Independent Researcher, Washington DC, USA.
Stat Med. 2024 May 20;43(11):2203-2215. doi: 10.1002/sim.10066. Epub 2024 Mar 28.
This study is to give a systematic account of sample size adaptation designs (SSADs) and to provide direct proof of the efficiency advantage of general SSADs over group sequential designs (GSDs) from a different perspective. For this purpose, a class of sample size mapping functions to define SSADs is introduced. Under the two-stage adaptive clinical trial setting, theorems are developed to describe the properties of SSADs. Sufficient conditions are derived and used to prove analytically that SSADs based on the weighted combination test can be uniformly more efficient than GSDs in a range of likely values of the true treatment difference . As shown in various scenarios, given a GSD, a fully adaptive SSAD can be obtained that has sufficient statistical power similar to that of the GSD but has a smaller average sample size for all in the range. The associated sample size savings can be substantial. A practical design example and suggestions on the steps to find efficient SSADs are also provided.
本研究旨在系统阐述样本量适应性设计（SSADs），并从不同角度为一般 SSADs 相对于群组序贯设计（GSDs）的效率优势提供直接证据。为此，引入了一类用于定义 SSADs 的样本量映射函数。在两阶段适应性临床试验设置下，开发了描述 SSADs 特性的定理。推导了充分条件，并进行了分析证明，基于加权组合检验的 SSADs 在真实治疗差异的可能值范围内可以比 GSDs 具有一致的更高效率。在各种情况下，对于给定的 GSD，可以获得完全自适应的 SSAD，其具有与 GSD 相似的充分统计功效，但对于范围中的所有 ，平均样本量更小。相关的样本量节省可能很大。还提供了一个实际的设计示例和有关寻找有效 SSADs 的步骤的建议。
