Jointly modeling multiple endpoints for efficient treatment effect estimation in randomized controlled trials
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联合建模多个终点以提高随机试验处理效应估计效率
- 研究问题
- 随机试验主要终点或亚组分析功效不足时，如何利用同时收集的次要终点提高主要终点处理效应的估计精度？
- 方法与思路
- 作者对主要和次要疗效终点联合建模，构造主要终点的处理效应估计器，并用模型平均缓解联合模型误设的影响。
- 材料陈述的贡献
- 摘要称模型正确时估计效率高于标准方法，模型平均提供误设稳健性；低尼古丁卷烟案例中标准误降低百分之二十七。
- 阅读关注点
- 应核对联合模型施加的跨终点约束、模型平均权重与误设稳健性的条件，以及亚组应用中的标准误和区间如何计算。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未给出模型平均细节，百分之二十七仅为所述案例，不能推广为一般增益。
原文摘要
Randomized controlled trials are the gold standard for evaluating the efficacy of an intervention. However, there is often a trade-off between selecting the most scientifically relevant primary endpoint versus a less relevant, but more powerful, endpoint. For example, in the context of tobacco regulatory science many trials evaluate cigarettes per day as the primary endpoint instead of abstinence from smoking due to limited power. Additionally, it is often of interest to consider subgroup analyses to answer additional questions; such analyses are rarely adequately powered. In practice, trials often collect multiple endpoints. Heuristically, if multiple endpoints demonstrate a similar treatment effect we would be more confident in the results of this trial. However, there is limited research on leveraging information from secondary endpoints besides using composite endpoints which can be difficult to interpret. In this paper, we develop an estimator for the treatment effect on the primary endpoint based on a joint model for primary and secondary efficacy endpoints. This estimator gains efficiency over the standard treatment effect estimator when the model is correctly specified but is robust to model misspecification via model averaging. We illustrate our approach by estimating the effect of very low nicotine content cigarettes on the proportion of Black people who smoke who achieve abstinence and find our approach reduces the standard error by 27%.