面向最优个体化治疗方案的半监督一致性学习
Semi-supervised Concordance Learning for Optimal Individual Treatment Regimes
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中文总结 AI 辅助
针对半监督数据框架下最优个体化治疗方案估计问题，提出结合单索引核平滑插补与一致性辅助学习的半参数推断方法，经模拟及MIMIC-III、ACTG175数据集验证，其效率与鲁棒性优于全监督方法。
中文摘要 AI 辅助
寻找将个体特征或上下文信息映射至治疗分配的最优个体化治疗方案，已在现有文献中得到广泛研究并具有大量实际应用。本文考虑在半监督数据框架（以电子病历数据为例）内估计最优治疗方案。在这类场景中，由于标注成本高、时间限制、数据隐私问题及其他约束，仅极小比例的观测值带有观测到的结局标签，而所有研究对象均包含协变量和治疗分配信息。我们开发了一种针对最优治疗方案的半参数推断方法，该方法利用具有完整协变量和治疗信息的未标注结局样本以提升估计效率。所提出的估计框架包含两个关键步骤：第一，通过单索引核平滑进行灵活的非参数插补；第二，基于一致性辅助学习估计最优治疗方案。我们证明了所提估计量的一致性和渐近正态性。数值模拟研究表明，在有限样本场景下，与全监督估计量相比，我们的方法实现了更高的效率和更强的鲁棒性。我们进一步使用MIMIC-III和ACTG175数据集验证了所提框架的实际价值。
英文摘要
Finding the optimal individualized treatment rule that maps individual characteristics or contextual information to treatment assignments has been extensively investigated in existing literature, with widespread practical applications. This paper considers the estimation of optimal treatment regimes within a semi-supervised data framework (exemplified by electronic medical record data). In such settings, only a tiny proportion of observations have observed outcome labels, owing to high labeling costs, time limitations, data privacy concerns, and other constraints, while covariates and treatment assignments are available for all study subjects. We develop a semi-parametric inference method for optimal treatment regimes, which leverages outcome- unlabeled samples with complete covariate and treatment information to enhance estimation efficiency. The proposed estimation framework consists of two key steps: first, flexible nonparametric imputation via single-index kernel smoothing; second, subsequent estimation of the optimal treatment regime based on concordance-assisted learning. We establish the consistency and asymptotic normality of our proposed estimators. Numerical simulation studies demonstrate that our method achieves higher efficiency and stronger robustness relative to fully supervised estimators under finite-sample settings. We further validate the practical value of our proposed framework using the MIMIC-III and ACTG175 datasets.
