Huang Francis L
Department of Educational, School, and Counseling Psychology, University of Missouri, 16 Hill Hall, Columbia, MO, 65211, USA.
Prev Sci. 2023 Apr;24(3):398-407. doi: 10.1007/s11121-021-01228-5. Epub 2021 Apr 6.
Binary outcomes are often encountered when analyzing cluster randomized trials (CRTs). A common approach to obtaining the average treatment effect of an intervention may involve using a logistic regression model. We outline some interpretive and statistical challenges associated with using logistic regression and discuss two alternative/supplementary approaches for analyzing clustered data with binary outcomes: the linear probability model (LPM) and the modified Poisson regression model. In our simulation and applied example, all models use a standard error adjustment that is effective even if a low number of clusters is present. Simulation results show that both the LPM and modified Poisson regression models can provide unbiased point estimates with acceptable coverage and type I error rates even with as little as 20 clusters.
在分析整群随机试验（CRT）时，经常会遇到二元结局。获得干预平均治疗效果的一种常见方法可能涉及使用逻辑回归模型。我们概述了与使用逻辑回归相关的一些解释性和统计挑战，并讨论了两种用于分析具有二元结局的聚类数据的替代/补充方法：线性概率模型（LPM）和修正泊松回归模型。在我们的模拟和应用示例中，所有模型都使用了一种标准误差调整方法，即使聚类数量较少时该方法也有效。模拟结果表明，即使只有20个聚类，LPM和修正泊松回归模型都可以提供具有可接受的覆盖率和I型错误率的无偏点估计。
