EFFICIENT SCREENING OF
COVARIATES IN POPULATION MODELS USING WALD’S APPROXIMATION TO THE LIKELIHOOD
RATIO TEST
Ken Kowalski and Matt Hutmacher
ASCPT Meeting
Abstract
We propose an efficient
algorithm for screening covariates in population model building using Wald’s
approximation to the likelihood ratio test (LRT) statistic in conjunction
with Schwarz’s Bayesian criterion. The algorithm can be applied to a full
model fit of k covariate parameters to calculate the approximate LRT for all
2k-1 possible restricted models. The algorithm’s efficiency
also permits internal validation of the model selection process via
bootstrap methods. We illustrate the use of this algorithm for both model
selection and validation with data from a Daypro® pediatric study. The
algorithm is easily implemented using standard statistical software such as
SAS/IML and S-Plus.
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SCREENING
COVARIATE MODELS USING WALD'S APPROXIMATION: AN EVALUATION OF THE WAM ALGORITHM
ON SEVERAL DATA SETS
Ken
Kowalski
9th
Annual MUFPADA Meeting
Outline
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WAM Algorithm Methodology
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Examples
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One compartment PK models with sparse
sampling.
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Two compartment PK model with dense
sampling.
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Sigmoid-Emax model with dense sampling.
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Ordered categorical response PD model
with dense sampling.
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Summary/Future Work
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Acknowledgements
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Bibliography
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