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Review
. 2023 Apr 17;3(1):53.
doi: 10.1038/s43856-023-00281-1.

The importance of appropriate selection of clinical endpoints in outpatient COVID-19 clinical trials

Affiliations
Review

The importance of appropriate selection of clinical endpoints in outpatient COVID-19 clinical trials

Kristian Thorlund et al. Commun Med (Lond). .

Abstract

Clinical trial endpoints must be carefully and intentionally selected so that the results of the trial can be used to inform policy- and decision-making. The relative importance of potential endpoints often depends on the stakeholder, with patients having different preferences to policymakers and regulators. The set up of clinical trials for COVID-19 was problematic, as endpoints that could be reasonably measured did not always match the efficacy endpoints usually required by guideline panels. Thus, different endpoints were used, which made the timely comparison and evaluation of interventions difficult. Here we discuss the evolution of the COVID-19 landscape and the effect this is having on the selection of consistent and measurable clinical trial endpoints. Using appropriate endpoints is crucial for researchers to offer the most reliable, valid, and interpretable results possible.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Sample size necessary for outpatient clinical trials with varied endpoint event rates and vaccine coverage.
We assumed background (control) event rates (CER) of 2%, 7·5%, 13%, 16% and 25% for death, hospitalization, emergency care use, a 2-point improvement on the WHO symptom scale and SPO2 decline, respectively. 14-day recovery was assumed to occur 92·5% of the time in the control arm (all but hospitalized). We then calculated the sample size required for 90% power to detect a 30% relative risk reduction (or equivalently, 0·7 hazard ratio in the case of time to recovery) effect at the 2·5% significance level, assuming zero vaccine coverage,. The power was then held fixed at 90% and modified sample size requirements were calculated, as the background rates changed according to CER(adjusted) = CER*(1-VE*p), where VE is the vaccine efficacy and p is the vaccine coverage in the general population, under the simplifying assumption that VE remains the same for all different outcomes. The increase in sample size relative to zero vaccine coverage is shown in the figure for all outcomes.

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