Zhao Zhizhen, Ni Ai, Xu Xinyi, Donneyong Macarius, Lu Bo
Department of Statistics, The Ohio State University, Columbus, Ohio, USA.
Division of Biostatistics, College of Public Health The Ohio State University, Columbus, Ohio, USA.
Stat Med. 2025 May;44(10-12):e70093. doi: 10.1002/sim.70093.
Individualized treatment rules (ITRs) tailor treatments to individuals based on their unique characteristics to optimize clinical outcomes and resource allocation. Current approaches use outcome modeling or propensity score weighting to control confounding in complex medical data. To avoid model misspecification and the impact of extreme weights, matched-learning (M-learning) was recently proposed for continuous outcomes. In this paper, we expand the existing M-learning methodology to estimate optimal ITRs under right-censored data, as time-to-event outcomes are common in medical research. We construct matched sets for individuals by comparing observed times and incorporate an inverse probability censoring weight into the value function to handle censored observations. Additionally, we consider a full matching design as a possible alternative to the matching with replacement in M-learning. We demonstrate that the proposed value function is unbiased for the true value function without censoring. To gain insight into the empirical performance, we conduct an extensive simulation study that compares M-learning with two matching designs and a weighed learning approach. Results are evaluated based on winning probabilities and estimated values. The simulation reveals that all methods are generally fine in the absence of unmeasured confounders, and different methods show somewhat different performances under various scenarios. But their performance drops substantially in the presence of unmeasured confounders. Finally, we apply these methods to estimate optimal ITRs for patients with atrial fibrillation (AF) complications from an electronic medical record database, where full matching design shows slightly better performance.
个体化治疗规则（ITRs）根据个体的独特特征为其量身定制治疗方案，以优化临床结果和资源分配。当前的方法使用结果建模或倾向得分加权来控制复杂医学数据中的混杂因素。为避免模型误设和极端权重的影响，最近针对连续结果提出了匹配学习（M-learning）。在本文中，由于事件发生时间结果在医学研究中很常见，我们扩展了现有的M-learning方法，以估计右删失数据下的最优ITRs。我们通过比较观察到的时间为个体构建匹配集，并将逆概率删失权重纳入价值函数以处理删失观测值。此外，我们考虑完全匹配设计作为M-learning中可重复抽样匹配的一种可能替代方案。我们证明，所提出的价值函数在无删失情况下对真实价值函数是无偏的。为深入了解实证性能，我们进行了一项广泛的模拟研究，将M-learning与两种匹配设计以及一种加权学习方法进行比较。结果根据获胜概率和估计值进行评估。模拟结果表明，在不存在未测量混杂因素的情况下，所有方法通常都表现良好，并且在各种情况下不同方法表现出略有不同的性能。但在存在未测量混杂因素的情况下，它们的性能会大幅下降。最后，我们将这些方法应用于从电子病历数据库中估计房颤（AF）并发症患者的最优ITRs，其中完全匹配设计表现出略好的性能。
