Robust Estimation of Moderated Causal Excursion Odds Ratio in Micro-Randomized Trials
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微随机试验中调节型因果游离优势比的稳健估计
- 研究问题
- 在具有纵向二元结果的微随机试验中，如何估计随调节变量变化的因果游离优势比，并应对随机化机制和辅助模型的复杂性？
- 方法与思路
- 作者区分调节变量充分刻画随机化与更一般情形，分别提出双重稳健和含关联辅助模型的估计器，并扩展至任意链接函数。
- 材料陈述的贡献
- 摘要称，两类估计器具有一致性和渐近正态性，一般估计器在无因果效应时容忍关联模型误设，并用于 Drink Less 试验。
- 阅读关注点
- 应核对游离效应的定义、调节变量对随机化机制的覆盖条件、两个辅助模型的具体角色，以及零效应稳健性的边界。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未说明完整正则条件，也未给出模拟或应用效应数值。
原文摘要
Micro-randomized trials (MRTs) have become increasingly popular for developing and evaluating mobile health interventions that promote healthy behaviors and manage chronic conditions. The recently proposed causal excursion effects have become the standard measure for interventions' marginal and moderated effect in MRTs. Existing methods for MRTs with binary outcomes focus on causal excursion effects on the relative risk scale. However, a causal excursion effect on the odds ratio scale is attractive for its interpretability and valid predicted probabilities, making it a valuable supplement to causal excursion relative risk. In this paper, we propose two novel estimators for the moderated causal excursion odds ratio for MRTs with longitudinal binary outcomes. When the prespecified moderator fully captures the way interventions are sequentially randomized, we propose a doubly robust estimator that remains consistent if either of two nuisance models is correctly specified. For more general settings in which treatment randomization depends on variables beyond the chosen moderator, we propose a general estimator that incorporates an association nuisance model. We further establish the general estimator's robustness to the misspecification of the association nuisance model under no causal effect, and extend the general estimator to accommodate any link functions. We establish the consistency and asymptotic normality of both estimators and demonstrate their performance through simulation studies. We apply the methods to Drink Less, a 30-day MRT for developing mobile health interventions to help reduce alcohol consumption, where the proximal outcome is whether the user opens the app in the hour following the notification.