A random effects meta-analysis model with Box-Cox transformation.

ABSTRACT:

Background

In a random effects meta-analysis model, true treatment effects for each study are routinely assumed to follow a normal distribution. However, normality is a restrictive assumption and the misspecification of the random effects distribution may result in a misleading estimate of overall mean for the treatment effect, an inappropriate quantification of heterogeneity across studies and a wrongly symmetric prediction interval.

Methods

We focus on problems caused by an inappropriate normality assumption of the random effects distribution, and propose a novel random effects meta-analysis model where a Box-Cox transformation is applied to the observed treatment effect estimates. The proposed model aims to normalise an overall distribution of observed treatment effect es

SUBMITTER: Yamaguchi Y 

PROVIDER: S-EPMC5517826 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature