title: Likelihood-Based Random-Effect Meta-Analysis of Binary Events.
abstract: Meta-analysis has been used extensively for evaluation of efficacy and safety of medical interventions. Its advantages and utilities are well known. However, recent studies have raised questions about the accuracy of the commonly used moment-based meta-analytic methods in general and for rare binary outcomes in particular. The issue is further complicated for studies with heterogeneous effect sizes. Likelihood-based mixed-effects modeling provides an alternative to moment-based methods such as inverse-variance weighted fixed- and random-effects estimators. In this article, we compare and contrast different mixed-effect modeling strategies in the context of meta-analysis. Their performance in estimation and testing of overall effect and heterogeneity are evaluated when combining results from studies with a binary outcome. Models that allow heterogeneity in both baseline rate and treatment effect across studies have low type I and type II error rates, and their estimates are the least biased among the models considered.
authors.affiliation: a Department of Health Sciences , New Mexico State University , Las Cruces , New Mexico , USA.
authors.forename: Anup
authors.initials: A
authors.lastname: Amatya
authors.forename: Dulal K
authors.initials: DK
authors.lastname: Bhaumik
authors.forename: Sharon-Lise
authors.initials: SL
authors.lastname: Normand
authors.forename: Joel
authors.initials: J
authors.lastname: Greenhouse
authors.forename: Eloise
authors.initials: E
authors.lastname: Kaizar
authors.forename: Brian
authors.initials: B
authors.lastname: Neelon
authors.forename: Robert D
authors.initials: RD
authors.lastname: Gibbons
mesh_terms: D035843:Biomedical Research / Q000706:statistics & numerical data*; D002317:Cardiovascular Agents / Q000627:therapeutic use; D016009:Chi-Square Distribution; D003198:Computer Simulation; D003327:Coronary Disease / Q000628:therapy; D003627:Data Interpretation, Statistical; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology; D016640:Diabetes, Gestational / Q000453:epidemiology; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D015201:Meta-Analysis as Topic; D009716:Numerical Analysis, Computer-Assisted; D016017:Odds Ratio; D062645:Percutaneous Coronary Intervention; D011247:Pregnancy; D012107:Research Design / Q000706:statistics & numerical data*; D018570:Risk Assessment; D012307:Risk Factors; D016896:Treatment Outcome
chemical_list: D002317:Cardiovascular Agents
keywords: Heterogeneity; Logistic regression; Random-effect; Rare event