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Frequentist evaluation of group sequential clinical trial designs
Author(s) -
Emerson Scott S.,
Kittelson John M.,
Gillen Daniel L.
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.2901
Subject(s) - frequentist inference , early stopping , bayesian probability , stopping rule , computer science , sequential analysis , type i and type ii errors , bayes' theorem , optimal stopping , clinical trial , econometrics , statistics , bayesian inference , mathematics , machine learning , medicine , artificial intelligence , mathematical optimization , artificial neural network , pathology
Group sequential stopping rules are often used as guidelines 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. Such stopping rules have been proposed based on a variety of different criteria, both scientific (e.g. estimates of treatment effect) and statistical (e.g. frequentist type I error, Bayesian posterior probabilities, stochastic curtailment). It is easily shown, however, that a stopping rule based on one of these criteria induces a stopping rule on all other criteria. Thus, the basis used to initially define a stopping rule is relatively unimportant so long as the operating characteristics of the stopping rule are fully investigated. In this paper we describe how the frequentist operatingcharacteristics of a particular stopping rule might be evaluated to ensure that the selected clinical trial design satisfies the constraints imposed by the many different disciplines represented by the clinical trial collaborators. Copyright © 2007 John Wiley & Sons, Ltd.