AI 中文总结
本研究针对存在竞争风险与右删失、时变混杂的场景，发展非参数效率理论，提出序贯双重稳健估计量，用于动态治疗方案的有效估计，且通过骨质疏松患者的临床骨折数据验证了框架的实用性。
AI 中文摘要
许多观察性研究评估时变干预措施的风险与获益，在这类纵向场景中，研究的主要结局常为被死亡（作为竞争风险）所阻碍的临床事件。动态治疗方案（DTR）的评估因时变混杂、右删失及随时间推移样本量逐步减少而变得复杂，传统方法如g公式或逆概率加权可能存在模型误设或极不稳定的问题。本研究发展了非参数效率理论，并推导了存在竞争风险与右删失时，某一DTR下事件累积发生率的有效影响函数。基于该结果，提出了一种序贯双重稳健估计量，其可对干扰项估计采用灵活的机器学习方法，同时保持根n一致性与渐近正态性。我们通过估计不同双膦酸盐“药物假期”方案下骨质疏松患者临床骨折的长期累积发生率，验证了所提框架的实用性。
英文摘要
Many observational studies evaluate the risks and benefits of time-varying interventions. In these longitudinal settings, the primary outcome of interest is often a clinical event that is precluded by mortality, which acts as a competing risk. Evaluating dynamic treatment regimes (DTRs) is complicated by time-varying confounding, right-censoring, and progressive sample size reduction over time, for which traditional methods such as the g-formula or inverse probability weighting may be misspecified or highly unstable. In this work, we develop the nonparametric efficiency theory and derive the efficient influence function for the cumulative incidence of an event under a DTR in the presence of a competing risk and right-censoring. Building on this result, we propose a sequentially doubly robust estimator that accommodates flexible machine learning for nuisance estimation while retaining root-n consistency and asymptotic normality. We illustrate the utility of our framework by estimating long-term cumulative incidence of clinical fractures under different bisphosphonate "drug holiday" regimes for osteoporosis.
Comments72 pages, 8 figures
