Jackknife empirical likelihood confidence intervals for assessing heterogeneity in meta-analysis of rare binary event data.

ABSTRACT: In meta-analysis, the heterogeneity of effect sizes across component studies is typically described by a variance parameter in a random-effects (Re) model. In the literature, methods for constructing confidence intervals (CIs) for the parameter often assume that study-level effect sizes are normally distributed. However, this assumption might be violated in practice, especially in meta-analysis of rare binary events. We propose to use jackknife empirical likelihood (JEL), a nonparametric approach that uses jackknife pseudo-values, to construct CIs for the heterogeneity parameter. To compute jackknife pseudo-values, we employ a moment-based estimator and consider two commonly used weighing schemes (i.e., equal and inverse variance weights). We prove that with each scheme, the resulting log

SUBMITTER: Wang G 

PROVIDER: S-EPMC8429181 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature