Pharmacostatistical modeling for observational data

Thaddeus H. Grasela; Lewis B. Sheiner

doi:10.1007/BF01371006

Pharmacostatistical modeling for observational data

Thaddeus H. Grasela
, Lewis B. Sheiner

Pharmaceutical Sciences

University of California at San Francisco

Research output: Contribution to journal › Article › peer-review

45 Scopus citations

Abstract

The analysis of observational data using a mixed effect model is more complex than the traditional approach to pharmacokinetic parameter estimation. Structural and statistical models must be explicitly formulated and implemented within the computer program NONMEM. This presents some unique challenges in the selection and evaluation of alternative models for the evaluation of interindividual and residual variability.

Original language	English
Pages (from-to)	25S-36S
Journal	Journal of Pharmacokinetics and Biopharmaceutics
Volume	19
Issue number	3 Supplement
DOIs	https://doi.org/10.1007/BF01371006
State	Published - Jun 1991

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title = "Pharmacostatistical modeling for observational data",

abstract = "The analysis of observational data using a mixed effect model is more complex than the traditional approach to pharmacokinetic parameter estimation. Structural and statistical models must be explicitly formulated and implemented within the computer program NONMEM. This presents some unique challenges in the selection and evaluation of alternative models for the evaluation of interindividual and residual variability.",

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AB - The analysis of observational data using a mixed effect model is more complex than the traditional approach to pharmacokinetic parameter estimation. Structural and statistical models must be explicitly formulated and implemented within the computer program NONMEM. This presents some unique challenges in the selection and evaluation of alternative models for the evaluation of interindividual and residual variability.

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JO - Journal of Pharmacokinetics and Biopharmaceutics

JF - Journal of Pharmacokinetics and Biopharmaceutics

IS - 3 Supplement

ER -

Pharmacostatistical modeling for observational data

Abstract

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