Alternatives to Logistic Regression Models when Analyzing Cluster Randomized Trials with Binary Outcomes
Alternatives to Logistic Regression Models when Analyzing Cluster Randomized Trials with Binary Outcomes
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DOI：
10.1007/s11121-021-01228-5
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发表时间：
2021-04-06
影响因子：
3.5
通讯作者：
Huang, Francis L.
中科院分区：
文献类型：
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作者：
Huang, Francis L.
在分析整群随机试验（CRT）时，经常会遇到二元结局。获得干预措施平均治疗效果的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和统计挑战，并讨论了两种替代/补充方法，用于分析具有二元结果的聚类数据：线性概率模型（LPM）和修改后的泊松回归模型。在我们的模拟和应用示例中，所有模型都使用标准误差调整，即使存在少量聚类也有效。仿真结果表明，LPM和修改的泊松回归模型可以提供无偏点估计与可接受的覆盖率和I类错误率，即使只有20个集群。
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.