Meta-analysis using flexible random-effects distribution models.

ABSTRACT:

Background

In meta-analysis, the normal distribution assumption has been adopted in most systematic reviews of random-effects distribution models due to its computational and conceptual simplicity. However, this restrictive model assumption is possibly unsuitable and might have serious influences in practices.

Methods

We provide two examples of real-world evidence that clearly show that the normal distribution assumption is explicitly unsuitable. We propose new random-effects meta-analysis methods using five flexible random-effects distribution models that can flexibly regulate skewness, kurtosis and tailweight: skew normal distribution, skew t-distribution, asymmetric Subbotin distribution, Jones-Faddy distribution, and sinh-arcsinh distribution. We also developed a statist

SUBMITTER: Noma H 

PROVIDER: S-EPMC9424185 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature