Open AccessRegression analysis of ordinal stroke clinical trial outcomes: An application to the NINDS t‐ PA trial
Author(s) -
DeSantis Stacia M.,
Lazaridis Christos,
Palesch Yuko,
Ramakrishnan Viswanathan
Publication year - 2014
Publication title -
international journal of stroke
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.375
H-Index - 74
eISSN - 1747-4949
pISSN - 1747-4930
DOI - 10.1111/ijs.12052
Subject(s) - ordinal regression , odds , ordered logit , odds ratio , ordinal data , medicine , statistics , modified rankin scale , ordinal scale , logistic regression , outcome (game theory) , type i and type ii errors , econometrics , mathematics , ischemic stroke , ischemia , mathematical economics
Background The modified R ankin 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. Aims 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. Methods 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 . Results 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). Conclusions 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.