# Simulation study of estimating between-study variance and overall effect in meta-analyses of log-response-ratio for lognormal data

Bakbergenuly, Ilyas, Hoaglin, David C. and Kulinskaya, Elena (2019) Simulation study of estimating between-study variance and overall effect in meta-analyses of log-response-ratio for lognormal data. ArXiv e-prints.

Full text not available from this repository. (Request a copy)

## Abstract

Methods for random-effects meta-analysis require an estimate of the between-study variance, $\tau^2$. The performance of estimators of $\tau^2$ (measured by bias and coverage) affects their usefulness in assessing heterogeneity of study-level effects, and also the performance of related estimators of the overall effect. For the effect measure log-response-ratio (LRR, also known as the logarithm of the ratio of means, RoM), we review four point estimators of $\tau^2$ (the popular methods of DerSimonian-Laird (DL), restricted maximum likelihood, and Mandel and Paule (MP), and the less-familiar method of Jackson), four interval estimators for $\tau^2$ (profile likelihood, Q-profile, Biggerstaff and Jackson, and Jackson), five point estimators of the overall effect (the four related to the point estimators of $\tau^2$ and an estimator whose weights use only study-level sample sizes), and seven interval estimators for the overall effect (four based on the point estimators for $\tau^2$, the Hartung-Knapp-Sidik-Jonkman (HKSJ) interval, a modification of HKSJ that uses the MP estimator of $\tau^2$ instead of the DL estimator, and an interval based on the sample-size-weighted estimator). We obtain empirical evidence from extensive simulations of data from lognormal distributions.

Item Type: Article 17 pages and full simulation results, comprising 160 figures, each presenting 12 combinations of sample sizes and numbers of studies stat.me,stat.ap Faculty of Science > School of Computing Sciences LivePure Connector 11 Jun 2020 01:28 05 Apr 2021 01:24 https://ueaeprints.uea.ac.uk/id/eprint/75543