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Sample size considerations for comparing dynamic treatment regimens in a sequential multiple-assignment randomized trial with a continuous longitudinal outcome

机译:用于比较动态治疗方案在连续的纵向结果中的动态治疗方案比较动态治疗方案的示例大小考虑因素

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Clinicians and researchers alike are increasingly interested in how best to personalize interventions. A dynamic treatment regimen is a sequence of prespecified decision rules which can be used to guide the delivery of a sequence of treatments or interventions that is tailored to the changing needs of the individual. The sequential multiple-assignment randomized trial is a research tool which allows for the construction of effective dynamic treatment regimens. We derive easy-to-use formulae for computing the total sample size for three common two-stage sequential multiple-assignment randomized trial designs in which the primary aim is to compare mean end-of-study outcomes for two embedded dynamic treatment regimens which recommend different first-stage treatments. The formulae are derived in the context of a regression model which leverages information from a longitudinal outcome collected over the entire study. We show that the sample size formula for a sequential multiple-assignment randomized trial can be written as the product of the sample size formula for a standard two-arm randomized trial, a deflation factor that accounts for the increased statistical efficiency resulting from a longitudinal analysis, and an inflation factor that accounts for the design of a sequential multiple-assignment randomized trial. The sequential multiple-assignment randomized trial design inflation factor is typically a function of the anticipated probability of response to first-stage treatment. We review modeling and estimation for dynamic treatment regimen effect analyses using a longitudinal outcome from a sequential multiple-assignment randomized trial, as well as the estimation of standard errors. We also present estimators for the covariance matrix for a variety of common working correlation structures. Methods are motivated using the ENGAGE study, a sequential multiple-assignment randomized trial aimed at developing a dynamic treatment regimen for increasing motivation to attend treatments among alcohol- and cocaine-dependent patients.
机译:临床医生和研究人员相似越来越感兴趣地是如何最好的干预措施。动态治疗方案是一系列预定的决策规则,可用于指导为改变个人不断变化的治疗或干预措施的交付。顺序多分配随机试验是一种研究工具,可以允许建造有效的动态治疗方案。我们推出了易于使用的公式,用于计算三个常见的两级顺序多分配随机试验设计的总样本大小,其中主要目的是比较推荐的两个嵌入式动态治疗方案的平均研究结果不同的第一阶段治疗。在回归模型的背景下导出公式,其利用在整个研究中收集的纵向结果中利用信息。我们表明,顺序多分配随机试验的样本大小公式可以写作标准双臂随机试验的样本大小公式的乘积,这是纵向分析所产生的统计效率增加的通货紧缩因子以及占设计顺序多分配随机试验的通胀因素。顺序多分配随机试验设计膨胀因子通常是预期对第一阶段治疗的响应概率的函数。我们审查了动态治疗方案效果分析的建模和估计,使用顺序多分配随机试验的纵向结果以及标准误差的估计。我们还为各种常见的工作相关结构提供协方差矩阵的估计。方法采用参赛研究,一种顺序多分配随机试验,旨在开发动态治疗方案,以增加运动和可卡因依赖性患者中治疗的动力。

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