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Fitting E max models to clinical trial dose–response data
Author(s) -
Kirby Simon,
Brain Phil,
Jones Byron
Publication year - 2011
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.432
Subject(s) - model selection , selection (genetic algorithm) , limiting , computer science , maximum likelihood , statistics , mathematics , econometrics , machine learning , mechanical engineering , engineering
We consider fitting E max models to the primary endpoint for a parallel group dose–response clinical trial. Such models can be difficult to fit using Maximum Likelihood if the data give little information about the maximum possible response. Consequently, we consider alternative models that can be derived as limiting cases, which can usually be fitted. Furthermore we propose two model selection procedures for choosing between the different models. These model selection procedures are compared with two model selection procedures which have previously been used. In a simulation study we find that the model selection procedure that performs best depends on the underlying true situation. One of the new model selection procedures gives what may be regarded as the most robust of the procedures. Copyright © 2010 John Wiley & Sons, Ltd.