Harmonizing Heterogeneous Endpoints in Coronavirus Disease 2019 Trials Without Loss of Information.

Objectives: Many trials investigate potential effects of treatments for coronavirus disease 2019. To provide sufficient information for all involveddecision-makers (clinicians, public health authorities, and drug regulatory agencies), a multiplicity of endpoints must be considered. The objectives are to provide hands-on statistical guidelines for harmonizing heterogeneous endpoints in coronavirus disease 2019 clinical trials.
Design: Randomized controlled trials for patients infected with coronavirus disease 2019.
Setting: General methods that apply to any randomized controlled trial for patients infected with coronavirus disease 2019.
Patients: Coronavirus disease 2019 positive individuals.
Interventions: None.
Measurements and Main Results: We develop a multistate model that is based on hospitalization, mechanical ventilation, death, and discharge. These events are both categories of the ordinal endpoint recommended by the World Health Organization and also within the core outcome set of the Core Outcome Measures in Effectiveness Trials initiative for coronavirus disease 2019 trials. To support our choice of states in the multistate model, we also perform a brief review of registered coronavirus disease 2019 clinical trials. Based on the multistate model, we give recommendation for compact, informative illustration of time-dynamic treatment effects and explorative statistical analysis. A majority of coronavirus disease 2019 clinical trials collect information on mechanical ventilation, hospitalization, and death. Using reconstructed and real data of coronavirus disease 2019 trials, we show how a stacked probability plot provides a detailed understanding of treatment effects on the patients' course of hospital stay. It contributes to harmonizing multiple endpoints and differing lengths of follow-up both within and between trials.
Conclusions: All ongoing clinical trials should include a stacked probability plot in their statistical analysis plan as descriptive analysis. While primary analysis should be on an early endpoint with appropriate capability to be a surrogate (parameter), our multistate model provides additional detailed descriptive information and links results within and between coronavirus disease 2019 trials.
Competing Interests: Dr. von Cube has been supported by the Education and Qualification for Postdocs Medical Scientist Program of the University of Freiburg. Dr. Lambert was partly supported by the Edmond de Rothschild Foundation Medical Fellowship Program. The remaining authors have disclosed that they do not have any potential conflicts of interest.
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