“How to Analyse Clinical Trial Data Collected during the Pandemic”
The Cancer Drug Development Forum held the third CDDF Live Webinar Series – Lessons Learnt from COVID-19: “How to Analyse Clinical Trial Data Collected during the Pandemics” on Thursday 23 September 2021 at 18:00-19:00 CEST. This webinar comprised a lecture given by Dr. Jan Bogaerts (EORTC, BE), followed by a discussion session moderated by Prof. Ruth Plummer (CDDF).
The COVID pandemic has affected clinical trials in several ways, ranging from accrual numbers to protocol deviations to being a competing risk factor in the patient’s outcome. We discussed how methodologists may consider the task of analyzing data in presence of the impact of COVID, using ideas from the new paradigm of estimands. We also looked into a number of ideas ranging from analysing the data “as is” to far-reaching efforts to consider COVID a confounder that needs to be eliminated from the analysis.
PREVIOUS WEBINARS ON COVID-19
- CDDF Live Webinar Series – Lessons Learnt from COVID-19:“A Cancer Institute’s experience in the operational cancer clinical trial performance during the COVID-pandemic”
- CDDF Live Webinar Series 2 – Lessons Learnt from COVID-19: “Treatment of Cancer Patients during the SARS-CoV2 Pandemic: Learnings and outlook after 18 months”
Jan Bogaerts gained his degree in mathematics (1986) and his PhD in mathematics (1993) at the Free University of Brussels (Belgium). In 1988 he also gained a degree in management at the Free University in Brussels.
He joined BMS in 1993 as statistician. Later, as Associate Director Statistics, he worked on several drugs in oncology, including several FDA and EMEA submissions.
In 2004 he joined the EORTC as statistician of the EORTC Breast Cancer Group. In 2010 he was promoted Head of the Statistics Department and, in 2017, Scientific Director. He contributed to the development of RECIST and sits on the RECIST Steering Committee. One of his previous key roles was statistician of the MINDACT trial (EORTC 10041 – BIG 3-04).
He serves as permanent statistician on the EMA Scientific Advisory Group – Oncology. He is member of the ESMO-Magnitude of Clinical Benefit Scale committee.
Current statistical interests include the use of and methodological issues around Progression Free Survival, alternative ways to use changes in tumor measurements as predictive markers, and the correct evaluation of the contribution of new markers to existing prognostic risk evaluation. He also has a high interest in closing the gap between clinical trials and day-to-day practice, and in increasing scientific learning from merging multiple data sources.