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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.