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Power and money in cluster randomized trials: when is it worth measuring a covariate?
Author(s) -
Moerbeek Mirjam
Publication year - 2006
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.2297
Subject(s) - covariate , cluster (spacecraft) , measure (data warehouse) , statistics , econometrics , outcome (game theory) , computer science , mathematics , data mining , mathematical economics , programming language
The power to detect a treatment effect in cluster randomized trials can be increased by increasing the number of clusters. An alternative is to include covariates into the regression model that relates treatment condition to outcome. In this paper, formulae are derived in order to evaluate both strategies on basis of their costs. It is shown that the strategy that uses covariates is more cost‐efficient in detecting a treatment effect when the costs to measure these covariates are small and the correlation between the covariates and outcome is sufficiently large. The minimum required correlation depends on the cluster size, and the costs to recruit a cluster and to measure the covariate, relative to the costs to recruit a person. Measuring a covariate that varies at the person level only is recommended when cluster sizes are small and the costs to recruit and measure a cluster are large. Measuring a cluster level covariate is recommended when cluster sizes are large and the costs to recruit and measure a cluster are small. An illustrative example shows the use of the formulae in a practical setting. Copyright © 2005 John Wiley & Sons, Ltd.