Classical and Bayesian random-effects meta-analysis models with sample quality weights in gene expression studies.

ABSTRACT:

Background

Random-effects (RE) models are commonly applied to account for heterogeneity in effect sizes in gene expression meta-analysis. The degree of heterogeneity may differ due to inconsistencies in sample quality. High heterogeneity can arise in meta-analyses containing poor quality samples. We applied sample-quality weights to adjust the study heterogeneity in the DerSimonian and Laird (DSL) and two-step DSL (DSLR2) RE models and the Bayesian random-effects (BRE) models with unweighted and weighted data, Gibbs and Metropolis-Hasting (MH) sampling algorithms, weighted common effect, and weighted between-study variance. We evaluated the performance of the models through simulations and illustrated application of the methods using Alzheimer's gene expression datasets.

Results

SUBMITTER: Siangphoe U 

PROVIDER: S-EPMC6327440 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature