School of Public Health and Preventive Medicine, 22457Monash University, Australia.
Stat Methods Med Res. 2021 Nov;30(11):2503-2525. doi: 10.1177/09622802211041754. Epub 2021 Sep 27.
In cluster-randomized trials, sometimes the effect of the intervention being studied differs between clusters, commonly referred to as treatment effect heterogeneity. In the analysis of stepped wedge and cluster-randomized crossover trials, it is possible to include terms in outcome regression models to allow for such treatment effect heterogeneity yet this is not frequently considered. Outside of some simulation studies of specific cases where the outcome is binary, the impact of failing to include terms for treatment effect heterogeneity on the variance of the treatment effect estimator is unknown. We analytically examine the impact of failing to include terms for treatment effect heterogeneity on the variance of the treatment effect estimator, when outcomes are continuous. Using analysis of variance and feasible generalized least squares we provide expressions for this variance. For both the cluster-randomized crossover design and the stepped wedge design, our analytic derivations indicate that failing to include treatment effect heterogeneity results in the estimates for variance of the treatment effect that are too small, leading to inflation of type I error rates. We therefore recommend assessing the sensitivity of sample size calculations and conclusions drawn from the analysis of cluster randomized trials to the inclusion of treatment effect heterogeneity.
在整群随机试验中，研究中的干预措施的效果在群组之间可能存在差异，通常称为处理效应异质性。在分析阶段式随机和整群随机交叉试验时，可以在结局回归模型中包含一些术语，以允许存在这种处理效应异质性，但这种情况并不常见。除了一些针对二项结局的特定情况的模拟研究外，未能包含处理效应异质性的术语对处理效应估计值的方差的影响是未知的。我们分析了当结局为连续变量时，未能包含处理效应异质性的术语对处理效应估计值方差的影响。我们使用方差分析和可行广义最小二乘法提供了这种方差的表达式。对于整群随机交叉设计和阶段式随机设计，我们的分析推导表明，未能包含处理效应异质性会导致对处理效应方差的估计值过小，从而导致Ⅰ型错误率的膨胀。因此，我们建议评估包含处理效应异质性对样本量计算和从聚类随机试验分析中得出的结论的敏感性。
