Dynamic Treatment Regimes (DTRs) provide a systematic framework for optimizing sequential decision-making in chronic disease management, where therapies must adapt to patients' evolving clinical profiles. Inverse probability weighting (IPW) is a cornerstone methodology for estimating regime values from observational data due to its intuitive formulation and established theoretical properties, yet standard IPW estimators face significant limitations, including variance instability and data inefficiency. A fundamental but underexplored source of inefficiency lies in the strict alignment requirement between observed and target treatment trajectories, which fails to account for partial compatibility and discards substantial information from individuals with only minimal deviations from the regime. We propose two novel methodologies that relax the strict inclusion rule through flexible compatibility mechanisms. Both methods provide computationally tractable alternatives that can be easily integrated into existing IPW workflows, offering more efficient approaches to DTR estimation. Theoretical analysis demonstrates that both estimators preserve consistency while achieving superior finite-sample efficiency compared to standard IPW, and comprehensive simulation studies confirm improved stability. We illustrate the practical utility of our methods through an application to HIV treatment data from the AIDS Clinical Trials Group Study 175 (ACTG175).

翻译：动态治疗方案（DTRs）为优化慢性疾病管理中序贯决策提供了系统性框架，其中治疗需根据患者不断变化的临床特征进行调整。逆概率加权（IPW）因其直观的公式和既定的理论性质，成为从观察数据估计治疗方案值的基础方法，但标准IPW估计器面临显著局限性，包括方差不稳定和数据效率低下。一个根本但未被充分探索的效率损失来源在于观察轨迹与目标治疗轨迹之间的严格对齐要求，这未能考虑部分兼容性，并丢弃了大量仅与治疗方案存在微小偏离的个体信息。我们提出两种通过灵活兼容机制放松严格纳入规则的新方法。这两种方法提供了计算上可行的替代方案，可轻松集成到现有IPW工作流中，为DTR估计提供更高效的途径。理论分析表明，两种估计器均保持一致性，同时相比标准IPW实现了更优的有限样本效率，综合模拟研究证实了其稳定性提升。我们通过艾滋病临床试验组研究175（ACTG175）中HIV治疗数据应用，展示了我们方法的实际效用。