Baseline-Covariate Adjusted Confidence Interval for Proportional Difference Between Two Treatment Groups in Clinical Trials
Baseline-Covariate Adjusted Confidence Interval for Proportional Difference Between Two Treatment Groups in Clinical Trials
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DOI：
10.1080/19466315.2019.1566087
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发表时间：
2019-04-25
影响因子：
1.8
通讯作者：
Liu, Fang
中科院分区：
文献类型：
--
作者：
Chen, Jingjing;Liu, Fang
治疗产品对二元终点的治疗效果通常表示为临床试验的治疗组和对照组之间具有感兴趣结果的受试者比例的差异。由于基线协变量与主要终点相关，对比例差异的分析和相关置信区间 (CI) 的构建通常很复杂。针对此类基线协变量进行调整的分析通常可以提高假设检验的效率和治疗效果估计的精度，并避免由基线协变量不平衡引起的可能偏差。大多数现有文献侧重于构建未调整或仅调整分类协变量的 CI，这对于不同统计方法如何执行以及哪种方法在构建比例差异的分类和连续基线协变量调整 CI 方面是最佳的提供了非常有限的建议。我们通过实际数据应用和模拟回顾和比较了三种常用的基于模型的方法以及传统的非参数加权差异方法的性能，用于构建比例差异的协变量调整 CI。检查 95% CI 的覆盖率、I 类错误控制和功效。我们还通过模拟研究了不同场景下导致模型收敛失败的因素。
The treatment effect of a therapeutic product on a binary endpoint is often expressed as the difference in proportions of subjects with the outcome of interest between the treated and control groups of a clinical trial. Analysis of the proportional difference and construction of the associated confidence interval (CI) is often complicated due to the baseline covariate(s) being associated with the primary endpoint. Analysis adjusting for such baseline covariate(s) generally improves efficiency of hypothesis testing and precision of treatment effect estimation, and avoids possible bias caused by baseline covariate imbalances. Most existing literatures focus on constructing unadjusted or categorical covariate(s) adjusted only CI, which provides very limited advice on how different statistical methods perform and which method is optimal in terms of constructing both categorical and continuous baseline covariate(s) adjusted CI for proportional difference. We review and compare the performance of three commonly used model-based methods as well as the traditional nonparametric weighted-difference methods for the construction of covariate-adjusted CI for proportional difference via a real data application and simulations. The coverage of 95% CI, Type I error control, and power are examined. We also examine the factors leading to the model convergence failure in different scenarios via simulations.