title: Evidence-based sample size estimation based upon an updated meta-regression analysis.
abstract: A traditional meta-analysis examines the overall effectiveness of an intervention by producing a pooled estimate of treatment efficacy. In contrast to this, a meta-regression model seeks to determine whether a study-level covariate (X) is a plausible source of heterogeneity in a set of treatment effects. Upon performing such an analysis, the results may suggest the presence of a meaningful amount of variation in the treatment effects because of the covariate; however, the current set of trials may not provide sufficient statistical power for such a conclusion. The proposed approach provides quantitative insight into the amount of support that a new trial may provide to the hypothesis that X is a meaningful source of variation in an updated meta-regression model, which includes both the previously completed and the proposed trial. This empirical algorithm allows examination of the potential feasibility of a planned study of various sizes to further support or refute the hypothesis that X is a statistically significant source of variation. A detailed example illustrates the sample size estimation algorithm for both a planned individually or cluster randomized trial to investigate the now commonly accepted impact of geographical latitude on the observed effectiveness of the Bacillus Calmette-Guérin vaccine in the prevention of tuberculosis. Copyright © 2012 John Wiley & Sons, Ltd.
authors.affiliation: School of Kinesiology and Health Science, York University, Toronto, Ontario, M3J 1P3, Canada. mrotondi@yorku.ca.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Rotondi
authors.affiliation: Department of Epidemiology and Biostatistics, The University of Western Ontario, London, Ontario, N6A 5C1, Canada.
authors.forename: Allan
authors.initials: A
authors.lastname: Donner
authors.affiliation: Department of Epidemiology and Biostatistics, The University of Western Ontario, London, Ontario, N6A 5C1, Canada.
authors.forename: John J
authors.initials: JJ
authors.lastname: Koval
keywords: evidence synthesis; heterogeneity; sample size estimation; study design