Increased risk of type I errors for detecting heterogeneity of treatment effects in cluster-randomized trials using mixed-effect models
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集群随机试验中用混合效应模型检验处理效应异质性的第一类错误风险
- 研究问题
- 在集群随机试验中，若不同亚组的组内相关程度不同，常用处理与亚组交互项检验是否会夸大处理效应异质性的证据？
- 方法与思路
- 作者用连续、二元和计数结局模拟，比较标准与允许亚组相关结构不同的广义线性混合模型，以及广义估计方程，并分析实际试验。
- 材料陈述的贡献
- 摘要称，标准混合模型的第一类错误率最高达47.2%；灵活相关结构改善校准，广义估计方程仍可能略有偏高，实际应用的结论也随模型改变。
- 阅读关注点
- 应核对模拟中相关结构与亚组构成、集群数量、交互项检验方式，以及灵活模型和广义估计方程在有限集群下的校准表现。
- 阅读边界
- 这是预印本，同行评审及期刊接收未核实。摘要未给出完整模拟设定，所报错误率及实际案例不能直接推广到所有集群试验。
原文摘要
Evaluating heterogeneity of treatment effects (HTE) across subgroups is common in both randomized trials and observational studies. Although several statistical challenges of HTE analyses including low statistical power and multiple comparisons are widely acknowledged, issues arising for clustered data, including cluster randomized trials (CRTs), have received less attention. Notably, the potential for model misspecification is increased given the complex clustering structure (e.g., due to correlation among individuals within a subgroup and cluster), which could impact inference and type 1 errors. To illicit this issue, we conducted a simulation study to evaluate the performance of common analytic approaches for testing the presence of HTE for continuous, binary, and count outcomes: generalized linear mixed models (GLMM) and generalized estimating equations (GEE) including interaction terms between treatment group and subgroup. We found that standard GLMM analyses that assume a common correlation of participants within clusters can lead to severely elevated type 1 error rates of up to 47.2% compared to the 5% nominal level if the within-cluster correlation varies across subgroups. A flexible GLMM, which allows subgroup-specific within-cluster correlations, achieved the nominal type 1 error rate, as did GEE (though rates were slightly elevated even with as many as 50 clusters). Applying the methods to a real-world CRT using the count outcome utilization of healthcare, we found a large impact of the model specification on inference: the standard GLMM yielded highly significant interaction by sex (P=0.01), whereas the interaction was non-statistically significant under the flexible GLMM and GEE (P=0.64 and 0.93, respectively). We recommend that HTE analyses using GLMM account for within-subgroup correlation to avoid anti-conservative inference.