Likelihood-based random-effects meta-analysis with few studies: empirical and simulation studies.

ABSTRACT:

Background

Standard random-effects meta-analysis methods perform poorly when applied to few studies only. Such settings however are commonly encountered in practice. It is unclear, whether or to what extent small-sample-size behaviour can be improved by more sophisticated modeling.

Methods

We consider likelihood-based methods, the DerSimonian-Laird approach, Empirical Bayes, several adjustment methods and a fully Bayesian approach. Confidence intervals are based on a normal approximation, or on adjustments based on the Student-t-distribution. In addition, a linear mixed model and two generalized linear mixed models (GLMMs) assuming binomial or Poisson distributed numbers of events per study arm are considered for pairwise binary meta-analyses. We extract an empirical data se

SUBMITTER: Seide SE 

PROVIDER: S-EPMC6330405 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature