Abstract
A clinical trial aims to uncover high-quality evidence for both efficacy and safety for an experimental treatment. Traditionally, statisticians evaluate the two aspects marginally, where, for example, efficacy considers the proportion of patients cured and safety considers the proportion of patients not showing adverse events. However, separate analyses could end up with misleading results, as the cumulative nature of clinical outcomes and the correlation between efficacy and safety endpoints are neglected. The desirability of outcome ranking (DOOR) method addresses such issues and provides a patient-centric approach to benefit:risk evaluation. A patient’s outcome is ranked based on pre-specified clinical criteria, where the most desirable rank represents a good outcome with no side effects and the least desirable rank is the worst possible clinical outcome. As the DOOR outcome is a temporal state that can have repeated measures, we propose a longitudinal approach that estimates and infers the temporal treatment effects. We develop a methodology for constructing simultaneous confidence bands by accounting for the correlations across time points. Additionally, we propose a weighted Mann-Whitney-U statistic to evaluate the treatment effect over the entire trial period. The performance of the proposed methodologies is examined through simulations and an application to a COVID-19 trial.
Published Version
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