Bayesian Statistics

In this episode of PharmaLex Talks, we are joined by Bruno Boulanger, Senior Director and Global Head of Statistics and Data Science at PharmaLex, who is recognized for his groundbreaking work in global statistics and data science, as highlighted by his award-winning publications. Bruno's innovative approach to statistics, aimed at enhancing predictive models and facilitating decision-making in the pharmaceutical industry, holds significant value for the field. Clement Laloux, Specialist in Statistics & Data Sciences at PharmaLex, provides additional insights, particularly on the influence of prior usage in predicting patient recruitment for clinical trials.

This episode delves into the critical role of patient enrolment in determining the success of clinical trials. Despite ongoing efforts to address recruitment challenges, many trials struggle to meet their enrolment targets on schedule, leading to delays in drug submission and potential shortages in hospitals. We explore various factors affecting decision-making and discuss the adaptation of prediction models, particularly Bayesian models, to address these challenges.

A key question addressed in this episode is the determination of the optimal number of additional centres required to ensure timely trial completion. We introduce a Bayesian methodology aimed at predicting the randomization dates of future patients in ongoing multicentre clinical trials.

If you're interested in learning more about this topic, we invite you to visit our webpage and access our complimentary webinar on the Bayesian approach to supply chain management: https://go.pharmalex.com/pharmalex_webinar_bayesianapproachsupplychain_ondemand