Baseline-adjusted proportional odds models for the quantification of treatment effects in trials with ordinal sum...

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.