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An introduction to mixed effect modeling: Concepts, definitions, and justification

  • Lewis B. Sheiner
  • Thaddeus H. GraselaJr.
Article

Summary

In spite of the vast amount of data generated during the new drug development process, much remains to be learned about the pharmacokinetics of a drug once it is released on the market. The ethical and logistical problems which arise during an experimental pharmacokinetic study frequently prevent the study of elderly, pediatric, or critically ill patients. The recognition of these limitations by scientists and regulators have prompted a desire to extract the maximum amount of information from data available during Phase III and Phase IV clinical trials and, in addition, to use information generated during the routine clinical care of patients. Mixed effect model analysis allows one to overcome the problems of analyzing observational data to obtain accurate and precise estimates of population pharmacokinetic parameters. Use of this approach expands the methodologies available to the data analyst and opens up a number of data sources which can now be considered for analysis.

Keywords

Observational Data Pharmacokinetic Parameter Mixed Effect Model Interindividual Variability Design Restriction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Plenum Publishing Corporation 1991

Authors and Affiliations

  • Lewis B. Sheiner
    • 1
  • Thaddeus H. GraselaJr.
    • 2
  1. 1.Departments of Laboratory Medicine and Medicine School of MedicineUniversity of California, San FranciscoSan Francisco
  2. 2.Departments of Pharmacy and Social and Preventive Medicine Schools of Pharmacy and MedicineState University of New YorkBuffalo

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