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.

Trying to be precise about vagueness.

Abstract: A previous investigation by Lambert et al., which used computer simulation to examine the influence of choice of prior distribution on inferences from Bayesian random effects meta-analysis, is critically examined from a number of viewpoints. The practical example used is shown to be problematic. The various prior distributions are shown to be unreasonable in terms of what they imply about the joint distribution of the overall treatment effect and the random effects variance. An alternative form of prior distribution is tentatively proposed. Finally, some practical recommendations are made that stress the value both of fixed effect analyses and of frequentist approaches as well as various diagnostic investigations.

Modelling the sampling design in the analysis of health surveys

Abstract: Health surveys typically have stratified multistage clustered designs in which individuals are sampled with differing probabilities The sampling design is taken into account in a classical survey analysis by using sample-weighted estimators and variance estimators calculated at the primary-sampling-unit level In this paper we investigate the possibility of modelling the sampling design using fixed and random effects to redefine target parameters, improve estimators of standard target parameters and improve standard variance estimators References in which this type of additional modelling was used in health surveys are given The problem of estimating population variance components is discussed in some detail, with an application involving estimation of between- and within-family variance components in the Hispanic Health and Nutrition Examination Survey

Correcting for population structure and kinship using the linear mixed model: theory and extensions.

Abstract: Population structure and kinship are widespread confounding factors in genome-wide association studies (GWAS). It has been standard practice to include principal components of the genotypes in a regression model in order to account for population structure. More recently, the linear mixed model (LMM) has emerged as a powerful method for simultaneously accounting for population structure and kinship. The statistical theory underlying the differences in empirical performance between modeling principal components as fixed versus random effects has not been thoroughly examined. We undertake an analysis to formalize the relationship between these widely used methods and elucidate the statistical properties of each. Moreover, we introduce a new statistic, effective degrees of freedom, that serves as a metric of model complexity and a novel low rank linear mixed model (LRLMM) to learn the dimensionality of the correction for population structure and kinship, and we assess its performance through simulations. A comparison of the results of LRLMM and a standard LMM analysis applied to GWAS data from the Multi-Ethnic Study of Atherosclerosis (MESA) illustrates how our theoretical results translate into empirical properties of the mixed model. Finally, the analysis demonstrates the ability of the LRLMM to substantially boost the strength of an association for HDL cholesterol in Europeans.