Optimal, two-stage, adaptive enrichment designs for randomized trials, using sparse linear programming
Optimal, two-stage, adaptive enrichment designs for randomized trials, using sparse linear programming
复制标题
使用稀疏线性规划进行随机试验的最佳两阶段自适应富集设计
DOI：
10.1111/rssb.12366
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Liu, Han
中科院分区：
文献类型：
--
作者：
Rosenblum, Michael;Fang, Ethan X.;Liu, Han
适应性丰富设计涉及预先计划的规则，用于根据随机试验中积累的数据修改登记标准。我们专注于将总体人口划分为两个预定义子群体的设计，例如基于基线测量的生物标志物或风险评分。目标是了解哪些人群从实验性治疗中受益。适应性丰富设计的两个关键组成部分是修改入学的决策规则和多重测试程序。我们提供了一种通用方法，用于同时优化这些组件，以实现两阶段、自适应富集设计。我们在功效和 I 类错误率的限制下最小化预期样本量。由于其非凸性，直接求解该优化问题在计算上是不可行的。我们方法的关键是将该优化问题新颖、离散地表示为稀疏线性程序，该程序很大，但在计算上可以通过使用现代优化技术来解决。我们提供了一个 R 包来实现我们的方法，并且与多种软件语言的线性程序求解器兼容。我们的方法产生了新的、近似最佳的试验设计。
Adaptive enrichment designs involve preplanned rules for modifying enrolment criteria based on accruing data in a randomized trial. We focus on designs where the overall population is partitioned into two predefined subpopulations, e.g. based on a biomarker or risk score measured at baseline. The goal is to learn which populations benefit from an experimental treatment. Two critical components of adaptive enrichment designs are the decision rule for modifying enrolment, and the multiple-testing procedure. We provide a general method for simultaneously optimizing these components for two-stage, adaptive enrichment designs. We minimize the expected sample size under constraints on power and the familywise type I error rate. It is computationally infeasible to solve this optimization problem directly because of its non-convexity. The key to our approach is a novel, discrete representation of this optimization problem as a sparse linear program, which is large but computationally feasible to solve by using modern optimization techniques. We provide an R package that implements our method and is compatible with linear program solvers in several software languages. Our approach produces new, approximately optimal trial designs.