Abstract
Methods for randomeffects metaanalysis require an estimate of the betweenstudy variance, $\tau^2$. The performance of estimators of $\tau^2$ (measured by bias and coverage) affects their usefulness in assessing heterogeneity of studylevel effects, and also the performance of related estimators of the overall effect. For the effect measure logresponseratio (LRR, also known as the logarithm of the ratio of means, RoM), we review four point estimators of $\tau^2$ (the popular methods of DerSimonianLaird (DL), restricted maximum likelihood, and Mandel and Paule (MP), and the lessfamiliar method of Jackson), four interval estimators for $\tau^2$ (profile likelihood, Qprofile, 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 studylevel sample sizes), and seven interval estimators for the overall effect (four based on the point estimators for $\tau^2$, the HartungKnappSidikJonkman (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 samplesizeweighted estimator). We obtain empirical evidence from extensive simulations of data from lognormal distributions.
Original language  English 

Journal  ArXiv eprints 
Publication status  Published  3 May 2019 
Keywords
 stat.ME
 stat.AP
Profiles

Elena Kulinskaya
 School of Computing Sciences  Emeritus Professor
 Norwich Epidemiology Centre  Member
 Data Science and AI  Member
Person: Honorary, Research Group Member