Power and money in cluster randomized trials: when is it worth measuring a covariate?

Abstract:

: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.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Moerbeek M

doi

10.1002/sim.2297

subject

Has Abstract

pub_date

2006-08-15 00:00:00

pages

2607-17

issue

15

eissn

0277-6715

issn

1097-0258

journal_volume

25

pub_type

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