Identifying a set that contains the best dynamic treatment regimes
Identifying a set that contains the best dynamic treatment regimes
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DOI：
10.1093/biostatistics/kxv025
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发表时间：
2016-01-01
期刊：
影响因子：
2.1
通讯作者：
Nahum-Shani, Inbal
中科院分区：
文献类型：
--
作者：
Ertefaie, Ashkan;Wu, Tianshuang;Nahum-Shani, Inbal
动态治疗方案（DTR）是一种治疗设计，旨在适应患者对治疗的异质性。DTR可以通过一系列决策规则来操作，这些规则将患者信息映射到特定决策点的治疗选项。序贯、多重分配、随机化试验（SMART）是一种试验设计，专门用于获取数据，以构建良好（即有效）的决策规则。激励SMART的科学问题之一涉及设计中嵌入的多个DTR的比较。用于识别最佳DTR的典型方法涉及嵌入在SMART中的DTR之间的所有可能的比较，其代价是在嵌入DTR（EDTR）的数量增加的程度上大大降低功率。在这里，我们提出了一种方法，使研究人员能够更有效地使用SMART研究数据来识别包含最有效EDTR的集合。我们的方法确保了真正的最佳EDTR至少以给定的概率包含在该集合中。仿真结果被用来评估所提出的方法，并分析纳洛酮SMART研究数据的扩展治疗效果，以说明其应用。
A dynamic treatment regime (DTR) is a treatment design that seeks to accommodate patient heterogeneity in response to treatment. DTRs can be operationalized by a sequence of decision rules that map patient information to treatment options at specific decision points. The sequential, multiple assignment, randomized trial (SMART) is a trial design that was developed specifically for the purpose of obtaining data that informs the construction of good (i.e. efficacious) decision rules. One of the scientific questions motivating a SMART concerns the comparison of multiple DTRs that are embedded in the design. Typical approaches for identifying the best DTRs involve all possible comparisons between DTRs that are embedded in a SMART, at the cost of greatly reduced power to the extent that the number of embedded DTRs (EDTRs) increase. Here, we propose a method that will enable investigators to use SMART study data more efficiently to identify the set that contains the most efficacious EDTRs. Our method ensures that the true best EDTRs are included in this set with at least a given probability. Simulation results are presented to evaluate the proposed method, and the Extending Treatment Effectiveness of Naltrexone SMART study data are analyzed to illustrate its application.