A novel estimator of between-study variance in random-effects models.

ABSTRACT:

Background

With the rapid development of high-throughput sequencing technologies, many datasets on the same biological subject are generated. A meta-analysis is an approach that combines results from different studies on the same topic. The random-effects model in a meta-analysis enables the modeling of differences between studies by incorporating the between-study variance.

Results

This paper proposes a moments estimator of the between-study variance that represents the across-study variation. A new random-effects method (DSLD2), which involves two-step estimation starting with the DSL estimate and the [Formula: see text] in the second step, is presented. The DSLD2 method is compared with 6 other meta-analysis methods based on effect sizes across 8 aspects under three hypot

SUBMITTER: Wang N 

PROVIDER: S-EPMC7014785 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature