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A Bayesian approach for incorporating economic factors in sample size design for clinical trials of individual drugs and portfolios of drugs
Author(s) -
Patel Nitin R.,
Ankolekar Suresh
Publication year - 2007
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2955
Subject(s) - sample size determination , portfolio , bayesian probability , satisficing , computer science , profit (economics) , budget constraint , sample (material) , econometrics , actuarial science , operations research , risk analysis (engineering) , economics , statistics , mathematics , artificial intelligence , microeconomics , medicine , finance , chemistry , chromatography
Classical approaches to clinical trial design ignore economic factors that determine economic viability of a new drug. We address the choice of sample size in Phase III trials as a decision theory problem using a hybrid approach that takes a Bayesian view from the perspective of a drug company and a classical Neyman–Pearson view from the perspective of regulatory authorities. We incorporate relevant economic factors in the analysis to determine the optimal sample size to maximize the expected profit for the company. We extend the analysis to account for risk by using a ‘satisficing’ objective function that maximizes the chance of meeting amanagement‐specified target level of profit. We extend the models for single drugs to a portfolio of clinical trials and optimize the sample sizes to maximize the expected profit subject to budget constraints. Further, we address the portfolio risk and optimize the sample sizes to maximize the probability of achieving a given target of expected profit. Copyright © 2007 John Wiley & Sons, Ltd.