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

Maja von Cube1,2, Marlon Grodd1,2, Martin Wolkewitz1,2

  • 1Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.

Critical Care Medicine
|November 5, 2020
PubMed
Abstract

Insights

This study offers statistical guidelines for harmonizing coronavirus disease 2019 (COVID-19) trial endpoints. A multistate model and stacked probability plot help analyze treatment effects for better decision-making.

Area of Science:

  • Statistics
  • Clinical Trials
  • Epidemiology

Background:

  • Numerous clinical trials are investigating treatments for coronavirus disease 2019 (COVID-19).
  • Effective decision-making by clinicians, public health authorities, and regulatory agencies requires comprehensive information from these trials.
  • Harmonizing heterogeneous endpoints across COVID-19 trials is crucial for synthesizing evidence.

Purpose of the Study:

  • To provide statistical guidelines for harmonizing diverse endpoints in COVID-19 clinical trials.
  • To introduce a multistate model for analyzing patient outcomes.
  • To recommend methods for illustrating time-dynamic treatment effects.

Main Methods:

  • Development of a multistate model incorporating hospitalization, mechanical ventilation, death, and discharge.
  • Review of registered COVID-19 clinical trials to support model state selection.
  • Application of a stacked probability plot for visualizing treatment effects on patient hospital course.

Main Results:

  • The multistate model effectively integrates key patient outcomes (hospitalization, ventilation, death, discharge).
  • Stacked probability plots offer detailed insights into treatment effects on patient trajectories.
  • The proposed methods facilitate the harmonization of multiple endpoints and varying follow-up durations.

Conclusions:

  • Ongoing COVID-19 trials should incorporate stacked probability plots for descriptive analysis.
  • The multistate model provides valuable descriptive information, linking results within and between trials.
  • This approach aids in understanding treatment effects and harmonizing data for robust evidence synthesis.

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