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.

Recovery of Interblock, Intergradient, and Intervariety Information in Incomplete Block and Lattice Rectangle Designed Experiments

Abstract: Spatial analysis and blocking analysis of experimental results are treated separately in the literature. Here we combine these analyses into a single analysis. The information arising from the random nature of different gradients within incomplete blocks is used to adjust treatment means. We extend Cox's (1958, Journal of the Royal Statistical Society, Series B 20, 193-204) idea of differential gradients within columns of a Latin square to within blocks for incomplete block and row-column designed experiments and, in addition, treat them as random effects. With this analysis, the restrictions on randomization due to blocking are taken into consideration whereas they are often ignored in spatial analysis literature. Some comments on designing experiments and analyzing experimental results to control heterogeneity are presented. A numerical example illustrates the computational procedure and indicates effect of alternative analyses. The class of augmented experiment designs has been found useful for experiments involving comparisons of standard check treatments with a set of new and untried treatments, usually with one replicate. Interreplicate, interblock, interrow, and/or intercolumn information is available to use in obtaining solutions for new treatment effects. Since the new treatment effects are often considered to be random effects, their distributional properties may be used to increase the efficiency of the experiment. We demonstrate the statistical procedures for recovering this information in block and row-column designs using mixed model procedures.

Sample size determination for hierarchical longitudinal designs with differential attrition rates.

Abstract: We consider the problem of sample size determination for three-level mixed-effects linear regression models for the analysis of clustered longitudinal data. Three-level designs are used in many areas, but in particular, multicenter randomized longitudinal clinical trials in medical or health-related research. In this case, level 1 represents measurement occasion, level 2 represents subject, and level 3 represents center. The model we consider involves random effects of the time trends at both the subject level and the center level. In the most common case, we have two random effects (constant and a single trend), at both subject and center levels. The approach presented here is general with respect to sampling proportions, number of groups, and attrition rates over time. In addition, we also develop a cost model, as an aid in selecting the most parsimonious of several possible competing models (i.e., different combinations of centers, subjects within centers, and measurement occasions). We derive sample size requirements (i.e., power characteristics) for a test of treatment-by-time interaction(s) for designs based on either subject-level or cluster-level randomization. The general methodology is illustrated using two characteristic examples.

A random effects model for ordinal responses from a crossover trial

Abstract: Crossover studies have been successfully conducted in the case of continuous responses. Existing procedures of analysis for ordinal responses, on the other hand, are rarely satisfactory unless strict, usually unrealistic, assumptions are made. In this paper we investigate a random effects model and show that the model is simple and general. Interpretation of parameters is easy, though with a complicated fitting procedure.

Translational methods in biostatistics: linear mixed effect regression models of alcohol consumption and HIV disease progression over time

Abstract: Longitudinal studies are helpful in understanding how subtle associations between factors of interest change over time. Our goal is to apply statistical methods which are appropriate for analyzing longitudinal data to a repeated measures epidemiological study as a tutorial in the appropriate use and interpretation of random effects models. To motivate their use, we study the association of alcohol consumption on markers of HIV disease progression in an observational cohort. To make valid inferences, the association among measurements correlated within a subject must be taken into account. We describe a linear mixed effects regression framework that accounts for the clustering of longitudinal data and that can be fit using standard statistical software. We apply the linear mixed effects model to a previously published dataset of HIV infected individuals with a history of alcohol problems who are receiving HAART (n = 197). The researchers were interested in determining the effect of alcohol use on HIV disease progression over time. Fitting a linear mixed effects multiple regression model with a random intercept and random slope for each subject accounts for the association of observations within subjects and yields parameters interpretable as in ordinary multiple regression. A significant interaction between alcohol use and adherence to HAART is found: subjects who use alcohol and are not fully adherent to their HIV medications had higher log RNA (ribonucleic acid) viral load levels than fully adherent non-drinkers, fully adherent alcohol users, and non-drinkers who were not fully adherent. Longitudinal studies are increasingly common in epidemiological research. Software routines that account for correlation between repeated measures using linear mixed effects methods are now generally available and straightforward to utilize. These models allow the relaxation of assumptions needed for approaches such as repeated measures ANOVA, and should be routinely incorporated into the analysis of cohort studies.

Two-Part and Related Regression Models for Longitudinal Data.

Abstract: Statistical models that involve a two-part mixture distribution are applicable in a variety of situations. Frequently, the two parts are a model for the binary response variable and a model for the outcome variable that is conditioned on the binary response. Two common examples are zero-inflated or hurdle models for count data and two-part models for semicontinuous data. Recently, there has been particular interest in the use of these models for the analysis of repeated measures of an outcome variable over time. The aim of this review is to consider motivations for the use of such models in this context and to highlight the central issues that arise with their use. We examine two-part models for semicontinuous and zero-heavy count data, and we also consider models for count data with a two-part random effects distribution.