Endpoints for randomized controlled clinical trials for COVID-19 treatments

TL;DR

Choosing appropriate endpoints in COVID-19 clinical trials is complex due to disease heterogeneity; time-to-event analyses offer advantages over fixed-time assessments, especially when optimal evaluation timing is uncertain.

Contribution

This study evaluates the statistical power of various trial endpoints for COVID-19 treatments using simulations and recent trial data, highlighting the benefits of time-to-event methods.

Findings

Time-to-event analyses have comparable power to fixed-time methods at optimal evaluation times.

Power depends heavily on the chosen evaluation time point in fixed-time assessments.

Time-to-event approaches are advantageous when the optimal evaluation time is unknown or for interim analyses.

Abstract

Introduction: Endpoint choice for randomized controlled trials of treatments for COVID-19 is complex. A new disease brings many uncertainties, but trials must start rapidly. COVID-19 is heterogeneous, ranging from mild disease that improves within days to critical disease that can last weeks and can end in death. While improvement in mortality would provide unquestionable evidence about clinical significance of a treatment, sample sizes for a study evaluating mortality are large and may be impractical. Furthermore, patient states in between "cure" and "death" represent meaningful distinctions. Clinical severity scores have been proposed as an alternative. However, the appropriate summary measure for severity scores has been the subject of debate, particularly in relating to the uncertainty about the time-course of COVID-19. Outcomes measured at fixed time-points may risk missing the time of clinical benefit. An endpoint such as time-to-improvement (or recovery), avoids the timing problem. However, some have argued that power losses will result from reducing the ordinal scale to a binary state of "recovered" vs "not recovered." Methods: We evaluate statistical power for possible trial endpoints for COVID-19 treatment trials using simulation models and data from two recent COVID-19 treatment trials. Results: Power for fixed-time point methods depends heavily on the time selected for evaluation. Time-to-improvement (or recovery) analyses do not specify a time-point. Time-to-event approaches have reasonable statistical power, even when compared to a fixed time-point method evaluated at the optimal time. Discussion: Time-to-event analyses methods have advantages in the COVID-19 setting, unless the optimal time for evaluating treatment effect is known in advance. Even when the optimal time is known, a time-to-event approach may increase power for interim analyses.