Conflicts of interest: The authors declare no potential conflict of interest.
Regression analysis of ordinal stroke clinical trial outcomes: An application to the NINDS t-PA trial
Article first published online: 27 JUN 2013
© 2013 The Authors. International Journal of Stroke © 2013 World Stroke Organization
International Journal of Stroke
Volume 9, Issue 2, pages 226–231, February 2014
How to Cite
DeSantis, S. M., Lazaridis, C., Palesch, Y. and Ramakrishnan, V. (2014), Regression analysis of ordinal stroke clinical trial outcomes: An application to the NINDS t-PA trial. International Journal of Stroke, 9: 226–231. doi: 10.1111/ijs.12052
Stacia M. DeSantis – Study concept and design, acquisition of data, analysis and interpretation, critical revision of the manuscript.
Christos Lazaridis – Study concept and design, interpretation, critical revision of the manuscript.
Yuko Palesch – Interpretation, critical revision of the manuscript.
Ramesh Ramakrishnan – Study concept and design, analysis and interpretation, critical revision of the manuscript.
- Issue published online: 20 JAN 2014
- Article first published online: 27 JUN 2013
- clinical trial;
- modified Rankin scale;
- ordinal regression;
- partial proportional odds;
- proportional odds;
- tissue plasminogen activator
The modified Rankin scale (mRS) is the most common functional outcome assessed in stroke trials. The proportional odds model is commonly used to analyze this ordinal outcome but it requires a restrictive assumption that a single odds ratio applies across the entire outcome scale.
The study aims to model the effect of tissue-type plasminogen activator on ordinal mRS, test model assumptions, and compare fits and predictive ability of the statistical models.
Several ordinal regression methods are presented and applied to a re-analysis of the 1995 NINDS tissue-type plasminogen activator study. Violations of the proportional odds assumption are demonstrated using graphs and statistical tests, and the partial proportional odds model is introduced and recommended as an alternative for the analysis of mRS.
The partial proportional odds model relaxes the assumptions about treatment effect on the ordinal outcome scale and provides a better fit to the data than the commonly used proportional odds model (likelihood ratio test chi-square = 8·05, P = 0·005). It provides easily interpretable odds ratios and it is able to detect efficacy at the lower end and a lack of efficacy at the upper end of the mRS scale. Further, it provides lower prediction error than the proportional odds model (0·002 versus 0·005).
Assuming proportional odds when it does not hold can mask differential treatment effects at the upper end of the ordinal mRS scale and has implications for reduced power when studies are designed under this assumption.