title: HELOW: a program for testing extreme homogeneity in meta-analysis.
abstract: Meta-analysis aims to synthesize results from different studies. Although, in a meta-analysis the presence of large between-study heterogeneity is routinely evaluated, in some instances is also important to probe whether there is extreme between-study homogeneity (i.e. extreme low between-study heterogeneity). HELOW (HEterogeneity LOW) is a program for testing extreme homogeneity in a meta-analysis of risk ratios when binary outcome and Mantel-Haenszel fixed effects summary risk ratio estimate are employed. The significance of extreme homogeneity is assessed using a Monte Carlo test. Extreme homogeneity may yield insights for the statistical and clinical interpretation of the data.
authors.affiliation: Department of Biomathematics, University of Thessaly School of Medicine, Larissa, Greece; Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA. Electronic address: zintza@med.uth.gr.
authors.forename: Elias
authors.initials: E
authors.lastname: Zintzaras
authors.affiliation: Stanford Prevention Research Center, Department of Medicine, Stanford, CA, USA; Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA, USA; Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, CA, USA.
authors.forename: John P
authors.initials: JP
authors.lastname: Ioannidis
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000379:methods*; D012984:Software; D012985:Software Design
keywords: Heterogeneity; Homogeneity; Meta-analysis; Monte Carlo; Risk ratio