The study of precision medicine involves dynamic treatment regimes (DTRs), which are sequences of treatment decision rules recommended by taking patient-level information as input. The primary goal of the DTR study is to identify an optimal DTR, a sequence of treatment decision rules that leads to the best expected clinical outcome. Statistical methods have been developed in recent years to estimate an optimal DTR, including Q-learning, a regression-based method in the DTR literature. Although there are many studies concerning Q-learning, little attention has been given in the presence of noisy data, such as misclassified outcomes. In this paper, we investigate the effect of outcome misclassification on Q-learning and propose a correction method to accommodate the misclassification effect. Simulation studies are conducted to demonstrate the satisfactory performance of the proposed method. We illustrate the proposed method in two examples from the National Health and Nutrition Examination Survey Data I Epidemiologic Follow-up Study and the smoking cessation program.

翻译：精准医学研究涉及动态治疗策略（DTR），即根据患者层面信息输入所推荐的治疗决策规则序列。DTR研究的核心目标是识别最优动态治疗策略，即一组能带来最佳预期临床结果的治疗决策规则序列。近年来，统计方法已被开发用于估计最优动态治疗策略，其中包括Q学习——一种基于回归的DTR研究方法。尽管已有大量关于Q学习的研究，但在存在噪声数据（例如结果误分类）的情况下，相关探讨仍显不足。本文研究了结果误分类对Q学习的影响，并提出了校正方法以消除误分类效应。通过模拟实验验证了所提方法的有效性，并在国家健康与营养调查I流行病学随访研究及戒烟计划两个案例中展示了该方法的应用。