期刊
STATISTICS IN MEDICINE
卷 27, 期 3, 页码 418-434出版社
WILEY
DOI: 10.1002/sim.2897
关键词
random effects; flexible modelling; skewing; predictive distribution; clustering; meta-analysis
类别
资金
- Medical Research Council [MC_U105260792] Funding Source: Medline
- Medical Research Council [MC_U105260792] Funding Source: researchfish
- MRC [MC_U105260792] Funding Source: UKRI
It is commonly assumed that random effects in hierarchical models follow a normal distribution. This can be extremely restrictive in practice. We explore the use of more flexible alternatives for this assumption, namely the I distribution, and skew extensions to the normal and I distributions, implemented using Markov Chain Monte Carlo methods. Models are compared in terms of parameter estimates, deviance information criteria, and predictive distributions. These methods are,applied to examples in meta-analysis and health-professional variation, where the distribution of the random effects is of direct interest. The results highlight the importance of allowing for potential skewing and heavy tails in random-effects distributions, especially when estimating a predictive distribution. We describe the extension of these random-effects models to the bivariate case, with application to a meta-analysis examining the relationship between treatment effect and baseline response. We conclude that inferences regarding the random effects can crucially depend on the assumptions made and recommend using a distribution, such as those suggested here, which is more flexible than the normal. Copyright (C) 2007 John Wiley & Sons, Ltd.
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