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COMPARISON OF POPULATION PHARMACOKINETIC MODELING METHODS USING SIMULATED DATA: RESULTS FROM THE POPULATION MODELING WORKGROUP
Author(s) -
ROE DENISE J.,
VONESH EDWARD F.,
WOLFINGER RUSSELL D.,
MESNIL FLORENCE,
MALLET ALAIN
Publication year - 1997
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/(sici)1097-0258(19970615)16:11<1241::aid-sim527>3.0.co;2-c
Subject(s) - workgroup , computer science , data set , population , statistics , replicate , statistical model , data mining , set (abstract data type) , regression analysis , econometrics , machine learning , artificial intelligence , mathematics , medicine , computer network , environmental health , programming language
Abstract Statistical modeling methods have had increasing use in drug disposition studies, both to estimate pharmacokinetic parameters and to develop regression models that relate these parameter estimates to patient characteristics. These methods are particularly flexible as they allow non‐linearity and sparse within‐patient information. In the past few years, multiple analysis methods have become available, but there is a lack of systematic comparisons of their estimates on the same data sets. Two simulated data sets were therefore developed by the Population Modeling Workgroup of the Biopharmaceutical Section of the American Statistical Association. We analysed these data sets using seven population modeling programs, some of which contain multiple analysis methods. Although each data set represents a single replicate from a given model and data collection design, the results suggest that the behaviour of some methods differs from that of the others. © 1997 by John Wiley & Sons, Ltd.