MethodsRobust Metrics and Sensitivity Analyses for Meta-analyses of Heterogeneous EffectsMathur, Maya B.a; VanderWeele, Tyler J.bAuthor Information From the aQuantitative Sciences Unit, Stanford University, Palo Alto, CA bDepartment of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA. Submitted October 9, 2019; accepted February 6, 2020. M.B.M. and T.J.V. were supported by NIH grant R01 CA222147. The funders had no role in the design, conduct, or reporting of this research. The authors report no conflicts of interest. All code and data required to reproduce the simulation study and applied example are publicly available (https://osf.io/6nyg8/). A general function to conduct the proposed analyses, prop_stronger, is publicly available in the R package MetaUtility. Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com). Epidemiology: May 2020 - Volume 31 - Issue 3 - p 356-358 doi: 10.1097/EDE.0000000000001180 Buy SDC Metrics Abstract We recently suggested new statistical metrics for routine reporting in random-effects meta-analyses to convey evidence strength for scientifically meaningful effects under effect heterogeneity. First, given a chosen threshold of meaningful effect size, we suggested reporting the estimated proportion of true population effect sizes above this threshold. Second, we suggested reporting the proportion of effect sizes below a second, possibly symmetric, threshold in the opposite direction from the estimated mean. Our previous methods applied when the true population effects are approximately normal, when the number of studies is relatively large, and when the proportion is between approximately 0.15 and 0.85. Here, we additionally describe robust methods for point estimation and inference that perform well under considerably more general conditions, as we validate in an extensive simulation study. The methods are implemented in the R package MetaUtility (function prop_stronger). We describe application of the robust methods to conducting sensitivity analyses for unmeasured confounding in meta-analyses. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.