariate analysis using PLS/OPLS models through the SIMCA P+12 statistical software package. The linear regression analysis results used for the identification of TCD predictors of stroke prognosis were confirmed through the OPLS modeling technique. Moreover, in comparison to linear regression, the OPLS model appeared to have higher sensitivity in detecting the predictors of ischemic stroke prognosis and detected several more predictors. Applying the OPLS model made it possible to use both single TCD measures/indicators and arbitrarily dichotomized measures of TCD single vessel involvement as well as the overall TCD result. In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.

Generalized linear mixed models with varying coefficients for longitudinal data.

PubMed

Zhang, Daowen

2004-03-01

The routinely assumed parametric functional form in the linear predictor of a generalized linear mixed model for longitudinal data may be too restrictive to represent true underlying covariate effects. We relax this assumption by representing these covariate effects by smooth but otherwise arbitrary functions of time, with random effects used to model the correlation induced by among-subject and within-subject variation. Due to the usually intractable integration involved in evaluating the quasi-likelihood function, the double penalized quasi-likelihood (DPQL) approach of Lin and Zhang (1999, Journal of the Royal Statistical Society, Series B61, 381-400) is used to estimate the varying coefficients and the variance components simultaneously by representing a nonparametric function by a linear combination of fixed effects and random effects. A scaled chi-squared test based on the mixed model representation of the proposed model is developed to test whether an underlying varying coefficient is a polynomial of certain degree. We evaluate the performance of the procedures through simulation studies and illustrate their application with Indonesian children infectious disease data.

Quality of life in breast cancer patients--a quantile regression analysis.

PubMed

Pourhoseingholi, Mohamad Amin; Safaee, Azadeh; Moghimi-Dehkordi, Bijan; Zeighami, Bahram; Faghihzadeh, Soghrat; Tabatabaee, Hamid Reza; Pourhoseingholi, Asma

2008-01-01

Quality of life study has an important role in health care especially in chronic diseases, in clinical judgment and in medical resources supplying. Statistical tools like linear regression are widely used to assess the predictors of quality of life. But when the response is not normal the results are misleading. The aim of this study is to determine the predictors of quality of life in breast cancer patients, using quantile regression model and compare to linear regression. A cross-sectional study conducted on 119 breast cancer patients that admitted and treated in chemotherapy ward of Namazi hospital in Shiraz. We used QLQ-C30 questionnaire to assessment quality of life in these patients. A quantile regression was employed to assess the assocciated factors and the results were compared to linear regression. All analysis carried out using SAS. The mean score for the global health status for breast cancer patients was 64.92+/-11.42. Linear regression showed that only grade of tumor, occupational status, menopausal status, financial difficulties and dyspnea were statistically significant. In spite of linear regression, financial difficulties were not significant in quantile regression analysis and dyspnea was only significant for first quartile. Also emotion functioning and duration of disease statistically predicted the QOL score in the third quartile. The results have demonstrated that using quantile regression leads to better interpretation and richer inference about predictors of the breast cancer patient quality of life.

Interpretation of commonly used statistical regression models.

PubMed

Kasza, Jessica; Wolfe, Rory

2014-01-01

A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.

Estimation of longitudinal stability and control derivatives for an icing research aircraft from flight data

NASA Technical Reports Server (NTRS)

Batterson, James G.; Omara, Thomas M.

1989-01-01

The results of applying a modified stepwise regression algorithm and a maximum likelihood algorithm to flight data from a twin-engine commuter-class icing research aircraft are presented. The results are in the form of body-axis stability and control derivatives related to the short-period, longitudinal motion of the aircraft. Data were analyzed for the baseline (uniced) and for the airplane with an artificial glaze ice shape attached to the leading edge of the horizontal tail. The results are discussed as to the accuracy of the derivative estimates and the difference between the derivative values found for the baseline and the iced airplane. Additional comparisons were made between the maximum likelihood results and the modified stepwise regression results with causes for any discrepancies postulated.

Use of probabilistic weights to enhance linear regression myoelectric control

NASA Astrophysics Data System (ADS)

Smith, Lauren H.; Kuiken, Todd A.; Hargrove, Levi J.

2015-12-01

Objective. Clinically available prostheses for transradial amputees do not allow simultaneous myoelectric control of degrees of freedom (DOFs). Linear regression methods can provide simultaneous myoelectric control, but frequently also result in difficulty with isolating individual DOFs when desired. This study evaluated the potential of using probabilistic estimates of categories of gross prosthesis movement, which are commonly used in classification-based myoelectric control, to enhance linear regression myoelectric control. Approach. Gaussian models were fit to electromyogram (EMG) feature distributions for three movement classes at each DOF (no movement, or movement in either direction) and used to weight the output of linear regression models by the probability that the user intended the movement. Eight able-bodied and two transradial amputee subjects worked in a virtual Fitts’ law task to evaluate differences in controllability between linear regression and probability-weighted regression for an intramuscular EMG-based three-DOF wrist and hand system. Main results. Real-time and offline analyses in able-bodied subjects demonstrated that probability weighting improved performance during single-DOF tasks (p < 0.05) by preventing extraneous movement at additional DOFs. Similar results were seen in experiments with two transradial amputees. Though goodness-of-fit evaluations suggested that the EMG feature distributions showed some deviations from the Gaussian, equal-covariance assumptions used in this experiment, the assumptions were sufficiently met to provide improved performance compared to linear regression control. Significance. Use of probability weights can improve the ability to isolate individual during linear regression myoelectric control, while maintaining the ability to simultaneously control multiple DOFs.

Simplified large African carnivore density estimators from track indices.

PubMed

Winterbach, Christiaan W; Ferreira, Sam M; Funston, Paul J; Somers, Michael J

2016-01-01

The range, population size and trend of large carnivores are important parameters to assess their status globally and to plan conservation strategies. One can use linear models to assess population size and trends of large carnivores from track-based surveys on suitable substrates. The conventional approach of a linear model with intercept may not intercept at zero, but may fit the data better than linear model through the origin. We assess whether a linear regression through the origin is more appropriate than a linear regression with intercept to model large African carnivore densities and track indices. We did simple linear regression with intercept analysis and simple linear regression through the origin and used the confidence interval for ß in the linear model y  =  αx  + ß, Standard Error of Estimate, Mean Squares Residual and Akaike Information Criteria to evaluate the models. The Lion on Clay and Low Density on Sand models with intercept were not significant ( P  > 0.05). The other four models with intercept and the six models thorough origin were all significant ( P  < 0.05). The models using linear regression with intercept all included zero in the confidence interval for ß and the null hypothesis that ß = 0 could not be rejected. All models showed that the linear model through the origin provided a better fit than the linear model with intercept, as indicated by the Standard Error of Estimate and Mean Square Residuals. Akaike Information Criteria showed that linear models through the origin were better and that none of the linear models with intercept had substantial support. Our results showed that linear regression through the origin is justified over the more typical linear regression with intercept for all models we tested. A general model can be used to estimate large carnivore densities from track densities across species and study areas. The formula observed track density = 3.26 × carnivore density can be used to estimate densities of large African carnivores using track counts on sandy substrates in areas where carnivore densities are 0.27 carnivores/100 km 2 or higher. To improve the current models, we need independent data to validate the models and data to test for non-linear relationship between track indices and true density at low densities.

Self-stigma of seeking treatment and being male predict an increased likelihood of having an undiagnosed eating disorder.

PubMed

Griffiths, Scott; Mond, Jonathan M; Li, Zhicheng; Gunatilake, Sanduni; Murray, Stuart B; Sheffield, Jeanie; Touyz, Stephen

2015-09-01

To examine whether self-stigma of seeking psychological help and being male would be associated with an increased likelihood of having an undiagnosed eating disorder. A multi-national sample of 360 individuals with diagnosed eating disorders and 125 individuals with undiagnosed eating disorders were recruited. Logistic regression was used to identify variables affecting the likelihood of having an undiagnosed eating disorder, including sex, self-stigma of seeking psychological help, and perceived stigma of having a mental illness, controlling for a broad range of covariates. Being male and reporting greater self-stigma of seeking psychological help was independently associated with an increased likelihood of being undiagnosed. Further, the association between self-stigma of seeking psychological help and increased likelihood of being undiagnosed was significantly stronger for males than for females. Perceived stigma associated with help-seeking may be a salient barrier to treatment for eating disorders-particularly among male sufferers. © 2015 Wiley Periodicals, Inc.

[From clinical judgment to linear regression model.

PubMed

Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O

2013-01-01

When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.

Financial, Academic, and Environmental Influences on the Retention and Graduation of Students

ERIC Educational Resources Information Center

Wohlgemuth, Darin; Whalen, Don; Sullivan, Julia; Nading, Carolyn; Shelley, Mack; Wang, Yongyi (Rebecca)

2007-01-01

Regression analysis was used to study retention and graduation for the fall 1996 entering class of students at a midwestern research extensive university (n = 3,610; 44% female, 8% minority, 77% in-state). Logistic regression was used to predict the likelihood of a student being retained for each of four years, and the outcome of graduation at the…

Land Suitability Modeling using a Geographic Socio-Environmental Niche-Based Approach: A Case Study from Northeastern Thailand

PubMed Central

Heumann, Benjamin W.; Walsh, Stephen J.; Verdery, Ashton M.; McDaniel, Phillip M.; Rindfuss, Ronald R.

2012-01-01

Understanding the pattern-process relations of land use/land cover change is an important area of research that provides key insights into human-environment interactions. The suitability or likelihood of occurrence of land use such as agricultural crop types across a human-managed landscape is a central consideration. Recent advances in niche-based, geographic species distribution modeling (SDM) offer a novel approach to understanding land suitability and land use decisions. SDM links species presence-location data with geospatial information and uses machine learning algorithms to develop non-linear and discontinuous species-environment relationships. Here, we apply the MaxEnt (Maximum Entropy) model for land suitability modeling by adapting niche theory to a human-managed landscape. In this article, we use data from an agricultural district in Northeastern Thailand as a case study for examining the relationships between the natural, built, and social environments and the likelihood of crop choice for the commonly grown crops that occur in the Nang Rong District – cassava, heavy rice, and jasmine rice, as well as an emerging crop, fruit trees. Our results indicate that while the natural environment (e.g., elevation and soils) is often the dominant factor in crop likelihood, the likelihood is also influenced by household characteristics, such as household assets and conditions of the neighborhood or built environment. Furthermore, the shape of the land use-environment curves illustrates the non-continuous and non-linear nature of these relationships. This approach demonstrates a novel method of understanding non-linear relationships between land and people. The article concludes with a proposed method for integrating the niche-based rules of land use allocation into a dynamic land use model that can address both allocation and quantity of agricultural crops. PMID:24187378

Future orientation and suicide ideation and attempts in depressed adults ages 50 and over.

PubMed

Hirsch, Jameson K; Duberstein, Paul R; Conner, Kenneth R; Heisel, Marnin J; Beckman, Anthony; Franus, Nathan; Conwell, Yeates

2006-09-01

The objective of this study was to test the hypothesis that future orientation is associated with lower levels of suicide ideation and lower likelihood of suicide attempt in a sample of patients in treatment for major depression. Two hundred two participants (116 female, 57%) ages 50-88 years were recruited from inpatient and outpatient settings. All were diagnosed with major depression using a structured diagnostic interview. Suicide ideation was assessed with the Scale for Suicide Ideation (both current and worst point ratings), and a measure of future orientation was created to assess future expectancies. The authors predicted that greater future orientation would be associated with less current and worst point suicide ideation, and would distinguish current and lifetime suicide attempters from nonattempters. Hypotheses were tested using multivariate logistic regression and linear regression analyses that accounted for age, gender, hopelessness, and depression. As hypothesized, higher future orientation scores were associated with lower current suicidal ideation, less intense suicidal ideation at its worst point, and lower probability of a history of attempted suicide after accounting for covariates. Future orientation was not associated with current attempt status. Future orientation holds promise as a cognitive variable associated with decreased suicide risk; a better understanding of its putative protective role is needed. Treatments designed to enhance future orientation might decrease suicide risk.

Fourier transform infrared reflectance spectra of latent fingerprints: a biometric gauge for the age of an individual.

PubMed

Hemmila, April; McGill, Jim; Ritter, David

2008-03-01

To determine if changes in fingerprint infrared spectra linear with age can be found, partial least squares (PLS1) regression of 155 fingerprint infrared spectra against the person's age was constructed. The regression produced a linear model of age as a function of spectrum with a root mean square error of calibration of less than 4 years, showing an inflection at about 25 years of age. The spectral ranges emphasized by the regression do not correspond to the highest concentration constituents of the fingerprints. Separate linear regression models for old and young people can be constructed with even more statistical rigor. The success of the regression demonstrates that a combination of constituents can be found that changes linearly with age, with a significant shift around puberty.

Linearity versus Nonlinearity of Offspring-Parent Regression: An Experimental Study of Drosophila Melanogaster

PubMed Central

Gimelfarb, A.; Willis, J. H.

1994-01-01

An experiment was conducted to investigate the offspring-parent regression for three quantitative traits (weight, abdominal bristles and wing length) in Drosophila melanogaster. Linear and polynomial models were fitted for the regressions of a character in offspring on both parents. It is demonstrated that responses by the characters to selection predicted by the nonlinear regressions may differ substantially from those predicted by the linear regressions. This is true even, and especially, if selection is weak. The realized heritability for a character under selection is shown to be determined not only by the offspring-parent regression but also by the distribution of the character and by the form and strength of selection. PMID:7828818

Semiparametric Estimation of the Impacts of Longitudinal Interventions on Adolescent Obesity using Targeted Maximum-Likelihood: Accessible Estimation with the ltmle Package

PubMed Central

Decker, Anna L.; Hubbard, Alan; Crespi, Catherine M.; Seto, Edmund Y.W.; Wang, May C.

2015-01-01

While child and adolescent obesity is a serious public health concern, few studies have utilized parameters based on the causal inference literature to examine the potential impacts of early intervention. The purpose of this analysis was to estimate the causal effects of early interventions to improve physical activity and diet during adolescence on body mass index (BMI), a measure of adiposity, using improved techniques. The most widespread statistical method in studies of child and adolescent obesity is multi-variable regression, with the parameter of interest being the coefficient on the variable of interest. This approach does not appropriately adjust for time-dependent confounding, and the modeling assumptions may not always be met. An alternative parameter to estimate is one motivated by the causal inference literature, which can be interpreted as the mean change in the outcome under interventions to set the exposure of interest. The underlying data-generating distribution, upon which the estimator is based, can be estimated via a parametric or semi-parametric approach. Using data from the National Heart, Lung, and Blood Institute Growth and Health Study, a 10-year prospective cohort study of adolescent girls, we estimated the longitudinal impact of physical activity and diet interventions on 10-year BMI z-scores via a parameter motivated by the causal inference literature, using both parametric and semi-parametric estimation approaches. The parameters of interest were estimated with a recently released R package, ltmle, for estimating means based upon general longitudinal treatment regimes. We found that early, sustained intervention on total calories had a greater impact than a physical activity intervention or non-sustained interventions. Multivariable linear regression yielded inflated effect estimates compared to estimates based on targeted maximum-likelihood estimation and data-adaptive super learning. Our analysis demonstrates that sophisticated, optimal semiparametric estimation of longitudinal treatment-specific means via ltmle provides an incredibly powerful, yet easy-to-use tool, removing impediments for putting theory into practice. PMID:26046009

Association of blood polychlorinated biphenyls and cholesterol levels among Canadian Inuit.

PubMed

Singh, Kavita; Chan, Hing Man

2018-01-01

It has generally been thought that Inuit populations have low risk of cardiovascular disease due to high consumption of omega-3 fatty acids found in traditional marine-based diets. However, results of recent surveys showed that Inuit populations are experiencing increasing rates of cardiovascular disease and related risk factors. The purpose of this study was to investigate if blood polychlorinated biphenyls (PCBs) are associated with high cholesterol and related parameters in Canadian Inuit, known risk factors for cardiovascular disease. The Adult Inuit Health Survey (IHS, 2007-2008) included 2595 Inuit participants from three regions of the Canadian Arctic, of which 2191 could be classified as with or without high cholesterol. The high cholesterol outcome was defined by LDL-C > 3.36mmol/L or taking medication(s) that reduce cholesterol, and was examined in adjusted logistic regression models with individual blood levels of PCB congeners, sum of dioxin-like PCBs (∑DL-PCBs), or sum of non-dioxin-like PCBs (∑NDL-PCBs). Statistically significant covariates for high cholesterol were ranked in importance according to the proportion of the model log likelihood explained. Continuous clinical parameters of total cholesterol, triglycerides, LDL-C, and HDL-C were examined in multiple linear regression models with ∑DL-PCBs or ∑NDL-PCBs. A total of 719 participants had high cholesterol (32.8%). PCBs were associated with increased risk of high cholesterol, and higher levels of serum triglycerides, total cholesterol, and LDL-C. No association was observed between PCBs and serum HDL-C. With respect to other statistically significant covariates for high cholesterol, the log likelihood ranking of PCBs generally fell between body mass index (BMI) and age. Further work is needed to corroborate the associations observed with PCBs and lipids in Canadian Inuit and to examine if they are causal in the direction anticipated. Copyright © 2017 Elsevier Inc. All rights reserved.

A preliminary evaluation of the generalized likelihood ratio for detecting and identifying control element failures in a transport aircraft

NASA Technical Reports Server (NTRS)

Bundick, W. T.

1985-01-01

The application of the Generalized Likelihood Ratio technique to the detection and identification of aircraft control element failures has been evaluated in a linear digital simulation of the longitudinal dynamics of a B-737 aircraft. Simulation results show that the technique has potential but that the effects of wind turbulence and Kalman filter model errors are problems which must be overcome.

Computing Maximum Likelihood Estimates of Loglinear Models from Marginal Sums with Special Attention to Loglinear Item Response Theory. [Project Psychometric Aspects of Item Banking No. 53.] Research Report 91-1.

ERIC Educational Resources Information Center

Kelderman, Henk

In this paper, algorithms are described for obtaining the maximum likelihood estimates of the parameters in log-linear models. Modified versions of the iterative proportional fitting and Newton-Raphson algorithms are described that work on the minimal sufficient statistics rather than on the usual counts in the full contingency table. This is…

Comparison of discriminant analysis methods: Application to occupational exposure to particulate matter

NASA Astrophysics Data System (ADS)

Ramos, M. Rosário; Carolino, E.; Viegas, Carla; Viegas, Sandra

2016-06-01

Health effects associated with occupational exposure to particulate matter have been studied by several authors. In this study were selected six industries of five different areas: Cork company 1, Cork company 2, poultry, slaughterhouse for cattle, riding arena and production of animal feed. The measurements tool was a portable device for direct reading. This tool provides information on the particle number concentration for six different diameters, namely 0.3 µm, 0.5 µm, 1 µm, 2.5 µm, 5 µm and 10 µm. The focus on these features is because they might be more closely related with adverse health effects. The aim is to identify the particles that better discriminate the industries, with the ultimate goal of classifying industries regarding potential negative effects on workers' health. Several methods of discriminant analysis were applied to data of occupational exposure to particulate matter and compared with respect to classification accuracy. The selected methods were linear discriminant analyses (LDA); linear quadratic discriminant analysis (QDA), robust linear discriminant analysis with selected estimators (MLE (Maximum Likelihood Estimators), MVE (Minimum Volume Elipsoid), "t", MCD (Minimum Covariance Determinant), MCD-A, MCD-B), multinomial logistic regression and artificial neural networks (ANN). The predictive accuracy of the methods was accessed through a simulation study. ANN yielded the highest rate of classification accuracy in the data set under study. Results indicate that the particle number concentration of diameter size 0.5 µm is the parameter that better discriminates industries.

Decomposition and model selection for large contingency tables.

PubMed

Dahinden, Corinne; Kalisch, Markus; Bühlmann, Peter

2010-04-01

Large contingency tables summarizing categorical variables arise in many areas. One example is in biology, where large numbers of biomarkers are cross-tabulated according to their discrete expression level. Interactions of the variables are of great interest and are generally studied with log-linear models. The structure of a log-linear model can be visually represented by a graph from which the conditional independence structure can then be easily read off. However, since the number of parameters in a saturated model grows exponentially in the number of variables, this generally comes with a heavy computational burden. Even if we restrict ourselves to models of lower-order interactions or other sparse structures, we are faced with the problem of a large number of cells which play the role of sample size. This is in sharp contrast to high-dimensional regression or classification procedures because, in addition to a high-dimensional parameter, we also have to deal with the analogue of a huge sample size. Furthermore, high-dimensional tables naturally feature a large number of sampling zeros which often leads to the nonexistence of the maximum likelihood estimate. We therefore present a decomposition approach, where we first divide the problem into several lower-dimensional problems and then combine these to form a global solution. Our methodology is computationally feasible for log-linear interaction models with many categorical variables each or some of them having many levels. We demonstrate the proposed method on simulated data and apply it to a bio-medical problem in cancer research.

Boosting multi-state models.

PubMed

Reulen, Holger; Kneib, Thomas

2016-04-01

One important goal in multi-state modelling is to explore information about conditional transition-type-specific hazard rate functions by estimating influencing effects of explanatory variables. This may be performed using single transition-type-specific models if these covariate effects are assumed to be different across transition-types. To investigate whether this assumption holds or whether one of the effects is equal across several transition-types (cross-transition-type effect), a combined model has to be applied, for instance with the use of a stratified partial likelihood formulation. Here, prior knowledge about the underlying covariate effect mechanisms is often sparse, especially about ineffectivenesses of transition-type-specific or cross-transition-type effects. As a consequence, data-driven variable selection is an important task: a large number of estimable effects has to be taken into account if joint modelling of all transition-types is performed. A related but subsequent task is model choice: is an effect satisfactory estimated assuming linearity, or is the true underlying nature strongly deviating from linearity? This article introduces component-wise Functional Gradient Descent Boosting (short boosting) for multi-state models, an approach performing unsupervised variable selection and model choice simultaneously within a single estimation run. We demonstrate that features and advantages in the application of boosting introduced and illustrated in classical regression scenarios remain present in the transfer to multi-state models. As a consequence, boosting provides an effective means to answer questions about ineffectiveness and non-linearity of single transition-type-specific or cross-transition-type effects.

Relationship between body mass index and perceived insufficient sleep among U.S. adults: an analysis of 2008 BRFSS data.

PubMed

Wheaton, Anne G; Perry, Geraldine S; Chapman, Daniel P; McKnight-Eily, Lela R; Presley-Cantrell, Letitia R; Croft, Janet B

2011-05-10

Over the past 50 years, the average sleep duration for adults in the United States has decreased while the prevalence of obesity and associated outcomes has increased. The objective of this study was to determine whether perceived insufficient sleep was associated with body mass index (BMI) in a national sample. We analyzed data from the 2008 Behavioral Risk Factor Surveillance System (BRFSS) survey (N=384,541) in which respondents were asked, "During the past 30 days, for about how many days have you felt you did not get enough rest or sleep?" We divided respondents into six BMI categories and used multivariable linear regression and logistic regression analyses to assess the association between BMI categories and days of insufficient sleep after adjusting for sociodemographic variables, smoking, physical activity, and frequent mental distress. Adjusted mean days of insufficient sleep ranged from 7.9 (95% confidence interval [CI]: 7.8, 8.0) days for people of normal weight to 10.5 (95% CI: 10.2, 10.9) days for those in the highest weight category (BMI≥40). Days of perceived insufficient sleep followed a linear trend across BMI categories. The likelihood of reporting ≥14 days of insufficient sleep in the previous 30 days was higher for respondents in the highest weight category than for those who were normal weight (34.9% vs. 25.2%; adjusted odds ratio=1.7 (95% CI: 1.5, 1.8]). Among U.S. adults, days of insufficient rest or sleep strongly correlated with BMI. Sleep sufficiency should be an important consideration in the assessment of the health of overweight and obese people and should be considered by developers of weight-reduction programs.

Medication Adherence Challenges among HIV Positive Substance Abusers: The Role of Food and Housing Insecurity

PubMed Central

Surratt, Hilary L.; O’Grady, Catherine L.; Levi-Minzi, Maria A.; Kurtz, Steven P.

2014-01-01

This study examines the prevalence of food/housing insecurity and its association with psychological, behavioral and environmental factors impacting ARV medication adherence and diversion among substance using HIV+ patients in South Florida. 503 HIV+ substance abusers were recruited through targeted sampling. Participants completed a standardized instrument assessing demographics, mental health status, sex risk behaviors, HIV diagnosis, treatment history and access, ARV adherence and diversion, and attitudes toward health care providers. Chi-square and t-tests were used to examine differences by food/housing status and a multivariate linear regression model examined food/housing insecurity and its associations to ARV adherence. Food/housing insecurity was reported by 43.3% of the sample and was associated with higher likelihood of severe psychological distress and substance dependence. Nearly 60% reported recent ARV diversion; only 47.2% achieved 95% medication adherence over one week. Food/housing insecure participants had deficits in their HIV care, including less time in consistent care, lower access to medical care, and less favorable attitudes toward care providers. Multivariate linear regression showed food/housing insecurity demonstrated significant main effects on adherence, including lower past week adherence. Medication diversion was also associated with reduced adherence. Our findings suggest that food/housing insecurity operates as a significant driver of ARV non-adherence and diversion in this population. In the pursuit of better long term health outcomes for vulnerable HIV+ individuals, it is essential for providers to understand the role of food and housing insecurity as a stressor that negatively impacts ARV adherence and treatment access, while also significantly contributing to higher levels distress and substance dependence. PMID:25314042

Medication adherence challenges among HIV positive substance abusers: the role of food and housing insecurity.

PubMed

Surratt, Hilary L; O'Grady, Catherine L; Levi-Minzi, Maria A; Kurtz, Steven P

2015-01-01

This study examines the prevalence of food/housing insecurity and its association with psychological, behavioral, and environmental factors impacting antiretroviral (ARV) medication adherence and diversion among substance using HIV+ patients in South Florida. Five hundred and three HIV+ substance abusers were recruited through targeted sampling. Participants completed a standardized instrument assessing demographics, mental health status, sex risk behaviors, HIV diagnosis, treatment history and access, ARV adherence and diversion, and attitudes toward health-care providers. Chi-square and t-tests were used to examine differences by food/housing status and a multivariate linear regression model examined food/housing insecurity and its associations to ARV adherence. Food/housing insecurity was reported by 43.3% of the sample and was associated with higher likelihood of severe psychological distress and substance dependence. Nearly 60% reported recent ARV diversion; only 47.2% achieved 95% medication adherence over one week. Food/housing insecure participants had deficits in their HIV care, including less time in consistent care, lower access to medical care, and less favorable attitudes toward care providers. Multivariate linear regression showed food/housing insecurity demonstrated significant main effects on adherence, including lower past week adherence. Medication diversion was also associated with reduced adherence. Our findings suggest that food/housing insecurity operates as a significant driver of ARV non-adherence and diversion in this population. In the pursuit of better long-term health outcomes for vulnerable HIV+ individuals, it is essential for providers to understand the role of food and housing insecurity as a stressor that negatively impacts ARV adherence and treatment access, while also significantly contributing to higher levels of distress and substance dependence.

Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control.

PubMed

Hahne, J M; Biessmann, F; Jiang, N; Rehbaum, H; Farina, D; Meinecke, F C; Muller, K-R; Parra, L C

2014-03-01

In recent years the number of active controllable joints in electrically powered hand-prostheses has increased significantly. However, the control strategies for these devices in current clinical use are inadequate as they require separate and sequential control of each degree-of-freedom (DoF). In this study we systematically compare linear and nonlinear regression techniques for an independent, simultaneous and proportional myoelectric control of wrist movements with two DoF. These techniques include linear regression, mixture of linear experts (ME), multilayer-perceptron, and kernel ridge regression (KRR). They are investigated offline with electro-myographic signals acquired from ten able-bodied subjects and one person with congenital upper limb deficiency. The control accuracy is reported as a function of the number of electrodes and the amount and diversity of training data providing guidance for the requirements in clinical practice. The results showed that KRR, a nonparametric statistical learning method, outperformed the other methods. However, simple transformations in the feature space could linearize the problem, so that linear models could achieve similar performance as KRR at much lower computational costs. Especially ME, a physiologically inspired extension of linear regression represents a promising candidate for the next generation of prosthetic devices.

Unitary Response Regression Models

ERIC Educational Resources Information Center

Lipovetsky, S.

2007-01-01

The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…

An Expert System for the Evaluation of Cost Models

DTIC Science & Technology

1990-09-01

contrast to the condition of equal error variance, called homoscedasticity. (Reference: Applied Linear Regression Models by John Neter - page 423...normal. (Reference: Applied Linear Regression Models by John Neter - page 125) Click Here to continue -> Autocorrelation Click Here for the index - Index...over time. Error terms correlated over time are said to be autocorrelated or serially correlated. (REFERENCE: Applied Linear Regression Models by John

Visual field progression with frequency-doubling matrix perimetry and standard automated perimetry in patients with glaucoma and in healthy controls.

PubMed

Redmond, Tony; O'Leary, Neil; Hutchison, Donna M; Nicolela, Marcelo T; Artes, Paul H; Chauhan, Balwantray C

2013-12-01

A new analysis method called permutation of pointwise linear regression measures the significance of deterioration over time at each visual field location, combines the significance values into an overall statistic, and then determines the likelihood of change in the visual field. Because the outcome is a single P value, individualized to that specific visual field and independent of the scale of the original measurement, the method is well suited for comparing techniques with different stimuli and scales. To test the hypothesis that frequency-doubling matrix perimetry (FDT2) is more sensitive than standard automated perimetry (SAP) in identifying visual field progression in glaucoma. Patients with open-angle glaucoma and healthy controls were examined by FDT2 and SAP, both with the 24-2 test pattern, on the same day at 6-month intervals in a longitudinal prospective study conducted in a hospital-based setting. Only participants with at least 5 examinations were included. Data were analyzed with permutation of pointwise linear regression. Permutation of pointwise linear regression is individualized to each participant, in contrast to current analyses in which the statistical significance is inferred from population-based approaches. Analyses were performed with both total deviation and pattern deviation. Sixty-four patients and 36 controls were included in the study. The median age, SAP mean deviation, and follow-up period were 65 years, -2.6 dB, and 5.4 years, respectively, in patients and 62 years, +0.4 dB, and 5.2 years, respectively, in controls. Using total deviation analyses, statistically significant deterioration was identified in 17% of patients with FDT2, in 34% of patients with SAP, and in 14% of patients with both techniques; in controls these percentages were 8% with FDT2, 31% with SAP, and 8% with both. Using pattern deviation analyses, statistically significant deterioration was identified in 16% of patients with FDT2, in 17% of patients with SAP, and in 3% of patients with both techniques; in controls these values were 3% with FDT2 and none with SAP. No evidence was found that FDT2 is more sensitive than SAP in identifying visual field deterioration. In about one-third of healthy controls, age-related deterioration with SAP reached statistical significance.

A non-linear relationship between the cumulative exposure to occupational stressors and nurses' burnout and the potentially emotion regulation factors.

PubMed

Sun, Ji-Wei; Lin, Ping-Zhen; Zhang, Hui-Hui; Li, Jia-Huan; Cao, Feng-Lin

2017-10-08

Stressful situations can increase the likelihood of nurses experiencing negative emotions, especially burnout. To explore the association of cumulative exposure to occupational stressors and emotion regulation strategies with nurses' burnout. Participants were 602 nurses from three general hospitals in Jinan, China. Social demographic characteristics, occupational stress, burnout, and emotion regulation strategies (cognitive reappraisal, expressive suppression, and rumination), were assessed. Nearly 70% of nurses reported that they were burnt out. Those with a moderate level and high level of stressors were 3.203 times and 26.444 times more likely to have burnout, respectively (x 2 trend = 62.732). Logistic regression revealed that nurses had higher cognitive reappraisal score (odds ratios (OR) = 0.941), scored lower for burnout. Those who had higher expressive suppression score (OR = 1.054), higher rumination score (OR = 1.037), and a higher level of stressors (OR = 2.779-18.259) scored higher for burnout. The results of sensitivity analysis were similar. A non-linear relationship exists between the cumulative exposure to occupational stressors and nurses' burnout. Those who less frequently use cognitive reappraisal, more frequently use rumination and expressive suppression, and have a high level of stressors may be more likely to experience burnout.

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

Otake, M.; Schull, W.J.

This paper investigates the quantitative relationship of ionizing radiation to the occurrence of posterior lenticular opacities among the survivors of the atomic bombings of Hiroshima and Nagasaki suggested by the DS86 dosimetry system. DS86 doses are available for 1983 (93.4%) of the 2124 atomic bomb survivors analyzed in 1982. The DS86 kerma neutron component for Hiroshima survivors is much smaller than its comparable T65DR component, but still 4.2-fold higher (0.38 Gy at 6 Gy) than that in Nagasaki (0.09 Gy at 6 Gy). Thus, if the eye is especially sensitive to neutrons, there may yet be some useful information onmore » their effects, particularly in Hiroshima. The dose-response relationship has been evaluated as a function of the separately estimated gamma-ray and neutron doses. Among several different dose-response models without and with two thresholds, we have selected as the best model the one with the smallest x2 or the largest log likelihood value associated with the goodness of fit. The best fit is a linear gamma-linear neutron relationship which assumes different thresholds for the two types of radiation. Both gamma and neutron regression coefficients for the best fitting model are positive and highly significant for the estimated DS86 eye organ dose.« less

Trellises and Trellis-Based Decoding Algorithms for Linear Block Codes. Part 3; A Recursive Maximum Likelihood Decoding

NASA Technical Reports Server (NTRS)

Lin, Shu; Fossorier, Marc

1998-01-01

The Viterbi algorithm is indeed a very simple and efficient method of implementing the maximum likelihood decoding. However, if we take advantage of the structural properties in a trellis section, other efficient trellis-based decoding algorithms can be devised. Recently, an efficient trellis-based recursive maximum likelihood decoding (RMLD) algorithm for linear block codes has been proposed. This algorithm is more efficient than the conventional Viterbi algorithm in both computation and hardware requirements. Most importantly, the implementation of this algorithm does not require the construction of the entire code trellis, only some special one-section trellises of relatively small state and branch complexities are needed for constructing path (or branch) metric tables recursively. At the end, there is only one table which contains only the most likely code-word and its metric for a given received sequence r = (r(sub 1), r(sub 2),...,r(sub n)). This algorithm basically uses the divide and conquer strategy. Furthermore, it allows parallel/pipeline processing of received sequences to speed up decoding.

An Extension of the Partial Credit Model with an Application to the Measurement of Change.

ERIC Educational Resources Information Center

Fischer, Gerhard H.; Ponocny, Ivo

1994-01-01

An extension to the partial credit model, the linear partial credit model, is considered under the assumption of a certain linear decomposition of the item x category parameters into basic parameters. A conditional maximum likelihood algorithm for estimating basic parameters is presented and illustrated with simulation and an empirical study. (SLD)

Indicators of Terrorism Vulnerability in Africa

DTIC Science & Technology

2015-03-26

the terror threat and vulnerabilities across Africa. Key words: Terrorism, Africa, Negative Binomial Regression, Classification Tree iv I would like...31 Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Log -likelihood...70 viii Page 5.3 Classification Tree Description

Learning quadratic receptive fields from neural responses to natural stimuli.

PubMed

Rajan, Kanaka; Marre, Olivier; Tkačik, Gašper

2013-07-01

Models of neural responses to stimuli with complex spatiotemporal correlation structure often assume that neurons are selective for only a small number of linear projections of a potentially high-dimensional input. In this review, we explore recent modeling approaches where the neural response depends on the quadratic form of the input rather than on its linear projection, that is, the neuron is sensitive to the local covariance structure of the signal preceding the spike. To infer this quadratic dependence in the presence of arbitrary (e.g., naturalistic) stimulus distribution, we review several inference methods, focusing in particular on two information theory-based approaches (maximization of stimulus energy and of noise entropy) and two likelihood-based approaches (Bayesian spike-triggered covariance and extensions of generalized linear models). We analyze the formal relationship between the likelihood-based and information-based approaches to demonstrate how they lead to consistent inference. We demonstrate the practical feasibility of these procedures by using model neurons responding to a flickering variance stimulus.

Compound Identification Using Penalized Linear Regression on Metabolomics

PubMed Central

Liu, Ruiqi; Wu, Dongfeng; Zhang, Xiang; Kim, Seongho

2014-01-01

Compound identification is often achieved by matching the experimental mass spectra to the mass spectra stored in a reference library based on mass spectral similarity. Because the number of compounds in the reference library is much larger than the range of mass-to-charge ratio (m/z) values so that the data become high dimensional data suffering from singularity. For this reason, penalized linear regressions such as ridge regression and the lasso are used instead of the ordinary least squares regression. Furthermore, two-step approaches using the dot product and Pearson’s correlation along with the penalized linear regression are proposed in this study. PMID:27212894

Control Variate Selection for Multiresponse Simulation.

DTIC Science & Technology

1987-05-01

M. H. Knuter, Applied Linear Regression Mfodels, Richard D. Erwin, Inc., Homewood, Illinois, 1983. Neuts, Marcel F., Probability, Allyn and Bacon...1982. Neter, J., V. Wasserman, and M. H. Knuter, Applied Linear Regression .fodels, Richard D. Erwin, Inc., Homewood, Illinois, 1983. Neuts, Marcel F...Aspects of J%,ultivariate Statistical Theory, John Wiley and Sons, New York, New York, 1982. dY Neter, J., W. Wasserman, and M. H. Knuter, Applied Linear Regression Mfodels

An Investigation of the Fit of Linear Regression Models to Data from an SAT[R] Validity Study. Research Report 2011-3

ERIC Educational Resources Information Center

Kobrin, Jennifer L.; Sinharay, Sandip; Haberman, Shelby J.; Chajewski, Michael

2011-01-01

This study examined the adequacy of a multiple linear regression model for predicting first-year college grade point average (FYGPA) using SAT[R] scores and high school grade point average (HSGPA). A variety of techniques, both graphical and statistical, were used to examine if it is possible to improve on the linear regression model. The results…

High correlations between MRI brain volume measurements based on NeuroQuant® and FreeSurfer.

PubMed

Ross, David E; Ochs, Alfred L; Tate, David F; Tokac, Umit; Seabaugh, John; Abildskov, Tracy J; Bigler, Erin D

2018-05-30

NeuroQuant ® (NQ) and FreeSurfer (FS) are commonly used computer-automated programs for measuring MRI brain volume. Previously they were reported to have high intermethod reliabilities but often large intermethod effect size differences. We hypothesized that linear transformations could be used to reduce the large effect sizes. This study was an extension of our previously reported study. We performed NQ and FS brain volume measurements on 60 subjects (including normal controls, patients with traumatic brain injury, and patients with Alzheimer's disease). We used two statistical approaches in parallel to develop methods for transforming FS volumes into NQ volumes: traditional linear regression, and Bayesian linear regression. For both methods, we used regression analyses to develop linear transformations of the FS volumes to make them more similar to the NQ volumes. The FS-to-NQ transformations based on traditional linear regression resulted in effect sizes which were small to moderate. The transformations based on Bayesian linear regression resulted in all effect sizes being trivially small. To our knowledge, this is the first report describing a method for transforming FS to NQ data so as to achieve high reliability and low effect size differences. Machine learning methods like Bayesian regression may be more useful than traditional methods. Copyright © 2018 Elsevier B.V. All rights reserved.

Quantile Regression in the Study of Developmental Sciences

PubMed Central

Petscher, Yaacov; Logan, Jessica A. R.

2014-01-01

Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of the outcome’s distribution. Using data from the High School and Beyond and U.S. Sustained Effects Study databases, quantile regression is demonstrated and contrasted with linear regression when considering models with: (a) one continuous predictor, (b) one dichotomous predictor, (c) a continuous and a dichotomous predictor, and (d) a longitudinal application. Results from each example exhibited the differential inferences which may be drawn using linear or quantile regression. PMID:24329596

To Leave or Not to Leave? A Regression Discontinuity Analysis of the Impact of Failing the High School Exit Exam

ERIC Educational Resources Information Center

Ou, Dongshu

2010-01-01

The high school exit exam (HSEE) is rapidly becoming a standardized assessment procedure for educational accountability in the United States. I use a unique, state-specific dataset to identify the effects of failing the HSEE on the likelihood of dropping out of high school based on a regression discontinuity design. The analysis shows that…

Improving validation methods for molecular diagnostics: application of Bland-Altman, Deming and simple linear regression analyses in assay comparison and evaluation for next-generation sequencing

PubMed Central

Misyura, Maksym; Sukhai, Mahadeo A; Kulasignam, Vathany; Zhang, Tong; Kamel-Reid, Suzanne; Stockley, Tracy L

2018-01-01

Aims A standard approach in test evaluation is to compare results of the assay in validation to results from previously validated methods. For quantitative molecular diagnostic assays, comparison of test values is often performed using simple linear regression and the coefficient of determination (R2), using R2 as the primary metric of assay agreement. However, the use of R2 alone does not adequately quantify constant or proportional errors required for optimal test evaluation. More extensive statistical approaches, such as Bland-Altman and expanded interpretation of linear regression methods, can be used to more thoroughly compare data from quantitative molecular assays. Methods We present the application of Bland-Altman and linear regression statistical methods to evaluate quantitative outputs from next-generation sequencing assays (NGS). NGS-derived data sets from assay validation experiments were used to demonstrate the utility of the statistical methods. Results Both Bland-Altman and linear regression were able to detect the presence and magnitude of constant and proportional error in quantitative values of NGS data. Deming linear regression was used in the context of assay comparison studies, while simple linear regression was used to analyse serial dilution data. Bland-Altman statistical approach was also adapted to quantify assay accuracy, including constant and proportional errors, and precision where theoretical and empirical values were known. Conclusions The complementary application of the statistical methods described in this manuscript enables more extensive evaluation of performance characteristics of quantitative molecular assays, prior to implementation in the clinical molecular laboratory. PMID:28747393

Estimating population diversity with CatchAll

PubMed Central

Bunge, John; Woodard, Linda; Böhning, Dankmar; Foster, James A.; Connolly, Sean; Allen, Heather K.

2012-01-01

Motivation: The massive data produced by next-generation sequencing require advanced statistical tools. We address estimating the total diversity or species richness in a population. To date, only relatively simple methods have been implemented in available software. There is a need for software employing modern, computationally intensive statistical analyses including error, goodness-of-fit and robustness assessments. Results: We present CatchAll, a fast, easy-to-use, platform-independent program that computes maximum likelihood estimates for finite-mixture models, weighted linear regression-based analyses and coverage-based non-parametric methods, along with outlier diagnostics. Given sample ‘frequency count’ data, CatchAll computes 12 different diversity estimates and applies a model-selection algorithm. CatchAll also derives discounted diversity estimates to adjust for possibly uncertain low-frequency counts. It is accompanied by an Excel-based graphics program. Availability: Free executable downloads for Linux, Windows and Mac OS, with manual and source code, at www.northeastern.edu/catchall. Contact: jab18@cornell.edu PMID:22333246

Testing goodness of fit in regression: a general approach for specified alternatives.

PubMed

Solari, Aldo; le Cessie, Saskia; Goeman, Jelle J

2012-12-10

When fitting generalized linear models or the Cox proportional hazards model, it is important to have tools to test for lack of fit. Because lack of fit comes in all shapes and sizes, distinguishing among different types of lack of fit is of practical importance. We argue that an adequate diagnosis of lack of fit requires a specified alternative model. Such specification identifies the type of lack of fit the test is directed against so that if we reject the null hypothesis, we know the direction of the departure from the model. The goodness-of-fit approach of this paper allows to treat different types of lack of fit within a unified general framework and to consider many existing tests as special cases. Connections with penalized likelihood and random effects are discussed, and the application of the proposed approach is illustrated with medical examples. Tailored functions for goodness-of-fit testing have been implemented in the R package global test. Copyright © 2012 John Wiley & Sons, Ltd.

A SEMIPARAMETRIC BAYESIAN MODEL FOR CIRCULAR-LINEAR REGRESSION

EPA Science Inventory

We present a Bayesian approach to regress a circular variable on a linear predictor. The regression coefficients are assumed to have a nonparametric distribution with a Dirichlet process prior. The semiparametric Bayesian approach gives added flexibility to the model and is usefu...

Can obviously intoxicated patrons still easily buy alcohol at on-premise establishments?

PubMed Central

Toomey, Traci L.; Lenk, Kathleen M.; Nederhoff, Dawn M.; Nelson, Toben F.; Ecklund, Alexandra M.; Horvath, Keith J.; Erickson, Darin J.

2015-01-01

Background Excessive alcohol consumption at licensed alcohol establishments (i.e., bars and restaurants) has been directly linked to alcohol-related problems such as traffic crashes and violence. Historically, alcohol establishments have had a high likelihood of selling alcohol to obviously intoxicated patrons (also referred to as “overservice”) despite laws prohibiting these sales. Given the risks associated with overservice and the need for up-to-date data, it is critical that we monitor the likelihood of sales to obviously intoxicated patrons. Methods To assess the current likelihood of a licensed alcohol establishment selling alcohol to an obviously intoxicated patron, we conducted pseudo-intoxicated purchase attempts (i.e., actors attempt to purchase alcohol while acting out obvious signs of intoxication) at 340 establishments in one Midwestern metropolitan area. We also measured characteristics of the establishments, the pseudo-intoxicated patrons, the servers, the managers, and the neighborhoods to assess whether these characteristics were associated with likelihood of sales of obviously intoxicated patrons. We assessed these associations with bivariate and multivariate regression models. Results Pseudo-intoxicated buyers were able to purchase alcohol at 82% of the establishments. In the fully adjusted multivariate regression model, only one of the characteristics we assessed was significantly associated with likelihood of selling to intoxicated patrons–establishments owned by a corporate entity had 3.6 greater odds of selling alcohol to a pseudo-intoxicated buyer compared to independently-owned establishments. Discussion Given the risks associated with overservice of alcohol, more resources should be devoted first to identify effective interventions for decreasing overservice of alcohol and then to educate practitioners who are working in their communities to address this public health problem. PMID:26891204

The retention of health human resources in primary healthcare centers in Lebanon: a national survey.

PubMed

Alameddine, Mohamad; Saleh, Shadi; El-Jardali, Fadi; Dimassi, Hani; Mourad, Yara

2012-11-22

Critical shortages of health human resources (HHR), associated with high turnover rates, have been a concern in many countries around the globe. Of particular interest is the effect of such a trend on the primary healthcare (PHC) sector; considered a cornerstone in any effective healthcare system. This study is a rare attempt to investigate PHC HHR work characteristics, level of burnout and likelihood to quit as well as the factors significantly associated with staff retention at PHC centers in Lebanon. A cross-sectional design was utilized to survey all health providers at 81 PHC centers dispersed in all districts of Lebanon. The questionnaire consisted of four sections: socio-demographic/ professional background, organizational/institutional characteristics, likelihood to quit and level of professional burnout (using the Maslach-Burnout Inventory). A total of 755 providers completed the questionnaire (60.5% response rate). Bivariate analyses and multinomial logistic regression were used to determine factors associated with likelihood to quit. Two out of five respondents indicated likelihood to quit their jobs within the next 1-3 years and an additional 13.4% were not sure about quitting. The top three reasons behind likelihood to quit were poor salary (54.4%), better job opportunities outside the country (35.1%) and lack of professional development (33.7%). A U-shaped relationship was observed between age and likelihood to quit. Regression analysis revealed that high levels of burnout, lower level of education and low tenure were all associated with increased likelihood to quit. The study findings reflect an unstable workforce and are not conducive to supporting an expanded role for PHC in the Lebanese healthcare system. While strategies aiming at improving staff retention would be important to develop and implement for all PHC HHR; targeted retention initiatives should focus on the young-new recruits and allied health professionals. Particular attention should be dedicated to enhancing providers' role satisfaction and sense of job security. Such initiatives are of pivotal importance to stabilize the workforce and ensure its longevity.

The retention of health human resources in primary healthcare centers in Lebanon: a national survey

PubMed Central

2012-01-01

Background Critical shortages of health human resources (HHR), associated with high turnover rates, have been a concern in many countries around the globe. Of particular interest is the effect of such a trend on the primary healthcare (PHC) sector; considered a cornerstone in any effective healthcare system. This study is a rare attempt to investigate PHC HHR work characteristics, level of burnout and likelihood to quit as well as the factors significantly associated with staff retention at PHC centers in Lebanon. Methods A cross-sectional design was utilized to survey all health providers at 81 PHC centers dispersed in all districts of Lebanon. The questionnaire consisted of four sections: socio-demographic/ professional background, organizational/institutional characteristics, likelihood to quit and level of professional burnout (using the Maslach-Burnout Inventory). A total of 755 providers completed the questionnaire (60.5% response rate). Bivariate analyses and multinomial logistic regression were used to determine factors associated with likelihood to quit. Results Two out of five respondents indicated likelihood to quit their jobs within the next 1–3 years and an additional 13.4% were not sure about quitting. The top three reasons behind likelihood to quit were poor salary (54.4%), better job opportunities outside the country (35.1%) and lack of professional development (33.7%). A U-shaped relationship was observed between age and likelihood to quit. Regression analysis revealed that high levels of burnout, lower level of education and low tenure were all associated with increased likelihood to quit. Conclusions The study findings reflect an unstable workforce and are not conducive to supporting an expanded role for PHC in the Lebanese healthcare system. While strategies aiming at improving staff retention would be important to develop and implement for all PHC HHR; targeted retention initiatives should focus on the young-new recruits and allied health professionals. Particular attention should be dedicated to enhancing providers’ role satisfaction and sense of job security. Such initiatives are of pivotal importance to stabilize the workforce and ensure its longevity. PMID:23173905

Pseudo second order kinetics and pseudo isotherms for malachite green onto activated carbon: comparison of linear and non-linear regression methods.

PubMed

Kumar, K Vasanth; Sivanesan, S

2006-08-25

Pseudo second order kinetic expressions of Ho, Sobkowsk and Czerwinski, Blanachard et al. and Ritchie were fitted to the experimental kinetic data of malachite green onto activated carbon by non-linear and linear method. Non-linear method was found to be a better way of obtaining the parameters involved in the second order rate kinetic expressions. Both linear and non-linear regression showed that the Sobkowsk and Czerwinski and Ritchie's pseudo second order model were the same. Non-linear regression analysis showed that both Blanachard et al. and Ho have similar ideas on the pseudo second order model but with different assumptions. The best fit of experimental data in Ho's pseudo second order expression by linear and non-linear regression method showed that Ho pseudo second order model was a better kinetic expression when compared to other pseudo second order kinetic expressions. The amount of dye adsorbed at equilibrium, q(e), was predicted from Ho pseudo second order expression and were fitted to the Langmuir, Freundlich and Redlich Peterson expressions by both linear and non-linear method to obtain the pseudo isotherms. The best fitting pseudo isotherm was found to be the Langmuir and Redlich Peterson isotherm. Redlich Peterson is a special case of Langmuir when the constant g equals unity.

The likelihood of achieving quantified road safety targets: a binary logistic regression model for possible factors.

PubMed

Sze, N N; Wong, S C; Lee, C Y

2014-12-01

In past several decades, many countries have set quantified road safety targets to motivate transport authorities to develop systematic road safety strategies and measures and facilitate the achievement of continuous road safety improvement. Studies have been conducted to evaluate the association between the setting of quantified road safety targets and road fatality reduction, in both the short and long run, by comparing road fatalities before and after the implementation of a quantified road safety target. However, not much work has been done to evaluate whether the quantified road safety targets are actually achieved. In this study, we used a binary logistic regression model to examine the factors - including vehicle ownership, fatality rate, and national income, in addition to level of ambition and duration of target - that contribute to a target's success. We analyzed 55 quantified road safety targets set by 29 countries from 1981 to 2009, and the results indicate that targets that are in progress and with lower level of ambitions had a higher likelihood of eventually being achieved. Moreover, possible interaction effects on the association between level of ambition and the likelihood of success are also revealed. Copyright © 2014 Elsevier Ltd. All rights reserved.

Likelihood of home death associated with local rates of home birth: influence of local area healthcare preferences on site of death.

PubMed

Silveira, Maria J; Copeland, Laurel A; Feudtner, Chris

2006-07-01

We tested whether local cultural and social values regarding the use of health care are associated with the likelihood of home death, using variation in local rates of home births as a proxy for geographic variation in these values. For each of 351110 adult decedents in Washington state who died from 1989 through 1998, we calculated the home birth rate in each zip code during the year of death and then used multivariate regression modeling to estimate the relation between the likelihood of home death and the local rate of home births. Individuals residing in local areas with higher home birth rates had greater adjusted likelihood of dying at home (odds ratio [OR]=1.04 for each percentage point increase in home birth rate; 95% confidence interval [CI] = 1.03, 1.05). Moreover, the likelihood of dying at home increased with local wealth (OR=1.04 per $10000; 95% CI=1.02, 1.06) but decreased with local hospital bed availability (OR=0.96 per 1000 beds; 95% CI=0.95, 0.97). The likelihood of home death is associated with local rates of home births, suggesting the influence of health care use preferences.

The adaptive and maladaptive faces of dependency in later life: links to physical and psychological health outcomes.

PubMed

Fiori, Katherine; Consedine, Nathan; Magai, Carol

2008-11-01

Negotiating the balance between reliance on others and desires for autonomy is a fundamental task of successful aging. The purpose of the present study was to replicate and extend a three-factor model of interpersonal dependency in a sample of older adults, and to examine the physical and psychological health correlates of this multifaceted construct. Data come from the third wave of a population-based study of older Americans (n = 166; mean age 80 years). We conducted an exploratory factor analysis of selected dependency items from two scales, and then conducted logistic and hierarchical linear regressions to analyze the association of dependency factors with self-reported health, use of hypertension medication, depressed affect and positive affect. We found three factors closely paralleling those of Bornstein and Languirand's (Psychological Bulletin, 112(1), 3-23, 2004) measure: destructive overdependence, healthy dependency and dysfunctional detachment, as well as a fourth factor we labeled 'healthy independence'. Healthy dependency was associated with better self-reported health. Dysfunctional detachment was related to a greater likelihood and healthy independence a lesser likelihood of taking hypertension medication. Whereas both healthy independence and healthy dependency were positively related to positive affect and negatively related to depressed affect, destructive overdependence was positively related to depressed affect. Understanding the complex nature of interpersonal dependency and autonomy in old age, as well as their implications for health and wellbeing, may enable practitioners to assist older adults in negotiating the task of balancing these needs.

Random regression models for the prediction of days to weight, ultrasound rib eye area, and ultrasound back fat depth in beef cattle.

PubMed

Speidel, S E; Peel, R K; Crews, D H; Enns, R M

2016-02-01

Genetic evaluation research designed to reduce the required days to a specified end point has received very little attention in pertinent scientific literature, given that its economic importance was first discussed in 1957. There are many production scenarios in today's beef industry, making a prediction for the required number of days to a single end point a suboptimal option. Random regression is an attractive alternative to calculate days to weight (DTW), days to ultrasound back fat (DTUBF), and days to ultrasound rib eye area (DTUREA) genetic predictions that could overcome weaknesses of a single end point prediction. The objective of this study was to develop random regression approaches for the prediction of the DTW, DTUREA, and DTUBF. Data were obtained from the Agriculture and Agri-Food Canada Research Centre, Lethbridge, AB, Canada. Data consisted of records on 1,324 feedlot cattle spanning 1999 to 2007. Individual animals averaged 5.77 observations with weights, ultrasound rib eye area (UREA), ultrasound back fat depth (UBF), and ages ranging from 293 to 863 kg, 73.39 to 129.54 cm, 1.53 to 30.47 mm, and 276 to 519 d, respectively. Random regression models using Legendre polynomials were used to regress age of the individual on weight, UREA, and UBF. Fixed effects in the model included an overall fixed regression of age on end point (weight, UREA, and UBF) nested within breed to account for the mean relationship between age and weight as well as a contemporary group effect consisting of breed of the animal (Angus, Charolais, and Charolais sired), feedlot pen, and year of measure. Likelihood ratio tests were used to determine the appropriate random polynomial order. Use of the quadratic polynomial did not account for any additional genetic variation in days for DTW ( > 0.11), for DTUREA ( > 0.18), and for DTUBF ( > 0.20) when compared with the linear random polynomial. Heritability estimates from the linear random regression for DTW ranged from 0.54 to 0.74, corresponding to end points of 293 and 863 kg, respectively. Heritability for DTUREA ranged from 0.51 to 0.34 and for DTUBF ranged from 0.55 to 0.37. These estimates correspond to UREA end points of 35 and 125 cm and UBF end points of 1.53 and 30 mm, respectively. This range of heritability shows DTW, DTUREA, and DTUBF to be highly heritable and indicates that selection pressure aimed at reducing the number of days to reach a finish weight end point can result in genetic change given sufficient data.

Comparison of Neural Network and Linear Regression Models in Statistically Predicting Mental and Physical Health Status of Breast Cancer Survivors

DTIC Science & Technology

2015-07-15

Long-term effects on cancer survivors’ quality of life of physical training versus physical training combined with cognitive-behavioral therapy ...COMPARISON OF NEURAL NETWORK AND LINEAR REGRESSION MODELS IN STATISTICALLY PREDICTING MENTAL AND PHYSICAL HEALTH STATUS OF BREAST...34Comparison of Neural Network and Linear Regression Models in Statistically Predicting Mental and Physical Health Status of Breast Cancer Survivors

Prediction of the Main Engine Power of a New Container Ship at the Preliminary Design Stage

NASA Astrophysics Data System (ADS)

Cepowski, Tomasz

2017-06-01

The paper presents mathematical relationships that allow us to forecast the estimated main engine power of new container ships, based on data concerning vessels built in 2005-2015. The presented approximations allow us to estimate the engine power based on the length between perpendiculars and the number of containers the ship will carry. The approximations were developed using simple linear regression and multivariate linear regression analysis. The presented relations have practical application for estimation of container ship engine power needed in preliminary parametric design of the ship. It follows from the above that the use of multiple linear regression to predict the main engine power of a container ship brings more accurate solutions than simple linear regression.

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…

Regression assumptions in clinical psychology research practice-a systematic review of common misconceptions.

PubMed

Ernst, Anja F; Albers, Casper J

2017-01-01

Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking.

Regression assumptions in clinical psychology research practice—a systematic review of common misconceptions

PubMed Central

Ernst, Anja F.

2017-01-01

Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking. PMID:28533971

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.

Relationship of Antibiotic Treatment to Recovery after Acute FEV1 Decline in Children with Cystic Fibrosis.

PubMed

Morgan, Wayne J; Wagener, Jeffrey S; Pasta, David J; Millar, Stefanie J; VanDevanter, Donald R; Konstan, Michael W

2017-06-01

Children with cystic fibrosis often experience acute declines in lung function. We previously showed that such declines are not always treated with antibiotics, but we did not assess whether treatment improves the likelihood of recovery. To determine whether new antibiotic treatment was associated with recovery from acute FEV 1 decline. We studied episodes of FEV 1 decline (≥10% from baseline) in the Epidemiologic Study of Cystic Fibrosis. Treatments were hospitalization, home intravenous antibiotic, new inhaled oral quinolone, or other oral antibiotic. We used logistic regression to evaluate whether treatment was associated with recovery to baseline or near baseline. Logistic regression of 9,875 patients showed that new antibiotic treatment was associated with an increased likelihood of recovery to 90% of baseline (P < 0.001), especially for hospitalization compared with no new antibiotic (odds ratio [OR], 2.79; 95% confidence interval, 2.41-3.23). All four outpatient treatments were associated with greater likelihood of recovery compared with no treatment (OR, 1.27-1.64). Inpatient treatment was better than outpatient treatment (OR, 1.94; 95% confidence interval, 1.68-2.23). Treatment-type ORs were similar across recovery criteria and levels of baseline lung function. New antibiotic therapy, and especially inpatient treatment, is associated with greater likelihood of recovery after acute decline in FEV 1 . Benefits extend across all disease stages and are especially important in patients with high lung function, who are at greatest risk for FEV 1 decline.

Comparing The Effectiveness of a90/95 Calculations (Preprint)

DTIC Science & Technology

2006-09-01

Nachtsheim, John Neter, William Li, Applied Linear Statistical Models , 5th ed., McGraw-Hill/Irwin, 2005 5. Mood, Graybill and Boes, Introduction...curves is based on methods that are only valid for ordinary linear regression. Requirements for a valid Ordinary Least-Squares Regression Model There... linear . For example is a linear model ; is not. 2. Uniform variance (homoscedasticity

Correlation and simple linear regression.

PubMed

Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G

2003-06-01

In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.

Improving validation methods for molecular diagnostics: application of Bland-Altman, Deming and simple linear regression analyses in assay comparison and evaluation for next-generation sequencing.

PubMed

Misyura, Maksym; Sukhai, Mahadeo A; Kulasignam, Vathany; Zhang, Tong; Kamel-Reid, Suzanne; Stockley, Tracy L

2018-02-01

A standard approach in test evaluation is to compare results of the assay in validation to results from previously validated methods. For quantitative molecular diagnostic assays, comparison of test values is often performed using simple linear regression and the coefficient of determination (R 2 ), using R 2 as the primary metric of assay agreement. However, the use of R 2 alone does not adequately quantify constant or proportional errors required for optimal test evaluation. More extensive statistical approaches, such as Bland-Altman and expanded interpretation of linear regression methods, can be used to more thoroughly compare data from quantitative molecular assays. We present the application of Bland-Altman and linear regression statistical methods to evaluate quantitative outputs from next-generation sequencing assays (NGS). NGS-derived data sets from assay validation experiments were used to demonstrate the utility of the statistical methods. Both Bland-Altman and linear regression were able to detect the presence and magnitude of constant and proportional error in quantitative values of NGS data. Deming linear regression was used in the context of assay comparison studies, while simple linear regression was used to analyse serial dilution data. Bland-Altman statistical approach was also adapted to quantify assay accuracy, including constant and proportional errors, and precision where theoretical and empirical values were known. The complementary application of the statistical methods described in this manuscript enables more extensive evaluation of performance characteristics of quantitative molecular assays, prior to implementation in the clinical molecular laboratory. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Modified Maxium Likelihood Estimation Method for Completely Separated and Quasi-Completely Separated Data for a Dose-Response Model

DTIC Science & Technology

2015-08-01

McCullagh, P.; Nelder, J.A. Generalized Linear Model , 2nd ed.; Chapman and Hall: London, 1989. 7. Johnston, J. Econometric Methods, 3rd ed.; McGraw...FOR A DOSE-RESPONSE MODEL ECBC-TN-068 Kyong H. Park Steven J. Lagan RESEARCH AND TECHNOLOGY DIRECTORATE August 2015 Approved for public release...Likelihood Estimation Method for Completely Separated and Quasi-Completely Separated Data for a Dose-Response Model 5a. CONTRACT NUMBER 5b. GRANT

U.S. Army Armament Research, Development and Engineering Center Grain Evaluation Software to Numerically Predict Linear Burn Regression for Solid Propellant Grain Geometries

DTIC Science & Technology

2017-10-01

ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID PROPELLANT GRAIN GEOMETRIES Brian...author(s) and should not be construed as an official Department of the Army position, policy, or decision, unless so designated by other documentation...U.S. ARMY ARMAMENT RESEARCH, DEVELOPMENT AND ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID

Performance and separation occurrence of binary probit regression estimator using maximum likelihood method and Firths approach under different sample size

NASA Astrophysics Data System (ADS)

Lusiana, Evellin Dewi

2017-12-01

The parameters of binary probit regression model are commonly estimated by using Maximum Likelihood Estimation (MLE) method. However, MLE method has limitation if the binary data contains separation. Separation is the condition where there are one or several independent variables that exactly grouped the categories in binary response. It will result the estimators of MLE method become non-convergent, so that they cannot be used in modeling. One of the effort to resolve the separation is using Firths approach instead. This research has two aims. First, to identify the chance of separation occurrence in binary probit regression model between MLE method and Firths approach. Second, to compare the performance of binary probit regression model estimator that obtained by MLE method and Firths approach using RMSE criteria. Those are performed using simulation method and under different sample size. The results showed that the chance of separation occurrence in MLE method for small sample size is higher than Firths approach. On the other hand, for larger sample size, the probability decreased and relatively identic between MLE method and Firths approach. Meanwhile, Firths estimators have smaller RMSE than MLEs especially for smaller sample sizes. But for larger sample sizes, the RMSEs are not much different. It means that Firths estimators outperformed MLE estimator.

Assessing the Effectiveness of E-learning Integration in College Physics in the Alamo Community Colleges District

NASA Astrophysics Data System (ADS)

Zhou, Qiaoying

Academic achievement and student participation in physics are lower than desired. Research has shown that there is a shortage of college students entering science and technology fields such as physics. E-learning may provide the technology-oriented Net Generation learner an option for taking courses such as physics in a course modality with which they are most comfortable thus garnering more participation and higher academic achievement. A quantitative ex-post facto study was performed to compare face-to-face and E-learning modalities on course completion and physics achievement for an entire introductory physics course. The theoretical framework for this study was based on the constructivist theory of education that implies a student-centered learning process. The sample consisted of 116 students enrolled in introductory physics courses at four 2-year community colleges in Texas. Course completion, SAT scores, Force Concept Inventory examination scores, as well as demographic information and employment information were examined. Linear and ordinal multiple regression analysis were used to determine if course modality is predictive of physics achievement while controlling for general scholastic aptitude, current employment, the presence of children in the home, and teacher evaluations. The results showed that students in the E-learning course performed better on the Force Concept Inventory than those in the traditional course both in the multiple regression analysis, beta = .61, p < .001, and in the ordinal regression analysis, Wald(1) = 18.83, p < .001. A chi-square test was used to determine if course completion rates differ between students in the two course modalities. The results showed no difference in course completion rates between students in the two course modalities, chi 2(1, n = 116) = 1.02, p = .312. It was concluded that students in an E-learning course modality had higher physics achievement but were no more likely to complete the introductory physics course than students were in a face-to-face modality. It was recommended that other colleges and universities should develop and test E-learning courses for introductory physics, that larger sample sizes should be used in future studies, and that additional outcome variables including the likelihood that a student chooses physics as a major or the likelihood that a student completes a physics degree should be examined.

Linear regression in astronomy. II

NASA Technical Reports Server (NTRS)

Feigelson, Eric D.; Babu, Gutti J.

1992-01-01

A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.

A Constrained Linear Estimator for Multiple Regression

ERIC Educational Resources Information Center

Davis-Stober, Clintin P.; Dana, Jason; Budescu, David V.

2010-01-01

"Improper linear models" (see Dawes, Am. Psychol. 34:571-582, "1979"), such as equal weighting, have garnered interest as alternatives to standard regression models. We analyze the general circumstances under which these models perform well by recasting a class of "improper" linear models as "proper" statistical models with a single predictor. We…

pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree

PubMed Central

2010-01-01

Background Likelihood-based phylogenetic inference is generally considered to be the most reliable classification method for unknown sequences. However, traditional likelihood-based phylogenetic methods cannot be applied to large volumes of short reads from next-generation sequencing due to computational complexity issues and lack of phylogenetic signal. "Phylogenetic placement," where a reference tree is fixed and the unknown query sequences are placed onto the tree via a reference alignment, is a way to bring the inferential power offered by likelihood-based approaches to large data sets. Results This paper introduces pplacer, a software package for phylogenetic placement and subsequent visualization. The algorithm can place twenty thousand short reads on a reference tree of one thousand taxa per hour per processor, has essentially linear time and memory complexity in the number of reference taxa, and is easy to run in parallel. Pplacer features calculation of the posterior probability of a placement on an edge, which is a statistically rigorous way of quantifying uncertainty on an edge-by-edge basis. It also can inform the user of the positional uncertainty for query sequences by calculating expected distance between placement locations, which is crucial in the estimation of uncertainty with a well-sampled reference tree. The software provides visualizations using branch thickness and color to represent number of placements and their uncertainty. A simulation study using reads generated from 631 COG alignments shows a high level of accuracy for phylogenetic placement over a wide range of alignment diversity, and the power of edge uncertainty estimates to measure placement confidence. Conclusions Pplacer enables efficient phylogenetic placement and subsequent visualization, making likelihood-based phylogenetics methodology practical for large collections of reads; it is freely available as source code, binaries, and a web service. PMID:21034504

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.

Assessing Reproductive Choices of Women and the Liklihood of Oocyte Cryopreservation in the Era of Elective Oocyte Freezing

PubMed Central

Milman, Lauren W.; Senapati, Suneeta; Sammel, Mary D.; Cameron, Katherine D.; Gracia, Clarisa

2017-01-01

OBJECTIVE To assess reproductive choices of US reproductive age women and factors that influence consideration of elective egg freezing (EF). DESIGN Cross-sectional Internet based survey of 1000 women METHODS An anonymous 63-item self-administered questionnaire was distributed to a representative cross section of reproductive age women age 21–45, stratified by age

MIXOR: a computer program for mixed-effects ordinal regression analysis.

PubMed

Hedeker, D; Gibbons, R D

1996-03-01

MIXOR provides maximum marginal likelihood estimates for mixed-effects ordinal probit, logistic, and complementary log-log regression models. These models can be used for analysis of dichotomous and ordinal outcomes from either a clustered or longitudinal design. For clustered data, the mixed-effects model assumes that data within clusters are dependent. The degree of dependency is jointly estimated with the usual model parameters, thus adjusting for dependence resulting from clustering of the data. Similarly, for longitudinal data, the mixed-effects approach can allow for individual-varying intercepts and slopes across time, and can estimate the degree to which these time-related effects vary in the population of individuals. MIXOR uses marginal maximum likelihood estimation, utilizing a Fisher-scoring solution. For the scoring solution, the Cholesky factor of the random-effects variance-covariance matrix is estimated, along with the effects of model covariates. Examples illustrating usage and features of MIXOR are provided.

Estimating residual fault hitting rates by recapture sampling

NASA Technical Reports Server (NTRS)

Lee, Larry; Gupta, Rajan

1988-01-01

For the recapture debugging design introduced by Nayak (1988) the problem of estimating the hitting rates of the faults remaining in the system is considered. In the context of a conditional likelihood, moment estimators are derived and are shown to be asymptotically normal and fully efficient. Fixed sample properties of the moment estimators are compared, through simulation, with those of the conditional maximum likelihood estimators. Properties of the conditional model are investigated such as the asymptotic distribution of linear functions of the fault hitting frequencies and a representation of the full data vector in terms of a sequence of independent random vectors. It is assumed that the residual hitting rates follow a log linear rate model and that the testing process is truncated when the gaps between the detection of new errors exceed a fixed amount of time.

Spatially resolved regression analysis of pre-treatment FDG, FLT and Cu-ATSM PET from post-treatment FDG PET: an exploratory study

PubMed Central

Bowen, Stephen R; Chappell, Richard J; Bentzen, Søren M; Deveau, Michael A; Forrest, Lisa J; Jeraj, Robert

2012-01-01

Purpose To quantify associations between pre-radiotherapy and post-radiotherapy PET parameters via spatially resolved regression. Materials and methods Ten canine sinonasal cancer patients underwent PET/CT scans of [18F]FDG (FDGpre), [18F]FLT (FLTpre), and [61Cu]Cu-ATSM (Cu-ATSMpre). Following radiotherapy regimens of 50 Gy in 10 fractions, veterinary patients underwent FDG PET/CT scans at three months (FDGpost). Regression of standardized uptake values in baseline FDGpre, FLTpre and Cu-ATSMpre tumour voxels to those in FDGpost images was performed for linear, log-linear, generalized-linear and mixed-fit linear models. Goodness-of-fit in regression coefficients was assessed by R2. Hypothesis testing of coefficients over the patient population was performed. Results Multivariate linear model fits of FDGpre to FDGpost were significantly positive over the population (FDGpost~0.17 FDGpre, p=0.03), and classified slopes of RECIST non-responders and responders to be different (0.37 vs. 0.07, p=0.01). Generalized-linear model fits related FDGpre to FDGpost by a linear power law (FDGpost~FDGpre0.93, p<0.001). Univariate mixture model fits of FDGpre improved R2 from 0.17 to 0.52. Neither baseline FLT PET nor Cu-ATSM PET uptake contributed statistically significant multivariate regression coefficients. Conclusions Spatially resolved regression analysis indicates that pre-treatment FDG PET uptake is most strongly associated with three-month post-treatment FDG PET uptake in this patient population, though associations are histopathology-dependent. PMID:22682748

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.

An Analysis of COLA (Cost of Living Adjustment) Allocation within the United States Coast Guard.

DTIC Science & Technology

1983-09-01

books Applied Linear Regression [Ref. 39], and Statistical Methods in Research and Production [Ref. 40], or any other book on regression. In the event...Indexes, Master’s Thesis, Air Force Institute of Technology, Wright-Patterson AFB, 1976. 39. Weisberg, Stanford, Applied Linear Regression , Wiley, 1980. 40

Testing hypotheses for differences between linear regression lines

Treesearch

Stanley J. Zarnoch

2009-01-01

Five hypotheses are identified for testing differences between simple linear regression lines. The distinctions between these hypotheses are based on a priori assumptions and illustrated with full and reduced models. The contrast approach is presented as an easy and complete method for testing for overall differences between the regressions and for making pairwise...

Graphical Description of Johnson-Neyman Outcomes for Linear and Quadratic Regression Surfaces.

ERIC Educational Resources Information Center

Schafer, William D.; Wang, Yuh-Yin

A modification of the usual graphical representation of heterogeneous regressions is described that can aid in interpreting significant regions for linear or quadratic surfaces. The standard Johnson-Neyman graph is a bivariate plot with the criterion variable on the ordinate and the predictor variable on the abscissa. Regression surfaces are drawn…

Teaching the Concept of Breakdown Point in Simple Linear Regression.

ERIC Educational Resources Information Center

Chan, Wai-Sum

2001-01-01

Most introductory textbooks on simple linear regression analysis mention the fact that extreme data points have a great influence on ordinary least-squares regression estimation; however, not many textbooks provide a rigorous mathematical explanation of this phenomenon. Suggests a way to fill this gap by teaching students the concept of breakdown…

Attitude determination and calibration using a recursive maximum likelihood-based adaptive Kalman filter

NASA Technical Reports Server (NTRS)

Kelly, D. A.; Fermelia, A.; Lee, G. K. F.

1990-01-01

An adaptive Kalman filter design that utilizes recursive maximum likelihood parameter identification is discussed. At the center of this design is the Kalman filter itself, which has the responsibility for attitude determination. At the same time, the identification algorithm is continually identifying the system parameters. The approach is applicable to nonlinear, as well as linear systems. This adaptive Kalman filter design has much potential for real time implementation, especially considering the fast clock speeds, cache memory and internal RAM available today. The recursive maximum likelihood algorithm is discussed in detail, with special attention directed towards its unique matrix formulation. The procedure for using the algorithm is described along with comments on how this algorithm interacts with the Kalman filter.

Estimating monotonic rates from biological data using local linear regression.

PubMed

Olito, Colin; White, Craig R; Marshall, Dustin J; Barneche, Diego R

2017-03-01

Accessing many fundamental questions in biology begins with empirical estimation of simple monotonic rates of underlying biological processes. Across a variety of disciplines, ranging from physiology to biogeochemistry, these rates are routinely estimated from non-linear and noisy time series data using linear regression and ad hoc manual truncation of non-linearities. Here, we introduce the R package LoLinR, a flexible toolkit to implement local linear regression techniques to objectively and reproducibly estimate monotonic biological rates from non-linear time series data, and demonstrate possible applications using metabolic rate data. LoLinR provides methods to easily and reliably estimate monotonic rates from time series data in a way that is statistically robust, facilitates reproducible research and is applicable to a wide variety of research disciplines in the biological sciences. © 2017. Published by The Company of Biologists Ltd.

Locally linear regression for pose-invariant face recognition.

PubMed

Chai, Xiujuan; Shan, Shiguang; Chen, Xilin; Gao, Wen

2007-07-01

The variation of facial appearance due to the viewpoint (/pose) degrades face recognition systems considerably, which is one of the bottlenecks in face recognition. One of the possible solutions is generating virtual frontal view from any given nonfrontal view to obtain a virtual gallery/probe face. Following this idea, this paper proposes a simple, but efficient, novel locally linear regression (LLR) method, which generates the virtual frontal view from a given nonfrontal face image. We first justify the basic assumption of the paper that there exists an approximate linear mapping between a nonfrontal face image and its frontal counterpart. Then, by formulating the estimation of the linear mapping as a prediction problem, we present the regression-based solution, i.e., globally linear regression. To improve the prediction accuracy in the case of coarse alignment, LLR is further proposed. In LLR, we first perform dense sampling in the nonfrontal face image to obtain many overlapped local patches. Then, the linear regression technique is applied to each small patch for the prediction of its virtual frontal patch. Through the combination of all these patches, the virtual frontal view is generated. The experimental results on the CMU PIE database show distinct advantage of the proposed method over Eigen light-field method.

Association of daily asthma emergency department visits and hospital admissions with ambient air pollutants among the pediatric Medicaid population in Detroit: time-series and time-stratified case-crossover analyses with threshold effects.

PubMed

Li, Shi; Batterman, Stuart; Wasilevich, Elizabeth; Wahl, Robert; Wirth, Julie; Su, Feng-Chiao; Mukherjee, Bhramar

2011-11-01

Asthma morbidity has been associated with ambient air pollutants in time-series and case-crossover studies. In such study designs, threshold effects of air pollutants on asthma outcomes have been relatively unexplored, which are of potential interest for exploring concentration-response relationships. This study analyzes daily data on the asthma morbidity experienced by the pediatric Medicaid population (ages 2-18 years) of Detroit, Michigan and concentrations of pollutants fine particles (PM2.5), CO, NO2 and SO2 for the 2004-2006 period, using both time-series and case-crossover designs. We use a simple, testable and readily implementable profile likelihood-based approach to estimate threshold parameters in both designs. Evidence of significant increases in daily acute asthma events was found for SO2 and PM2.5, and a significant threshold effect was estimated for PM2.5 at 13 and 11 μg m(-3) using generalized additive models and conditional logistic regression models, respectively. Stronger effect sizes above the threshold were typically noted compared to standard linear relationship, e.g., in the time series analysis, an interquartile range increase (9.2 μg m(-3)) in PM2.5 (5-day-moving average) had a risk ratio of 1.030 (95% CI: 1.001, 1.061) in the generalized additive models, and 1.066 (95% CI: 1.031, 1.102) in the threshold generalized additive models. The corresponding estimates for the case-crossover design were 1.039 (95% CI: 1.013, 1.066) in the conditional logistic regression, and 1.054 (95% CI: 1.023, 1.086) in the threshold conditional logistic regression. This study indicates that the associations of SO2 and PM2.5 concentrations with asthma emergency department visits and hospitalizations, as well as the estimated PM2.5 threshold were fairly consistent across time-series and case-crossover analyses, and suggests that effect estimates based on linear models (without thresholds) may underestimate the true risk. Copyright © 2011 Elsevier Inc. All rights reserved.

The influence of local food environments on adolescents' food purchasing behaviors.

PubMed

He, Meizi; Tucker, Patricia; Gilliland, Jason; Irwin, Jennifer D; Larsen, Kristian; Hess, Paul

2012-04-01

This study examined the relationship between the neighborhood food environment and the food purchasing behaviors among adolescents. Grade 7 and 8 students (n = 810) at 21 elementary schools in London, Ontario, Canada completed a questionnaire assessing their food purchasing behaviors. Parents of participants also completed a brief questionnaire providing residential address and demographic information. A Geographic Information System (GIS) was used to assess students' home and school neighborhood food environment and land use characteristics. Logistic regression analysis was conducted to assess the influence of the home neighborhood food environment on students' food purchasing behaviors, while two-level Hierarchical Non-Linear Regression Models were used to examine the effects of school neighborhood food environment factors on students' food purchasing behaviors. The study showed that approximately 65% of participants reported self-purchasing foods from fast-food outlets or convenience stores. Close proximity (i.e., less than 1 km) to the nearest fast-food outlet or convenience store in the home neighborhood increased the likelihood of food purchasing from these food establishments at least once per week by adolescents (p < 0.05). High fast-food outlet density in both home and school neighborhoods was associated with increased fast-food purchasing by adolescents (i.e., at least once per week; p < 0.05). In conclusion, macro-level regulations and policies are required to amend the health-detracting neighborhood food environment surrounding children and youth's home and school.

Using financial incentives to improve the care of tuberculosis patients.

PubMed

Lee, Cheng-Yi; Chi, Mei-Ju; Yang, Shiang-Lin; Lo, Hsiu-Yun; Cheng, Shou-Hsia

2015-01-01

Tuberculosis (TB) is a serious public health concern, and Taiwan has implemented a pay-for-performance (P4P) program to incentivize healthcare professionals to provide comprehensive care to TB patients. This study aims to examine the effects of the TB P4P program on treatment outcomes and related expenses. A population-based natural experimental design with intervention and comparison groups. Propensity score matching was conducted to increase the comparability between the P4P and non-P4P group. A total of 12,018 subjects were included in the analysis, with 6009 cases in each group. Generalized linear models and multinomial logistic regression were employed to examine the effects of the P4P program. The regression models indicated that patients enrolled in the P4P program had 14% more ambulatory visits than non-P4P patients (P < .001), but there were no differences in hospitalization rates. On average, P4P enrollees spent $215 (4.6%) less on TB-related expenses than their counterparts. In addition, P4P enrollees had a higher likelihood of being successfully treated (odds ratio, 1.56; P < .001) and were less likely to die compared with nonenrollees. Patients in the P4P program were less likely to die, were more likely to be treated successfully, and incurred lower costs. Providing financial incentives to healthcare institutions could be a feasible model for better TB control.

Accession medical waivers and deployment duration in the U.S. Army.

PubMed

Gubata, Marlene E; Oetting, Alexis A; Niebuhr, David W; Cowan, David N

2013-06-01

To examine the performance of active duty U.S. Army personnel with accession medical waivers during combat deployments, the deployment duration and likelihood of early return from theater for medically waived (n = 18,093) and medically qualified (n = 250,209) personnel deploying between September 2001 and March 2011 were determined. The mean and median deployment duration for waived men (309.4 ± 107.5 and 346) and for waived women (291.8 ± 115.3 and 341) was not shorter than for medically qualified men (304.6 ± 112.1 and 346) and women (289.5 ± 116.3 and 337). When adjusted for other accession factors in a multivariate linear regression model, neither waived men (p = 1.00) nor women (p = 0.7713) had significantly shorter deployments. In a case-control analysis, 24,369 men and 3,094 women were defined as having a short deployment. Multivariate logistic regression found that medically waived men (odds ratio [OR] = 0.87, 95% confidence interval [CI] = 0.82-0.92) and women (OR = 1.02, 95% CI = 0.87-1.19) were not more likely to have shorter deployments compared to medically qualified individuals. These findings show that those with an accession medical waiver were not more likely to have shorter deployments or more likely to return early from deployment than those without waivers. Reprint & Copyright © 2013 Association of Military Surgeons of the U.S.

Comparison of robustness to outliers between robust poisson models and log-binomial models when estimating relative risks for common binary outcomes: a simulation study.

PubMed

Chen, Wansu; Shi, Jiaxiao; Qian, Lei; Azen, Stanley P

2014-06-26

To estimate relative risks or risk ratios for common binary outcomes, the most popular model-based methods are the robust (also known as modified) Poisson and the log-binomial regression. Of the two methods, it is believed that the log-binomial regression yields more efficient estimators because it is maximum likelihood based, while the robust Poisson model may be less affected by outliers. Evidence to support the robustness of robust Poisson models in comparison with log-binomial models is very limited. In this study a simulation was conducted to evaluate the performance of the two methods in several scenarios where outliers existed. The findings indicate that for data coming from a population where the relationship between the outcome and the covariate was in a simple form (e.g. log-linear), the two models yielded comparable biases and mean square errors. However, if the true relationship contained a higher order term, the robust Poisson models consistently outperformed the log-binomial models even when the level of contamination is low. The robust Poisson models are more robust (or less sensitive) to outliers compared to the log-binomial models when estimating relative risks or risk ratios for common binary outcomes. Users should be aware of the limitations when choosing appropriate models to estimate relative risks or risk ratios.

Reproductive and Birth Outcomes in Haiti Before and After the 2010 Earthquake.

PubMed

Harville, Emily W; Do, Mai

2016-02-01

We aimed to examine the relationship between exposure to the 2010 Haiti earthquake and pregnancy wantedness, interpregnancy interval, and birth weight. From the nationally representative Haiti 2012 Demographic and Health Survey, information on "size of child at birth" (too small or not) was available for 7280 singleton births in the previous 5 years, whereas information on birth weight was available for 1607 births. Pregnancy wantedness, short (<1 year) interpregnancy interval, and maternal-reported birth weight were compared before and after the earthquake and by level of damage. Multiple logistic regression and linear regression analyses were conducted. Post-earthquake births were less likely to be wanted and more likely to be born after a short interpregnancy interval. Earthquake exposure was associated with increased likelihood of a child being born too small: timing of birth (after earthquake vs. before earthquake, adjusted odds ratio [aOR]: 1.27, 95% confidence interval [CI]: 1.12-1.45), region (hardest-hit vs. rest of country; aOR: 1.43, 95% CI: 1.14- 1.80), and house damage (aOR: 1.27 95% CI: 1.02-1.58). Mean birth weight was 150 to 300 g lower in those exposed to the earthquake. Experience with the earthquake was associated with worse reproductive and birth outcomes, which underscores the need to provide reproductive health services as part of relief efforts.

Phobic Anxiety and Plasma Levels of Global Oxidative Stress in Women.

PubMed

Hagan, Kaitlin A; Wu, Tianying; Rimm, Eric B; Eliassen, A Heather; Okereke, Olivia I

2015-01-01

Psychological distress has been hypothesized to be associated with adverse biologic states such as higher oxidative stress and inflammation. Yet, little is known about associations between a common form of distress - phobic anxiety - and global oxidative stress. Thus, we related phobic anxiety to plasma fluorescent oxidation products (FlOPs), a global oxidative stress marker. We conducted a cross-sectional analysis among 1,325 women (aged 43-70 years) from the Nurses' Health Study. Phobic anxiety was measured using the Crown-Crisp Index (CCI). Adjusted least-squares mean log-transformed FlOPs were calculated across phobic categories. Logistic regression models were used to calculate odds ratios (OR) comparing the highest CCI category (≥6 points) vs. lower scores, across FlOPs quartiles. No association was found between phobic anxiety categories and mean FlOP levels in multivariable adjusted linear models. Similarly, in multivariable logistic regression models there were no associations between FlOPs quartiles and likelihood of being in the highest phobic category. Comparing women in the highest vs. lowest FlOPs quartiles: FlOP_360: OR=0.68 (95% CI: 0.40-1.15); FlOP_320: OR=0.99 (95% CI: 0.61-1.61); FlOP_400: OR=0.92 (95% CI: 0.52, 1.63). No cross-sectional association was found between phobic anxiety and a plasma measure of global oxidative stress in this sample of middle-aged and older women.

Circular Regression in a Dual-Phase Lock-In Amplifier for Coherent Detection of Weak Signal

PubMed Central

Wang, Gaoxuan; Reboul, Serge; Fertein, Eric

2017-01-01

Lock-in amplification (LIA) is an effective approach for recovery of weak signal buried in noise. Determination of the input signal amplitude in a classical dual-phase LIA is based on incoherent detection which leads to a biased estimation at low signal-to-noise ratio. This article presents, for the first time to our knowledge, a new architecture of LIA involving phase estimation with a linear-circular regression for coherent detection. The proposed phase delay estimate, between the input signal and a reference, is defined as the maximum-likelihood of a set of observations distributed according to a von Mises distribution. In our implementation this maximum is obtained with a Newton Raphson algorithm. We show that the proposed LIA architecture provides an unbiased estimate of the input signal amplitude. Theoretical simulations with synthetic data demonstrate that the classical LIA estimates are biased for SNR of the input signal lower than −20 dB, while the proposed LIA is able to accurately recover the weak signal amplitude. The novel approach is applied to an optical sensor for accurate measurement of NO2 concentrations at the sub-ppbv level in the atmosphere. Side-by-side intercomparison measurements with a commercial LIA (SR830, Stanford Research Inc., Sunnyvale, CA, USA ) demonstrate that the proposed LIA has an identical performance in terms of measurement accuracy and precision but with simplified hardware architecture. PMID:29135951

The Influence of Local Food Environments on Adolescents’ Food Purchasing Behaviors

PubMed Central

He, Meizi; Tucker, Patricia; Gilliland, Jason; Irwin, Jennifer D.; Larsen, Kristian; Hess, Paul

2012-01-01

This study examined the relationship between the neighborhood food environment and the food purchasing behaviors among adolescents. Grade 7 and 8 students (n = 810) at 21 elementary schools in London, Ontario, Canada completed a questionnaire assessing their food purchasing behaviors. Parents of participants also completed a brief questionnaire providing residential address and demographic information. A Geographic Information System (GIS) was used to assess students’ home and school neighborhood food environment and land use characteristics. Logistic regression analysis was conducted to assess the influence of the home neighborhood food environment on students’ food purchasing behaviors, while two-level Hierarchical Non-Linear Regression Models were used to examine the effects of school neighborhood food environment factors on students’ food purchasing behaviors. The study showed that approximately 65% of participants reported self-purchasing foods from fast-food outlets or convenience stores. Close proximity (i.e., less than 1 km) to the nearest fast-food outlet or convenience store in the home neighborhood increased the likelihood of food purchasing from these food establishments at least once per week by adolescents (p < 0.05). High fast-food outlet density in both home and school neighborhoods was associated with increased fast-food purchasing by adolescents (i.e., at least once per week; p < 0.05). In conclusion, macro-level regulations and policies are required to amend the health-detracting neighborhood food environment surrounding children and youth’s home and school. PMID:22690205

Associations of linear growth and relative weight gain during early life with adult health and human capital in countries of low and middle income: findings from five birth cohort studies.

PubMed

Adair, Linda S; Fall, Caroline H D; Osmond, Clive; Stein, Aryeh D; Martorell, Reynaldo; Ramirez-Zea, Manuel; Sachdev, Harshpal Singh; Dahly, Darren L; Bas, Isabelita; Norris, Shane A; Micklesfield, Lisa; Hallal, Pedro; Victora, Cesar G

2013-08-10

Fast weight gain and linear growth in children in low-income and middle-income countries are associated with enhanced survival and improved cognitive development, but might increase risk of obesity and related adult cardiometabolic diseases. We investigated how linear growth and relative weight gain during infancy and childhood are related to health and human capital outcomes in young adults. We used data from five prospective birth cohort studies from Brazil, Guatemala, India, the Philippines, and South Africa. We investigated body-mass index, systolic and diastolic blood pressure, plasma glucose concentration, height, years of attained schooling, and related categorical indicators of adverse outcomes in young adults. With linear and logistic regression models, we assessed how these outcomes relate to birthweight and to statistically independent measures representing linear growth and weight gain independent of linear growth (relative weight gain) in three age periods: 0-2 years, 2 years to mid-childhood, and mid-childhood to adulthood. We obtained data for 8362 participants who had at least one adult outcome of interest. A higher birthweight was consistently associated with an adult body-mass index of greater than 25 kg/m(2) (odds ratio 1·28, 95% CI 1·21-1·35) and a reduced likelihood of short adult stature (0·49, 0·44-0·54) and of not completing secondary school (0·82, 0·78-0·87). Faster linear growth was strongly associated with a reduced risk of short adult stature (age 2 years: 0·23, 0·20-0·52; mid-childhood: 0·39, 0·36-0·43) and of not completing secondary school (age 2 years: 0·74, 0·67-0·78; mid-childhood: 0·87, 0·83-0·92), but did raise the likelihood of overweight (age 2 years: 1·24, 1·17-1·31; mid-childhood: 1·12, 1·06-1·18) and elevated blood pressure (age 2 years: 1·12, 1·06-1·19; mid-childhood: 1·07, 1·01-1·13). Faster relative weight gain was associated with an increased risk of adult overweight (age 2 years: 1·51, 1·43-1·60; mid-childhood: 1·76, 1·69-1·91) and elevated blood pressure (age 2 years: 1·07, 1·01-1·13; mid-childhood: 1·22, 1·15-1·30). Linear growth and relative weight gain were not associated with dysglycaemia, but a higher birthweight was associated with decreased risk of the disorder (0·89, 0·81-0·98). Interventions in countries of low and middle income to increase birthweight and linear growth during the first 2 years of life are likely to result in substantial gains in height and schooling and give some protection from adult chronic disease risk factors, with few adverse trade-offs. Wellcome Trust and Bill & Melinda Gates Foundation. Copyright © 2013 Elsevier Ltd. All rights reserved.

Effect of Malmquist bias on correlation studies with IRAS data base

NASA Technical Reports Server (NTRS)

Verter, Frances

1993-01-01

The relationships between galaxy properties in the sample of Trinchieri et al. (1989) are reexamined with corrections for Malmquist bias. The linear correlations are tested and linear regressions are fit for log-log plots of L(FIR), L(H-alpha), and L(B) as well as ratios of these quantities. The linear correlations for Malmquist bias are corrected using the method of Verter (1988), in which each galaxy observation is weighted by the inverse of its sampling volume. The linear regressions are corrected for Malmquist bias by a new method invented here in which each galaxy observation is weighted by its sampling volume. The results of correlation and regressions among the sample are significantly changed in the anticipated sense that the corrected correlation confidences are lower and the corrected slopes of the linear regressions are lower. The elimination of Malmquist bias eliminates the nonlinear rise in luminosity that has caused some authors to hypothesize additional components of FIR emission.

Maximum likelihood estimation for Cox's regression model under nested case-control sampling.

PubMed

Scheike, Thomas H; Juul, Anders

2004-04-01

Nested case-control sampling is designed to reduce the costs of large cohort studies. It is important to estimate the parameters of interest as efficiently as possible. We present a new maximum likelihood estimator (MLE) for nested case-control sampling in the context of Cox's proportional hazards model. The MLE is computed by the EM-algorithm, which is easy to implement in the proportional hazards setting. Standard errors are estimated by a numerical profile likelihood approach based on EM aided differentiation. The work was motivated by a nested case-control study that hypothesized that insulin-like growth factor I was associated with ischemic heart disease. The study was based on a population of 3784 Danes and 231 cases of ischemic heart disease where controls were matched on age and gender. We illustrate the use of the MLE for these data and show how the maximum likelihood framework can be used to obtain information additional to the relative risk estimates of covariates.

Obstetrician gender and the likelihood of performing a maternal request for a cesarean delivery.

PubMed

Liu, Tsai-Ching; Lin, Herng-Ching; Chen, Chin-Shyan; Lee, Hsin-Chien

2008-01-01

To examine the relationship between obstetrician gender and the likelihood of maternal request for cesarean section (CS) within different healthcare institutions (medical centers, regional hospitals, district hospitals, and obstetric and gynecology clinics). Five years of population-based data from Taiwan covering 857,920 singleton deliveries without a clinical indication for a CS were subjected to a multiple logistic regression to examine the association between obstetrician gender and the likelihood of maternal request for a CS. After adjusting for physician and institutional characteristics, it was found that male obstetricians were more likely to perform a requested CS than female obstetricians in district hospitals (OR=1.53) and clinics (OR=2.26), while obstetrician gender had no discernible associations with the likelihood of a CS upon maternal request in medical centers and regional hospitals. While obstetrician gender had the greatest association with delivery mode decisions in the lowest obstetric care units, those associations were diluted in higher-level healthcare institutions.

A primer for biomedical scientists on how to execute model II linear regression analysis.

PubMed

Ludbrook, John

2012-04-01

1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.

Analyzing Multilevel Data: Comparing Findings from Hierarchical Linear Modeling and Ordinary Least Squares Regression

ERIC Educational Resources Information Center

Rocconi, Louis M.

2013-01-01

This study examined the differing conclusions one may come to depending upon the type of analysis chosen, hierarchical linear modeling or ordinary least squares (OLS) regression. To illustrate this point, this study examined the influences of seniors' self-reported critical thinking abilities three ways: (1) an OLS regression with the student…

Short communication: Principal components and factor analytic models for test-day milk yield in Brazilian Holstein cattle.

PubMed

Bignardi, A B; El Faro, L; Rosa, G J M; Cardoso, V L; Machado, P F; Albuquerque, L G

2012-04-01

A total of 46,089 individual monthly test-day (TD) milk yields (10 test-days), from 7,331 complete first lactations of Holstein cattle were analyzed. A standard multivariate analysis (MV), reduced rank analyses fitting the first 2, 3, and 4 genetic principal components (PC2, PC3, PC4), and analyses that fitted a factor analytic structure considering 2, 3, and 4 factors (FAS2, FAS3, FAS4), were carried out. The models included the random animal genetic effect and fixed effects of the contemporary groups (herd-year-month of test-day), age of cow (linear and quadratic effects), and days in milk (linear effect). The residual covariance matrix was assumed to have full rank. Moreover, 2 random regression models were applied. Variance components were estimated by restricted maximum likelihood method. The heritability estimates ranged from 0.11 to 0.24. The genetic correlation estimates between TD obtained with the PC2 model were higher than those obtained with the MV model, especially on adjacent test-days at the end of lactation close to unity. The results indicate that for the data considered in this study, only 2 principal components are required to summarize the bulk of genetic variation among the 10 traits. Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Analyzing Multilevel Data: An Empirical Comparison of Parameter Estimates of Hierarchical Linear Modeling and Ordinary Least Squares Regression

ERIC Educational Resources Information Center

Rocconi, Louis M.

2011-01-01

Hierarchical linear models (HLM) solve the problems associated with the unit of analysis problem such as misestimated standard errors, heterogeneity of regression and aggregation bias by modeling all levels of interest simultaneously. Hierarchical linear modeling resolves the problem of misestimated standard errors by incorporating a unique random…

Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis

ERIC Educational Resources Information Center

Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.

2006-01-01

Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…

Modulation/demodulation techniques for satellite communications. Part 2: Advanced techniques. The linear channel

NASA Technical Reports Server (NTRS)

Omura, J. K.; Simon, M. K.

1982-01-01

A theory is presented for deducing and predicting the performance of transmitter/receivers for bandwidth efficient modulations suitable for use on the linear satellite channel. The underlying principle used is the development of receiver structures based on the maximum-likelihood decision rule. The application of the performance prediction tools, e.g., channel cutoff rate and bit error probability transfer function bounds to these modulation/demodulation techniques.

Comparison of two-concentration with multi-concentration linear regressions: Retrospective data analysis of multiple regulated LC-MS bioanalytical projects.

PubMed

Musuku, Adrien; Tan, Aimin; Awaiye, Kayode; Trabelsi, Fethi

2013-09-01

Linear calibration is usually performed using eight to ten calibration concentration levels in regulated LC-MS bioanalysis because a minimum of six are specified in regulatory guidelines. However, we have previously reported that two-concentration linear calibration is as reliable as or even better than using multiple concentrations. The purpose of this research is to compare two-concentration with multiple-concentration linear calibration through retrospective data analysis of multiple bioanalytical projects that were conducted in an independent regulated bioanalytical laboratory. A total of 12 bioanalytical projects were randomly selected: two validations and two studies for each of the three most commonly used types of sample extraction methods (protein precipitation, liquid-liquid extraction, solid-phase extraction). When the existing data were retrospectively linearly regressed using only the lowest and the highest concentration levels, no extra batch failure/QC rejection was observed and the differences in accuracy and precision between the original multi-concentration regression and the new two-concentration linear regression are negligible. Specifically, the differences in overall mean apparent bias (square root of mean individual bias squares) are within the ranges of -0.3% to 0.7% and 0.1-0.7% for the validations and studies, respectively. The differences in mean QC concentrations are within the ranges of -0.6% to 1.8% and -0.8% to 2.5% for the validations and studies, respectively. The differences in %CV are within the ranges of -0.7% to 0.9% and -0.3% to 0.6% for the validations and studies, respectively. The average differences in study sample concentrations are within the range of -0.8% to 2.3%. With two-concentration linear regression, an average of 13% of time and cost could have been saved for each batch together with 53% of saving in the lead-in for each project (the preparation of working standard solutions, spiking, and aliquoting). Furthermore, examples are given as how to evaluate the linearity over the entire concentration range when only two concentration levels are used for linear regression. To conclude, two-concentration linear regression is accurate and robust enough for routine use in regulated LC-MS bioanalysis and it significantly saves time and cost as well. Copyright © 2013 Elsevier B.V. All rights reserved.

A Linear Regression and Markov Chain Model for the Arabian Horse Registry

DTIC Science & Technology

1993-04-01

as a tax deduction? Yes No T-4367 68 26. Regardless of previous equine tax deductions, do you consider your current horse activities to be... (Mark one...E L T-4367 A Linear Regression and Markov Chain Model For the Arabian Horse Registry Accesion For NTIS CRA&I UT 7 4:iC=D 5 D-IC JA" LI J:13tjlC,3 lO...the Arabian Horse Registry, which needed to forecast its future registration of purebred Arabian horses . A linear regression model was utilized to

ITS impacts on safety and traffic management : an investigation of secondary crash causes

DOT National Transportation Integrated Search

1999-01-01

In this paper, the authors focus on identifying potential savings from lowering the likelihood of secondary crash occurrences in incidents. Logistic regression models are developed to examine which primary crash characteristics are likely to influenc...

An improved multiple linear regression and data analysis computer program package

NASA Technical Reports Server (NTRS)

Sidik, S. M.

1972-01-01

NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.

Genetic mixed linear models for twin survival data.

PubMed

Ha, Il Do; Lee, Youngjo; Pawitan, Yudi

2007-07-01

Twin studies are useful for assessing the relative importance of genetic or heritable component from the environmental component. In this paper we develop a methodology to study the heritability of age-at-onset or lifespan traits, with application to analysis of twin survival data. Due to limited period of observation, the data can be left truncated and right censored (LTRC). Under the LTRC setting we propose a genetic mixed linear model, which allows general fixed predictors and random components to capture genetic and environmental effects. Inferences are based upon the hierarchical-likelihood (h-likelihood), which provides a statistically efficient and unified framework for various mixed-effect models. We also propose a simple and fast computation method for dealing with large data sets. The method is illustrated by the survival data from the Swedish Twin Registry. Finally, a simulation study is carried out to evaluate its performance.

CO2 flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression

NASA Astrophysics Data System (ADS)

Kutzbach, L.; Schneider, J.; Sachs, T.; Giebels, M.; Nykänen, H.; Shurpali, N. J.; Martikainen, P. J.; Alm, J.; Wilmking, M.

2007-07-01

Closed (non-steady state) chambers are widely used for quantifying carbon dioxide (CO2) fluxes between soils or low-stature canopies and the atmosphere. It is well recognised that covering a soil or vegetation by a closed chamber inherently disturbs the natural CO2 fluxes by altering the concentration gradients between the soil, the vegetation and the overlying air. Thus, the driving factors of CO2 fluxes are not constant during the closed chamber experiment, and no linear increase or decrease of CO2 concentration over time within the chamber headspace can be expected. Nevertheless, linear regression has been applied for calculating CO2 fluxes in many recent, partly influential, studies. This approach was justified by keeping the closure time short and assuming the concentration change over time to be in the linear range. Here, we test if the application of linear regression is really appropriate for estimating CO2 fluxes using closed chambers over short closure times and if the application of nonlinear regression is necessary. We developed a nonlinear exponential regression model from diffusion and photosynthesis theory. This exponential model was tested with four different datasets of CO2 flux measurements (total number: 1764) conducted at three peatland sites in Finland and a tundra site in Siberia. The flux measurements were performed using transparent chambers on vegetated surfaces and opaque chambers on bare peat surfaces. Thorough analyses of residuals demonstrated that linear regression was frequently not appropriate for the determination of CO2 fluxes by closed-chamber methods, even if closure times were kept short. The developed exponential model was well suited for nonlinear regression of the concentration over time c(t) evolution in the chamber headspace and estimation of the initial CO2 fluxes at closure time for the majority of experiments. CO2 flux estimates by linear regression can be as low as 40% of the flux estimates of exponential regression for closure times of only two minutes and even lower for longer closure times. The degree of underestimation increased with increasing CO2 flux strength and is dependent on soil and vegetation conditions which can disturb not only the quantitative but also the qualitative evaluation of CO2 flux dynamics. The underestimation effect by linear regression was observed to be different for CO2 uptake and release situations which can lead to stronger bias in the daily, seasonal and annual CO2 balances than in the individual fluxes. To avoid serious bias of CO2 flux estimates based on closed chamber experiments, we suggest further tests using published datasets and recommend the use of nonlinear regression models for future closed chamber studies.

Fisher's method of scoring in statistical image reconstruction: comparison of Jacobi and Gauss-Seidel iterative schemes.

PubMed

Hudson, H M; Ma, J; Green, P

1994-01-01

Many algorithms for medical image reconstruction adopt versions of the expectation-maximization (EM) algorithm. In this approach, parameter estimates are obtained which maximize a complete data likelihood or penalized likelihood, in each iteration. Implicitly (and sometimes explicitly) penalized algorithms require smoothing of the current reconstruction in the image domain as part of their iteration scheme. In this paper, we discuss alternatives to EM which adapt Fisher's method of scoring (FS) and other methods for direct maximization of the incomplete data likelihood. Jacobi and Gauss-Seidel methods for non-linear optimization provide efficient algorithms applying FS in tomography. One approach uses smoothed projection data in its iterations. We investigate the convergence of Jacobi and Gauss-Seidel algorithms with clinical tomographic projection data.

How users adopt healthcare information: An empirical study of an online Q&A community.

PubMed

Jin, Jiahua; Yan, Xiangbin; Li, Yijun; Li, Yumei

2016-02-01

The emergence of social media technology has led to the creation of many online healthcare communities, where patients can easily share and look for healthcare-related information from peers who have experienced a similar problem. However, with increased user-generated content, there is a need to constantly analyse which content should be trusted as one sifts through enormous amounts of healthcare information. This study aims to explore patients' healthcare information seeking behavior in online communities. Based on dual-process theory and the knowledge adoption model, we proposed a healthcare information adoption model for online communities. This model highlights that information quality, emotional support, and source credibility are antecedent variables of adoption likelihood of healthcare information, and competition among repliers and involvement of recipients moderate the relationship between the antecedent variables and adoption likelihood. Empirical data were collected from the healthcare module of China's biggest Q&A community-Baidu Knows. Text mining techniques were adopted to calculate the information quality and emotional support contained in each reply text. A binary logistics regression model and hierarchical regression approach were employed to test the proposed conceptual model. Information quality, emotional support, and source credibility have significant and positive impact on healthcare information adoption likelihood, and among these factors, information quality has the biggest impact on a patient's adoption decision. In addition, competition among repliers and involvement of recipients were tested as moderating effects between these antecedent factors and the adoption likelihood. Results indicate competition among repliers positively moderates the relationship between source credibility and adoption likelihood, and recipients' involvement positively moderates the relationship between information quality, source credibility, and adoption decision. In addition to information quality and source credibility, emotional support has significant positive impact on individuals' healthcare information adoption decisions. Moreover, the relationships between information quality, source credibility, emotional support, and adoption decision are moderated by competition among repliers and involvement of recipients. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Genetic risk of major depressive disorder: the moderating and mediating effects of neuroticism and psychological resilience on clinical and self-reported depression.

PubMed

Navrady, L B; Adams, M J; Chan, S W Y; Ritchie, S J; McIntosh, A M

2017-11-29

Polygenic risk scores (PRS) for depression correlate with depression status and chronicity, and provide causal anchors to identify depressive mechanisms. Neuroticism is phenotypically and genetically positively associated with depression, whereas psychological resilience demonstrates negative phenotypic associations. Whether increased neuroticism and reduced resilience are downstream mediators of genetic risk for depression, and whether they contribute independently to risk remains unknown. Moderating and mediating relationships between depression PRS, neuroticism, resilience and both clinical and self-reported depression were examined in a large, population-based cohort, Generation Scotland: Scottish Family Health Study (N = 4166), using linear regression and structural equation modelling. Neuroticism and resilience were measured by the Eysenck Personality Scale Short Form Revised and the Brief Resilience Scale, respectively. PRS for depression was associated with increased likelihood of self-reported and clinical depression. No interaction was found between PRS and neuroticism, or between PRS and resilience. Neuroticism was associated with increased likelihood of self-reported and clinical depression, whereas resilience was associated with reduced risk. Structural equation modelling suggested the association between PRS and self-reported and clinical depression was mediated by neuroticism (43-57%), while resilience mediated the association in the opposite direction (37-40%). For both self-reported and clinical diagnoses, the genetic risk for depression was independently mediated by neuroticism and resilience. Findings suggest polygenic risk for depression increases vulnerability for self-reported and clinical depression through independent effects on increased neuroticism and reduced psychological resilience. In addition, two partially independent mechanisms - neuroticism and resilience - may form part of the pathway of vulnerability to depression.

Associations of prognostic awareness/acceptance with psychological distress, existential suffering, and quality of life in terminally ill cancer patients' last year of life.

PubMed

Tang, Siew Tzuh; Chang, Wen-Cheng; Chen, Jen-Shi; Chou, Wen-Chi; Hsieh, Chia-Hsun; Chen, Chen H

2016-04-01

Whether prognostic awareness benefits terminally ill cancer patients' psychological-existential well-being and quality of life (QOL) is unclear because of lack of well-controlled longitudinal studies. This study longitudinally evaluated the associations of accurate prognostic awareness and prognostic acceptance with psychological distress, existential suffering, and QOL while comprehensively controlling for confounders in Taiwanese terminally ill cancer patients' last year of life. A convenience sample of 325 cancer patients was followed until death. Psychological distress and existential suffering were assessed by severe anxiety and depressive symptoms and high self-perceived sense of burden to others, respectively. Dichotomized and continuous (QOL) outcome variables were evaluated by multivariate logistic and linear regression modeling with the generalized estimating equation, respectively. Accurate prognostic awareness was not associated with the likelihood of severe anxiety or depressive symptoms but significantly increased the likelihood of high self-perceived sense of burden to others and was associated with poorer QOL in participants' last year of life. Participants who knew and highly accepted their prognosis were significantly less likely to experience severe anxiety symptoms than those who were unaware of or knew their prognosis but had difficulty accepting it. Knowing one's poor prognosis and confronting one's impending death without full acceptance and adequate professional psycho-spiritual support may harm more than benefit terminally ill cancer patients' psychological state, existential well-being, and QOL. These findings highlight the importance of tailoring psycho-spiritual support to cancer patients' psychological and existential needs when prognostic information is disclosed. Copyright © 2015 John Wiley & Sons, Ltd.

Parent-child relationships, parental attitudes towards sex, and birth outcomes among adolescents.

PubMed

Harville, Emily W; Madkour, Aubrey Spriggs; Xie, Yiqiong

2014-10-01

To examine how parent-child relationships, parental control, and parental attitudes towards sex were related to pregnancy outcomes among adolescent mothers. Prospective cohort study. Parental report of relationship satisfaction, disapproval of adolescent having sex, discussion around sexual health, and sexual communication attitudes, and adolescent report of relationship satisfaction, parental control, and parental disapproval of sex were examined as predictors of self-reported birth outcomes. Weighted multivariable linear regression models were run incorporating interactions by race. United States. 632 females who participated in Waves I and IV of the National Longitudinal Study of Adolescent Health (Add Health), a nationally-representative sample of students enrolled in grades 7-12 in 1994-95 and followed up in 2007-2008. Birthweight and gestational age. For Black adolescents, better parent-child relationship was associated with higher birthweight (0.14 kg, P < .05) and gestational age (0.75 weeks, P < .01), while higher parental disapproval of having sex (adjusted beta 0.15 kg, P < .05) were associated with higher birthweight. For non-Black adolescents, a moderate amount of discussion of birth control was associated with higher birthweight (0.19 kg, P < .01 and lower child-perceived parental disapproval of having sex was associated with higher birthweight (0.08 kg, P < .05) and gestational age (0.37 weeks, P < .05). Higher parental control was associated with a reduced likelihood of smoking during pregnancy and a greater likelihood of early prenatal care. Parent-child relationships and attitudes about sex affect outcomes of pregnant adolescents. Copyright © 2014 North American Society for Pediatric and Adolescent Gynecology. Published by Elsevier Inc. All rights reserved.

Parent-child Relationships, Parental Attitudes towards Sex, and Birth Outcomes among Adolescents

PubMed Central

Harville, Emily W.; Madkour, Aubrey Spriggs; Xie, Yiqiong

2014-01-01

Study objective To examine how parent-child relationships, parental control, and parental attitudes towards sex were related to pregnancy outcomes among adolescent mothers. Design Prospective cohort study. Parental report of relationship satisfaction, disapproval of adolescent having sex, discussion around sexual health, and sexual communication attitudes, and adolescent report of relationship satisfaction, parental control, and parental disapproval of sex were examined as predictors of self-reported birth outcomes. Weighted multivariable linear regression models were run incorporating interactions by race. Setting United States Participants 632 females who participated in Waves I and IV of the National Longitudinal Study of Adolescent Health (Add Health), a nationally-representative sample of students enrolled in grades 7–12 in 1994–95 and followed up in 2007–2008 Main Outcome Measures birthweight and gestational age Results For Black adolescents, better parent-child relationship was associated with higher birthweight (0.14 kg, p<0.05) and gestational age (0.75 weeks, p<0.01), while higher parental disapproval of having sex (adjusted beta 0.15 kg, p<0.05) were associated with higher birthweight. For non-Black adolescents, a moderate amount of discussion of birth control was associated with higher birthweight (0.19 kg, p<0.01 and lower child-perceived parental disapproval of having sex was associated with higher birthweight (0.08 kg, p<0.05) and gestational age (0.37 weeks, p<0.05). Higher parental control was associated with a reduced likelihood of smoking during pregnancy and a greater likelihood of early prenatal care. Conclusion Parent-child relationships and attitudes about sex affect outcomes of pregnant adolescents. PMID:25023982

Artificial pancreas (AP) clinical trial participants' acceptance of future AP technology.

PubMed

Bevier, Wendy C; Fuller, Serena M; Fuller, Ryan P; Rubin, Richard R; Dassau, Eyal; Doyle, Francis J; Jovanovič, Lois; Zisser, Howard C

2014-09-01

Artificial pancreas (AP) systems are currently an active field of diabetes research. This pilot study examined the attitudes of AP clinical trial participants toward future acceptance of the technology, having gained firsthand experience. After possible influencers of AP technology adoption were considered, a 34-question questionnaire was developed. The survey assessed current treatment satisfaction, dimensions of clinical trial participant motivation, and variables of the technology acceptance model (TAM). Forty-seven subjects were contacted to complete the survey. The reliability of the survey scales was tested using Cronbach's α. The relationship of the factors to the likelihood of AP technology adoption was explored using regression analysis. Thirty-six subjects (76.6%) completed the survey. Of the respondents, 86.1% were either highly likely or likely to adopt the technology once available. Reliability analysis of the survey dimensions revealed good internal consistency, with scores of >0.7 for current treatment satisfaction, convenience (motivation), personal health benefit (motivation), perceived ease of use (TAM), and perceived usefulness (TAM). Linear modeling showed that future acceptance of the AP was significantly associated with TAM and the motivation variables of convenience plus the individual item benefit to others (R(2)=0.26, P=0.05). When insulin pump and continuous glucose monitor use were added, the model significance improved (R(2)=0.37, P=0.02). This pilot study demonstrated that individuals with direct AP technology experience expressed high likelihood of future acceptance. Results support the factors of personal benefit, convenience, perceived usefulness, and perceived ease of use as reliable scales that suggest system adoption in this highly motivated patient population.

Feasibility of transferring intensive cared preterm infants from incubator to open crib at 1600 grams.

PubMed

Barone, Giovanni; Corsello, Mirta; Papacci, Patrizia; Priolo, Francesca; Romagnoli, Costantino; Zecca, Enrico

2014-05-03

Ability to maintain a normal body temperature in an open crib is an important physiologic competency generally requested to discharge preterm infants from the hospital. The aim of this study is to assess the feasibility of an early weaning protocol from incubator in preterm newborns in a Neonatal Intensive Care Unit. 101 infants with birth weight < 1600 g were included in this feasibility study. We compared 80 newborns successfully transferred from an incubator to open crib at 1600 g with 21 infants transferred at weight ≥ 1700 g. The primary outcome was to evaluate feasibility of the protocol and the reasons for the eventual delay. Secondary outcomes were the identification of factors that would increase the likelihood of early weaning, the impact of an earlier weaning on discharge timing, and the incidence of adverse outcomes. Newborns in the study period were then compared with an historical control group with similar characteristics. Early weaning was achieved in 79.2% of infants without significant adverse effects on temperature stability or weight gain. Delayed weaning was mainly due to the need of respiratory support. Gestational age affected the likelihood of early weaning (OR 1.7282 95% CI: 1.3071 - 2.2850). In the multivariate linear regression, early weaning reduced length of stay (LOS) by 25.8 days (p < 0.0001). Preterm infants can be weaned successfully from an incubator to an open crib at weight as low as 1600 grams without significant adverse effect. Early weaning significantly reduces LOS in preterm newborns.

Nutrition Facts Use in Relation to Eating Behaviors and Healthy and Unhealthy Weight Control Behaviors.

PubMed

Christoph, Mary J; Loth, Katie A; Eisenberg, Marla E; Haynos, Ann F; Larson, Nicole; Neumark-Sztainer, Dianne

2018-03-01

Investigate the relationship between use of Nutrition Facts labels on packaged foods and weight-related behaviors. Cross-sectional survey in 2015-2016. Young adult respondents (n = 1,817; 57% women; average age 31.0 ± 1.6 years) to the Project Eating and Activity in Teens and Young Adults-IV survey, the fourth wave of a longitudinal cohort study. Use of Nutrition Facts labels on packaged foods; healthy, unhealthy, and extreme weight control behaviors; intuitive eating; binge eating. Linear and logistic regression models were adjusted for age, ethnicity/race, education, income, and weight status. In women, greater Nutrition Facts use was associated with a 23% and 10% greater likelihood of engaging in healthy and unhealthy weight control behaviors, respectively, and a 17% greater chance of engaging in binge eating. In men, greater label use was associated with a 27% and 17% greater likelihood of engaging in healthy and unhealthy weight control behaviors, respectively, and a lower level of intuitive eating. Professionals advising patients and clients on weight management may consider possible gender differences in response to weight loss and management guidance. Since label use was related to engagement in some unhealthy behaviors in addition to healthy behaviors, it is important to consider how individuals may use labels, particularly those at risk for, or engaging in, disordered eating behaviors. Future research investigating potential relationships between Nutrition Facts use, intuitive eating, and binge eating is needed. Copyright © 2017 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

Biostatistics Series Module 6: Correlation and Linear Regression.

PubMed

Hazra, Avijit; Gogtay, Nithya

2016-01-01

Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson's correlation coefficient ( r ). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman's rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P < 0.05. A 95% confidence interval of the correlation coefficient can also be calculated for an idea of the correlation in the population. The value r 2 denotes the proportion of the variability of the dependent variable y that can be attributed to its linear relation with the independent variable x and is called the coefficient of determination. Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation ( y = a + bx ), such that given the value of one variable the other may be predicted. In general, the method of least squares is applied to obtain the equation of the regression line. Correlation and linear regression analysis are based on certain assumptions pertaining to the data sets. If these assumptions are not met, misleading conclusions may be drawn. The first assumption is that of linear relationship between the two variables. A scatter plot is essential before embarking on any correlation-regression analysis to show that this is indeed the case. Outliers or clustering within data sets can distort the correlation coefficient value. Finally, it is vital to remember that though strong correlation can be a pointer toward causation, the two are not synonymous.

Biostatistics Series Module 6: Correlation and Linear Regression

PubMed Central

Hazra, Avijit; Gogtay, Nithya

2016-01-01

Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson's correlation coefficient (r). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman's rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P < 0.05. A 95% confidence interval of the correlation coefficient can also be calculated for an idea of the correlation in the population. The value r2 denotes the proportion of the variability of the dependent variable y that can be attributed to its linear relation with the independent variable x and is called the coefficient of determination. Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation (y = a + bx), such that given the value of one variable the other may be predicted. In general, the method of least squares is applied to obtain the equation of the regression line. Correlation and linear regression analysis are based on certain assumptions pertaining to the data sets. If these assumptions are not met, misleading conclusions may be drawn. The first assumption is that of linear relationship between the two variables. A scatter plot is essential before embarking on any correlation-regression analysis to show that this is indeed the case. Outliers or clustering within data sets can distort the correlation coefficient value. Finally, it is vital to remember that though strong correlation can be a pointer toward causation, the two are not synonymous. PMID:27904175

Using the Coefficient of Determination "R"[superscript 2] to Test the Significance of Multiple Linear Regression

ERIC Educational Resources Information Center

Quinino, Roberto C.; Reis, Edna A.; Bessegato, Lupercio F.

2013-01-01

This article proposes the use of the coefficient of determination as a statistic for hypothesis testing in multiple linear regression based on distributions acquired by beta sampling. (Contains 3 figures.)

Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

PubMed

Faul, Franz; Erdfelder, Edgar; Buchner, Axel; Lang, Albert-Georg

2009-11-01

G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.

Guidelines and Procedures for Computing Time-Series Suspended-Sediment Concentrations and Loads from In-Stream Turbidity-Sensor and Streamflow Data

USGS Publications Warehouse

Rasmussen, Patrick P.; Gray, John R.; Glysson, G. Douglas; Ziegler, Andrew C.

2009-01-01

In-stream continuous turbidity and streamflow data, calibrated with measured suspended-sediment concentration data, can be used to compute a time series of suspended-sediment concentration and load at a stream site. Development of a simple linear (ordinary least squares) regression model for computing suspended-sediment concentrations from instantaneous turbidity data is the first step in the computation process. If the model standard percentage error (MSPE) of the simple linear regression model meets a minimum criterion, this model should be used to compute a time series of suspended-sediment concentrations. Otherwise, a multiple linear regression model using paired instantaneous turbidity and streamflow data is developed and compared to the simple regression model. If the inclusion of the streamflow variable proves to be statistically significant and the uncertainty associated with the multiple regression model results in an improvement over that for the simple linear model, the turbidity-streamflow multiple linear regression model should be used to compute a suspended-sediment concentration time series. The computed concentration time series is subsequently used with its paired streamflow time series to compute suspended-sediment loads by standard U.S. Geological Survey techniques. Once an acceptable regression model is developed, it can be used to compute suspended-sediment concentration beyond the period of record used in model development with proper ongoing collection and analysis of calibration samples. Regression models to compute suspended-sediment concentrations are generally site specific and should never be considered static, but they represent a set period in a continually dynamic system in which additional data will help verify any change in sediment load, type, and source.

CO2 flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression

NASA Astrophysics Data System (ADS)

Kutzbach, L.; Schneider, J.; Sachs, T.; Giebels, M.; Nykänen, H.; Shurpali, N. J.; Martikainen, P. J.; Alm, J.; Wilmking, M.

2007-11-01

Closed (non-steady state) chambers are widely used for quantifying carbon dioxide (CO2) fluxes between soils or low-stature canopies and the atmosphere. It is well recognised that covering a soil or vegetation by a closed chamber inherently disturbs the natural CO2 fluxes by altering the concentration gradients between the soil, the vegetation and the overlying air. Thus, the driving factors of CO2 fluxes are not constant during the closed chamber experiment, and no linear increase or decrease of CO2 concentration over time within the chamber headspace can be expected. Nevertheless, linear regression has been applied for calculating CO2 fluxes in many recent, partly influential, studies. This approach has been justified by keeping the closure time short and assuming the concentration change over time to be in the linear range. Here, we test if the application of linear regression is really appropriate for estimating CO2 fluxes using closed chambers over short closure times and if the application of nonlinear regression is necessary. We developed a nonlinear exponential regression model from diffusion and photosynthesis theory. This exponential model was tested with four different datasets of CO2 flux measurements (total number: 1764) conducted at three peatlands sites in Finland and a tundra site in Siberia. Thorough analyses of residuals demonstrated that linear regression was frequently not appropriate for the determination of CO2 fluxes by closed-chamber methods, even if closure times were kept short. The developed exponential model was well suited for nonlinear regression of the concentration over time c(t) evolution in the chamber headspace and estimation of the initial CO2 fluxes at closure time for the majority of experiments. However, a rather large percentage of the exponential regression functions showed curvatures not consistent with the theoretical model which is considered to be caused by violations of the underlying model assumptions. Especially the effects of turbulence and pressure disturbances by the chamber deployment are suspected to have caused unexplainable curvatures. CO2 flux estimates by linear regression can be as low as 40% of the flux estimates of exponential regression for closure times of only two minutes. The degree of underestimation increased with increasing CO2 flux strength and was dependent on soil and vegetation conditions which can disturb not only the quantitative but also the qualitative evaluation of CO2 flux dynamics. The underestimation effect by linear regression was observed to be different for CO2 uptake and release situations which can lead to stronger bias in the daily, seasonal and annual CO2 balances than in the individual fluxes. To avoid serious bias of CO2 flux estimates based on closed chamber experiments, we suggest further tests using published datasets and recommend the use of nonlinear regression models for future closed chamber studies.

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.

Genetic modelling of test day records in dairy sheep using orthogonal Legendre polynomials.

PubMed

Kominakis, A; Volanis, M; Rogdakis, E

2001-03-01

Test day milk yields of three lactations in Sfakia sheep were analyzed fitting a random regression (RR) model, regressing on orthogonal polynomials of the stage of the lactation period, i.e. days in milk. Univariate (UV) and multivariate (MV) analyses were also performed for four stages of the lactation period, represented by average days in milk, i.e. 15, 45, 70 and 105 days, to compare estimates obtained from RR models with estimates from UV and MV analyses. The total number of test day records were 790, 1314 and 1041 obtained from 214, 342 and 303 ewes in the first, second and third lactation, respectively. Error variances and covariances between regression coefficients were estimated by restricted maximum likelihood. Models were compared using likelihood ratio tests (LRTs). Log likelihoods were not significantly reduced when the rank of the orthogonal Legendre polynomials (LPs) of lactation stage was reduced from 4 to 2 and homogenous variances for lactation stages within lactations were considered. Mean weighted heritability estimates with RR models were 0.19, 0.09 and 0.08 for first, second and third lactation, respectively. The respective estimates obtained from UV analyses were 0.14, 0.12 and 0.08, respectively. Mean permanent environmental variance, as a proportion of the total, was high at all stages and lactations ranging from 0.54 to 0.71. Within lactations, genetic and permanent environmental correlations between lactation stages were in the range from 0.36 to 0.99 and 0.76 to 0.99, respectively. Genetic parameters for additive genetic and permanent environmental effects obtained from RR models were different from those obtained from UV and MV analyses.

A method for fitting regression splines with varying polynomial order in the linear mixed model.

PubMed

Edwards, Lloyd J; Stewart, Paul W; MacDougall, James E; Helms, Ronald W

2006-02-15

The linear mixed model has become a widely used tool for longitudinal analysis of continuous variables. The use of regression splines in these models offers the analyst additional flexibility in the formulation of descriptive analyses, exploratory analyses and hypothesis-driven confirmatory analyses. We propose a method for fitting piecewise polynomial regression splines with varying polynomial order in the fixed effects and/or random effects of the linear mixed model. The polynomial segments are explicitly constrained by side conditions for continuity and some smoothness at the points where they join. By using a reparameterization of this explicitly constrained linear mixed model, an implicitly constrained linear mixed model is constructed that simplifies implementation of fixed-knot regression splines. The proposed approach is relatively simple, handles splines in one variable or multiple variables, and can be easily programmed using existing commercial software such as SAS or S-plus. The method is illustrated using two examples: an analysis of longitudinal viral load data from a study of subjects with acute HIV-1 infection and an analysis of 24-hour ambulatory blood pressure profiles.

Variational Bayesian Parameter Estimation Techniques for the General Linear Model

PubMed Central

Starke, Ludger; Ostwald, Dirk

2017-01-01

Variational Bayes (VB), variational maximum likelihood (VML), restricted maximum likelihood (ReML), and maximum likelihood (ML) are cornerstone parametric statistical estimation techniques in the analysis of functional neuroimaging data. However, the theoretical underpinnings of these model parameter estimation techniques are rarely covered in introductory statistical texts. Because of the widespread practical use of VB, VML, ReML, and ML in the neuroimaging community, we reasoned that a theoretical treatment of their relationships and their application in a basic modeling scenario may be helpful for both neuroimaging novices and practitioners alike. In this technical study, we thus revisit the conceptual and formal underpinnings of VB, VML, ReML, and ML and provide a detailed account of their mathematical relationships and implementational details. We further apply VB, VML, ReML, and ML to the general linear model (GLM) with non-spherical error covariance as commonly encountered in the first-level analysis of fMRI data. To this end, we explicitly derive the corresponding free energy objective functions and ensuing iterative algorithms. Finally, in the applied part of our study, we evaluate the parameter and model recovery properties of VB, VML, ReML, and ML, first in an exemplary setting and then in the analysis of experimental fMRI data acquired from a single participant under visual stimulation. PMID:28966572

Empirical best linear unbiased prediction method for small areas with restricted maximum likelihood and bootstrap procedure to estimate the average of household expenditure per capita in Banjar Regency

NASA Astrophysics Data System (ADS)

Aminah, Agustin Siti; Pawitan, Gandhi; Tantular, Bertho

2017-03-01

So far, most of the data published by Statistics Indonesia (BPS) as data providers for national statistics are still limited to the district level. Less sufficient sample size for smaller area levels to make the measurement of poverty indicators with direct estimation produced high standard error. Therefore, the analysis based on it is unreliable. To solve this problem, the estimation method which can provide a better accuracy by combining survey data and other auxiliary data is required. One method often used for the estimation is the Small Area Estimation (SAE). There are many methods used in SAE, one of them is Empirical Best Linear Unbiased Prediction (EBLUP). EBLUP method of maximum likelihood (ML) procedures does not consider the loss of degrees of freedom due to estimating β with β ^. This drawback motivates the use of the restricted maximum likelihood (REML) procedure. This paper proposed EBLUP with REML procedure for estimating poverty indicators by modeling the average of household expenditures per capita and implemented bootstrap procedure to calculate MSE (Mean Square Error) to compare the accuracy EBLUP method with the direct estimation method. Results show that EBLUP method reduced MSE in small area estimation.

GIS Tools to Estimate Average Annual Daily Traffic

DOT National Transportation Integrated Search

2012-06-01

This project presents five tools that were created for a geographical information system to estimate Annual Average Daily : Traffic using linear regression. Three of the tools can be used to prepare spatial data for linear regression. One tool can be...

Supporting employees' work-family needs improves health care quality: Longitudinal evidence from long-term care.

PubMed

Okechukwu, Cassandra A; Kelly, Erin L; Bacic, Janine; DePasquale, Nicole; Hurtado, David; Kossek, Ellen; Sembajwe, Grace

2016-05-01

We analyzed qualitative and quantitative data from U.S.-based employees in 30 long-term care facilities. Analysis of semi-structured interviews from 154 managers informed quantitative analyses. Quantitative data include 1214 employees' scoring of their supervisors and their organizations on family supportiveness (individual scores and aggregated to facility level), and three outcomes: (1), care quality indicators assessed at facility level (n = 30) and collected monthly for six months after employees' data collection; (2), employees' dichotomous survey response on having additional off-site jobs; and (3), proportion of employees with additional jobs at each facility. Thematic analyses revealed that managers operate within the constraints of an industry that simultaneously: (a) employs low-wage employees with multiple work-family challenges, and (b) has firmly institutionalized goals of prioritizing quality of care and minimizing labor costs. Managers universally described providing work-family support and prioritizing care quality as antithetical to each other. Concerns surfaced that family-supportiveness encouraged employees to work additional jobs off-site, compromising care quality. Multivariable linear regression analysis of facility-level data revealed that higher family-supportive supervision was associated with significant decreases in residents' incidence of all pressure ulcers (-2.62%) and other injuries (-9.79%). Higher family-supportive organizational climate was associated with significant decreases in all falls (-17.94%) and falls with injuries (-7.57%). Managers' concerns about additional jobs were not entirely unwarranted: multivariable logistic regression of employee-level data revealed that among employees with children, having family-supportive supervision was associated with significantly higher likelihood of additional off-site jobs (RR 1.46, 95%CI 1.08-1.99), but family-supportive organizational climate was associated with lower likelihood (RR 0.76, 95%CI 0.59-0.99). However, proportion of workers with additional off-site jobs did not significantly predict care quality at facility levels. Although managers perceived providing work-family support and ensuring high care quality as conflicting goals, results suggest that family-supportiveness is associated with better care quality. Copyright © 2016 Elsevier Ltd. All rights reserved.

Supporting employees’ work-family needs improves health care quality: longitudinal evidence from long-term care

PubMed Central

Okechukwu, Cassandra A.; Kelly, Erin L.; Bacic, Janine; DePasquale, Nicole; Hurtado, David; Kossek, Ellen; Sembajwe, Grace

2016-01-01

We analyzed qualitative and quantitative data from U.S.-based employees in 30 long-term care facilities. Analysis of semi-structured interviews from 154 managers informed quantitative analyses. Quantitative data include 1,214 employees’ scoring of their supervisors and their organizations on family supportiveness (individual scores and aggregated to facility level), and three outcomes: (1), care quality indicators assessed at facility level (n=30) and collected monthly for six months after employees’ data collection; (2), employees’ dichotomous survey response on having additional off-site jobs; and (3), proportion of employees with additional jobs at each facility. Thematic analyses revealed that managers operate within the constraints of an industry that simultaneously: (a) employs low-wage employees with multiple work-family challenges, and (b) has firmly institutionalized goals of prioritizing quality of care and minimizing labor costs. Managers universally described providing work-family support and prioritizing care quality as antithetical to each other. Concerns surfaced that family-supportiveness encouraged employees to work additional jobs off-site, compromising care quality. Multivariable linear regression analysis of facility-level data revealed that higher family-supportive supervision was associated with significant decreases in residents’ incidence of all pressure ulcers (−2.62%) and other injuries (−9.79%). Higher family-supportive organizational climate was associated with significant decreases in all falls (−17.94%) and falls with injuries (−7.57%). Managers’ concerns about additional jobs were not entirely unwarranted: multivariable logistic regression of employee-level data revealed that among employees with children, having family-supportive supervision was associated with significantly higher likelihood of additional off-site jobs (RR 1.46, 95%CI 1.08-1.99), but family-supportive organizational climate was associated with lower likelihood (RR 0.76, 95%CI 0.59-0.99). However, proportion of workers with additional off-site jobs did not significantly predict care quality at facility levels. Although managers perceived providing work-family support and ensuring high care quality as conflicting goals, results suggest that family-supportiveness is associated with better care quality. PMID:27082022

Drug Authorization for Sofosbuvir/Ledipasvir (Harvoni) for Chronic HCV Infection in a Real-World Cohort: A New Barrier in the HCV Care Cascade

PubMed Central

Liapakis, AnnMarie; Cohen, Elizabeth; Chau, Hong; Bertuccio, Claudia; Sapir, Dana; Wright, Jessica; Eggers, Carol; Drozd, Kristine; Ciarleglio, Maria; Deng, Yanhong; Lim, Joseph K.

2015-01-01

Background New treatments for hepatitis C (HCV) infection hold great promise for cure, but numerous challenges to diagnosing, establishing care, and receiving therapy exist. There are limited data on insurance authorization for these medications. Materials and Methods We performed a retrospective chart review of patients receiving sofosbuvir/ledipasvir (SOF/LED) from October 11-December 31, 2014 to determine rates and timing of drug authorization. We also determined predictors of approval, and those factors associated with faster decision and approval times. Results Of 174 patients prescribed HCV therapy during this period, 129 requests were made for SOF/LED, of whom 100 (77.5%) received initial approval, and an additional 17 patients (13.9%) ultimately received approval through the appeals process. Faster approval times were seen in patients with Child-Pugh Class B disease (14.4 vs. 24.7 days, p = 0.048). A higher proportion of patients were initially approved in those with Medicare/Medicaid coverage (92.2% vs. 71.4%, p = 0.002) and those with baseline viral load ≥6 million IU/mL (84.1% vs. 62.5%, p = 0.040). Linear regression modeling identified advanced fibrosis, high Model of End Stage Liver Disease (MELD) score, and female gender as significant predictors of shorter decision and approval times. On logistic regression, Medicare/Medicaid coverage (OR 5.96, 95% CI 1.66–21.48) and high viral load (OR 4.52, 95% CI 1.08–19.08) were significant predictors for initial approval. Conclusions Early analysis of real-world drug authorization outcomes between October-December 2014 reveals that nearly one in four patients are initially denied access to SOF/LED upon initial prescription, although most patients are eventually approved through appeal, which delays treatment initiation. Having Medicare/Medicaid and advanced liver disease resulted in a higher likelihood of approval as well as earlier decision and approval times. More studies are needed to determine factors resulting in higher likelihood of denial and to evaluate approval rates and times after implementation of restrictive prior authorization guidelines. PMID:26312999

Drug Authorization for Sofosbuvir/Ledipasvir (Harvoni) for Chronic HCV Infection in a Real-World Cohort: A New Barrier in the HCV Care Cascade.

PubMed

Do, Albert; Mittal, Yash; Liapakis, AnnMarie; Cohen, Elizabeth; Chau, Hong; Bertuccio, Claudia; Sapir, Dana; Wright, Jessica; Eggers, Carol; Drozd, Kristine; Ciarleglio, Maria; Deng, Yanhong; Lim, Joseph K

2015-01-01

New treatments for hepatitis C (HCV) infection hold great promise for cure, but numerous challenges to diagnosing, establishing care, and receiving therapy exist. There are limited data on insurance authorization for these medications. We performed a retrospective chart review of patients receiving sofosbuvir/ledipasvir (SOF/LED) from October 11-December 31, 2014 to determine rates and timing of drug authorization. We also determined predictors of approval, and those factors associated with faster decision and approval times. Of 174 patients prescribed HCV therapy during this period, 129 requests were made for SOF/LED, of whom 100 (77.5%) received initial approval, and an additional 17 patients (13.9%) ultimately received approval through the appeals process. Faster approval times were seen in patients with Child-Pugh Class B disease (14.4 vs. 24.7 days, p = 0.048). A higher proportion of patients were initially approved in those with Medicare/Medicaid coverage (92.2% vs. 71.4%, p = 0.002) and those with baseline viral load ≥ 6 million IU/mL (84.1% vs. 62.5%, p = 0.040). Linear regression modeling identified advanced fibrosis, high Model of End Stage Liver Disease (MELD) score, and female gender as significant predictors of shorter decision and approval times. On logistic regression, Medicare/Medicaid coverage (OR 5.96, 95% CI 1.66-21.48) and high viral load (OR 4.52, 95% CI 1.08-19.08) were significant predictors for initial approval. Early analysis of real-world drug authorization outcomes between October-December 2014 reveals that nearly one in four patients are initially denied access to SOF/LED upon initial prescription, although most patients are eventually approved through appeal, which delays treatment initiation. Having Medicare/Medicaid and advanced liver disease resulted in a higher likelihood of approval as well as earlier decision and approval times. More studies are needed to determine factors resulting in higher likelihood of denial and to evaluate approval rates and times after implementation of restrictive prior authorization guidelines.

Estimating extent of mortality associated with the Douglas-fir beetle in the Central and Northern Rockies

Treesearch

Jose F. Negron; Willis C. Schaupp; Kenneth E. Gibson; John Anhold; Dawn Hansen; Ralph Thier; Phil Mocettini

1999-01-01

Data collected from Douglas-fir stands infected by the Douglas-fir beetle in Wyoming, Montana, Idaho, and Utah, were used to develop models to estimate amount of mortality in terms of basal area killed. Models were built using stepwise linear regression and regression tree approaches. Linear regression models using initial Douglas-fir basal area were built for all...

[Prediction model of health workforce and beds in county hospitals of Hunan by multiple linear regression].

PubMed

Ling, Ru; Liu, Jiawang

2011-12-01

To construct prediction model for health workforce and hospital beds in county hospitals of Hunan by multiple linear regression. We surveyed 16 counties in Hunan with stratified random sampling according to uniform questionnaires,and multiple linear regression analysis with 20 quotas selected by literature view was done. Independent variables in the multiple linear regression model on medical personnels in county hospitals included the counties' urban residents' income, crude death rate, medical beds, business occupancy, professional equipment value, the number of devices valued above 10 000 yuan, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, and utilization rate of hospital beds. Independent variables in the multiple linear regression model on county hospital beds included the the population of aged 65 and above in the counties, disposable income of urban residents, medical personnel of medical institutions in county area, business occupancy, the total value of professional equipment, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, utilization rate of hospital beds, and length of hospitalization. The prediction model shows good explanatory and fitting, and may be used for short- and mid-term forecasting.

The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models

PubMed Central

Mahara, Gehendra; Wang, Chao; Yang, Kun; Chen, Sipeng; Guo, Jin; Gao, Qi; Wang, Wei; Wang, Quanyi; Guo, Xiuhua

2016-01-01

(1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (2) Methods: A retrospective ecological study was carried out to distinguish the epidemic characteristics of scarlet fever incidence in Beijing districts from 2013 to 2014. Daily incidence and corresponding air pollutant and meteorological data were used to develop the model. Global Moran’s I statistic and Anselin’s local Moran’s I (LISA) were applied to detect the spatial autocorrelation (spatial dependency) and clusters of scarlet fever incidence. The spatial lag model (SLM) and spatial error model (SEM) including ordinary least squares (OLS) models were then applied to probe the association between scarlet fever incidence and meteorological including air pollution factors. (3) Results: Among the 5491 cases, more than half (62%) were male, and more than one-third (37.8%) were female, with the annual average incidence rate 14.64 per 100,000 population. Spatial autocorrelation analysis exhibited the existence of spatial dependence; therefore, we applied spatial regression models. After comparing the values of R-square, log-likelihood and the Akaike information criterion (AIC) among the three models, the OLS model (R2 = 0.0741, log likelihood = −1819.69, AIC = 3665.38), SLM (R2 = 0.0786, log likelihood = −1819.04, AIC = 3665.08) and SEM (R2 = 0.0743, log likelihood = −1819.67, AIC = 3665.36), identified that the spatial lag model (SLM) was best for model fit for the regression model. There was a positive significant association between nitrogen oxide (p = 0.027), rainfall (p = 0.036) and sunshine hour (p = 0.048), while the relative humidity (p = 0.034) had an adverse association with scarlet fever incidence in SLM. (4) Conclusions: Our findings indicated that meteorological, as well as air pollutant factors may increase the incidence of scarlet fever; these findings may help to guide scarlet fever control programs and targeting the intervention. PMID:27827946

The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models.

PubMed

Mahara, Gehendra; Wang, Chao; Yang, Kun; Chen, Sipeng; Guo, Jin; Gao, Qi; Wang, Wei; Wang, Quanyi; Guo, Xiuhua

2016-11-04

(1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (2) Methods: A retrospective ecological study was carried out to distinguish the epidemic characteristics of scarlet fever incidence in Beijing districts from 2013 to 2014. Daily incidence and corresponding air pollutant and meteorological data were used to develop the model. Global Moran's I statistic and Anselin's local Moran's I (LISA) were applied to detect the spatial autocorrelation (spatial dependency) and clusters of scarlet fever incidence. The spatial lag model (SLM) and spatial error model (SEM) including ordinary least squares (OLS) models were then applied to probe the association between scarlet fever incidence and meteorological including air pollution factors. (3) Results: Among the 5491 cases, more than half (62%) were male, and more than one-third (37.8%) were female, with the annual average incidence rate 14.64 per 100,000 population. Spatial autocorrelation analysis exhibited the existence of spatial dependence; therefore, we applied spatial regression models. After comparing the values of R-square, log-likelihood and the Akaike information criterion (AIC) among the three models, the OLS model (R² = 0.0741, log likelihood = -1819.69, AIC = 3665.38), SLM (R² = 0.0786, log likelihood = -1819.04, AIC = 3665.08) and SEM (R² = 0.0743, log likelihood = -1819.67, AIC = 3665.36), identified that the spatial lag model (SLM) was best for model fit for the regression model. There was a positive significant association between nitrogen oxide ( p = 0.027), rainfall ( p = 0.036) and sunshine hour ( p = 0.048), while the relative humidity ( p = 0.034) had an adverse association with scarlet fever incidence in SLM. (4) Conclusions: Our findings indicated that meteorological, as well as air pollutant factors may increase the incidence of scarlet fever; these findings may help to guide scarlet fever control programs and targeting the intervention.

Influences of peers, teachers, and climate on students' willingness to intervene when witnessing anti-transgender harassment.

PubMed

Wernick, Laura J; Kulick, Alex; Inglehart, M H

2014-08-01

Transgender young people are at increased risk for bullying, harassment, and negative mental health and academic outcomes compared to the general population as well as compared to other members of lesbian, gay, bisexual, transgender, queer, questioning, and similarly identified (LGBTQQ) communities. To inform interventions to support transgender students, the present study investigates students' willingness to intervene when witnessing anti-transgender harassment, using data collected from a participatory action research project investigating school climate. Multi-step linear regression was used to test the impacts of hearing transphobic language and witnessing teachers and others students intervene, while controlling for demographics and school. Hostile climate negatively predicted intervention intentions while witnessing peer intervention positively predicted likelihood to intervene. Witnessing teacher intervention did not significantly predict the outcome. These findings suggest that youth-led interventions in peer networks might be effective in diminishing transphobic bullying and supporting the healthy development of transgender young people. Copyright © 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

A unified procedure for meta-analytic evaluation of surrogate end points in randomized clinical trials

PubMed Central

Dai, James Y.; Hughes, James P.

2012-01-01

The meta-analytic approach to evaluating surrogate end points assesses the predictiveness of treatment effect on the surrogate toward treatment effect on the clinical end point based on multiple clinical trials. Definition and estimation of the correlation of treatment effects were developed in linear mixed models and later extended to binary or failure time outcomes on a case-by-case basis. In a general regression setting that covers nonnormal outcomes, we discuss in this paper several metrics that are useful in the meta-analytic evaluation of surrogacy. We propose a unified 3-step procedure to assess these metrics in settings with binary end points, time-to-event outcomes, or repeated measures. First, the joint distribution of estimated treatment effects is ascertained by an estimating equation approach; second, the restricted maximum likelihood method is used to estimate the means and the variance components of the random treatment effects; finally, confidence intervals are constructed by a parametric bootstrap procedure. The proposed method is evaluated by simulations and applications to 2 clinical trials. PMID:22394448

Inequality in Disability in Bangladesh

PubMed Central

Tareque, Md. Ismail; Begum, Sharifa; Saito, Yasuhiko

2014-01-01

Objective To investigate inequality in disability in Bangladesh. Methods The study used both household level and individual level data from a large nationally representative data set, Bangladesh’s Household Income and Expenditure Survey - 2010. Principal component analysis was used to construct a wealth index based on household assets from household level data. Then, using data from 49,809 individuals aged 5 years and over, chi-square tests and logistic regression were performed to test the association between wealth level and disability. Findings Women and older people are significantly more likely to report having disabilities than men and younger people. For middle and rich families, respectively, there is a 14 percent lower likelihood of reporting disabilities than for poor families. Changes in the probability of having disabilities are linear with increasing wealth. In addition, the study identifies some significant factors affecting disability, namely, age, sex, education, marital status, and place of residence including divisional differences. Conclusion In Bangladesh, worse health among the poor argues for policies prioritizing this group while at the same time giving special attention to women and the elderly. PMID:25075513

Correlates of resilience in the face of adversity for Korean women immigrating to the US.

PubMed

Lee, Hei-Sung; Brown, Stephen L; Mitchell, Mary M; Schiraldi, Glenn R

2008-10-01

To explore the association between resilience and psychosocial variables of theoretical relevance such as self-esteem, optimism, religiousness, cultural interdependency, and belief in higher education in a population of elderly Korean women and their daughters who experienced great adversity. Surveys were conducted with 200 elderly Korean women and 170 of their daughters in several community locations. Both mothers and daughters experienced great adversities in their lives such as psychological and physical losses from war as well as current and past difficulties with relocation. The mothers' bivariate correlations indicate that self-esteem, optimism, religiousness, and cultural interdependency were significantly correlated with resilience. Length of time in the US, age entering the US, physical and psychological war-related adversities, current relocation difficulties, self-esteem, optimism, cultural interdependency, and belief in education were all significantly associated with daughters' resilience. In linear regression, self-esteem and optimism were significant predictors of resilience in both mothers and daughters. Self-esteem and optimism deserve further attention as psychological factors that may increase the likelihood of developing resilience. Implications of these findings for health professionals are discussed.

Measuring and improving student engagement in clinical training.

PubMed

Lee, Simin; Valtis, Yannis K; Jun, Tomi; Wang, David; Zhang, Biqi; Chung, Esther H; Yu, Amy; Williams, Rachael; Cohen, Marya J

2018-01-01

Volunteer service learning activities, including Student Run Clinics (SRCs), are becoming an increasingly popular extracurricular component of medical education. While there are reports that student clinicians generally enjoy their educational experiences at SRCs, it is not understood how to optimize and measure student engagement in them. To identify key drivers of student engagement a tool was created to measure volunteer experience at the Crimson Care Collaborative (CCC), a primary care SRC. CCC volunteers were asked to complete an online engagement survey. Cross-sectional survey data were collected for 149 CCC volunteers (53% response rate). Multivariate linear regression showed that overall 'likelihood to recommend CCC to a friend' was significantly associated with students' perception of the clarity of their role within the clinic, frequency of interprofessional interactions, and overall quality of medical education. Students who volunteer more frequently and for longer periods of time had higher engagement scores. Measuring engagement is feasible in volunteer settings. Engagement appears to be dependent on both structural and experiential components. Easily modifiable components of job design (role definition, expected frequency of volunteering), are key drivers of volunteer engagement.

A national survey of skin infections, care behaviors and MRSA knowledge in the United States.

PubMed

Wilder, Jocelyn R; Wegener, Duane T; David, Michael Z; Macal, Charles; Daum, Robert; Lauderdale, Diane S

2014-01-01

A nationally representative sample of approximately 2000 individuals was surveyed to assess SSTI infections over their lifetime and then prospectively over six-months. Knowledge of MRSA, future likelihood to self-treat a SSTI and self-care behaviors was also queried. Chi square tests, linear and multinomial regression were used for analysis. About 50% of those with a reported history of a SSTI typical of MRSA had sought medical treatment. MRSA knowledge was low: 28% of respondents could describe MRSA. Use of protective self-care behaviors that may reduce transmission, such as covering a lesion, differed with knowledge of MRSA and socio-demographics. Those reporting a history of a MRSA-like SSTI were more likely to respond that they would self-treat than those without such a history (OR 2.05 95% CI 1.40, 3.01; p<0.001). Since half of respondents reported not seeking care for past lesions, incidence determined from clinical encounters would greatly underestimate true incidence. MRSA knowledge was not associated with seeking medical care, but was associated with self-care practices that may decrease transmission.

A new approach to correct the QT interval for changes in heart rate using a nonparametric regression model in beagle dogs.

PubMed

Watanabe, Hiroyuki; Miyazaki, Hiroyasu

2006-01-01

Over- and/or under-correction of QT intervals for changes in heart rate may lead to misleading conclusions and/or masking the potential of a drug to prolong the QT interval. This study examines a nonparametric regression model (Loess Smoother) to adjust the QT interval for differences in heart rate, with an improved fitness over a wide range of heart rates. 240 sets of (QT, RR) observations collected from each of 8 conscious and non-treated beagle dogs were used as the materials for investigation. The fitness of the nonparametric regression model to the QT-RR relationship was compared with four models (individual linear regression, common linear regression, and Bazett's and Fridericia's correlation models) with reference to Akaike's Information Criterion (AIC). Residuals were visually assessed. The bias-corrected AIC of the nonparametric regression model was the best of the models examined in this study. Although the parametric models did not fit, the nonparametric regression model improved the fitting at both fast and slow heart rates. The nonparametric regression model is the more flexible method compared with the parametric method. The mathematical fit for linear regression models was unsatisfactory at both fast and slow heart rates, while the nonparametric regression model showed significant improvement at all heart rates in beagle dogs.

Linear regression analysis: part 14 of a series on evaluation of scientific publications.

PubMed

Schneider, Astrid; Hommel, Gerhard; Blettner, Maria

2010-11-01

Regression analysis is an important statistical method for the analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication. This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience. After a brief introduction of the uni- and multivariable regression models, illustrative examples are given to explain what the important considerations are before a regression analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately. The performance and interpretation of linear regression analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying linear regression analysis and its limitations are presented.

The impact of sleep disorders on driving safety-findings from the Second Strategic Highway Research Program naturalistic driving study.

PubMed

Liu, Shu-Yuan; Perez, Miguel A; Lau, Nathan

2018-04-01

This study investigated the association between driving safety and seven sleep disorders amongst 3541 participants of the Second Strategic Highway Research Program (SHRP 2) naturalistic driving study. SHRP 2 collected naturalistic driving data from participants between 16 and 98 years old by instrumenting participants' vehicles. The analyses used logistic regression to determine the likelihood of crash or near-crash involvement, Poisson log-linear regression to assess crash or near-crash rate, and ordinal logistic regression to assess driver maneuver appropriateness and crash or near-crash severity. These analyses did not account for any medical treatments for the sleep disorders. Females with restless legs syndrome/Willis-Ekbom disease (RLS/WED), drivers with insomnia or narcolepsy, are associated with significantly higher risk of crash or near-crash. Drivers with shift work sleep disorder (SWSD) are associated with significantly increased crash or near-crash rate. Females with RLS/WED or sleep apnea and drivers with SWSD are associated with less safe driver maneuver and drivers with periodic limb movement disorder are associated with more severe events. The four analyses provide no evidence of safety decrements associated with migraine. This study is the first examination on the association between seven sleep disorders and different measures of driving risk using large-scale naturalistic driving study data. The results corroborate much of the existing simulator and epidemiological research related to sleep-disorder patients and their driving safety, but add ecological validity to those findings. These results contribute to the empirical basis for medical professionals, policy makers, and employers in making decisions to aid individuals with sleep disorders in balancing safety and personal mobility.

The Roles of IL-6, IL-10, and IL-1RA in Obesity and Insulin Resistance in African-Americans

PubMed Central

Doumatey, Ayo; Huang, Hanxia; Zhou, Jie; Chen, Guanjie; Shriner, Daniel; Adeyemo, Adebowale

2011-01-01

Objective: The aim of the study was to investigate the associations between IL-1 receptor antagonist (IL-1RA), IL-6, IL-10, measures of obesity, and insulin resistance in African-Americans. Research Design and Methods: Nondiabetic participants (n = 1025) of the Howard University Family Study were investigated for associations between serum IL (IL-1RA, IL-6, IL-10), measures of obesity, and insulin resistance, with adjustment for age and sex. Measures of obesity included body mass index, waist circumference, hip circumference, waist-to-hip ratio, and percent fat mass. Insulin resistance was assessed using the homeostasis model assessment of insulin resistance (HOMA-IR). Data were analyzed with R statistical software using linear regression and likelihood ratio tests. Results: IL-1RA and IL-6 were associated with measures of obesity and insulin resistance, explaining 4–12.7% of the variance observed (P values < 0.001). IL-1RA was bimodally distributed and therefore was analyzed based on grouping those with low vs. high IL-1RA levels. High IL-1RA explained up to 20 and 12% of the variance in measures of obesity and HOMA-IR, respectively. Among the IL, only high IL-1RA improved the fit of models regressing HOMA-IR on measures of obesity. In contrast, all measures of obesity improved the fit of models regressing HOMA-IR on IL. IL-10 was not associated with obesity measures or HOMA-IR. Conclusions: High IL-1RA levels and obesity measures are associated with HOMA-IR in this population-based sample of African-Americans. The results suggest that obesity and increased levels of IL-1RA both contribute to the development of insulin resistance. PMID:21956416

Using reduced rank regression methods to identify dietary patterns associated with obesity: a cross-country study among European and Australian adolescents.

PubMed

Huybrechts, Inge; Lioret, Sandrine; Mouratidou, Theodora; Gunter, Marc J; Manios, Yannis; Kersting, Mathilde; Gottrand, Frederic; Kafatos, Anthony; De Henauw, Stefaan; Cuenca-García, Magdalena; Widhalm, Kurt; Gonzales-Gross, Marcela; Molnar, Denes; Moreno, Luis A; McNaughton, Sarah A

2017-01-01

This study aims to examine repeatability of reduced rank regression (RRR) methods in calculating dietary patterns (DP) and cross-sectional associations with overweight (OW)/obesity across European and Australian samples of adolescents. Data from two cross-sectional surveys in Europe (2006/2007 Healthy Lifestyle in Europe by Nutrition in Adolescence study, including 1954 adolescents, 12-17 years) and Australia (2007 National Children's Nutrition and Physical Activity Survey, including 1498 adolescents, 12-16 years) were used. Dietary intake was measured using two non-consecutive, 24-h recalls. RRR was used to identify DP using dietary energy density, fibre density and percentage of energy intake from fat as the intermediate variables. Associations between DP scores and body mass/fat were examined using multivariable linear and logistic regression as appropriate, stratified by sex. The first DP extracted (labelled 'energy dense, high fat, low fibre') explained 47 and 31 % of the response variation in Australian and European adolescents, respectively. It was similar for European and Australian adolescents and characterised by higher consumption of biscuits/cakes, chocolate/confectionery, crisps/savoury snacks, sugar-sweetened beverages, and lower consumption of yogurt, high-fibre bread, vegetables and fresh fruit. DP scores were inversely associated with BMI z-scores in Australian adolescent boys and borderline inverse in European adolescent boys (so as with %BF). Similarly, a lower likelihood for OW in boys was observed with higher DP scores in both surveys. No such relationships were observed in adolescent girls. In conclusion, the DP identified in this cross-country study was comparable for European and Australian adolescents, demonstrating robustness of the RRR method in calculating DP among populations. However, longitudinal designs are more relevant when studying diet-obesity associations, to prevent reverse causality.

Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines.

PubMed

Grajeda, Laura M; Ivanescu, Andrada; Saito, Mayuko; Crainiceanu, Ciprian; Jaganath, Devan; Gilman, Robert H; Crabtree, Jean E; Kelleher, Dermott; Cabrera, Lilia; Cama, Vitaliano; Checkley, William

2016-01-01

Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p < 0.001) when using a linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p < 0.001) and slopes (p < 0.001) of the individual growth trajectories. We also identified important serial correlation within the structure of the data (ρ = 0.66; 95 % CI 0.64 to 0.68; p < 0.001), which we modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19,598, respectively). While the regression parameters are more complex to interpret in the former, we argue that inference for any problem depends more on the estimated curve or differences in curves rather than the coefficients. Moreover, use of cubic regression splines provides biological meaningful growth velocity and acceleration curves despite increased complexity in coefficient interpretation. Through this stepwise approach, we provide a set of tools to model longitudinal childhood data for non-statisticians using linear mixed-effect models.

Inverse Ising problem in continuous time: A latent variable approach

NASA Astrophysics Data System (ADS)

Donner, Christian; Opper, Manfred

2017-12-01

We consider the inverse Ising problem: the inference of network couplings from observed spin trajectories for a model with continuous time Glauber dynamics. By introducing two sets of auxiliary latent random variables we render the likelihood into a form which allows for simple iterative inference algorithms with analytical updates. The variables are (1) Poisson variables to linearize an exponential term which is typical for point process likelihoods and (2) Pólya-Gamma variables, which make the likelihood quadratic in the coupling parameters. Using the augmented likelihood, we derive an expectation-maximization (EM) algorithm to obtain the maximum likelihood estimate of network parameters. Using a third set of latent variables we extend the EM algorithm to sparse couplings via L1 regularization. Finally, we develop an efficient approximate Bayesian inference algorithm using a variational approach. We demonstrate the performance of our algorithms on data simulated from an Ising model. For data which are simulated from a more biologically plausible network with spiking neurons, we show that the Ising model captures well the low order statistics of the data and how the Ising couplings are related to the underlying synaptic structure of the simulated network.

College Student Invulnerability Beliefs and HIV Vaccine Acceptability

ERIC Educational Resources Information Center

Ravert, Russell D.; Zimet, Gregory D.

2009-01-01

Objective: To examine behavioral history, beliefs, and vaccine characteristics as predictors of HIV vaccine acceptability. Methods: Two hundred forty-five US under graduates were surveyed regarding their sexual history, risk beliefs, and likelihood of accepting hypothetical HIV vaccines. Results: Multivariate regression analysis indicated that…

Regression Model Term Selection for the Analysis of Strain-Gage Balance Calibration Data

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert Manfred; Volden, Thomas R.

2010-01-01

The paper discusses the selection of regression model terms for the analysis of wind tunnel strain-gage balance calibration data. Different function class combinations are presented that may be used to analyze calibration data using either a non-iterative or an iterative method. The role of the intercept term in a regression model of calibration data is reviewed. In addition, useful algorithms and metrics originating from linear algebra and statistics are recommended that will help an analyst (i) to identify and avoid both linear and near-linear dependencies between regression model terms and (ii) to make sure that the selected regression model of the calibration data uses only statistically significant terms. Three different tests are suggested that may be used to objectively assess the predictive capability of the final regression model of the calibration data. These tests use both the original data points and regression model independent confirmation points. Finally, data from a simplified manual calibration of the Ames MK40 balance is used to illustrate the application of some of the metrics and tests to a realistic calibration data set.

A Regional Analysis of Non-Methane Hydrocarbons And Meteorology of The Rural Southeast United States

DTIC Science & Technology

1996-01-01

Zt is an ARIMA time series. This is a typical regression model , except that it allows for autocorrelation in the error term Z. In this work, an ARMA...data=folder; var residual; run; II Statistical output of 1992 regression model on 1993 ozone data ARIMA Procedure Maximum Likelihood Estimation Approx...at each of the sites, and to show the effect of synoptic meteorology on high ozone by examining NOAA daily weather maps and climatic data

Individual relocation decisions after tornadoes: a multi-level analysis.

PubMed

Cong, Zhen; Nejat, Ali; Liang, Daan; Pei, Yaolin; Javid, Roxana J

2018-04-01

This study examines how multi-level factors affected individuals' relocation decisions after EF4 and EF5 (Enhanced Fujita Tornado Intensity Scale) tornadoes struck the United States in 2013. A telephone survey was conducted with 536 respondents, including oversampled older adults, one year after these two disaster events. Respondents' addresses were used to associate individual information with block group-level variables recorded by the American Community Survey. Logistic regression revealed that residential damage and homeownership are important predictors of relocation. There was also significant interaction between these two variables, indicating less difference between homeowners and renters at higher damage levels. Homeownership diminished the likelihood of relocation among younger respondents. Random effects logistic regression found that the percentage of homeownership and of higher income households in the community buffered the effect of damage on relocation; the percentage of older adults reduced the likelihood of this group relocating. The findings are assessed from the standpoint of age difference, policy implications, and social capital and vulnerability. © 2018 The Author(s). Disasters © Overseas Development Institute, 2018.

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

PubMed

Yang, Miao; Yuan, Ke-Hai

2016-01-01

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

Prediction of primary vs secondary hypertension in children.

PubMed

Baracco, Rossana; Kapur, Gaurav; Mattoo, Tej; Jain, Amrish; Valentini, Rudolph; Ahmed, Maheen; Thomas, Ronald

2012-05-01

Despite current guidelines, variability exists in the workup of hypertensive children due to physician preferences. The study evaluates primary vs secondary hypertension diagnosis from investigations routinely performed in hypertensive children. This retrospective study included children 5 to 19 years with primary and secondary hypertension. The proportions of abnormal laboratory and imaging tests were compared between primary and secondary hypertension groups. Risk factors for primary vs secondary hypertension were evaluated by logistic regression and likelihood function analysis. Patients with secondary hypertension were younger (5-12 years) and had a higher proportion of abnormal creatinine, renal ultrasound, and echocardiogram findings. There was no significant difference in abnormal results of thyroid function, urine catecholamines, plasma renin, and aldosterone. Abnormal renal ultrasound findings and age were predictors of secondary hypertension by regression and likelihood function analysis. Children aged 5 to 12 years with abnormal renal ultrasound findings and high diastolic blood pressures are at higher risk for secondary hypertension that requires detailed evaluation. © 2012 Wiley Periodicals, Inc.

Testing Multivariate Adaptive Regression Splines (MARS) as a Method of Land Cover Classification of TERRA-ASTER Satellite Images.

PubMed

Quirós, Elia; Felicísimo, Angel M; Cuartero, Aurora

2009-01-01

This work proposes a new method to classify multi-spectral satellite images based on multivariate adaptive regression splines (MARS) and compares this classification system with the more common parallelepiped and maximum likelihood (ML) methods. We apply the classification methods to the land cover classification of a test zone located in southwestern Spain. The basis of the MARS method and its associated procedures are explained in detail, and the area under the ROC curve (AUC) is compared for the three methods. The results show that the MARS method provides better results than the parallelepiped method in all cases, and it provides better results than the maximum likelihood method in 13 cases out of 17. These results demonstrate that the MARS method can be used in isolation or in combination with other methods to improve the accuracy of soil cover classification. The improvement is statistically significant according to the Wilcoxon signed rank test.

Efficient logistic regression designs under an imperfect population identifier.

PubMed

Albert, Paul S; Liu, Aiyi; Nansel, Tonja

2014-03-01

Motivated by actual study designs, this article considers efficient logistic regression designs where the population is identified with a binary test that is subject to diagnostic error. We consider the case where the imperfect test is obtained on all participants, while the gold standard test is measured on a small chosen subsample. Under maximum-likelihood estimation, we evaluate the optimal design in terms of sample selection as well as verification. We show that there may be substantial efficiency gains by choosing a small percentage of individuals who test negative on the imperfect test for inclusion in the sample (e.g., verifying 90% test-positive cases). We also show that a two-stage design may be a good practical alternative to a fixed design in some situations. Under optimal and nearly optimal designs, we compare maximum-likelihood and semi-parametric efficient estimators under correct and misspecified models with simulations. The methodology is illustrated with an analysis from a diabetes behavioral intervention trial. © 2013, The International Biometric Society.

Objective Structured Assessment of Technical Skills (OSATS) evaluation of hysteroscopy training: a prospective study.

PubMed

Alici, Ferizan; Buerkle, Bernd; Tempfer, Clemens B

2014-07-01

To describe the performance curve of hysteroscopy-naïve probands repeatedly working through a surgery algorithm on a hysteroscopy trainer. We prospectively recruited medical students to a 30min demonstration session teaching a standardized surgery algorithm. Subjects subsequently performed three training courses immediately after training (T1) and after 24h (T2) and 48h (T3). Skills were recorded with a 20-item Objective Structured Assessment of Technical Skills (OSATS) at T1, T2, and T3. The presence of a sustained OSATS score improvement from T1 to T3 was the primary outcome. Performance time (PT) and self assessment (SA) were secondary outcomes. Statistics were performed using paired T-test and multiple linear regression analysis. 92 subjects were included. OSATS scores significantly improved over time from T1 to T2 (15.21±1.95 vs. 16.02±2.06, respectively; p<0.0001) and from T2 to T3 (16.02±2.06 vs. 16.95±1.61, respectively; p<0.0001). The secondary outcomes PT (414±119s vs. 357±88s vs. 304±91s; p<0.0001) and SA (3.02±0.85 vs. 3.80±0.76 vs. 4.41±0.67; p<0.0001) also showed an improvement over time with quicker performance and higher confidence. SA, but not PT demonstrated construct validity. In a multiple linear regression analysis, gender (odds ratio (OR) 0.96; 95% confidence interval (CI) 0.35-2.71; p=0.9) did not independently influence the likelihood of OSATS score improvement. In a hysteroscopy-naïve population, there is a continuous and sustained improvement of surgical proficiency and confidence after multiple training courses on a hysteroscopy trainer. Serial hysteroscopy trainings may be helpful for teaching hysteroscopy skills. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

A Spanish-language patient safety questionnaire to measure medical and nursing students' attitudes and knowledge.

PubMed

Mira, José J; Navarro, Isabel M; Guilabert, Mercedes; Poblete, Rodrigo; Franco, Astolfo L; Jiménez, Pilar; Aquino, Margarita; Fernández-Trujillo, Francisco J; Lorenzo, Susana; Vitaller, Julián; de Valle, Yohana Díaz; Aibar, Carlos; Aranaz, Jesús M; De Pedro, José A

2015-08-01

To design and validate a questionnaire for assessing attitudes and knowledge about patient safety using a sample of medical and nursing students undergoing clinical training in Spain and four countries in Latin America. In this cross-sectional study, a literature review was carried out and total of 786 medical and nursing students were surveyed at eight universities from five countries (Chile, Colombia, El Salvador, Guatemala, and Spain) to develop and refine a Spanish-language questionnaire on knowledge and attitudes about patient safety. The scope of the questionnaire was based on five dimensions (factors) presented in studies related to patient safety culture found in PubMed and Scopus. Based on the five factors, 25 reactive items were developed. Composite reliability indexes and Cronbach's alpha statistics were estimated for each factor, and confirmatory factor analysis was conducted to assess validity. After a pilot test, the questionnaire was refined using confirmatory models, maximum-likelihood estimation, and the variance-covariance matrix (as input). Multiple linear regression models were used to confirm external validity, considering variables related to patient safety culture as dependent variables and the five factors as independent variables. The final instrument was a structured five-point Likert self-administered survey (the "Latino Student Patient Safety Questionnaire") consisting of 21 items grouped into five factors. Compound reliability indexes (Cronbach's alpha statistic) calculated for the five factors were about 0.7 or higher. The results of the multiple linear regression analyses indicated good model fit (goodness-of-fit index: 0.9). Item-total correlations were higher than 0.3 in all cases. The convergent-discriminant validity was adequate. The questionnaire designed and validated in this study assesses nursing and medical students' attitudes and knowledge about patient safety. This instrument could be used to indirectly evaluate whether or not students in health disciplines are acquiring and thus likely to put into practice the professional skills currently considered most appropriate for patient safety.

Predicting Rotator Cuff Tears Using Data Mining and Bayesian Likelihood Ratios

PubMed Central

Lu, Hsueh-Yi; Huang, Chen-Yuan; Su, Chwen-Tzeng; Lin, Chen-Chiang

2014-01-01

Objectives Rotator cuff tear is a common cause of shoulder diseases. Correct diagnosis of rotator cuff tears can save patients from further invasive, costly and painful tests. This study used predictive data mining and Bayesian theory to improve the accuracy of diagnosing rotator cuff tears by clinical examination alone. Methods In this retrospective study, 169 patients who had a preliminary diagnosis of rotator cuff tear on the basis of clinical evaluation followed by confirmatory MRI between 2007 and 2011 were identified. MRI was used as a reference standard to classify rotator cuff tears. The predictor variable was the clinical assessment results, which consisted of 16 attributes. This study employed 2 data mining methods (ANN and the decision tree) and a statistical method (logistic regression) to classify the rotator cuff diagnosis into “tear” and “no tear” groups. Likelihood ratio and Bayesian theory were applied to estimate the probability of rotator cuff tears based on the results of the prediction models. Results Our proposed data mining procedures outperformed the classic statistical method. The correction rate, sensitivity, specificity and area under the ROC curve of predicting a rotator cuff tear were statistical better in the ANN and decision tree models compared to logistic regression. Based on likelihood ratios derived from our prediction models, Fagan's nomogram could be constructed to assess the probability of a patient who has a rotator cuff tear using a pretest probability and a prediction result (tear or no tear). Conclusions Our predictive data mining models, combined with likelihood ratios and Bayesian theory, appear to be good tools to classify rotator cuff tears as well as determine the probability of the presence of the disease to enhance diagnostic decision making for rotator cuff tears. PMID:24733553

A score to estimate the likelihood of detecting advanced colorectal neoplasia at colonoscopy

PubMed Central

Kaminski, Michal F; Polkowski, Marcin; Kraszewska, Ewa; Rupinski, Maciej; Butruk, Eugeniusz; Regula, Jaroslaw

2014-01-01

Objective This study aimed to develop and validate a model to estimate the likelihood of detecting advanced colorectal neoplasia in Caucasian patients. Design We performed a cross-sectional analysis of database records for 40-year-old to 66-year-old patients who entered a national primary colonoscopy-based screening programme for colorectal cancer in 73 centres in Poland in the year 2007. We used multivariate logistic regression to investigate the associations between clinical variables and the presence of advanced neoplasia in a randomly selected test set, and confirmed the associations in a validation set. We used model coefficients to develop a risk score for detection of advanced colorectal neoplasia. Results Advanced colorectal neoplasia was detected in 2544 of the 35 918 included participants (7.1%). In the test set, a logistic-regression model showed that independent risk factors for advanced colorectal neoplasia were: age, sex, family history of colorectal cancer, cigarette smoking (p<0.001 for these four factors), and Body Mass Index (p=0.033). In the validation set, the model was well calibrated (ratio of expected to observed risk of advanced neoplasia: 1.00 (95% CI 0.95 to 1.06)) and had moderate discriminatory power (c-statistic 0.62). We developed a score that estimated the likelihood of detecting advanced neoplasia in the validation set, from 1.32% for patients scoring 0, to 19.12% for patients scoring 7–8. Conclusions Developed and internally validated score consisting of simple clinical factors successfully estimates the likelihood of detecting advanced colorectal neoplasia in asymptomatic Caucasian patients. Once externally validated, it may be useful for counselling or designing primary prevention studies. PMID:24385598

A score to estimate the likelihood of detecting advanced colorectal neoplasia at colonoscopy.

PubMed

Kaminski, Michal F; Polkowski, Marcin; Kraszewska, Ewa; Rupinski, Maciej; Butruk, Eugeniusz; Regula, Jaroslaw

2014-07-01

This study aimed to develop and validate a model to estimate the likelihood of detecting advanced colorectal neoplasia in Caucasian patients. We performed a cross-sectional analysis of database records for 40-year-old to 66-year-old patients who entered a national primary colonoscopy-based screening programme for colorectal cancer in 73 centres in Poland in the year 2007. We used multivariate logistic regression to investigate the associations between clinical variables and the presence of advanced neoplasia in a randomly selected test set, and confirmed the associations in a validation set. We used model coefficients to develop a risk score for detection of advanced colorectal neoplasia. Advanced colorectal neoplasia was detected in 2544 of the 35,918 included participants (7.1%). In the test set, a logistic-regression model showed that independent risk factors for advanced colorectal neoplasia were: age, sex, family history of colorectal cancer, cigarette smoking (p<0.001 for these four factors), and Body Mass Index (p=0.033). In the validation set, the model was well calibrated (ratio of expected to observed risk of advanced neoplasia: 1.00 (95% CI 0.95 to 1.06)) and had moderate discriminatory power (c-statistic 0.62). We developed a score that estimated the likelihood of detecting advanced neoplasia in the validation set, from 1.32% for patients scoring 0, to 19.12% for patients scoring 7-8. Developed and internally validated score consisting of simple clinical factors successfully estimates the likelihood of detecting advanced colorectal neoplasia in asymptomatic Caucasian patients. Once externally validated, it may be useful for counselling or designing primary prevention studies. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Quantile regression in the presence of monotone missingness with sensitivity analysis

PubMed Central

Liu, Minzhao; Daniels, Michael J.; Perri, Michael G.

2016-01-01

In this paper, we develop methods for longitudinal quantile regression when there is monotone missingness. In particular, we propose pattern mixture models with a constraint that provides a straightforward interpretation of the marginal quantile regression parameters. Our approach allows sensitivity analysis which is an essential component in inference for incomplete data. To facilitate computation of the likelihood, we propose a novel way to obtain analytic forms for the required integrals. We conduct simulations to examine the robustness of our approach to modeling assumptions and compare its performance to competing approaches. The model is applied to data from a recent clinical trial on weight management. PMID:26041008

Mapping the Information Trace in Local Field Potentials by a Computational Method of Two-Dimensional Time-Shifting Synchronization Likelihood Based on Graphic Processing Unit Acceleration.

PubMed

Zhao, Zi-Fang; Li, Xue-Zhu; Wan, You

2017-12-01

The local field potential (LFP) is a signal reflecting the electrical activity of neurons surrounding the electrode tip. Synchronization between LFP signals provides important details about how neural networks are organized. Synchronization between two distant brain regions is hard to detect using linear synchronization algorithms like correlation and coherence. Synchronization likelihood (SL) is a non-linear synchronization-detecting algorithm widely used in studies of neural signals from two distant brain areas. One drawback of non-linear algorithms is the heavy computational burden. In the present study, we proposed a graphic processing unit (GPU)-accelerated implementation of an SL algorithm with optional 2-dimensional time-shifting. We tested the algorithm with both artificial data and raw LFP data. The results showed that this method revealed detailed information from original data with the synchronization values of two temporal axes, delay time and onset time, and thus can be used to reconstruct the temporal structure of a neural network. Our results suggest that this GPU-accelerated method can be extended to other algorithms for processing time-series signals (like EEG and fMRI) using similar recording techniques.

Development and validation of an environmental fragility index (EFI) for the neotropical savannah biome.

PubMed

Macedo, Diego R; Hughes, Robert M; Kaufmann, Philip R; Callisto, Marcos

2018-04-23

Augmented production and transport of fine sediments resulting from increased human activities are major threats to freshwater ecosystems, including reservoirs and their ecosystem services. To support large scale assessment of the likelihood of soil erosion and reservoir sedimentation, we developed and validated an environmental fragility index (EFI) for the Brazilian neotropical savannah. The EFI was derived from measured geoclimatic controls on sediment production (rainfall, variation of elevation and slope, geology) and anthropogenic pressures (natural cover, road density, distance from roads and urban centers) in 111 catchments upstream of four large hydroelectric reservoirs. We evaluated the effectiveness of the EFI by regressing it against a relative bed stability index (LRBS) that assesses the degree to which stream sites draining into the reservoirs are affected by excess fine sediments. We developed the EFI on 111 of these sites and validated our model on the remaining 37 independent sites. We also compared the effectiveness of the EFI in predicting LRBS with that of a multiple linear regression model (via best-subset procedure) using 7 independent variables. The EFI was significantly correlated with the LRBS, with regression R 2 values of 0.32 and 0.40, respectively, in development and validation sites. Although the EFI and multiple regression explained similar amounts of variability (R 2  = 0.32 vs 0.36), the EFI had a higher F-ratio (51.6 vs 8.5) and better AICc value (333 vs 338). Because the sites were randomly selected and well-distributed across geoclimatic controlling factors, we were able to calculate spatially-explicit EFI values for all hydrologic units within the study area (~38,500 km 2 ). This model-based inference showed that over 65% of those units had high or extreme fragility. This methodology has great potential for application in the management, recovery, and preservation of hydroelectric reservoirs and streams in tropical river basins. Copyright © 2018 Elsevier B.V. All rights reserved.

Scoring and staging systems using cox linear regression modeling and recursive partitioning.

PubMed

Lee, J W; Um, S H; Lee, J B; Mun, J; Cho, H

2006-01-01

Scoring and staging systems are used to determine the order and class of data according to predictors. Systems used for medical data, such as the Child-Turcotte-Pugh scoring and staging systems for ordering and classifying patients with liver disease, are often derived strictly from physicians' experience and intuition. We construct objective and data-based scoring/staging systems using statistical methods. We consider Cox linear regression modeling and recursive partitioning techniques for censored survival data. In particular, to obtain a target number of stages we propose cross-validation and amalgamation algorithms. We also propose an algorithm for constructing scoring and staging systems by integrating local Cox linear regression models into recursive partitioning, so that we can retain the merits of both methods such as superior predictive accuracy, ease of use, and detection of interactions between predictors. The staging system construction algorithms are compared by cross-validation evaluation of real data. The data-based cross-validation comparison shows that Cox linear regression modeling is somewhat better than recursive partitioning when there are only continuous predictors, while recursive partitioning is better when there are significant categorical predictors. The proposed local Cox linear recursive partitioning has better predictive accuracy than Cox linear modeling and simple recursive partitioning. This study indicates that integrating local linear modeling into recursive partitioning can significantly improve prediction accuracy in constructing scoring and staging systems.

Comparison of Linear and Non-linear Regression Analysis to Determine Pulmonary Pressure in Hyperthyroidism.

PubMed

Scarneciu, Camelia C; Sangeorzan, Livia; Rus, Horatiu; Scarneciu, Vlad D; Varciu, Mihai S; Andreescu, Oana; Scarneciu, Ioan

2017-01-01

This study aimed at assessing the incidence of pulmonary hypertension (PH) at newly diagnosed hyperthyroid patients and at finding a simple model showing the complex functional relation between pulmonary hypertension in hyperthyroidism and the factors causing it. The 53 hyperthyroid patients (H-group) were evaluated mainly by using an echocardiographical method and compared with 35 euthyroid (E-group) and 25 healthy people (C-group). In order to identify the factors causing pulmonary hypertension the statistical method of comparing the values of arithmetical means is used. The functional relation between the two random variables (PAPs and each of the factors determining it within our research study) can be expressed by linear or non-linear function. By applying the linear regression method described by a first-degree equation the line of regression (linear model) has been determined; by applying the non-linear regression method described by a second degree equation, a parabola-type curve of regression (non-linear or polynomial model) has been determined. We made the comparison and the validation of these two models by calculating the determination coefficient (criterion 1), the comparison of residuals (criterion 2), application of AIC criterion (criterion 3) and use of F-test (criterion 4). From the H-group, 47% have pulmonary hypertension completely reversible when obtaining euthyroidism. The factors causing pulmonary hypertension were identified: previously known- level of free thyroxin, pulmonary vascular resistance, cardiac output; new factors identified in this study- pretreatment period, age, systolic blood pressure. According to the four criteria and to the clinical judgment, we consider that the polynomial model (graphically parabola- type) is better than the linear one. The better model showing the functional relation between the pulmonary hypertension in hyperthyroidism and the factors identified in this study is given by a polynomial equation of second degree where the parabola is its graphical representation.

Duration of U.S. residence and suicidality among racial/ethnic minority immigrants.

PubMed

Brown, Monique J; Cohen, Steven A; Mezuk, Briana

2015-02-01

The immigration experience embodies a range of factors including different cultural norms and expectations, which may be particularly important for groups who become racial/ethnic minorities when they migrate to the U.S. However, little is known about the correlates of mental health indicators among these groups. The primary and secondary aims were to determine the association between duration of U.S. residence and suicidality, and 12-month mood, anxiety, and substance use disorders, respectively, among racial/ethnic minority immigrants. Data were obtained from the National Survey of American Life and the National Latino and Asian American Survey. Multivariable logistic regression was used to determine the association between duration of US residence, and suicidality and 12-month psychopathology. Among Afro-Caribbeans, there was a modest positive association between duration of U.S. residence and 12-month psychopathology (P linear trend = 0.016). Among Asians there was a modest positive association between duration of US residence and suicidal ideation and attempts (P linear trend = 0.018, 0.063, respectively). Among Latinos, there was a positive association between duration of US residence, and suicidal ideation, attempts and 12-month psychopathology (P linear trend = 0.001, 0.012, 0.002, respectively). Latinos who had been in the U.S. for >20 years had 2.6 times greater likelihood of suicidal ideation relative to those who had been in the U.S. for <5 years (95% CI 1.01-6.78). The association between duration of US residence and suicidality and psychopathology varies across racial/ethnic minority groups. The results for Latino immigrants are broadly consistent with the goal-striving or acculturation stress hypothesis.

Effect of a laboratory result pager on provider behavior in a neonatal intensive care unit.

PubMed

Samal, L; Stavroudis, Ta; Miller, Re; Lehmann, Hp; Lehmann, Cu

2011-01-01

A computerized laboratory result paging system (LRPS) that alerts providers about abnormal results ("push") may improve upon active laboratory result review ("pull"). However, implementing such a system in the intensive care setting may be hindered by low signal-to-noise ratio, which may lead to alert fatigue. To evaluate the impact of an LRPS in a Neonatal Intensive Care Unit. Utilizing paper chart review, we tallied provider orders following an abnormal laboratory result before and after implementation of an LRPS. Orders were compared with a predefined set of appropriate orders for such an abnormal result. The likelihood of a provider response in the post-implementation period as compared to the pre-implementation period was analyzed using logistic regression. The provider responses were analyzed using logistic regression to control for potential confounders. The likelihood of a provider response to an abnormal laboratory result did not change significantly after implementation of an LRPS. (Odds Ratio 0.90, 95% CI 0.63-1.30, p-value 0.58) However, when providers did respond to an alert, the type of response was different. The proportion of repeat laboratory tests increased. (26/378 vs. 7/278, p-value = 0.02). Although the laboratory result pager altered healthcare provider behavior in the Neonatal Intensive Care Unit, it did not increase the overall likelihood of provider response.

SOME STATISTICAL ISSUES RELATED TO MULTIPLE LINEAR REGRESSION MODELING OF BEACH BACTERIA CONCENTRATIONS

EPA Science Inventory

As a fast and effective technique, the multiple linear regression (MLR) method has been widely used in modeling and prediction of beach bacteria concentrations. Among previous works on this subject, however, several issues were insufficiently or inconsistently addressed. Those is...

A simplified competition data analysis for radioligand specific activity determination.

PubMed

Venturino, A; Rivera, E S; Bergoc, R M; Caro, R A

1990-01-01

Non-linear regression and two-step linear fit methods were developed to determine the actual specific activity of 125I-ovine prolactin by radioreceptor self-displacement analysis. The experimental results obtained by the different methods are superposable. The non-linear regression method is considered to be the most adequate procedure to calculate the specific activity, but if its software is not available, the other described methods are also suitable.

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

Electricity Consumption in the Industrial Sector of Jordan: Application of Multivariate Linear Regression and Adaptive Neuro-Fuzzy Techniques

NASA Astrophysics Data System (ADS)

Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.

2009-08-01

In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.

Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

PubMed Central

Carvalho, Carlos; Gomes, Danielo G.; Agoulmine, Nazim; de Souza, José Neuman

2011-01-01

This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction. PMID:22346626

Equalization of nonlinear transmission impairments by maximum-likelihood-sequence estimation in digital coherent receivers.

PubMed

Khairuzzaman, Md; Zhang, Chao; Igarashi, Koji; Katoh, Kazuhiro; Kikuchi, Kazuro

2010-03-01

We describe a successful introduction of maximum-likelihood-sequence estimation (MLSE) into digital coherent receivers together with finite-impulse response (FIR) filters in order to equalize both linear and nonlinear fiber impairments. The MLSE equalizer based on the Viterbi algorithm is implemented in the offline digital signal processing (DSP) core. We transmit 20-Gbit/s quadrature phase-shift keying (QPSK) signals through a 200-km-long standard single-mode fiber. The bit-error rate performance shows that the MLSE equalizer outperforms the conventional adaptive FIR filter, especially when nonlinear impairments are predominant.

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.

Probabilistic inference using linear Gaussian importance sampling for hybrid Bayesian networks

NASA Astrophysics Data System (ADS)

Sun, Wei; Chang, K. C.

2005-05-01

Probabilistic inference for Bayesian networks is in general NP-hard using either exact algorithms or approximate methods. However, for very complex networks, only the approximate methods such as stochastic sampling could be used to provide a solution given any time constraint. There are several simulation methods currently available. They include logic sampling (the first proposed stochastic method for Bayesian networks, the likelihood weighting algorithm) the most commonly used simulation method because of its simplicity and efficiency, the Markov blanket scoring method, and the importance sampling algorithm. In this paper, we first briefly review and compare these available simulation methods, then we propose an improved importance sampling algorithm called linear Gaussian importance sampling algorithm for general hybrid model (LGIS). LGIS is aimed for hybrid Bayesian networks consisting of both discrete and continuous random variables with arbitrary distributions. It uses linear function and Gaussian additive noise to approximate the true conditional probability distribution for continuous variable given both its parents and evidence in a Bayesian network. One of the most important features of the newly developed method is that it can adaptively learn the optimal important function from the previous samples. We test the inference performance of LGIS using a 16-node linear Gaussian model and a 6-node general hybrid model. The performance comparison with other well-known methods such as Junction tree (JT) and likelihood weighting (LW) shows that LGIS-GHM is very promising.

Multidimensional stochastic approximation using locally contractive functions

NASA Technical Reports Server (NTRS)

Lawton, W. M.

1975-01-01

A Robbins-Monro type multidimensional stochastic approximation algorithm which converges in mean square and with probability one to the fixed point of a locally contractive regression function is developed. The algorithm is applied to obtain maximum likelihood estimates of the parameters for a mixture of multivariate normal distributions.

A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.

PubMed

Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne

2016-04-01

Existing evidence suggests that ambient ultrafine particles (UFPs) (<0.1µm) may contribute to acute cardiorespiratory morbidity. However, few studies have examined the long-term health effects of these pollutants owing in part to a need for exposure surfaces that can be applied in large population-based studies. To address this need, we developed a land use regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

Factors influencing dental utilization by U.S. military recruits.

PubMed

Chisick, M C; Poindexter, F R; York, A K

1996-12-01

This study identifies factors that influence dental utilization by U.S. military recruits prior to entering military service. Data were collected between February and June 1994 at one recruit in-processing site per service using self-administered questionnaires. A prestratified, systematic, random sample of 2,711 Army, Air Force, Navy, and Marine recruits was drawn. Women and minorities were oversampled. Using logistic regression to determine what factors contribute to the likelihood of recruits having seen a dentist over the past year reveals that the likelihood is greater for females, singles, Air Force personnel, Native Americans, and the better educated; the likelihood is less for recruits who are black, above 19 years old, from the Midwest, Southwest, or Pacific regions of the U.S., and who perceive a need for dental care. Education is the strongest predictor of dental utilization prior to entering military service.

Performance of the likelihood ratio difference (G2 Diff) test for detecting unidimensionality in applications of the multidimensional Rasch model.

PubMed

Harrell-Williams, Leigh; Wolfe, Edward W

2014-01-01

Previous research has investigated the influence of sample size, model misspecification, test length, ability distribution offset, and generating model on the likelihood ratio difference test in applications of item response models. This study extended that research to the evaluation of dimensionality using the multidimensional random coefficients multinomial logit model (MRCMLM). Logistic regression analysis of simulated data reveal that sample size and test length have a large effect on the capacity of the LR difference test to correctly identify unidimensionality, with shorter tests and smaller sample sizes leading to smaller Type I error rates. Higher levels of simulated misfit resulted in fewer incorrect decisions than data with no or little misfit. However, Type I error rates indicate that the likelihood ratio difference test is not suitable under any of the simulated conditions for evaluating dimensionality in applications of the MRCMLM.

Depression and Chronic Health Conditions Among Latinos: The Role of Social Networks.

PubMed

Soto, Sandra; Arredondo, Elva M; Villodas, Miguel T; Elder, John P; Quintanar, Elena; Madanat, Hala

2016-12-01

The purpose of this study was to examine the "buffering hypothesis" of social network characteristics in the association between chronic conditions and depression among Latinos. Cross-sectional self-report data from the San Diego Prevention Research Center's community survey of Latinos were used (n = 393). Separate multiple logistic regression models tested the role of chronic conditions and social network characteristics in the likelihood of moderate-to-severe depressive symptoms. Having a greater proportion of the network comprised of friends increased the likelihood of depression among those with high cholesterol. Having a greater proportion of women in the social network was directly related to the increased likelihood of depression, regardless of the presence of chronic health conditions. Findings suggest that network characteristics may play a role in the link between chronic conditions and depression among Latinos. Future research should explore strategies targeting the social networks of Latinos to improve health outcomes.

Does the portrayal of tanning in Australian women's magazines relate to real women's tanning beliefs and behavior?

PubMed

Dixon, Helen G; Warne, Charles D; Scully, Maree L; Wakefield, Melanie A; Dobbinson, Suzanne J

2011-04-01

Content analysis data on the tans of 4,422 female Caucasian models sampled from spring and summer magazine issues were combined with readership data to generate indices of potential exposure to social modeling of tanning via popular women's magazines over a 15-year period (1987 to 2002). Associations between these indices and cross-sectional telephone survey data from the same period on 5,675 female teenagers' and adults' tanning attitudes, beliefs, and behavior were examined using logistic regression models. Among young women, greater exposure to tanning in young women's magazines was associated with increased likelihood of endorsing pro-tan attitudes and beliefs. Among women of all ages, greater exposure to tanned models via the most popular women's magazines was associated with increased likelihood of attempting to get a tan but lower likelihood of endorsing pro-tan attitudes. Popular women's magazines may promote and reflect real women's tanning beliefs and behavior.

The Use of Female Commercial Sex Workers’ Services by Latino Day Laborers

PubMed Central

Galván, Frank H.; Ortiz, Daniel J.; Martinez, Victor; Bing, Eric G.

2010-01-01

This article reports the characteristics of Latino day laborers who have sex with female commercial sex workers (CSWs). A sample of 450 day laborers in Los Angeles was utilized. Multivariate logistic regression was used to determine the association of independent variables with the likelihood of having sex with a CSW. Overall, 26% of the 450 day laborers reported having had sex with a CSW in the previous 12 months. A lower likelihood of having sex with a CSW was found for those with more than six years of education and for those who were married and living with their spouses. A higher likelihood of having sex with a CSW was found for those who met the criteria for harmful drinking or drug dependence. Commercial sex work has been associated with sexually transmitted infections and other problems among clients of CSWs and warrants further attention by providers working with day laborers. PMID:20354572

Work-related injury factors and safety climate perception in truck drivers.

PubMed

Anderson, Naomi J; Smith, Caroline K; Byrd, Jesse L

2017-08-01

The trucking industry has a high burden of work-related injuries. This study examined factors, such as safety climate perceptions, that may impact injury risk. A random sample of 9800 commercial driver's license holders (CDL) were sent surveys, only 4360 were eligible truck drivers. Descriptive statistics and logistic regression models were developed to describe the population and identify variables associated with work-related injury. 2189 drivers completed the pertinent interview questions. Driving less-than-truckload, daytime sleepiness, pressure to work faster, and having a poor composite score for safety perceptions were all associated with increased likelihood of work-related injury. Positive safety perception score was protective for odds of work-related injury, and increased claim filing when injured. Positive psychological safety climate is associated with decreased likelihood of work-related injury and increased likelihood that a driver injured on the job files a workers' compensation claim. © 2017 Wiley Periodicals, Inc.

Alzheimer's Disease Detection by Pseudo Zernike Moment and Linear Regression Classification.

PubMed

Wang, Shui-Hua; Du, Sidan; Zhang, Yin; Phillips, Preetha; Wu, Le-Nan; Chen, Xian-Qing; Zhang, Yu-Dong

2017-01-01

This study presents an improved method based on "Gorji et al. Neuroscience. 2015" by introducing a relatively new classifier-linear regression classification. Our method selects one axial slice from 3D brain image, and employed pseudo Zernike moment with maximum order of 15 to extract 256 features from each image. Finally, linear regression classification was harnessed as the classifier. The proposed approach obtains an accuracy of 97.51%, a sensitivity of 96.71%, and a specificity of 97.73%. Our method performs better than Gorji's approach and five other state-of-the-art approaches. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

A simple bias correction in linear regression for quantitative trait association under two-tail extreme selection.

PubMed

Kwan, Johnny S H; Kung, Annie W C; Sham, Pak C

2011-09-01

Selective genotyping can increase power in quantitative trait association. One example of selective genotyping is two-tail extreme selection, but simple linear regression analysis gives a biased genetic effect estimate. Here, we present a simple correction for the bias.

Estimating overall exposure effects for the clustered and censored outcome using random effect Tobit regression models.

PubMed

Wang, Wei; Griswold, Michael E

2016-11-30

The random effect Tobit model is a regression model that accommodates both left- and/or right-censoring and within-cluster dependence of the outcome variable. Regression coefficients of random effect Tobit models have conditional interpretations on a constructed latent dependent variable and do not provide inference of overall exposure effects on the original outcome scale. Marginalized random effects model (MREM) permits likelihood-based estimation of marginal mean parameters for the clustered data. For random effect Tobit models, we extend the MREM to marginalize over both the random effects and the normal space and boundary components of the censored response to estimate overall exposure effects at population level. We also extend the 'Average Predicted Value' method to estimate the model-predicted marginal means for each person under different exposure status in a designated reference group by integrating over the random effects and then use the calculated difference to assess the overall exposure effect. The maximum likelihood estimation is proposed utilizing a quasi-Newton optimization algorithm with Gauss-Hermite quadrature to approximate the integration of the random effects. We use these methods to carefully analyze two real datasets. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

The Inverse Problem for Confined Aquifer Flow: Identification and Estimation With Extensions

NASA Astrophysics Data System (ADS)

Loaiciga, Hugo A.; MariñO, Miguel A.

1987-01-01

The contributions of this work are twofold. First, a methodology for estimating the elements of parameter matrices in the governing equation of flow in a confined aquifer is developed. The estimation techniques for the distributed-parameter inverse problem pertain to linear least squares and generalized least squares methods. The linear relationship among the known heads and unknown parameters of the flow equation provides the background for developing criteria for determining the identifiability status of unknown parameters. Under conditions of exact or overidentification it is possible to develop statistically consistent parameter estimators and their asymptotic distributions. The estimation techniques, namely, two-stage least squares and three stage least squares, are applied to a specific groundwater inverse problem and compared between themselves and with an ordinary least squares estimator. The three-stage estimator provides the closer approximation to the actual parameter values, but it also shows relatively large standard errors as compared to the ordinary and two-stage estimators. The estimation techniques provide the parameter matrices required to simulate the unsteady groundwater flow equation. Second, a nonlinear maximum likelihood estimation approach to the inverse problem is presented. The statistical properties of maximum likelihood estimators are derived, and a procedure to construct confidence intervals and do hypothesis testing is given. The relative merits of the linear and maximum likelihood estimators are analyzed. Other topics relevant to the identification and estimation methodologies, i.e., a continuous-time solution to the flow equation, coping with noise-corrupted head measurements, and extension of the developed theory to nonlinear cases are also discussed. A simulation study is used to evaluate the methods developed in this study.

Association of fish and n-3 fatty acid intake with the risk of type 2 diabetes: a meta-analysis of prospective studies.

PubMed

Zhou, Yunping; Tian, Changwei; Jia, Chongqi

2012-08-01

Results from observational studies on the association of fish and n-3 fatty acid consumption with type 2 diabetes mellitus (T2DM) risk are conflicting. Hence, a meta-analysis was performed to investigate this association from cohort studies. A comprehensive search was then conducted to identify cohort studies on the association of fish and/or n-3 fatty acid intake with T2DM risk. In the highest v. lowest categorical analyses, the fixed or random-effect model was selected based on the homogeneity test among studies. Linear and non-linear dose-response relationships were also assessed by univariate and bivariate random-effect meta-regression with restricted maximum likelihood estimation. In the highest v. lowest categorical analyses, the pooled relative risk (RR) of T2DM for intake of fish and n-3 fatty acid was 1·146 (95 % CI 0·975, 1·346) and 1·076 (95 % CI 0·955, 1·213), respectively. In the linear dose-response relationship, the pooled RR for an increment of one time (about 105 g)/week of fish intake (four times/month) and of 0·1 g/d of n-3 fatty acid intake was 1·042 (95 % CI 1·026, 1·058) and 1·057 (95 % CI 1·042, 1·073), respectively. The significant non-linear dose-response associations of fish and n-3 fatty acid intake with T2DM risk were not observed. The present evidence from observational studies suggests that the intake of both fish and n-3 fatty acids might be weakly positively associated with the T2DM risk. Further studies are needed to confirm these results.

A Common Mechanism for Resistance to Oxime Reactivation of Acetylcholinesterase Inhibited by Organophosphorus Compounds

DTIC Science & Technology

2013-01-01

application of the Hammett equation with the constants rph in the chemistry of organophosphorus compounds, Russ. Chem. Rev. 38 (1969) 795–811. [13...of oximes and OP compounds and the ability of oximes to reactivate OP- inhibited AChE. Multiple linear regression equations were analyzed using...phosphonate pairs, 21 oxime/ phosphoramidate pairs and 12 oxime/phosphate pairs. The best linear regression equation resulting from multiple regression anal

Phobic Anxiety and Plasma Levels of Global Oxidative Stress in Women

PubMed Central

Hagan, Kaitlin A.; Wu, Tianying; Rimm, Eric B.; Eliassen, A. Heather; Okereke, Olivia I.

2015-01-01

Background and Objectives Psychological distress has been hypothesized to be associated with adverse biologic states such as higher oxidative stress and inflammation. Yet, little is known about associations between a common form of distress – phobic anxiety – and global oxidative stress. Thus, we related phobic anxiety to plasma fluorescent oxidation products (FlOPs), a global oxidative stress marker. Methods We conducted a cross-sectional analysis among 1,325 women (aged 43-70 years) from the Nurses’ Health Study. Phobic anxiety was measured using the Crown-Crisp Index (CCI). Adjusted least-squares mean log-transformed FlOPs were calculated across phobic categories. Logistic regression models were used to calculate odds ratios (OR) comparing the highest CCI category (≥6 points) vs. lower scores, across FlOPs quartiles. Results No association was found between phobic anxiety categories and mean FlOP levels in multivariable adjusted linear models. Similarly, in multivariable logistic regression models there were no associations between FlOPs quartiles and likelihood of being in the highest phobic category. Comparing women in the highest vs. lowest FlOPs quartiles: FlOP_360: OR=0.68 (95% CI: 0.40-1.15); FlOP_320: OR=0.99 (95% CI: 0.61-1.61); FlOP_400: OR=0.92 (95% CI: 0.52, 1.63). Conclusions No cross-sectional association was found between phobic anxiety and a plasma measure of global oxidative stress in this sample of middle-aged and older women. PMID:26635425

Integrative Understanding of Familial Impulsivity, Early Adversity and Suicide Risk.

PubMed

Lima, Isabela M M; Malloy-Diniz, Leandro F; de Miranda, Débora M; Da Silva, Antônio G; Neves, Fernando S; Johnson, Sheri L

2017-01-01

Introduction: Impulsivity is a core characteristic of bipolar disorder and it was observed as elevated in individuals with the disorder and in their relatives. Both impulsivity and history of maltreatment are risk factors for suicide attempts, however, these two key variables may not be independent, given the fact that parental impulsivity and associated social context could increase the risk of child maltreatment. In this study it was examined the association between the impulsivity of relatives and child maltreatment taking into consideration the conjoint and unique effects of these two variables on the risk of suicide attempts among the patients. Materials and Methods: Participants of the study consisted of 117 patients diagnosed with bipolar disorder and 25 first-degree relatives. Linear regression model was conducted to describe associations between facets of impulsivity of relatives and levels of child maltreatment reported by patients. The independent associations of suicide attempt history with the dimensions of impulsivity of the patient and maltreatment were tested by multinomial logistic regression. Results: Impulsivity of relatives and, more specifically, inhibitory control can predict the maltreatment of the patient. Inhibitory control and emotional abuse were related, conjointly, to a greater likelihood of having a history of more than one suicide attempt. Discussion: Considering that the impulsivity of relatives predicts child maltreatment, it is possible that a genetically shared impulsivity is an underlying feature associated with the history of multiple suicide attempts. These findings highlight the importance of considering child maltreatment, impulsivity and suicide attempt history in integrative models.

Integrative Understanding of Familial Impulsivity, Early Adversity and Suicide Risk

PubMed Central

Lima, Isabela M. M.; Malloy-Diniz, Leandro F.; de Miranda, Débora M.; Da Silva, Antônio G.; Neves, Fernando S.; Johnson, Sheri L.

2017-01-01

Introduction: Impulsivity is a core characteristic of bipolar disorder and it was observed as elevated in individuals with the disorder and in their relatives. Both impulsivity and history of maltreatment are risk factors for suicide attempts, however, these two key variables may not be independent, given the fact that parental impulsivity and associated social context could increase the risk of child maltreatment. In this study it was examined the association between the impulsivity of relatives and child maltreatment taking into consideration the conjoint and unique effects of these two variables on the risk of suicide attempts among the patients. Materials and Methods: Participants of the study consisted of 117 patients diagnosed with bipolar disorder and 25 first-degree relatives. Linear regression model was conducted to describe associations between facets of impulsivity of relatives and levels of child maltreatment reported by patients. The independent associations of suicide attempt history with the dimensions of impulsivity of the patient and maltreatment were tested by multinomial logistic regression. Results: Impulsivity of relatives and, more specifically, inhibitory control can predict the maltreatment of the patient. Inhibitory control and emotional abuse were related, conjointly, to a greater likelihood of having a history of more than one suicide attempt. Discussion: Considering that the impulsivity of relatives predicts child maltreatment, it is possible that a genetically shared impulsivity is an underlying feature associated with the history of multiple suicide attempts. These findings highlight the importance of considering child maltreatment, impulsivity and suicide attempt history in integrative models. PMID:29312082

Outcome and Life Satisfaction of Adults with Myelomeningocele

PubMed Central

Cope, Heidi; McMahon, Kelly; Heise, Elizabeth; Eubanks, Sonja; Garrett, Melanie; Gregory, Simon; Ashley-Koch, Allison

2013-01-01

Background Myelomeningocele (MMC) commonly causes impairments in body structure and functions as well as cognitive disabilities that can have an adverse effect on adult life. Improved medical care has resulted in increased numbers of individuals with MMC surviving to adulthood, however little is known about the impact of MMC on the lives of adults age 25 years or older. Objective To gain a better understanding of outcomes in education, employment, relationships, reproduction and life satisfaction of adults with MMC. Methods A primarily quantitative multiple-choice questionnaire designed to capture outcomes in education, employment, relationships and reproduction, along with a previously validated life satisfaction checklist (LiSat-11), was completed by adults with MMC. Relationships between demographic variables, outcomes and life satisfaction were determined using cross tabulation analysis, logistic regression and linear regression. Results Ninety adults with MMC, age 25 to 85 years (median age 32), reported a diverse range of outcomes in education, employment, relationships and reproduction. The most consistent variable associated with difficulty attaining adult milestones was hydrocephalus, the presence of which reduced the likelihood of living independently (p=<0.001), having a partner (p=0.003) and reproducing (p=<0.001), but did not contribute to reduced life satisfaction. Conclusions Adults with MMC, especially those without hydrocephalus, can obtain gainful employment, live independently, form partner relationships and have children, and these achievements contribute to life satisfaction. While MMC does not affect overall reported life satisfaction for adults, attention should be paid to specific domains with less reported satisfaction. PMID:23769483

The relationship between mental disorders, quality of life, and pregnancy: findings from a nationally representative sample.

PubMed

Mota, Natalie; Cox, Brian J; Enns, Murray W; Calhoun, Laura; Sareen, Jitender

2008-08-01

The present study examined health-related quality of life (HRQOL) and the prevalence of mental disorders in pregnant and past-year pregnant women compared to non-pregnant women. Data came from the National Epidemiologic Survey on Alcohol and Related Conditions. Three groups of women (ages 18-44) were compared: currently pregnant (n=451), past-year pregnant (n=1061), and not pregnant (n=10,544). Past-year mood, anxiety and substance use disorders were assessed by the Alcohol Use Disorder and Associated Disabilities Interview Schedule-DSM-IV version. HRQOL was measured by the Medical Outcomes Study Short Form (SF-12). All analyses included sociodemographics as covariates. Multiple logistic regression analyses showed that pregnant women were less likely than non-pregnant women to have depression and alcohol abuse or dependence; and less likely than past-year pregnant women to have depression and mania. Past-year pregnant women were less likely than non-pregnant women to have social phobia and alcohol dependence or abuse. Multiple linear regression analyses demonstrated that pregnant and non-pregnant women had higher mental component scores than past-year pregnant women. Physical component scores were lower in pregnant women than in non-pregnant and past-year pregnant groups. This was a cross-sectional survey and the causality of relationships cannot be inferred. Results suggest that pregnant women have a lower likelihood of mental disorder than both non-pregnant and past-year pregnant women.

Social relationships and longitudinal changes in body mass index and waist circumference: the coronary artery risk development in young adults study.

PubMed

Kershaw, Kiarri N; Hankinson, Arlene L; Liu, Kiang; Reis, Jared P; Lewis, Cora E; Loria, Catherine M; Carnethon, Mercedes R

2014-03-01

Few studies have examined longitudinal associations between close social relationships and weight change. Using data from 3,074 participants in the Coronary Artery Risk Development in Young Adults Study who were examined in 2000, 2005, and 2010 (at ages 33-45 years in 2000), we estimated separate logistic regression random-effects models to assess whether patterns of exposure to supportive and negative relationships were associated with 10% or greater increases in body mass index (BMI) (weight (kg)/height (m)(2)) and waist circumference. Linear regression random-effects modeling was used to examine associations of social relationships with mean changes in BMI and waist circumference. Participants with persistently high supportive relationships were significantly less likely to increase their BMI values and waist circumference by 10% or greater compared with those with persistently low supportive relationships after adjustment for sociodemographic characteristics, baseline BMI/waist circumference, depressive symptoms, and health behaviors. Persistently high negative relationships were associated with higher likelihood of 10% or greater increases in waist circumference (odds ratio = 1.62, 95% confidence interval: 1.15, 2.29) and marginally higher BMI increases (odds ratio = 1.50, 95% confidence interval: 1.00, 2.24) compared with participants with persistently low negative relationships. Increasingly negative relationships were associated with increases in waist circumference only. These findings suggest that supportive relationships may minimize weight gain, and that adverse relationships may contribute to weight gain, particularly via central fat accumulation.

Social Relationships and Longitudinal Changes in Body Mass Index and Waist Circumference: The Coronary Artery Risk Development in Young Adults Study

PubMed Central

Kershaw, Kiarri N.; Hankinson, Arlene L.; Liu, Kiang; Reis, Jared P.; Lewis, Cora E.; Loria, Catherine M.; Carnethon, Mercedes R.

2014-01-01

Few studies have examined longitudinal associations between close social relationships and weight change. Using data from 3,074 participants in the Coronary Artery Risk Development in Young Adults Study who were examined in 2000, 2005, and 2010 (at ages 33–45 years in 2000), we estimated separate logistic regression random-effects models to assess whether patterns of exposure to supportive and negative relationships were associated with 10% or greater increases in body mass index (BMI) (weight (kg)/height (m)2) and waist circumference. Linear regression random-effects modeling was used to examine associations of social relationships with mean changes in BMI and waist circumference. Participants with persistently high supportive relationships were significantly less likely to increase their BMI values and waist circumference by 10% or greater compared with those with persistently low supportive relationships after adjustment for sociodemographic characteristics, baseline BMI/waist circumference, depressive symptoms, and health behaviors. Persistently high negative relationships were associated with higher likelihood of 10% or greater increases in waist circumference (odds ratio = 1.62, 95% confidence interval: 1.15, 2.29) and marginally higher BMI increases (odds ratio = 1.50, 95% confidence interval: 1.00, 2.24) compared with participants with persistently low negative relationships. Increasingly negative relationships were associated with increases in waist circumference only. These findings suggest that supportive relationships may minimize weight gain, and that adverse relationships may contribute to weight gain, particularly via central fat accumulation. PMID:24389018

Updating The Navy’s Recruit Quality Matrix: An Analysis of Educational Credentials and the Success of First-Term Sailors

DTIC Science & Technology

2004-03-01

Allison , Logistic Regression: Using the SAS System (Cary, NC: SAS Institute, Inc, 2001), 57. 23 using the likelihood ratio that SAS generates...21, respectively. 33 Jesse M. Rothstein, College Performance Predictions and the SAT ( Berkely , CA: UC

Information loss in approximately bayesian data assimilation: a comparison of generative and discriminative approaches to estimating agricultural yield

USDA-ARS?s Scientific Manuscript database

Data assimilation and regression are two commonly used methods for predicting agricultural yield from remote sensing observations. Data assimilation is a generative approach because it requires explicit approximations of the Bayesian prior and likelihood to compute the probability density function...

A Study of the Relationship between Kindergarten Nonverbal Ability and Third-Grade Reading Achievement

ERIC Educational Resources Information Center

Wills, Aaron J.

2012-01-01

Increased scrutiny of educational proficiency targets has intensified the urgency for educators to identify measurements that indicate students' likelihood of eventual achievement in reading. This regression analysis explored the relationship between nonverbal ability in kindergarten as measured by the Naglieri Nonverbal Ability Test (NNAT) and…

School-Related Factors Affecting High School Seniors' Methamphetamine Use

ERIC Educational Resources Information Center

Stanley, Jarrod M.; Lo, Celia C.

2009-01-01

Data from the 2005 Monitoring the Future survey were used to examine relationships between school-related factors and high school seniors' lifetime methamphetamine use. The study applied logistic regression techniques to evaluate effects of social bonding variables and social learning variables on likelihood of lifetime methamphetamine use. The…

Factors Associated with Living in Developmental Centers in California

ERIC Educational Resources Information Center

Harrington, Charlene; Kang, Taewoon; Chang, Jamie

2009-01-01

This study examined need, predisposing, market, and regional factors that predicted the likelihood of individuals with developmental disabilities living in state developmental centers (DCs) compared with living at home, in community care, or in intermediate care (ICFs) and other facilities. Secondary data analysis using logistic regression models…

Severe Pain Predicts Greater Likelihood of Subsequent Suicide

ERIC Educational Resources Information Center

Ilgen, Mark A.; Zivin, Kara; Austin, Karen L.; Bohnert, Amy S. B.; Czyz, Ewa K.; Valenstein, Marcia; Kilbourne, Amy M.

2010-01-01

Using data from the 1999 Large Health Survey of Veterans, Veterans Affairs' medical records, and the National Death Index (N = 260,254), the association between self-reported pain severity and suicide among veterans as examined, after accounting for demographic variables and psychiatric diagnoses. A Cox proportional hazards regression demonstrated…

Effects of Employing Ridge Regression in Structural Equation Models.

ERIC Educational Resources Information Center

McQuitty, Shaun

1997-01-01

LISREL 8 invokes a ridge option when maximum likelihood or generalized least squares are used to estimate a structural equation model with a nonpositive definite covariance or correlation matrix. Implications of the ridge option for model fit, parameter estimates, and standard errors are explored through two examples. (SLD)

Specialization Agreements in the Council for Mutual Economic Assistance

DTIC Science & Technology

1988-02-01

proportions to stabilize variance (S. Weisberg, Applied Linear Regression , 2nd ed., John Wiley & Sons, New York, 1985, p. 134). If the dependent...27, 1986, p. 3. Weisberg, S., Applied Linear Regression , 2nd ed., John Wiley & Sons, New York, 1985, p. 134. Wiles, P. J., Communist International

Radio Propagation Prediction Software for Complex Mixed Path Physical Channels

DTIC Science & Technology

2006-08-14

63 4.4.6. Applied Linear Regression Analysis in the Frequency Range 1-50 MHz 69 4.4.7. Projected Scaling to...4.4.6. Applied Linear Regression Analysis in the Frequency Range 1-50 MHz In order to construct a comprehensive numerical algorithm capable of

INTRODUCTION TO A COMBINED MULTIPLE LINEAR REGRESSION AND ARMA MODELING APPROACH FOR BEACH BACTERIA PREDICTION

EPA Science Inventory

Due to the complexity of the processes contributing to beach bacteria concentrations, many researchers rely on statistical modeling, among which multiple linear regression (MLR) modeling is most widely used. Despite its ease of use and interpretation, there may be time dependence...

Data Transformations for Inference with Linear Regression: Clarifications and Recommendations

ERIC Educational Resources Information Center

Pek, Jolynn; Wong, Octavia; Wong, C. M.

2017-01-01

Data transformations have been promoted as a popular and easy-to-implement remedy to address the assumption of normally distributed errors (in the population) in linear regression. However, the application of data transformations introduces non-ignorable complexities which should be fully appreciated before their implementation. This paper adds to…

USING LINEAR AND POLYNOMIAL MODELS TO EXAMINE THE ENVIRONMENTAL STABILITY OF VIRUSES

EPA Science Inventory

The article presents the development of model equations for describing the fate of viral infectivity in environmental samples. Most of the models were based upon the use of a two-step linear regression approach. The first step employs regression of log base 10 transformed viral t...

Identifying the Factors That Influence Change in SEBD Using Logistic Regression Analysis

ERIC Educational Resources Information Center

Camilleri, Liberato; Cefai, Carmel

2013-01-01

Multiple linear regression and ANOVA models are widely used in applications since they provide effective statistical tools for assessing the relationship between a continuous dependent variable and several predictors. However these models rely heavily on linearity and normality assumptions and they do not accommodate categorical dependent…

Simple and multiple linear regression: sample size considerations.

PubMed

Hanley, James A

2016-11-01

The suggested "two subjects per variable" (2SPV) rule of thumb in the Austin and Steyerberg article is a chance to bring out some long-established and quite intuitive sample size considerations for both simple and multiple linear regression. This article distinguishes two of the major uses of regression models that imply very different sample size considerations, neither served well by the 2SPV rule. The first is etiological research, which contrasts mean Y levels at differing "exposure" (X) values and thus tends to focus on a single regression coefficient, possibly adjusted for confounders. The second research genre guides clinical practice. It addresses Y levels for individuals with different covariate patterns or "profiles." It focuses on the profile-specific (mean) Y levels themselves, estimating them via linear compounds of regression coefficients and covariates. By drawing on long-established closed-form variance formulae that lie beneath the standard errors in multiple regression, and by rearranging them for heuristic purposes, one arrives at quite intuitive sample size considerations for both research genres. Copyright © 2016 Elsevier Inc. All rights reserved.

A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression

PubMed Central

Jiang, Feng; Han, Ji-zhong

2018-01-01

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods. PMID:29623088

A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression.

PubMed

Yu, Xu; Lin, Jun-Yu; Jiang, Feng; Du, Jun-Wei; Han, Ji-Zhong

2018-01-01

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.

Bit Error Probability for Maximum Likelihood Decoding of Linear Block Codes

NASA Technical Reports Server (NTRS)

Lin, Shu; Fossorier, Marc P. C.; Rhee, Dojun

1996-01-01

In this paper, the bit error probability P(sub b) for maximum likelihood decoding of binary linear codes is investigated. The contribution of each information bit to P(sub b) is considered. For randomly generated codes, it is shown that the conventional approximation at high SNR P(sub b) is approximately equal to (d(sub H)/N)P(sub s), where P(sub s) represents the block error probability, holds for systematic encoding only. Also systematic encoding provides the minimum P(sub b) when the inverse mapping corresponding to the generator matrix of the code is used to retrieve the information sequence. The bit error performances corresponding to other generator matrix forms are also evaluated. Although derived for codes with a generator matrix randomly generated, these results are shown to provide good approximations for codes used in practice. Finally, for decoding methods which require a generator matrix with a particular structure such as trellis decoding or algebraic-based soft decision decoding, equivalent schemes that reduce the bit error probability are discussed.

Offline Versus Online Suicide-Related Help Seeking: Changing Domains, Changing Paradigms.

PubMed

Seward, Amy-Lee; Harris, Keith M

2016-06-01

Suicidal individuals are among the most reluctant help-seekers, which limits opportunities for treating and preventing unnecessary suffering and self-inflicted deaths. This study aimed to assist outreach, prevention, and treatment efforts by elucidating relationships between suicidality and both online and offline help seeking. An anonymous online survey provided data on 713 participants, aged 18-71 years. Measures included an expanded General Help-Seeking Questionnaire and the Suicidal Affect-Behavior-Cognition Scale. General linear modeling results showed that, as predicted, face-to-face help-seeking willingness decreased as risk level increased. However, for emerging adults help-seeking likelihood increased with informal online sources as risk increased, while other online help-seeking attitudes differed little by risk level. Linear regression modeling determined that, for suicidal individuals, willingness to seek help from online mental health professionals and online professional support sites was strongly related (ps < .001). Help seeking from social networking sites and anonymous online forums was also interrelated, but more complex, demonstrating the importance of age and social support factors (ps < .001). These findings show that the Internet has altered the suicide-related help-seeking paradigm. Online help seeking for suicidality was not more popular than face-to-face help seeking, even for emerging adults. However, treatment and prevention professionals have good reasons to increase their online efforts, because that is where some of the highest risk individuals are going for help with their most severe personal problems. © 2016 Wiley Periodicals, Inc.

Degradation modeling of mid-power white-light LEDs by using Wiener process.

PubMed

Huang, Jianlin; Golubović, Dušan S; Koh, Sau; Yang, Daoguo; Li, Xiupeng; Fan, Xuejun; Zhang, G Q

2015-07-27

The IES standard TM-21-11 provides a guideline for lifetime prediction of LED devices. As it uses average normalized lumen maintenance data and performs non-linear regression for lifetime modeling, it cannot capture dynamic and random variation of the degradation process of LED devices. In addition, this method cannot capture the failure distribution, although it is much more relevant in reliability analysis. Furthermore, the TM-21-11 only considers lumen maintenance for lifetime prediction. Color shift, as another important performance characteristic of LED devices, may also render significant degradation during service life, even though the lumen maintenance has not reached the critical threshold. In this study, a modified Wiener process has been employed for the modeling of the degradation of LED devices. By using this method, dynamic and random variations, as well as the non-linear degradation behavior of LED devices, can be easily accounted for. With a mild assumption, the parameter estimation accuracy has been improved by including more information into the likelihood function while neglecting the dependency between the random variables. As a consequence, the mean time to failure (MTTF) has been obtained and shows comparable result with IES TM-21-11 predictions, indicating the feasibility of the proposed method. Finally, the cumulative failure distribution was presented corresponding to different combinations of lumen maintenance and color shift. The results demonstrate that a joint failure distribution of LED devices could be modeled by simply considering their lumen maintenance and color shift as two independent variables.

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

Genetic Programming Transforms in Linear Regression Situations

NASA Astrophysics Data System (ADS)

Castillo, Flor; Kordon, Arthur; Villa, Carlos

The chapter summarizes the use of Genetic Programming (GP) inMultiple Linear Regression (MLR) to address multicollinearity and Lack of Fit (LOF). The basis of the proposed method is applying appropriate input transforms (model respecification) that deal with these issues while preserving the information content of the original variables. The transforms are selected from symbolic regression models with optimal trade-off between accuracy of prediction and expressional complexity, generated by multiobjective Pareto-front GP. The chapter includes a comparative study of the GP-generated transforms with Ridge Regression, a variant of ordinary Multiple Linear Regression, which has been a useful and commonly employed approach for reducing multicollinearity. The advantages of GP-generated model respecification are clearly defined and demonstrated. Some recommendations for transforms selection are given as well. The application benefits of the proposed approach are illustrated with a real industrial application in one of the broadest empirical modeling areas in manufacturing - robust inferential sensors. The chapter contributes to increasing the awareness of the potential of GP in statistical model building by MLR.

Artificial neural network, genetic algorithm, and logistic regression applications for predicting renal colic in emergency settings.

PubMed

Eken, Cenker; Bilge, Ugur; Kartal, Mutlu; Eray, Oktay

2009-06-03

Logistic regression is the most common statistical model for processing multivariate data in the medical literature. Artificial intelligence models like an artificial neural network (ANN) and genetic algorithm (GA) may also be useful to interpret medical data. The purpose of this study was to perform artificial intelligence models on a medical data sheet and compare to logistic regression. ANN, GA, and logistic regression analysis were carried out on a data sheet of a previously published article regarding patients presenting to an emergency department with flank pain suspicious for renal colic. The study population was composed of 227 patients: 176 patients had a diagnosis of urinary stone, while 51 ultimately had no calculus. The GA found two decision rules in predicting urinary stones. Rule 1 consisted of being male, pain not spreading to back, and no fever. In rule 2, pelvicaliceal dilatation on bedside ultrasonography replaced no fever. ANN, GA rule 1, GA rule 2, and logistic regression had a sensitivity of 94.9, 67.6, 56.8, and 95.5%, a specificity of 78.4, 76.47, 86.3, and 47.1%, a positive likelihood ratio of 4.4, 2.9, 4.1, and 1.8, and a negative likelihood ratio of 0.06, 0.42, 0.5, and 0.09, respectively. The area under the curve was found to be 0.867, 0.720, 0.715, and 0.713 for all applications, respectively. Data mining techniques such as ANN and GA can be used for predicting renal colic in emergency settings and to constitute clinical decision rules. They may be an alternative to conventional multivariate analysis applications used in biostatistics.

Naval Research Logistics Quarterly. Volume 28. Number 3,

DTIC Science & Technology

1981-09-01

denotes component-wise maximum. f has antone (isotone) differences on C x D if for cl < c2 and d, < d2, NAVAL RESEARCH LOGISTICS QUARTERLY VOL. 28...or negative correlations and linear or nonlinear regressions. Given are the mo- ments to order two and, for special cases, (he regression function and...data sets. We designate this bnb distribution as G - B - N(a, 0, v). The distribution admits only of positive correlation and linear regressions

Automating approximate Bayesian computation by local linear regression.

PubMed

Thornton, Kevin R

2009-07-07

In several biological contexts, parameter inference often relies on computationally-intensive techniques. "Approximate Bayesian Computation", or ABC, methods based on summary statistics have become increasingly popular. A particular flavor of ABC based on using a linear regression to approximate the posterior distribution of the parameters, conditional on the summary statistics, is computationally appealing, yet no standalone tool exists to automate the procedure. Here, I describe a program to implement the method. The software package ABCreg implements the local linear-regression approach to ABC. The advantages are: 1. The code is standalone, and fully-documented. 2. The program will automatically process multiple data sets, and create unique output files for each (which may be processed immediately in R), facilitating the testing of inference procedures on simulated data, or the analysis of multiple data sets. 3. The program implements two different transformation methods for the regression step. 4. Analysis options are controlled on the command line by the user, and the program is designed to output warnings for cases where the regression fails. 5. The program does not depend on any particular simulation machinery (coalescent, forward-time, etc.), and therefore is a general tool for processing the results from any simulation. 6. The code is open-source, and modular.Examples of applying the software to empirical data from Drosophila melanogaster, and testing the procedure on simulated data, are shown. In practice, the ABCreg simplifies implementing ABC based on local-linear regression.

Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

NASA Astrophysics Data System (ADS)

Jakubowski, J.; Stypulkowski, J. B.; Bernardeau, F. G.

2017-12-01

The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

The influence of genital tract status in postpartum period on the subsequent reproductive performance in high producing dairy cows.

PubMed

López-Helguera, I; López-Gatius, F; Garcia-Ispierto, I

2012-04-15

The aim of the present study was to characterize the early postpartum period in clinically healthy dairy cows by ultrasonography (US), endometrial cytology (EC), and white blood cell counts, and determine possible relationships between postpartum findings and subsequent reproductive performance. Fifty-three dairy cows were examined on Days 15 to 21 (Visit 1), 22 to 28 (Visit 2), and 29 to 35 (Visit 3) postpartum. The clinical examination included: examination of vaginal fluid, EC, transrectal palpation and ultrasonography of the genital tract (cervical diameter, endometrial thickness, presence of a corpus luteum [CL] or intrauterine fluid [IUF] and its echogenicity). Luteal activity (presence of a CL in a single visit), return to cyclicity (presence of a CL in 2 consecutive visits), and conception rate at 70 and 120 days postpartum were considered as the dependent variables in four consecutive binary logistic regression analyses. Factors affecting leukocyte counts were established by general linear model (GLM) repeated measures analysis of variance. Based on the odds ratio (OR), the likelihood of luteal activity was higher in multiparous than primiparous cows (OR = 3.75) and tended to diminish in cows showing increased endometrial thickness in Visit 1 (V1) (OR = 0.06). The likelihood of returning to cyclicity decreased for each centimeter increase in cervical diameter in V1 (OR = 0.14) and that of conception on Day 70 was lower in cows showing the presence of echogenic or anechogenic IUF in V1 (OR = 0.09 or OR = 0.13, respectively) compared with cows lacking IUF. Effects of parity and IUF were observed on neutrophil counts. Positive EC results were unrelated to the cumulative conception rate at 70 and 120 days in milk, whereas cows returning a positive EC result in V1 showed a greater likelihood of increased endometrial thickness. In conclusion, measuring cervical diameter, endometrial thickness, and detecting the echogenicity of IUF by ultrasonography from Days 15 to 21 postpartum in clinically normal cows is an appropriate tool to predict subsequent reproductive performance. Vaginal examination and transrectal palpation alone did not emerge as valuable predictors. Copyright © 2012 Elsevier Inc. All rights reserved.

Effect of radiance-to-reflectance transformation and atmosphere removal on maximum likelihood classification accuracy of high-dimensional remote sensing data

NASA Technical Reports Server (NTRS)

Hoffbeck, Joseph P.; Landgrebe, David A.

1994-01-01

Many analysis algorithms for high-dimensional remote sensing data require that the remotely sensed radiance spectra be transformed to approximate reflectance to allow comparison with a library of laboratory reflectance spectra. In maximum likelihood classification, however, the remotely sensed spectra are compared to training samples, thus a transformation to reflectance may or may not be helpful. The effect of several radiance-to-reflectance transformations on maximum likelihood classification accuracy is investigated in this paper. We show that the empirical line approach, LOWTRAN7, flat-field correction, single spectrum method, and internal average reflectance are all non-singular affine transformations, and that non-singular affine transformations have no effect on discriminant analysis feature extraction and maximum likelihood classification accuracy. (An affine transformation is a linear transformation with an optional offset.) Since the Atmosphere Removal Program (ATREM) and the log residue method are not affine transformations, experiments with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were conducted to determine the effect of these transformations on maximum likelihood classification accuracy. The average classification accuracy of the data transformed by ATREM and the log residue method was slightly less than the accuracy of the original radiance data. Since the radiance-to-reflectance transformations allow direct comparison of remotely sensed spectra with laboratory reflectance spectra, they can be quite useful in labeling the training samples required by maximum likelihood classification, but these transformations have only a slight effect or no effect at all on discriminant analysis and maximum likelihood classification accuracy.

Inverse problems-based maximum likelihood estimation of ground reflectivity for selected regions of interest from stripmap SAR data [Regularized maximum likelihood estimation of ground reflectivity from stripmap SAR data

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

West, R. Derek; Gunther, Jacob H.; Moon, Todd K.

In this study, we derive a comprehensive forward model for the data collected by stripmap synthetic aperture radar (SAR) that is linear in the ground reflectivity parameters. It is also shown that if the noise model is additive, then the forward model fits into the linear statistical model framework, and the ground reflectivity parameters can be estimated by statistical methods. We derive the maximum likelihood (ML) estimates for the ground reflectivity parameters in the case of additive white Gaussian noise. Furthermore, we show that obtaining the ML estimates of the ground reflectivity requires two steps. The first step amounts tomore » a cross-correlation of the data with a model of the data acquisition parameters, and it is shown that this step has essentially the same processing as the so-called convolution back-projection algorithm. The second step is a complete system inversion that is capable of mitigating the sidelobes of the spatially variant impulse responses remaining after the correlation processing. We also state the Cramer-Rao lower bound (CRLB) for the ML ground reflectivity estimates.We show that the CRLB is linked to the SAR system parameters, the flight path of the SAR sensor, and the image reconstruction grid.We demonstrate the ML image formation and the CRLB bound for synthetically generated data.« less

Inverse problems-based maximum likelihood estimation of ground reflectivity for selected regions of interest from stripmap SAR data [Regularized maximum likelihood estimation of ground reflectivity from stripmap SAR data

DOE PAGES

West, R. Derek; Gunther, Jacob H.; Moon, Todd K.

2016-12-01

In this study, we derive a comprehensive forward model for the data collected by stripmap synthetic aperture radar (SAR) that is linear in the ground reflectivity parameters. It is also shown that if the noise model is additive, then the forward model fits into the linear statistical model framework, and the ground reflectivity parameters can be estimated by statistical methods. We derive the maximum likelihood (ML) estimates for the ground reflectivity parameters in the case of additive white Gaussian noise. Furthermore, we show that obtaining the ML estimates of the ground reflectivity requires two steps. The first step amounts tomore » a cross-correlation of the data with a model of the data acquisition parameters, and it is shown that this step has essentially the same processing as the so-called convolution back-projection algorithm. The second step is a complete system inversion that is capable of mitigating the sidelobes of the spatially variant impulse responses remaining after the correlation processing. We also state the Cramer-Rao lower bound (CRLB) for the ML ground reflectivity estimates.We show that the CRLB is linked to the SAR system parameters, the flight path of the SAR sensor, and the image reconstruction grid.We demonstrate the ML image formation and the CRLB bound for synthetically generated data.« less

Spectral-Spatial Shared Linear Regression for Hyperspectral Image Classification.

PubMed

Haoliang Yuan; Yuan Yan Tang

2017-04-01

Classification of the pixels in hyperspectral image (HSI) is an important task and has been popularly applied in many practical applications. Its major challenge is the high-dimensional small-sized problem. To deal with this problem, lots of subspace learning (SL) methods are developed to reduce the dimension of the pixels while preserving the important discriminant information. Motivated by ridge linear regression (RLR) framework for SL, we propose a spectral-spatial shared linear regression method (SSSLR) for extracting the feature representation. Comparing with RLR, our proposed SSSLR has the following two advantages. First, we utilize a convex set to explore the spatial structure for computing the linear projection matrix. Second, we utilize a shared structure learning model, which is formed by original data space and a hidden feature space, to learn a more discriminant linear projection matrix for classification. To optimize our proposed method, an efficient iterative algorithm is proposed. Experimental results on two popular HSI data sets, i.e., Indian Pines and Salinas demonstrate that our proposed methods outperform many SL methods.

Simple linear and multivariate regression models.

PubMed

Rodríguez del Águila, M M; Benítez-Parejo, N

2011-01-01

In biomedical research it is common to find problems in which we wish to relate a response variable to one or more variables capable of describing the behaviour of the former variable by means of mathematical models. Regression techniques are used to this effect, in which an equation is determined relating the two variables. While such equations can have different forms, linear equations are the most widely used form and are easy to interpret. The present article describes simple and multiple linear regression models, how they are calculated, and how their applicability assumptions are checked. Illustrative examples are provided, based on the use of the freely accessible R program. Copyright © 2011 SEICAP. Published by Elsevier Espana. All rights reserved.

A data reconnaissance on the effect of suspended-sediment concentrations on dissolved-solids concentrations in rivers and tributaries in the Upper Colorado River Basin

USGS Publications Warehouse

Tillman, Fred D.; Anning, David W.

2014-01-01

The Colorado River is one of the most important sources of water in the western United States, supplying water to over 35 million people in the U.S. and 3 million people in Mexico. High dissolved-solids loading to the River and tributaries are derived primarily from geologic material deposited in inland seas in the mid-to-late Cretaceous Period, but this loading may be increased by human activities. High dissolved solids in the River causes substantial damages to users, primarily in reduced agricultural crop yields and corrosion. The Colorado River Basin Salinity Control Program was created to manage dissolved-solids loading to the River and has focused primarily on reducing irrigation-related loading from agricultural areas. This work presents a reconnaissance of existing data from sites in the Upper Colorado River Basin (UCRB) in order to highlight areas where suspended-sediment control measures may be useful in reducing dissolved-solids concentrations. Multiple linear regression was used on data from 164 sites in the UCRB to develop dissolved-solids models that include combinations of explanatory variables of suspended sediment, flow, and time. Results from the partial t-test, overall likelihood ratio, and partial likelihood ratio on the models were used to group the sites into categories of strong, moderate, weak, and no-evidence of a relation between suspended-sediment and dissolved-solids concentrations. Results show 68 sites have strong or moderate evidence of a relation, with drainage areas for many of these sites composed of a large percentage of clastic sedimentary rocks. These results could assist water managers in the region in directing field-scale evaluation of suspended-sediment control measures to reduce UCRB dissolved-solids loading.

Does More Therapy in Skilled Nursing Facilities Lead to Better Outcomes in Patients With Hip Fracture?

PubMed Central

Trivedi, Amal N.; Grabowski, David C.; Mor, Vincent

2016-01-01

Background Skilled nursing facilities (SNFs) have increasingly been providing more therapy hours to beneficiaries of Medicare. It is not known whether these increases have improved patient outcomes. Objective The study objectives were: (1) to examine temporal trends in therapy hour volumes and (2) to evaluate whether more therapy hours are associated with improved patient outcomes. Design This was a retrospective cohort study. Methods Data sources included the Minimum Data Set, Medicare inpatient claims, and the Online Survey, Certification, and Reporting System. The study population consisted of 481,908 beneficiaries of Medicare fee-for-service who were admitted to 15,496 SNFs after hip fracture from 2000 to 2009. Linear regression models with facility and time fixed effects were used to estimate the association between the quantity of therapy provided in SNFs and the likelihood of discharge to home. Results The average number of therapy hours increased by 52% during the study period, with relatively little change in case mix at SNF admission. An additional hour of therapy per week was associated with a 3.1-percentage-point (95% confidence interval=3.0, 3.1) increase in the likelihood of discharge to home. The effect of additional therapy decreased as the Resource Utilization Group category increased, and additional therapy did not benefit patients in the highest Resource Utilization Group category. Limitations Minimum Data Set assessments did not cover details of therapeutic interventions throughout the entire SNF stay and captured only a 7-day retrospective period for measures of the quantity of therapy provided. Conclusions Increases in the quantity of therapy during the study period cannot be explained by changes in case mix at SNF admission. More therapy hours in SNFs appear to improve outcomes, except for patients with the greatest need. PMID:26586858

[Influence of habitual chocolate consumption over the Mini-Mental State Examination in Spanish older adults].

PubMed

Orozco Arbelaez, Edilbeto; Banegas, José Ramón; Rodríguez Artalejo, Fernando; López García, Esther

2017-07-28

There are associations described between dementia, mild cognitive impairment (MCI) and foods with a high content of polyphenols. To assess the infl uence of habitual chocolate consumption over the MMSE in Spanish older adults. Cross-sectional study, using data of the follow-up of the Seniors-Study on Nutrition and Cardiovascular Risk in Spain (ENRICA) cohort. Habitual chocolate consumption in the last year was assessed with a computerized dietary history; differences between dark chocolate and milk chocolate were recorded. Chocolate intake was classified into the following categories: no consumption, < 10 g/day, and ≥ 10 g/day. Validated MMSE scores for Spain were obtained during an interview and different cutoff points were used to define ≤ 25, ≤ 24 and ≤ 23. Linear and logistic regression models were used to calculate adjusted beta coefficients and odds ratios (OR). Compared to non-consumers, participants with a habitual chocolate consumption of ≥ 10 g/d had a better MMSE score (adjusted beta coefficient and 95% confidence interval: 0.26 (0.02-0.50; p trend = 0.05); for dark chocolate, the results were also statistically significant (0.48 [0.18-0.78]; p trend < 0.001). Total chocolate consumption was not associated with higher likelihood of having MCI. However, dark chocolate consumption was associated with less likelihood of MCI (OR and 95%CI for MMSE ≤ 25: 0.39 [0.20-0.77]; for MMSE ≤ 24: 0.26 [0.10-0.67]; and for MMSE ≤ 23: 0.25 [0.07-0.82]). Our results suggest that habitual dark chocolate consumption might improve cognitive function among the older population.

Does More Therapy in Skilled Nursing Facilities Lead to Better Outcomes in Patients With Hip Fracture?

PubMed

Jung, Hye-Young; Trivedi, Amal N; Grabowski, David C; Mor, Vincent

2016-01-01

Skilled nursing facilities (SNFs) have increasingly been providing more therapy hours to beneficiaries of Medicare. It is not known whether these increases have improved patient outcomes. The study objectives were: (1) to examine temporal trends in therapy hour volumes and (2) to evaluate whether more therapy hours are associated with improved patient outcomes. This was a retrospective cohort study. Data sources included the Minimum Data Set, Medicare inpatient claims, and the Online Survey, Certification, and Reporting System. The study population consisted of 481,908 beneficiaries of Medicare fee-for-service who were admitted to 15,496 SNFs after hip fracture from 2000 to 2009. Linear regression models with facility and time fixed effects were used to estimate the association between the quantity of therapy provided in SNFs and the likelihood of discharge to home. The average number of therapy hours increased by 52% during the study period, with relatively little change in case mix at SNF admission. An additional hour of therapy per week was associated with a 3.1-percentage-point (95% confidence interval=3.0, 3.1) increase in the likelihood of discharge to home. The effect of additional therapy decreased as the Resource Utilization Group category increased, and additional therapy did not benefit patients in the highest Resource Utilization Group category. Minimum Data Set assessments did not cover details of therapeutic interventions throughout the entire SNF stay and captured only a 7-day retrospective period for measures of the quantity of therapy provided. Increases in the quantity of therapy during the study period cannot be explained by changes in case mix at SNF admission. More therapy hours in SNFs appear to improve outcomes, except for patients with the greatest need. © 2016 American Physical Therapy Association.

Leveraging cues from person-generated health data for peer matching in online communities

PubMed Central

Hartzler, Andrea L; Taylor, Megan N; Park, Albert; Griffiths, Troy; Backonja, Uba; McDonald, David W; Wahbeh, Sam; Brown, Cory; Pratt, Wanda

2016-01-01

Objective Online health communities offer a diverse peer support base, yet users can struggle to identify suitable peer mentors as these communities grow. To facilitate mentoring connections, we designed a peer-matching system that automatically profiles and recommends peer mentors to mentees based on person-generated health data (PGHD). This study examined the profile characteristics that mentees value when choosing a peer mentor. Materials and Methods Through a mixed-methods user study, in which cancer patients and caregivers evaluated peer mentor recommendations, we examined the relative importance of four possible profile elements: health interests, language style, demographics, and sample posts. Playing the role of mentees, the study participants ranked mentors, then rated both the likelihood that they would hypothetically contact each mentor and the helpfulness of each profile element in helping the make that decision. We analyzed the participants’ ratings with linear regression and qualitatively analyzed participants’ feedback for emerging themes about choosing mentors and improving profile design. Results Of the four profile elements, only sample posts were a significant predictor for the likelihood of a mentee contacting a mentor. Communication cues embedded in posts were critical for helping the participants choose a compatible mentor. Qualitative themes offer insight into the interpersonal characteristics that mentees sought in peer mentors, including being knowledgeable, sociable, and articulate. Additionally, the participants emphasized the need for streamlined profiles that minimize the time required to choose a mentor. Conclusion Peer-matching systems in online health communities offer a promising approach for leveraging PGHD to connect patients. Our findings point to interpersonal communication cues embedded in PGHD that could prove critical for building mentoring relationships among the growing membership of online health communities. PMID:26911825

Treatment, survival, and costs of laryngeal cancer care in the elderly.

PubMed

Gourin, Christine G; Dy, Sydney M; Herbert, Robert J; Blackford, Amanda L; Quon, Harry; Forastiere, Arlene A; Eisele, David W; Frick, Kevin D

2014-08-01

To examine associations between treatment and volume with survival and costs in elderly patients with laryngeal squamous cell cancer (SCCA). Retrospective cross-sectional analysis of Surveillance, Epidemiology, and End Results-Medicare data. We evaluated 2,370 patients diagnosed with laryngeal SCCA from 2004 to 2007 using cross-tabulations, multivariate logistic and generalized linear regression modeling, and survival analysis. Chemoradiation was significantly associated with supraglottic tumors (relative risk ratio: 2.6, 95% confidence interval [CI]: 1.7-4.0), additional cancer-directed treatment (odds ratio [OR]: 1.8, 95% CI: 1.2-2.7), and a reduced likelihood of surgical salvage (OR: 0.3, 95% CI: 0.2-0.6). Surgery with postoperative radiation was associated with significantly improved survival (hazard ratio [HR]: 0.7, 95% CI: 0.6-0.9), after controlling for patient and tumor variables including salvage. High-volume care was not associated with survival for nonoperative treatment but was associated with improved survival (HR: 0.7, 95% CI: 0.5-0.8) among surgical patients. Initial treatment and 5-year overall costs for chemoradiation were higher than for all other treatment categories. High-volume care was associated with significantly lower costs of care for surgical patients but was not associated with differences in costs of care for nonoperative treatment. Chemoradiation in elderly patients with laryngeal cancer was associated with increased costs, additional cancer-directed treatment, and a reduced likelihood of surgical salvage. Surgery with postoperative radiation was associated with improved survival in this cohort, and high-volume hospital surgical care was associated with improved survival and lower costs. These findings have implications for improving the quality of laryngeal cancer treatment at a time of both rapid growth in the elderly population and diminishing healthcare resources. © 2014 The American Laryngological, Rhinological and Otological Society, Inc.

Sleep Disruption Medical Intervention Forecasting (SDMIF) Module for the Integrated Medical Model

NASA Technical Reports Server (NTRS)

Lewandowski, Beth; Brooker, John; Mallis, Melissa; Hursh, Steve; Caldwell, Lynn; Myers, Jerry

2011-01-01

The NASA Integrated Medical Model (IMM) assesses the risk, including likelihood and impact of occurrence, of all credible in-flight medical conditions. Fatigue due to sleep disruption is a condition that could lead to operational errors, potentially resulting in loss of mission or crew. Pharmacological consumables are mitigation strategies used to manage the risks associated with sleep deficits. The likelihood of medical intervention due to sleep disruption was estimated with a well validated sleep model and a Monte Carlo computer simulation in an effort to optimize the quantity of consumables. METHODS: The key components of the model are the mission parameter program, the calculation of sleep intensity and the diagnosis and decision module. The mission parameter program was used to create simulated daily sleep/wake schedules for an ISS increment. The hypothetical schedules included critical events such as dockings and extravehicular activities and included actual sleep time and sleep quality. The schedules were used as inputs to the Sleep, Activity, Fatigue and Task Effectiveness (SAFTE) Model (IBR Inc., Baltimore MD), which calculated sleep intensity. Sleep data from an ISS study was used to relate calculated sleep intensity to the probability of sleep medication use, using a generalized linear model for binomial regression. A human yes/no decision process using a binomial random number was also factored into sleep medication use probability. RESULTS: These probability calculations were repeated 5000 times resulting in an estimate of the most likely amount of sleep aids used during an ISS mission and a 95% confidence interval. CONCLUSIONS: These results were transferred to the parent IMM for further weighting and integration with other medical conditions, to help inform operational decisions. This model is a potential planning tool for ensuring adequate sleep during sleep disrupted periods of a mission.

Metabolic syndrome and subclinical carotid atherosclerosis among Chinese elderly people living in a rural community.

PubMed

Yan, Zhongrui; Liang, Yajun; Jiang, Hui; Cai, Chuanzhu; Sun, Binglun; Qiu, Chengxuan

2014-06-01

The relationship between the metabolic syndrome and subclinical atherosclerosis among elderly people remains controversial. We seek to investigate the associations of metabolic syndrome and its components with subclinical carotid atherosclerosis in older people living in rural China. This cross-sectional study included 911 participants (age ≥60 years, 59.4% women) of the community-based Confucius Hometown Aging Project in Shandong, China, who were free of clinical cardiovascular disease. In 2010-2011, data were collected through interviews, clinical examinations, and laboratory tests. Carotid intima media thickness (cIMT) and carotid artery plaque were assessed using ultrasonography. Metabolic syndrome was defined by four sets of criteria. Data were analyzed with multiple general linear and logistic regression models. Carotid plaque was detected in 514 (56.4%) persons. Depending on the defining criteria, prevalence of metabolic syndrome ranged from 30.6% to 56.9%. Hypertension, elevated blood pressure (≥130/85 mmHg), diabetes/prediabetes, and high fasting plasma glucose (≥5.6 mmol/L) were significantly associated with an increased likelihood of carotid plaque, whereas an increased cIMT was associated with elevated blood pressure and hypertension (P<0.05). Obesity, high triglycerides, and dyslipidemia were not associated with carotid artery plaque or cIMT. The significant association of metabolic syndrome with an increased likelihood of carotid plaque disappeared when elevated blood pressure and high plasma glucose were added to the models. Metabolic syndrome was not associated with cIMT. Metabolic syndrome is associated with subclinical carotid plaque among Chinese elderly people, driven primarily by elevated blood pressure and hyperglycemia. Older adults with metabolic syndrome, especially those with high blood pressure and hyperglycemia, may benefit from evaluation for subclinical atherosclerosis.

Toll-like Receptor 4 and Comorbid Pain in Interstitial Cystitis/Bladder Pain Syndrome: A Multidisciplinary Approach to the Study of Chronic Pelvic Pain Research Network Study

PubMed Central

Schrepf, Andrew; Bradley, Catherine S.; O'Donnell, Michael; Luo, Yi; Harte, Steven E.; Kreder, Karl; Lutgendorf, Susan

2015-01-01

Background Interstitial Cystitis/Bladder Pain Syndrome (IC/BPS) is a condition characterized by pelvic pain and urinary symptoms. Some IC/BPS patients have pain confined to the pelvic region, while others suffer widespread pain. Inflammatory processes have previously been linked to pelvic pain in IC/BPS, but their association with widespread pain in IC/BPS has not been characterized. Methods Sixty-six women meeting criteria for IC/BPS completed self-report measures of pain as part of the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP), collected 3 days of saliva for cortisol assays, and provided blood samples. Peripheral blood mononuclear cells (PBMCs) were stimulated with Toll-Like Receptor (TLR) 2 and 4 agonists and cytokines were measured in supernatant; IL-6 was also measured in plasma. Associations between inflammatory variables and the likelihood of endorsing extra-pelvic pain, or the presence of a comorbid syndrome, were tested by logistic regression and General Linear Models, respectively. A subset of patients (n=32) completed Quantitative Sensory Testing. Results A one standard deviation increase in TLR-4 inflammatory response was associated with a 1.59 greater likelihood of endorsing extra-pelvic pain (p = .019). Participants with comorbid syndromes also had higher inflammatory responses to TLR-4 stimulation in PBMCs (p = .016). Lower pressure pain thresholds were marginally associated with higher TLR-4 inflammatory responses (p = .062), and significantly associated with higher IL-6 in plasma (p = .031). Conclusions TLR-4 inflammatory responses in PBMCs are a marker of widespread pain in IC/BPS, and should be explored in other conditions characterized by medically unexplained pain. PMID:25771510

Zero-inflated Conway-Maxwell Poisson Distribution to Analyze Discrete Data.

PubMed

Sim, Shin Zhu; Gupta, Ramesh C; Ong, Seng Huat

2018-01-09

In this paper, we study the zero-inflated Conway-Maxwell Poisson (ZICMP) distribution and develop a regression model. Score and likelihood ratio tests are also implemented for testing the inflation/deflation parameter. Simulation studies are carried out to examine the performance of these tests. A data example is presented to illustrate the concepts. In this example, the proposed model is compared to the well-known zero-inflated Poisson (ZIP) and the zero- inflated generalized Poisson (ZIGP) regression models. It is shown that the fit by ZICMP is comparable or better than these models.

A Powerful Test for Comparing Multiple Regression Functions.

PubMed

Maity, Arnab

2012-09-01

In this article, we address the important problem of comparison of two or more population regression functions. Recently, Pardo-Fernández, Van Keilegom and González-Manteiga (2007) developed test statistics for simple nonparametric regression models: Y(ij) = θ(j)(Z(ij)) + σ(j)(Z(ij))∊(ij), based on empirical distributions of the errors in each population j = 1, … , J. In this paper, we propose a test for equality of the θ(j)(·) based on the concept of generalized likelihood ratio type statistics. We also generalize our test for other nonparametric regression setups, e.g, nonparametric logistic regression, where the loglikelihood for population j is any general smooth function [Formula: see text]. We describe a resampling procedure to obtain the critical values of the test. In addition, we present a simulation study to evaluate the performance of the proposed test and compare our results to those in Pardo-Fernández et al. (2007).

Estimating the exceedance probability of rain rate by logistic regression

NASA Technical Reports Server (NTRS)

Chiu, Long S.; Kedem, Benjamin

1990-01-01

Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.

Soft-decision decoding techniques for linear block codes and their error performance analysis

NASA Technical Reports Server (NTRS)

Lin, Shu

1996-01-01

The first paper presents a new minimum-weight trellis-based soft-decision iterative decoding algorithm for binary linear block codes. The second paper derives an upper bound on the probability of block error for multilevel concatenated codes (MLCC). The bound evaluates difference in performance for different decompositions of some codes. The third paper investigates the bit error probability code for maximum likelihood decoding of binary linear codes. The fourth and final paper included in this report is concerns itself with the construction of multilevel concatenated block modulation codes using a multilevel concatenation scheme for the frequency non-selective Rayleigh fading channel.

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

PubMed

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

2017-04-01

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

London Measure of Unplanned Pregnancy: guidance for its use as an outcome measure

PubMed Central

Hall, Jennifer A; Barrett, Geraldine; Copas, Andrew; Stephenson, Judith

2017-01-01

Background The London Measure of Unplanned Pregnancy (LMUP) is a psychometrically validated measure of the degree of intention of a current or recent pregnancy. The LMUP is increasingly being used worldwide, and can be used to evaluate family planning or preconception care programs. However, beyond recommending the use of the full LMUP scale, there is no published guidance on how to use the LMUP as an outcome measure. Ordinal logistic regression has been recommended informally, but studies published to date have all used binary logistic regression and dichotomized the scale at different cut points. There is thus a need for evidence-based guidance to provide a standardized methodology for multivariate analysis and to enable comparison of results. This paper makes recommendations for the regression method for analysis of the LMUP as an outcome measure. Materials and methods Data collected from 4,244 pregnant women in Malawi were used to compare five regression methods: linear, logistic with two cut points, and ordinal logistic with either the full or grouped LMUP score. The recommendations were then tested on the original UK LMUP data. Results There were small but no important differences in the findings across the regression models. Logistic regression resulted in the largest loss of information, and assumptions were violated for the linear and ordinal logistic regression. Consequently, robust standard errors were used for linear regression and a partial proportional odds ordinal logistic regression model attempted. The latter could only be fitted for grouped LMUP score. Conclusion We recommend the linear regression model with robust standard errors to make full use of the LMUP score when analyzed as an outcome measure. Ordinal logistic regression could be considered, but a partial proportional odds model with grouped LMUP score may be required. Logistic regression is the least-favored option, due to the loss of information. For logistic regression, the cut point for un/planned pregnancy should be between nine and ten. These recommendations will standardize the analysis of LMUP data and enhance comparability of results across studies. PMID:28435343

Improved statistical analysis of moclobemide dose effects on panic disorder treatment.

PubMed

Ross, Donald C; Klein, Donald F; Uhlenhuth, E H

2010-04-01

Clinical trials with several measurement occasions are frequently analyzed using only the last available observation as the dependent variable [last observation carried forward (LOCF)]. This ignores intermediate observations. We reanalyze, with complete data methods, a clinical trial previously reported using LOCF, comparing placebo and five dosage levels of moclobemide in the treatment of outpatients with panic disorder to illustrate the superiority of methods using repeated observations. We initially analyzed unprovoked and situational, major and minor attacks as the four dependent variables, by repeated measures maximum likelihood methods. The model included parameters for linear and curvilinear time trends and regression of measures during treatment on baseline measures. Significance tests using this method take into account the structure of the error covariance matrix. This makes the sphericity assumption irrelevant. Missingness is assumed to be unrelated to eventual outcome and the residuals are assumed to have a multivariate normal distribution. No differential treatment effects for limited attacks were found. Since similar results were obtained for both types of major attack, data for the two types of major attack were combined. Overall downward linear and negatively accelerated downward curvilinear time trends were found. There were highly significant treatment differences in the regression slopes of scores during treatment on baseline observations. For major attacks, all treatment groups improved over time. The flatter regression slopes, obtained with higher doses, indicated that higher doses result in uniformly lower attack rates regardless of initial severity. Lower doses do not lower the attack rate of severely ill patients to those achieved in the less severely ill. The clinical implication is that more severe patients require higher doses to attain best benefit. Further, the significance levels obtained by LOCF analyses were only in the 0.05-0.01 range, while significance levels of <0.00001 were obtained by these repeated measures analyses indicating increased power. The greater sensitivity to treatment effect of this complete data method is illustrated. To increase power, it is often recommended to increase sample size. However, this is often impractical since a major proportion of the cost per subject is due to the initial evaluation. Increasing the number of repeated observations increases power economically and also allows detailed longitudinal trajectory analyses.