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Activity Number:
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537
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Type:
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Invited
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Date/Time:
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Thursday, August 6, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Biopharmaceutical Section
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| Abstract - #303180 |
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Title:
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Computational Methods for Nonlinear Mixed Models
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Author(s):
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Douglas M. Bates*+
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Companies:
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University of Wisconsin-Madison
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Address:
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1300 University Ave, Statistics Dept, Madison, WI, 53706,
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Keywords:
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mixed-effects ; pharmacokinetics ; lme4
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Abstract:
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Population pharmacokinetics data are often modeled using nonlinear mixed-effects models; nonlinear because the pharmacokinetic parameters---rate constants, clearance rates, etc.---occur nonlinearly in the model function and mixed-effects because the models involve both fixed-effects parameters, applying to the population or well-defined subsets of the population, and random effects associated with particular experimental or observational units under study. Many algorithms for estimating the parameters in such models have been proposed and implemented but often the estimation algorithm is confused with the parameter estimation criterion - maximum likelihood. I suggest we concentrate on evaluating or approximating the log-likelihood to be optimized and will describe how this is done in the nlmer function in the lme4 package for R.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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