The focus of precision medicine is on decision support, often in the form of dynamic treatment regimes (DTRs), which are sequences of decision rules. At each decision point, the decision rules determine the next treatment according to the patient's baseline characteristics, the information on treatments and responses accrued by that point, and the patient's current health status, including symptom severity and other measures. However, DTR estimation with ordinal outcomes is rarely studied, and rarer still in the context of interference - where one patient's treatment may affect another's outcome. In this paper, we introduce the proposed weighted proportional odds model (WPOM): a regression-based, doubly-robust approach to single-stage DTR estimation for ordinal outcomes. This method also accounts for the possibility of interference between individuals sharing a household through the use of covariate balancing weights derived from joint propensity scores. Examining different types of balancing weights, we verify the double robustness of WPOM with our adjusted weights via simulation studies. We further extend WPOM to multi-stage DTR estimation with household interference. Lastly, we demonstrate our proposed methodology in the analysis of longitudinal survey data from the Population Assessment of Tobacco and Health study, which motivates this work.

翻译：精准医学的核心聚焦于决策支持，通常以动态治疗方案（DTR）形式呈现，即一系列决策规则序列。在每个决策时间点，决策规则需根据患者基线特征、截至该时间点累计的治疗与应答信息、当前健康状况（包括症状严重程度及其他指标）来确定下一步治疗方案。然而，针对有序结局的动态治疗方案估计研究尚属罕见，涉及干扰效应（即某位患者的治疗可能影响其他患者的结局）的研究则更为稀缺。本文提出加权比例优势模型（WPOM）：一种基于回归的双稳健方法，用于有序结局的单阶段动态治疗方案估计。该方法通过联合倾向性得分的协变量平衡权重，考虑同一家庭个体间可能存在的干扰效应。通过考察不同类型的平衡权重，我们通过模拟研究验证了使用调整后权重的WPOM的双稳健性。我们进一步将WPOM扩展到存在家庭干扰的多阶段动态治疗方案估计。最终，我们通过分析本研究的动机来源——烟草与健康人群评估纵向调查数据，展示了所提出方法的实际应用。