Sample size calculations for evaluating treatment policies in multi-stage designs
Sample size calculations for evaluating treatment policies in multi-stage designs
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DOI：
10.1177/1740774510376418
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发表时间：
2010-12-01
期刊：
影响因子：
2.7
通讯作者：
Lavori, Philip W.
中科院分区：
文献类型：
--
作者：
Dawson, Ree;Lavori, Philip W.
序贯多重分配随机（SMAR）设计用于评价治疗策略，也称为适应性治疗策略（ATS）。SMAR样本量的确定是具有挑战性的，因为ATS的顺序和自适应的性质，和多阶段的随机分配用于evaluating them.Purpose我们推导出适合连续SMAR随机化的嵌套结构的样本量公式。这种嵌套会产生具有重叠数据的ATS，因此会产生策略间协方差。我们专注于当协方差是足够大的情况下，通过提高推理efficiency.Methods减少样本量，我们的设计计算借鉴两种不同的方法SMAR试验，使用的最佳半参数和贝叶斯预测估计的标准误的平等。这种“混合”的方法产生的t-检验功率计算，是在熟悉的trialist.Results模拟研究支持的基本假设的合理性，以及足够的近似策略之间的协方差时，它是实质性的，进行了概括。公式的敏感性误指定的调查表明，最大的影响是由于影响大小的变化，这是一个先验的临床判断的一部分trialist.Limitations我们已经限制模拟调查SMAR研究的两个和三个阶段，尽管这些方法是完全通用的，因为它们适用于“K”，结论需要实际指导，以允许试验者使用导出的方法来确定SMAR设计的大小。为此，我们将ATS定义为“独特的”，当它们至少在被认为具有临床相关性的（最小）效应大小上存在差异时。模拟结果表明，区分不同的策略所需的受试者的数量将显着减少调整协方差只有当小的影响是感兴趣的。临床试验2010; 7：643-652。http://ctj.sagepub.com
Background Sequential multiple assignment randomized (SMAR) designs are used to evaluate treatment policies, also known as adaptive treatment strategies (ATS). The determination of SMAR sample sizes is challenging because of the sequential and adaptive nature of ATS, and the multi-stage randomized assignment used to evaluate them.Purpose We derive sample size formulae appropriate for the nested structure of successive SMAR randomizations. This nesting gives rise to ATS that have overlapping data, and hence between-strategy covariance. We focus on the case when covariance is substantial enough to reduce sample size through improved inferential efficiency.Methods Our design calculations draw upon two distinct methodologies for SMAR trials, using the equality of the optimal semi-parametric and Bayesian predictive estimators of standard error. This 'hybrid' approach produces a generalization of the t-test power calculation that is carried out in terms of effect size and regression quantities familiar to the trialist.Results Simulation studies support the reasonableness of underlying assumptions as well as the adequacy of the approximation to between-strategy covariance when it is substantial. Investigation of the sensitivity of formulae to misspecification shows that the greatest influence is due to changes in effect size, which is an a priori clinical judgment on the part of the trialist.Limitations We have restricted simulation investigation to SMAR studies of two and three stages, although the methods are fully general in that they apply to 'K-stage' trials.Conclusions Practical guidance is needed to allow the trialist to size a SMAR design using the derived methods. To this end, we define ATS to be 'distinct' when they differ by at least the (minimal) size of effect deemed to be clinically relevant. Simulation results suggest that the number of subjects needed to distinguish distinct strategies will be significantly reduced by adjustment for covariance only when small effects are of interest. Clinical Trials 2010; 7: 643-652. http://ctj.sagepub.com