Event / Webinar

Bayesian Statistics: Model to predict recruitment and inform supply chain

The smooth running of patient enrolment is a key determinant of success for clinical trials. Yet many trials fail to complete on time due to delays in patient recruitment. Indeed, more than 80% of clinical trials do not reach recruitment targets on schedule (Huang et al., 2018). Despite efforts over multiple decades to identify and address barriers, recruitment challenges persist. This causes delays in drug submissions, then shorten the duration of the licence of exploitation of the product, and so a delay generates a significant loss of income. These delays may also end up with a premature termination of the trial due to lack of recruitment, leading to major financial issues but also to ethical issues when patients have been followed in a trial that cannot give robust results due to a restricted sample size. Thus, accurate predictions of the duration to complete recruitment is therefore of crucial importance for conducting of clinical trials

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Speakers

Bruno Boulanger

Senior Director, Global Head Statistics and Data Science

Clément Laloux

Specialist Statistics & Data Sciences

This webinar presents a statistical model which addresses this issue by providing crucial information regarding two practical questions. First, given the current status of an ongoing trial, how long will it take to recruit the remaining patients needed? Second, how many additional centres should be opened in order to ensure completion within timelines? More practically, the proposed methodology is carried out under the Bayesian framework and aims at predicting the randomisation dates of future patients in the context of ongoing multicentre clinical trials. At any time during the conduct of a trial, informative and quantitative metrics for decision-making are provided: First, it gives predictive probabilities of completion at a given date in the future; Second, it provides, for a required level of credibility, the recruitment period estimated to complete the study.

Additionally, the approach can be adapted to follow additional event occurrences, e.g. Progression Free Survival (PFS) or Overall Survival (OS) in oncology trials. Also, the predictive results can be used to feed the supply chain with valuable information such as time when each centre can be short of supply and need shipping of material, prediction of clinical material needed over time and therefore optimal time to initiate production and shipping to minimize the probability of short supply. All that valuable information can be derived under various scenarios to minimize the loss in clinical material and therefore considerably reduce the costs.

Key learning objectives:

  • Time-to-event approach within a Bayesian modeling framework allows to adapt for multiple recruitment scenarios
  • Bayesian approach proposed has been shown in our cases studies to be effective to support commissioning decisions
  • The approach can be adapted to more complex design e.g. trials with additional event occurrences as in oncology
  • The approach can be extended to address other fields of clinical trials such as supply chain
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