协变量
可比性
医学
子群分析
临床试验
随机化
基线(sea)
选择(遗传算法)
统计
计量经济学
计算机科学
荟萃分析
数学
内科学
机器学习
地质学
组合数学
海洋学
作者
Stuart Pocock,Susan E. Assmann,Laura E. Enos,Linda Kasten
摘要
Abstract Clinical trial investigators often record a great deal of baseline data on each patient at randomization. When reporting the trial's findings such baseline data can be used for (i) subgroup analyses which explore whether there is evidence that the treatment difference depends on certain patient characteristics, (ii) covariate‐adjusted analyses which aim to refine the analysis of the overall treatment difference by taking account of the fact that some baseline characteristics are related to outcome and may be unbalanced between treatment groups, and (iii) baseline comparisons which compare the baseline characteristics of patients in each treatment group for any possible (unlucky) differences. This paper examines how these issues are currently tackled in the medical journals, based on a recent survey of 50 trial reports in four major journals. The statistical ramifications are explored, major problems are highlighted and recommendations for future practice are proposed. Key issues include: the overuse and overinterpretation of subgroup analyses; the underuse of appropriate statistical tests for interaction; inconsistencies in the use of covariate‐adjustment; the lack of clear guidelines on covariate selection; the overuse of baseline comparisons in some studies; the misuses of significance tests for baseline comparability, and the need for trials to have a predefined statistical analysis plan for all these uses of baseline data. Copyright © 2002 John Wiley & Sons, Ltd.
科研通智能强力驱动
Strongly Powered by AbleSci AI