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A simple sample size formula for analysis of covariance in cluster randomized trials
Author(s) -
Teerenstra Steven,
Eldridge Sandra,
Graff Maud,
Hoop Esther,
Borm George F.
Publication year - 2012
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5352
Subject(s) - analysis of covariance , autocorrelation , statistics , sample size determination , correlation , covariance , cluster (spacecraft) , outcome (game theory) , cluster randomised controlled trial , mathematics , baseline (sea) , sample (material) , range (aeronautics) , econometrics , randomized controlled trial , medicine , computer science , geometry , oceanography , mathematical economics , chemistry , chromatography , materials science , composite material , programming language , geology
For cluster randomized trials with a continuous outcome, the sample size is often calculated as if an analysis of the outcomes at the end of the treatment period (follow‐up scores) would be performed. However, often a baseline measurement of the outcome is available or feasible to obtain. An analysis of covariance (ANCOVA) using both the baseline and follow‐up score of the outcome will then have more power. We calculate the efficiency of an ANCOVA analysis using the baseline scores compared with an analysis on follow‐up scores only. The sample size for such an ANCOVA analysis is a factor r 2 smaller, where r is the correlation of the cluster means between baseline and follow‐up. This correlation can be expressed in clinically interpretable parameters: the correlation between baseline and follow‐up of subjects (subject autocorrelation) and that of clusters (cluster autocorrelation). Because of this, subject matter knowledge can be used to provide (range of) plausible values for these correlations, when estimates from previous studies are lacking. Depending on how large the subject and cluster autocorrelations are, analysis of covariance can substantially reduce the number of clusters needed. Copyright © 2012 John Wiley & Sons, Ltd.