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Evidence‐based sample size estimation based upon an updated meta‐regression analysis
Author(s) -
Rotondi Michael A.,
Donner Allan,
Koval John J.
Publication year - 2012
Publication title -
research synthesis methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.376
H-Index - 35
eISSN - 1759-2887
pISSN - 1759-2879
DOI - 10.1002/jrsm.1055
Subject(s) - covariate , meta regression , sample size determination , statistics , meta analysis , econometrics , regression analysis , computer science , set (abstract data type) , regression , sample (material) , contrast (vision) , statistical power , estimation , mathematics , medicine , artificial intelligence , chemistry , management , chromatography , economics , programming language
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.