To promote precision medicine, individualized treatment regimes (ITRs) are crucial for optimizing the expected clinical outcome based on patient-specific characteristics. However, existing ITR research has primarily focused on scenarios with categorical treatment options and a single outcome. In reality, clinicians often encounter scenarios with continuous treatment options and multiple, potentially competing outcomes, such as medicine efficacy and unavoidable toxicity. To balance these outcomes, a proper weight is necessary, which should be learned in a data-driven manner that considers both patient preference and clinician expertise. In this paper, we present a novel algorithm for developing individualized treatment regimes (ITRs) that incorporate continuous treatment options and multiple outcomes, utilizing observational data. Our approach assumes that clinicians are optimizing individualized patient utilities with sub-optimal treatment decisions that are at least better than random assignment. Treatment assignment is assumed to directly depend on the true underlying utility of the treatment rather than patient characteristics. The proposed method simultaneously estimates the weighting of composite outcomes and the decision-making process, allowing for construction of individualized treatment regimes with continuous doses. The proposed estimators can be used for inference and variable selection, facilitating the identification of informative treatment assignments and preference-associated variables. We evaluate the finite sample performance of our proposed method via simulation studies and apply it to a real data application of radiation oncology analysis.

翻译：为促进精准医学，个体化治疗方案（ITRs）对于基于患者特异性特征优化预期临床结局至关重要。然而，现有ITR研究主要聚焦于分类治疗选项和单一结局场景。现实中，临床医生常面临连续治疗选项与多重潜在竞争结局（如药物疗效与不可避免的毒性反应）共存的场景。为平衡这些结局，需引入恰当权重，该权重应通过数据驱动方式学习，同时兼顾患者偏好与临床专家经验。本文提出一种新算法，利用观察性数据开发包含连续治疗选项和多重结局的个体化治疗方案。我们假设临床医生正在优化个体化患者效用，其治疗决策虽非最优但至少优于随机分配。治疗分配被假设直接取决于治疗的真实潜在效用而非患者特征。该方法同步估计复合结局的权重和决策过程，从而构建含连续剂量的个体化治疗方案。所提估计量可用于推断和变量选择，有助于识别具有信息量的治疗分配与偏好相关变量。通过模拟研究评估所提方法的有限样本性能，并应用于放射肿瘤学分析的真实数据场景。