Department of Biostatistics, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, Zurich, CH-8001, Switzerland.
University Hospital Balgrist, Spinal Cord Injury Center, Forchstrasse 340, Zurich, CH-8008, Switzerland.
BMC Med Res Methodol. 2020 May 6;20(1):104. doi: 10.1186/s12874-020-00984-2.
Sum scores of ordinal outcomes are common in randomized clinical trials. The approaches routinely employed for assessing treatment effects, such as t-tests or Wilcoxon tests, are not particularly powerful in detecting changes in relevant parameters or lack the ability to incorporate baseline information. Hence, tailored statistical methods are needed for the analysis of ordinal outcomes in clinical research.
We propose baseline-adjusted proportional odds logistic regression models to overcome previous limitations in the analysis of ordinal outcomes in randomized clinical trials. For the validation of our method, we focus on common ordinal sum score outcomes of neurological clinical trials such as the upper extremity motor score, the spinal cord independence measure, and the self-care subscore of the latter. We compare the statistical power of our models to other conventional approaches in a large simulation study of two-arm randomized clinical trials based on data from the European Multicenter Study about Spinal Cord Injury (EMSCI, ClinicalTrials.gov Identifier: NCT01571531). We also use the new method as an alternative analysis of the historical Sygen®clinical trial.
The simulation study of all postulated trial settings demonstrated that the statistical power of the novel method was greater than that of conventional methods. Baseline adjustments were more suited for the analysis of the upper extremity motor score compared to the spinal cord independence measure and its self-care subscore.
The proposed baseline-adjusted proportional odds models allow the global treatment effect to be directly interpreted. This clear interpretation, the superior statistical power compared to the conventional analysis approaches, and the availability of open-source software support the application of this novel method for the analysis of ordinal outcomes of future clinical trials.
在随机临床试验中，有序结局的总和评分很常见。用于评估治疗效果的方法，如 t 检验或 Wilcoxon 检验，在检测相关参数的变化或缺乏纳入基线信息的能力方面并不特别有效。因此，需要针对临床研究中有序结局的分析制定专门的统计方法。
我们提出了基线调整的比例优势逻辑回归模型，以克服以前在随机临床试验中分析有序结局的局限性。为了验证我们的方法，我们关注神经科临床试验中常见的有序总和评分结局，如上肢运动评分、脊髓独立性测量和后者的自理子评分。我们在一项基于欧洲多中心脊髓损伤研究（EMSCI，ClinicalTrials.gov 标识符：NCT01571531）数据的两臂随机临床试验的大型模拟研究中，比较了我们的模型与其他传统方法的统计功效。我们还将新方法作为 Sygen®临床试验的历史数据的替代分析。
所有假定试验设置的模拟研究表明，新方法的统计功效大于传统方法。与脊髓独立性测量及其自理子评分相比，基线调整更适合上肢运动评分的分析。
所提出的基线调整比例优势模型允许直接解释总体治疗效果。这种清晰的解释、与传统分析方法相比的优越统计功效，以及开源软件的可用性支持了该新方法在未来临床试验中分析有序结局的应用。
