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PSI Conference 2019: Enabling the use of Bayesian Methods for Interim Analyses in Early Clinical Development at Pfizer

05 June 2019

In drug development, most early clinical studies utilise quantitative decision criteria when determining the success or failure of a compound, Within a Bayesian framework applied to clinical trials, predictive probabilities can be used at an interim analysis to determine the probability that the end of study decision criteria will be met based on the existing interim data. For example, for a study with a single decision criterion (e.g. at least 95% confidence treatment effect over placebo > 0) an interim analysis could be conducted when x% of the planned subjects have completed. A predictive probability calculation can then be made to determine the probability that the criterion will be met at the study end, based on the observed interim data. This framework facilitates efficient decision making in an intuitive way as criteria based on the predictive probabilities at the interim(s) can be pre-specified to make decisions regarding final study criteria such as: stop study for futility ;accelerate clinical development planning ;modify design. In this presentation I will discuss the use of interim analyses in early clinical development at Pfizer through practical examples using Bayesian predictive probabilities for decision-making. I will discuss practical difficulties in applying these methods (technical burden, lack of analytical solutions to more complex scenarios such as mixture priors, inconsistent approaches and assumptions) and how we have enabled and standardised this approach at Pfizer through the production of a guidance document and internal software that calculates design operating characteristics and predictive probability calculations for a range of scenarios.

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