Abstract
Designing a study for independent confirmation of a treatment effect is sometimes not practical due to required large sample size. Post hoc pooling of studies including those for learning purposes is subject to selection bias and therefore generally not suitable for confirmation of a treatment effect. We propose a Bayesian approach which calibrates the role of prior information from historical studies for learning and confirming purposes. The amount of prior information to be combined with current study data for the purpose of hypothesis confirmation depends on the overall strength of prior information for hypothesis generation. The method is illustrated in the analysis of mortality data for the pirfenidone NDA. The Bayesian analysis provides a formal method to calibrate the role of information from historical evidence in the overall interpretation of results from both historical and concurrent clinical studies. The increased efficiency of using all available data is especially important in drug development for rare diseases with serious consequences, where limited patient source prohibits large trials, and unmet medical needs demand rapid access to treatment options.
Highlights
IntroductionEarly phase studies are designed for learning, for generating and testing hypotheses
In clinical drug development, early phase studies are designed for learning, for generating and testing hypotheses
Data pooling from multiple studies can provide reasonable sample size for hypothesis confirmation, post hoc data pooling including those for hypothesis generation purposes is not scientifically solid, and pre-specification of data pooling without early learning is often unrealistic
Summary
Early phase studies are designed for learning, for generating and testing hypotheses. Later phase studies are designed for confirmation of treatment effects for regulatory approval. The process of developing and confirming hypotheses applies to a collection of several studies as well as individual studies. The setting to confirm a hypothesis based on data exclusively from an individual study can be inefficient and sometimes not feasible in practice due to required large sample size, especially in the case of low event rate for a rare disease. Data pooling from multiple studies can provide reasonable sample size for hypothesis confirmation, post hoc data pooling including those for hypothesis generation purposes is not scientifically solid, and pre-specification of data pooling without early learning is often unrealistic
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