School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Rd, Melbourne, 3004, Australia.
BMC Med Res Methodol. 2023 Mar 13;23(1):60. doi: 10.1186/s12874-023-01878-9.
Baseline imbalance in covariates associated with the primary outcome in clinical trials leads to bias in the reporting of results. Standard practice is to mitigate that bias by stratifying by those covariates in the randomization. Additionally, for continuously valued outcome variables, precision of estimates can be (and should be) improved by controlling for those covariates in analysis. Continuously valued covariates are commonly thresholded for the purpose of performing stratified randomization, with participants being allocated to arms such that balance between arms is achieved within each stratum. Often the thresholding consists of a simple dichotomization. For simplicity, it is also common practice to dichotomize the covariate when controlling for it at the analysis stage. This latter dichotomization is unnecessary, and has been shown in the literature to result in a loss of precision when compared with controlling for the covariate in its raw, continuous form. Analytic approaches to quantifying the magnitude of the loss of precision are generally confined to the most convenient case of a normally distributed covariate. This work generalises earlier findings, examining the effect on treatment effect estimation of dichotomizing skew-normal covariates, which are characteristic of a far wider range of real-world scenarios than their normal equivalents.
临床试验中与主要结局相关的协变量基线不平衡会导致结果报告产生偏差。标准做法是通过在随机分组中对这些协变量进行分层来减轻这种偏差。此外，对于连续值的结局变量，通过在分析中控制这些协变量，可以（并且应该）提高估计的精度。连续值协变量通常为了进行分层随机化而进行阈值处理，将参与者分配到各个手臂，使得在每个分层内实现手臂之间的平衡。通常，阈值处理包括简单的二分类。为了简单起见，在分析阶段控制协变量时也通常将其二分类。这种后一种二分类是不必要的，并且文献已经表明，与以原始连续形式控制协变量相比，这会导致精度损失。量化精度损失幅度的分析方法通常仅限于最方便的正态分布协变量的情况。这项工作扩展了早期的发现，研究了将偏态正态协变量二分类对治疗效果估计的影响，偏态正态协变量比正态分布协变量更能代表更广泛的现实情况。
