Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each point where a clinical decision is made based on each patient's time-varying characteristics and intermediate outcomes observed at earlier points in time. The complexity, patient heterogeneity, and chronicity of mental disorders call for learning optimal DTRs to dynamically adapt treatment to an individual's response over time. The Sequential Multiple Assignment Randomized Trial (SMARTs) design allows for estimating causal effects of DTRs. Modern statistical tools have been developed to optimize DTRs based on personalized variables and intermediate outcomes using rich data collected from SMARTs; these statistical methods can also be used to recommend tailoring variables for designing future SMART studies. This paper introduces DTRs and SMARTs using two examples in mental health studies, discusses two machine learning methods for estimating optimal DTR from SMARTs data, and demonstrates the performance of the statistical methods using simulated data.
动态治疗方案（Dynamic treatment regimens，DTRs）是一种序贯决策规则，是根据每个患者随时间变化而变化的特征和先前观察到的中间结果而量身定制的临床决策。精神障碍具有慢性和复杂性的特点，精神障碍患者具有异质性特点。这就要求随时间推移，根据个体对治疗反应的不同而分析出最佳的治疗方案，并动态地应用到患者之后的治疗中。多重方案随机序贯试验（Sequential Multiple Assignment Randomized Trial，SMARTs）的设计可以估计DTRs的治疗效应。SMARTs收集到大量的个体化变量和中间结果，在此基础上应用已有的现代统计工具可以优化DTRs。这些统计方法也可为今后的SMARTs研究设计推荐量身定制的变量。本文通过两个精神卫生研究案例介绍了DTRs和SMARTs，讨论了从SMARTs数据估算出最佳DTR的两种不同的计算机自动分析方法，并使用模拟数据演示这两种统计方法的性能。
Keywords: O-learning; Q-learning; SMART; double robust estimation; dynamic treatment regimes; personalized medicine.