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Issues in the use of adaptive clinical trial designs
Author(s) -
Emerson Scott S.
Publication year - 2006
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.2626
Subject(s) - flexibility (engineering) , inefficiency , computer science , stopping rule , adaptation (eye) , early stopping , adaptive sampling , adaptive design , stopping time , risk analysis (engineering) , econometrics , clinical trial , machine learning , statistics , medicine , psychology , mathematical optimization , mathematics , economics , pathology , neuroscience , artificial neural network , monte carlo method , microeconomics
Sequential sampling plans are often used in the monitoring of clinical trials in order to address the ethical and efficiency issues inherent in human testing of a new treatment or preventive agent for disease. Group sequential stopping rules are perhaps the most commonly used approaches, but in recent years, a number of authors have proposed adaptive methods of choosing a stopping rule. In general, such adaptive approaches come at a price of inefficiency (almost always) and clouding of the scientific question (sometimes). In this paper, I review the degree of adaptation possible within the largely prespecified group sequential stopping rules, and discuss the operating characteristics that can be characterized fully prior to collection of the data. I then discuss the greater flexibility possible when using several of the adaptive approaches receiving the greatest attention in the statistical literature and conclude with a discussion of the scientific and statistical issues raised by their use. Copyright © 2006 John Wiley & Sons, Ltd.