Interpretable Treatment Effect Summary Measures for Randomized Controlled Trials with Ordinal Composite Outcomes

Music City Center 

In randomized controlled trials, using an ordinal outcome is often more statistically efficient than using a binary composite outcome. The treatment effect on an ordinal outcome is frequently described as the odds ratio from a proportional odds model; however, this summary measure lacks transparency when proportional odds is violated. We propose transparent treatment effect summary measures for ordinal outcomes, including 'weighted mean' risk differences and 'weighted geometric mean' odds ratios and relative risks, along with Bayesian estimators based on non-proportional odds models that facilitate covariate adjustment with marginalization via the Bayesian bootstrap. We propose weighting schemes that ensure inference is invariant to whether the ordinal outcome is ordered from best to worst versus worst to best, and invariant to the insertion of a outcome level with zero probability. Using computer simulation, we show that comparative testing based on the proposed treatment effect summary measures performs well relative to the traditional proportional odds approach. We provide an analysis using the proposed framework of the COVID-OUT trial which exhibits evidence of non-proportional odds effects.

Bayesian methods

Clinical trials

Estimand framework

Non-proportional odds

Partial proportional odds