Design aspects of COVID-19 treatment trials: Improving probability and time of favourable events

TL;DR

This paper discusses how to optimize COVID-19 treatment trial designs to increase recovery probability and speed, while maintaining safety, by focusing on endpoint selection, sample size, and adaptive methods.

Contribution

It provides guidance on selecting endpoints, analyzing trial data, and applying adaptive designs specifically for COVID-19 treatment trials.

Findings

Optimal endpoint choice improves trial efficiency.

Adaptive designs can accelerate favourable event detection.

Sample size considerations are crucial for timely results.

Abstract

As a reaction to the pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a multitude of clinical trials for the treatment of SARS-CoV-2 or the resulting corona disease (COVID-19) are globally at various stages from planning to completion. Although some attempts were made to standardize study designs, this was hindered by the ferocity of the pandemic and the need to set up trials quickly. We take the view that a successful treatment of COVID-19 patients (i) increases the probability of a recovery or improvement within a certain time interval, say 28 days; (ii) aims to expedite favourable events within this time frame; and (iii) does not increase mortality over this time period. On this background we discuss the choice of endpoint and its analysis. Furthermore, we consider consequences of this choice for other design aspects including sample size and power and provide some guidance on the application of adaptive designs in this particular context.