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It has long been noticed that the efficacy observed in small early phasestudies is generally better than that observed in later larger studies.Historically, the inflation of the efficacy results from early proof-of-conceptstudies is either ignored, or adjusted empirically using a frequentist orBayesian approach. In this article, we systematically explained the underlyingreason for the inflation of efficacy results in small early phase studies fromthe perspectives of measurement error models and selection bias. A systematicmethod was built to adjust the early phase study results from both frequentistand Bayesian perspectives. A hierarchical model was proposed to estimate thedistribution of the efficacy for a portfolio of compounds, which can serve asthe prior distribution for the Bayesian approach. We showed through theory thatthe systematic adjustment provides an unbiased estimator for the true meanefficacy for a portfolio of compounds. The adjustment was applied to paireddata for the efficacy in early small and later larger studies for a set ofcompounds in diabetes and immunology. After the adjustment, the bias in theearly phase small studies seems to be diminished.

Understanding and adjusting the selection bias from a proof-of-concept study to a more confirmatory study