Topic
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.
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TL;DR: It will be demonstrated that a common practice of testing for homogeneity of effect size, and acting upon the inference to decide between fixed vs random effects, can lead to potentially misleading results.
Abstract: Meta-analyses can be powerful tools to combine the results of randomized clinical trials and observational studies to make consensus inferences about a medical issue. It will be demonstrated that a common practice of testing for homogeneity of effect size, and acting upon the inference to decide between fixed vs random effects, can lead to potentially misleading results. A by-product of this paper is a new ratio estimator approach to random effects meta-analysis of a large set of studies with low event rates. As a case study, we shall use the recent Rosiglitazone example, where diagnostic testing failed to reject homogeneity, leading the investigators to use fixed effects. The results for the fixed and random effects analyses are discordant. In the fixed (random) effects analysis, the p-values for myocardial infarction were 0.03 (0.11) while those for cardiac death were 0.06 (0.0017). Had the fixed effects analysis controlled the study error for multiple testing via a Bonferonni correction, the joint 95+ per cent confidence rectangle for the two outcomes would have included odds ratios of (1.0, 1.0). For the Rosiglitazone example, random effects analysis, where all studies receive the same weight, is the superior choice over fixed effects, where two large studies dominate.
106 citations
TL;DR: In this article, Park and Simar extended the results of Hausman and Taylor (1981) and Cornwell, Schmidt and Sickles (1990) by examining the semiparametric efficient estimation of panel models in which the random effects and regressors have certain patterns of correlation.
106 citations
TL;DR: In this article, the authors model the infant and early childhood survival using family and community random effect multipliers on the fixed effect proportional hazards model, which allows the dependence between observations in the same family and communities into the model.
Abstract: The Malawi Demographic and Health Survey conducted in 1992 collected the retrospective birth histories for a national sample of 4,878 women aged between 15 and 49 years. The sample was randomly selected by a two-stage sampling design. The data consist of biological, demographic, and social variables collected for each birth. This article models the infant and early childhood survival using family and community random effect multipliers on the fixed effect proportional hazards model, which allows the dependence between observations in the same family and community into the model. A Markov chain Monte Carlo sample from the posterior distribution of the parameters given the data is found. The standard errors of the fixed effect estimates are more correct than those found from the standard model, which are underestimated because of the ignored correlation structure.
105 citations
Journal Article•
TL;DR: The h-likelihood provides a unified framework for this new class of models and gives a single algorithm for fitting all members of the class, which will enable models with heavy-tailed distributions to be explored and provide robust estimation against outliers.
Abstract: We propose a class of double hierarchical generalized linear models in which random effects can be specified for both the mean and dispersion. Heteroscedasticity between clusters can be modelled by introducing random effects in the dispersion model, as is heterogeneity between clusters in the mean model. This class will, among other things, enable models with heavy-tailed distributions to be explored, providing robust estimation against outliers. The h-likelihood provides a unified framework for this new class of models and gives a single algorithm for fitting all members of the class. This algorithm does not require quadrature or prior probabilities.
105 citations
TL;DR: An in depth description of several highly efficient sampling schemes that allow to estimate complex models with several hierarchy levels and a large number of observations within a couple of minutes (often even seconds) is provided.
Abstract: Models with structured additive predictor provide a very broad and rich framework for complex regression modeling. They can deal simultaneously with nonlinear covariate effects and time trends, unit- or cluster-specific heterogeneity, spatial heterogeneity and complex interactions between covariates of different type. In this paper, we propose a hierarchical or multilevel version of regression models with structured additive predictor where the regression coefficients of a particular nonlinear term may obey another regression model with structured additive predictor. In that sense, the model is composed of a hierarchy of complex structured additive regression models. The proposed model may be regarded as an extended version of a multilevel model with nonlinear covariate terms in every level of the hierarchy. The model framework is also the basis for generalized random slope modeling based on multiplicative random effects. Inference is fully Bayesian and based on Markov chain Monte Carlo simulation techniques. We provide an in depth description of several highly efficient sampling schemes that allow to estimate complex models with several hierarchy levels and a large number of observations within a couple of minutes (often even seconds). We demonstrate the practicability of the approach in a complex application on childhood undernutrition with large sample size and three hierarchy levels.
105 citations