Meaningful and unbiased statistical analysis of endpoints in Covid-19-related trials - by Dr. Tobias Bluhmki and Dr. Simon Rückinger
In order to win the fight against COVID-19, the fast discovery, development, and approval of effective therapies and targeted vaccinations via well-designed clinical trials and real-world evidence are of utmost importance. Throughout a development program, critically important decisions have to be made regarding data collection as well as the analysis, interpretation, and discussion of study results. Examples in the context of Covid-19 pneumonia are:
- What are meaningful endpoints of primary interest, e.g.
- Clinical response based on pre-set criteria
- Vasopressor-/ICU-free days
- Time on mechanical ventilation
- All-cause/cause-specific mortality
- What are optimal statistical analysis strategies in order to ensure an accurate and robust estimation of intervention effects?
- How can complex problems of competing risks (patients who die are unable to reach further endpoints but have to be included in the analysis) and multiple states (re-start of ventilation in previously ventilator-free patients, re-admission to ICU in already discharged patients) be adequately handled by statistical methods?
Clear advice is inevitable to overcome these challenging barriers. Our interdisciplinary team is equipped with up-to-date expertise and is familiar with all relevant industry/regulatory standards. We provide informed guidance in a timely and focused manner to initiate future studies as well as to accelerate/facilitate ongoing research.