Robust estimation of optimal dynamic treatment regimes with nonignorable missing covariates
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协变量不可忽略缺失下最优动态治疗方案的稳健估计
- 研究问题
- 观察性临床数据中协变量因信息性监测而缺失时，如何估计最优动态治疗方案，并减少后期模型误设向早期阶段传播的误差？
- 方法与思路
- 作者用直接搜索优化治疗方案，以非响应工具变量估计缺失概率，并结合 RKHS 协变量平衡权重与平滑样条构造价值估计量。
- 材料陈述的贡献
- 摘要称方法降低加权 Q 学习的模型误设风险，给出渐近性质和模拟比较，并用 MIMIC 数据研究脓毒症患者的补液策略。
- 阅读关注点
- 应核对非响应工具变量的识别要求、各阶段治疗分配与缺失机制、价值估计的稳健条件，以及策略搜索的范围和优化误差。
- 阅读边界
- 这是 arXiv 预印本，同行评审与接收未核实；摘要未列出工具变量及因果识别条件，模拟优势不能直接证明临床策略有效。
原文摘要
Estimating optimal dynamic treatment regimes (DTRs) using observational data is often challenged by nonignorable missing covariates arsing from informative monitoring of patients in clinical practice. To address nonignorable missingness of pseudo-outcomes induced by nonignorable missing covariates, a weighted Q-learning approach using parametric Q-function models and a semiparametric missingness propensity model has recently been proposed. However, misspecification of parametric Q-functions at later stages of a DTR can propagate estimation errors to earlier stages via the pseudo-outcomes themselves and indirectly through biased estimation of the missingness propensity of the pseudo-outcomes. This robustness concern motivates us to develop a direct-search-based optimal DTR estimator built on a robust and efficient value estimator, where nonparametric methods are employed for treatment propensity and Q-function estimation, and inverse probability weighting is applied using missingness propensity estimated with the aid of nonresponse instrumental variables. Specifically, in our value estimator, we replace weights estimated by prediction models of treatment propensity with stable weights estimated by balancing covariate functions in a reproducing-kernel Hilbert space (RKHS). Augmented by Q-functions estimated by RKHS-based smoothing splines, our value estimator mitigates the misspecification risk of the weighted Q-learning approach while maintaining the efficiency gain from employing pseudo-outcomes in missing data scenarios. The asymptotic properties of the proposed estimator are derived, and simulations demonstrate its superior performance over weighted Q-learning under model misspecification. We apply the proposed methods to investigate the optimal fluid strategy for sepsis patients using data from the MIMIC database.