Moerbeek Mirjam
Department of Methodology and Statistics, Utrecht University, The Netherlands.
Stat Med. 2006 Aug 15;25(15):2607-17. doi: 10.1002/sim.2297.
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.
在整群随机试验中，可通过增加整群数量来提高检测治疗效果的效能。另一种方法是在将治疗条件与结局相关联的回归模型中纳入协变量。本文推导了公式，以便根据成本评估这两种策略。结果表明，当测量这些协变量的成本较低且协变量与结局之间的相关性足够大时，使用协变量的策略在检测治疗效果方面更具成本效益。所需的最小相关性取决于整群大小，以及相对于招募一个人的成本而言，招募一个整群和测量协变量的成本。当整群规模较小且招募和测量一个整群的成本较高时，建议仅测量个体水平上变化的协变量。当整群规模较大且招募和测量一个整群的成本较低时，建议测量整群水平的协变量。一个示例说明了这些公式在实际场景中的应用。
