Office of Biostatistics and Pharmacovigilance, Center for Biologics Evaluation and Research (CBER), U.S. Food and Drug Administration (FDA), Silver Spring, MD, United States of America.
Office of Vaccines Research and Review, CBER, FDA, Silver Spring, MD, United States of America.
PLoS One. 2023 Sep 14;18(9):e0291533. doi: 10.1371/journal.pone.0291533. eCollection 2023.
We previously introduced a three-stage design and associated end-of-stage analyses for allergen immunotherapy (AIT) trials. End-of-stage differences alone may not provide a fuller picture of Stages 2 and 3 effects because they may depend upon stage-specific durations. Therefore, we introduce an additional trend analysis to evaluate the difference in progression curves of two groups over the entire stage. Results from such analysis are used to inform persistence of end-of-stage benefit and thus provide evidence for stagewise effects beyond the study periods. We jointly apply end-of-stage and trend analyses to support the enhanced three-stage design to determine treatment response over time and sustained response to AIT. A simulation study was performed to illustrate the statistical properties (bias and power) of trend analyses under varying statistical missing mechanisms and effect sizes. The extent of bias depended on the missing mechanism and magnitude. Powers were largely driven by effect and sample sizes as well as pre-specified success margins, particularly of relative trend. As an illustration, assuming relative treatment differences of 25-30%, stagewise dropout rate of 15%, and parallel outcome progressions, a sample size of 200 per group may achieve 97% power to demonstrate a treatment effect and 53% power to demonstrate a sustained effect post-treatment. Trend analysis supplements the end-of-stage analysis to enhance the statistical claims of stagewise effects. Inferential statistics support our proposed trend analysis for evaluating benefits of AIT over time and inform clinical understanding and decisions.
我们之前介绍了过敏原免疫治疗 (AIT) 试验的三阶段设计和相关的阶段末分析。仅阶段末差异可能无法全面反映第 2 阶段和第 3 阶段的效果，因为它们可能取决于阶段特异性持续时间。因此，我们引入了额外的趋势分析，以评估两组在整个阶段的进展曲线差异。此类分析的结果用于告知阶段末获益的持续性，从而为研究期间之外的阶段效果提供证据。我们联合应用阶段末和趋势分析来支持增强的三阶段设计，以确定随时间推移的治疗反应和对 AIT 的持续反应。进行了一项模拟研究，以说明在不同的统计缺失机制和效应大小下，趋势分析的统计特性（偏差和功效）。偏差的程度取决于缺失机制和大小。功效主要受效应和样本量以及预定的成功幅度（特别是相对趋势）驱动。例如，假设治疗差异相对为 25-30%，阶段内失效率为 15%，并且结果呈平行进展，每组 200 个样本可能具有 97%的功效来证明治疗效果，以及 53%的功效来证明治疗后具有持续效果。趋势分析补充了阶段末分析，以增强阶段效果的统计结论。推断统计学支持我们提出的用于评估 AIT 随时间推移的获益的趋势分析，并为临床理解和决策提供信息。
