Random effects model

About: Random effects model is a research topic. Over the lifetime, 8388 publications have been published within this topic receiving 438823 citations. The topic is also known as: random effects & random effect.

Smooth random effects distribution in a linear mixed model.

Abstract: A linear mixed model with a smooth random effects density is proposed. A similar approach to P-spline smoothing of Eilers and Marx (1996, Statistical Science 11, 89-121) is applied to yield a more flexible estimate of the random effects density. Our approach differs from theirs in that the B-spline basis functions are replaced by approximating Gaussian densities. Fitting the model involves maximizing a penalized marginal likelihood. The best penalty parameters minimize Akaike's Information Criterion employing Gray's (1992, Journal of the American Statistical Association 87, 942-951) results. Although our method is applicable to any dimensions of the random effects structure, in this article the two-dimensional case is explored. Our methodology is conceptually simple, and it is relatively easy to fit in practice and is applied to the cholesterol data first analyzed by Zhang and Davidian (2001, Biometrics 57, 795-802). A simulation study shows that our approach yields almost unbiased estimates of the regression and the smoothing parameters in small sample settings. Consistency of the estimates is shown in a particular case.

Analysis of Longitudinal Data in the Presence of Informative Observational Times and a Dependent Terminal Event, with Application to Medical Cost Data

Abstract: In longitudinal observational studies, repeated measures are often taken at informative observation times. Also, there may exist a dependent terminal event such as death that stops the follow-up. For example, patients in poorer health are more likely to seek medical treatment and their medical cost for each visit tends to be higher. They are also subject to a higher mortality rate. In this article, we propose a random effects model of repeated measures in the presence of both informative observation times and a dependent terminal event. Three submodels are used, respectively, for (1) the intensity of recurrent observation times, (2) the amount of repeated measure at each observation time, and (3) the hazard of death. Correlated random effects are incorporated to join the three submodels. The estimation can be conveniently accomplished by Gaussian quadrature techniques, e.g., SAS Proc NLMIXED. An analysis of the cost-accrual process of chronic heart failure patients from the clinical data repository at the University of Virginia Health System is presented to illustrate the proposed method.

Mixture of linear mixed models for clustering gene expression profiles from repeated microarray experiments

Abstract: Data variability can be important in microarray data analysis. Thus, when clustering gene expression profiles, it could be judicious to make use of repeated data. In this paper, the problem of anal...

Random effects meta-analysis: Coverage performance of 95% confidence and prediction intervals following REML estimation.

Abstract: A random effects meta-analysis combines the results of several independent studies to summarise the evidence about a particular measure of interest, such as a treatment effect. The approach allows for unexplained between-study heterogeneity in the true treatment effect by incorporating random study effects about the overall mean. The variance of the mean effect estimate is conventionally calculated by assuming that the between study variance is known; however, it has been demonstrated that this approach may be inappropriate, especially when there are few studies. Alternative methods that aim to account for this uncertainty, such as Hartung-Knapp, Sidik-Jonkman and Kenward-Roger, have been proposed and shown to improve upon the conventional approach in some situations. In this paper, we use a simulation study to examine the performance of several of these methods in terms of the coverage of the 95% confidence and prediction intervals derived from a random effects meta-analysis estimated using restricted maximum likelihood. We show that, in terms of the confidence intervals, the Hartung-Knapp correction performs well across a wide-range of scenarios and outperforms other methods when heterogeneity was large and/or study sizes were similar. However, the coverage of the Hartung-Knapp method is slightly too low when the heterogeneity is low (I2 30%) and study sizes are similar. In other situations, especially when heterogeneity is small and the study sizes are quite varied, the coverage is far too low and could not be consistently improved by either increasing the number of studies, altering the degrees of freedom or using variance inflation methods. Therefore, researchers should be cautious in deriving 95% prediction intervals following a frequentist random-effects meta-analysis until a more reliable solution is identified. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Some approaches to the analysis of recurrent event data

Abstract: Methodological research in biostatistics has been dominated over the last twenty years by further development of Cox's regression model for life tables and of Nelder and Wedderburn's formulation of generalized linear models. In both of these areas the need to address the problems introduced by subject level heterogeneity has provided a major motivation, and the analysis of data concerning recurrent events has been widely discussed within both frameworks. This paper reviews this work, drawing together the parallel development of 'marginal' and 'conditional' approaches in survival analysis and in generalized linear models. Frailty models are shown to be a special case of a random effects generalization of generalized linear models, whereas marginal models for multivariate failure time data are more closely related to the generalized estimating equation approach to longitudinal generalized linear models. Computational methods for inference are discussed, including the Bayesian Markov chain Monte Carlo approach.