Title: Good Statistical Practices to Tackle the lack of Reproducibility from Discovery to Clinical Research
Date: Tuesday, April 21, 2020
Time: 04:00 PM Central European Summer Time
Duration: 1 hour
For 15 years the scientific literature repeatedly underlines the important lack of reproducibility and replicability of studies in biomedical and psychological research. As a consequence, several scientific organizations (journal, scientific societies, universities, national agencies) have identified some root causes to this issue and proposed good research practices to improve the reproducibility of the results. The misuse of statistical concepts, from design of studies to analysis of data to decision-making is at the heart of the crisis, even if not the only cause. In this webinar we will explain how to understand the sequences of issues and how to fix it in order to drastically improve the replicability of the results. Through example the webinar will cover concepts such as OFAT vs DoE, p-values and Bayesian statistics and Power vs Assurance as easy opportunities to improve robustness of decisions.
- The value of good design of experiments
- Consider Bayesian statistics to answer your question
- P-values is not always what you’re looking for
- Adopt a life-cycle over the long-run, not just study by study
Dr. Bruno Boulanger
CSO, Statistical Solutions
Bruno has 25 years of experience in several areas of pharmaceutical research and industry including discovery, toxicology, CMC and early clinical phases. He holds various positions in Europe and in The US. Bruno joined UCB Pharma in 2007 as Director of Exploratory Statistics has been a Lecturer at the Université of Liège, in the School of Pharmacy, teaching Design of Experiments and Statistics since 2000. He is also a USP Expert, member of the Committee of Experts in Statistics since 2010. Bruno has authored or co-authored more than 100 publications in applied statistics.
Senior Manager, Statistics and Pharmacometrics
Timothy Mutsvari has more than 6yrs of experience in pharmaceutical industry. He did his PhD in Biostatistics from KULeuven, Belgium where he focused on Bayesian methodology in multilevel and misclassification. In 2012 he joined UCB as a post-doc in pharmacometrics. During his post-doc he also contributed immensely to early phase clinical studies, focusing on assessing the quality of prior distributions, including topics such as prior-data conflict, meta-analytic predictive priors, etc. In 2015 Timothy joined Pharmalex where he is involved in CMC support, PKPD modeling and Biosimilarity.