Department of Neurology and Rehabilitation Medicine, University of Cincinnati, OH (E.A.M., P.K.).
Department of Public Health Sciences, Medical University of South Carolina, Charleston (S.D.Y.).
Stroke. 2022 Apr;53(4):e150-e155. doi: 10.1161/STROKEAHA.121.034859. Epub 2022 Jan 11.
National Institutes of Health Stroke Scale (NIHSS), measured a few hours to days after stroke onset, is an attractive outcome measure for stroke research. NIHSS at the time of presentation (baseline NIHSS) strongly predicts the follow-up NIHSS. Because of the need to account for the baseline NIHSS in the analysis of follow-up NIHSS as an outcome measure, a common and intuitive approach is to define study outcome as the change in NIHSS from baseline to follow-up (ΔNIHSS). However, this approach has important limitations. Analyzing ΔNIHSS implies a very strong assumption about the relationship between baseline and follow-up NIHSS that is unlikely to be satisfied, drawing into question the validity of the resulting statistical analysis. This reduces the precision of the estimates of treatment effects and the power of clinical trials that use this approach to analysis. ANCOVA allows for the analysis of follow-up NIHSS as the dependent variable while adjusting for baseline NIHSS as a covariate in the model and addresses several challenges of using ΔNIHSS outcome using simple bivariate comparisons (eg, a test, Wilcoxon rank-sum, linear regression without adjustment for baseline) for stroke research. In this article, we use clinical trial simulations to illustrate that variability in NIHSS outcome is less when follow-up NIHSS is adjusted for baseline compared to ΔNIHSS and how a reduction in this variability improves the power. We outline additional, important clinical and statistical arguments to support the superiority of ANCOVA using the final measurement of the NIHSS adjusted for baseline over, and caution against using, the simple bivariate comparison of absolute NIHSS change (ie, delta).
美国国立卫生研究院卒中量表（NIHSS）在卒中发病后数小时至数天内进行测量，是卒中研究中一种有吸引力的结局指标。发病时的 NIHSS（基线 NIHSS）强烈预测随访时的 NIHSS。由于需要在分析随访 NIHSS 作为结局指标时考虑基线 NIHSS，一种常见且直观的方法是将研究结局定义为 NIHSS 从基线到随访的变化（ΔNIHSS）。然而，这种方法存在重要的局限性。分析ΔNIHSS 意味着对基线和随访 NIHSS 之间关系的假设非常强烈，而这种假设不太可能得到满足，从而对由此产生的统计分析的有效性提出质疑。这降低了使用这种方法进行分析的临床试验中治疗效果估计的精度和效力。协方差分析允许在模型中调整基线 NIHSS 作为协变量，从而分析随访 NIHSS 作为因变量，并解决了使用 ΔNIHSS 结局进行卒中研究的简单双变量比较（例如 t 检验、Wilcoxon 秩和检验、未调整基线的线性回归）的几个挑战。在本文中，我们使用临床试验模拟来阐明，与 ΔNIHSS 相比，调整基线 NIHSS 后 NIHSS 结局的变异性更小，以及这种变异性的降低如何提高功效。我们概述了额外的重要临床和统计论据，以支持使用经基线调整的最终 NIHSS 测量的协方差分析优于且不建议使用简单的 NIHSS 绝对变化（即 delta）的双变量比较。
