Chen Yuan, Wang Yuanjia, Zeng Donglin
Department of Biostatistics, Columbia University, New York, New York, USA.
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Stat Med. 2020 Dec 10;39(28):4107-4119. doi: 10.1002/sim.8712. Epub 2020 Aug 17.
Dynamic treatment regimes (DTRs) adaptively prescribe treatments based on patients' intermediate responses and evolving health status over multiple treatment stages. Data from sequential multiple assignment randomization trials (SMARTs) are recommended to be used for learning DTRs. However, due to re-randomization of the same patients over multiple treatment stages and a prolonged follow-up period, SMARTs are often difficult to implement and costly to manage, and patient adherence is always a concern in practice. To lessen such practical challenges, we propose an alternative approach to learn optimal DTRs by synthesizing independent trials over different stages. Specifically, at each stage, data from a single randomized trial along with patients' natural medical history and health status in previous stages are used. We use a backward learning method to estimate optimal treatment decisions at a particular stage, where patients' future optimal outcome increments are estimated using data observed from independent trials with future stages' information. Under some conditions, we show that the proposed method yields consistent estimation of the optimal DTRs and we obtain the same learning rates as those from SMARTs. We conduct simulation studies to demonstrate the advantage of the proposed method. Finally, we learn optimal DTRs for treating major depressive disorder (MDD) by stagewise synthesis of two randomized trials. We perform a validation study on independent subjects and show that the synthesized DTRs lead to the greatest MDD symptom reduction compared to alternative methods.
动态治疗方案（DTRs）基于患者在多个治疗阶段的中期反应和不断变化的健康状况，适应性地规定治疗方法。建议使用来自序贯多重分配随机试验（SMARTs）的数据来学习DTRs。然而，由于同一患者在多个治疗阶段需要重新随机分组，且随访期延长，SMARTs往往难以实施且管理成本高昂，并且在实际操作中患者的依从性始终是一个问题。为了减轻这些实际挑战，我们提出了一种替代方法，通过综合不同阶段的独立试验来学习最优的DTRs。具体而言，在每个阶段，使用来自单个随机试验的数据以及患者在前一阶段的自然病史和健康状况。我们使用一种反向学习方法来估计特定阶段的最优治疗决策，其中使用从具有未来阶段信息的独立试验中观察到的数据来估计患者未来的最优结果增量。在某些条件下，我们表明所提出的方法能够对最优DTRs进行一致估计，并且我们获得的学习率与SMARTs相同。我们进行了模拟研究以证明所提出方法的优势。最后，我们通过对两项随机试验进行逐阶段综合来学习治疗重度抑郁症（MDD）的最优DTRs。我们对独立受试者进行了验证研究，并表明与其他方法相比，综合后的DTRs能最大程度地减轻MDD症状。
