NDLI: Use of Prior Information to Stabilize a Population Data Analysis

Content Provider	Springer Nature Link
Author	Gisleskog, Per O. Karlsson, Mats O. Beal, Stuart L.
Copyright Year	2002
Abstract	When modeling new data with a complex population pharmacokinetic/pharmacodynamic model, there may not be sufficient information to obtain estimates of all parameters. In this case information from previous studies can also be used to help stabilize estimation. Using simulated data, we explored three different ways to do this. (i) Some parameter values were fixed to estimates obtained from earlier data. (ii) The earlier data were combined with the current data. (iii) The objective function based on the current data was augmented by a penalty function expressing summary information obtained from the earlier data. This last method is similar to the use of a Bayesian prior. It may be particularly useful when either the combined data set of method (ii) is very large and leads to large computation times or when the early data are not readily available. With this method, two different types of penalty functions were used. With our examples, the three methods all resulted in stabilized estimation. Methods (ii) and (iii) gave similar results for parameter and standard error estimation, especially with respect to fixed effects parameters. For hypothesis testing, results obtained with method (i) are very problematic. There are also problems with the results obtained with method (iii), but they are much less severe, and when the design for the earlier data is known, they can be corrected by using a computer-intensive simulation test procedure.
Starting Page	473
Ending Page	505
Page Count	33
File Format	PDF
ISSN	1567567X
Journal	Journal of Pharmacokinetics and Pharmacodynamics
Volume Number	29
Issue Number	5-6
e-ISSN	15738744
Language	English
Publisher	Kluwer Academic Publishers-Plenum Publishers
Publisher Date	2002-01-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Pharmacology/Toxicology Pharmacy Veterinary Medicine Biochemistry Biomedical Engineering
Content Type	Text
Resource Type	Article
Subject	Pharmacology

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