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 weighted proportional odds model (WPOM): a regression-based, approximate 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 approximate double robustness of WPOM with our adjusted weights via simulation studies. We further extend WPOM to multi-stage DTR estimation with household interference, namely dWPOM (dynamic WPOM). 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. Furthermore, considering interference, we provide optimal treatment strategies for households to achieve smoking cessation of the pair in the household.

翻译：精准医学的核心在于决策支持，通常以动态治疗方案（DTRs）的形式呈现，即一组决策规则序列。在每个决策节点，这些规则依据患者基线特征、截至该节点累积的治疗应答信息以及患者当前健康状况（包括症状严重程度等指标）确定后续治疗方案。然而，针对序数结局的DTR估计研究尚属罕见，在存在干扰效应（即某患者的治疗方案可能影响另一患者的结局）的背景下更是如此。本文提出加权比例优势模型（WPOM）：一种基于回归的近似双稳健方法，用于序数结局的单阶段DTR估计。该方法通过联合倾向性得分导出的协变量平衡权重，同时考量家庭内个体间可能存在的干扰效应。通过仿真实验验证不同类型平衡权重，证明经调整权重后的WPOM具有近似双稳健性。进一步将WPOM扩展至存在家庭干扰的多阶段DTR估计，即动态WPOM（dWPOM）。最后，本文将所提方法应用于烟草与健康人群评估研究中的纵向调查数据分析（该研究直接促成本项工作），并在考虑干扰效应的情况下，为家庭内夫妻对实现戒烟提供最优治疗策略。