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Some general estimation methods for nonlinear mixed-effects models

M Davidian1, D M Giltinan

  • 1Department of Statistics, North Carolina State University, Raleigh 27695-8203.

Journal of Biopharmaceutical Statistics
|March 1, 1993
PubMed
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This study introduces a flexible nonlinear mixed-effects model for repeated measurements. The model accounts for covariates and complex within-individual data structures, simplifying analysis for growth and assay development.

Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Repeated measurement data presents unique statistical challenges.
  • Existing models may not fully capture complex intraindividual variability and covariate dependencies.
  • Accurate characterization of such data is crucial in fields like growth analysis and assay development.

Purpose of the Study:

  • To describe a nonlinear mixed-effects model for repeated measurement data.
  • To allow random coefficients to depend on covariates.
  • To accommodate general intraindividual covariance structures, including mean-dependent variance and autocorrelation.

Main Methods:

  • Development of a nonlinear mixed-effects model.
  • Incorporation of covariate dependence for random coefficients.

Related Experiment Videos

  • Specification of common intraindividual covariance structures.
  • Two classes of estimation procedures are presented, including parameter estimation for the covariance structure.

    • Main Results:

    • The described model effectively characterizes repeated measurement data.
    • The model accommodates complex dependencies, including covariate effects and intraindividual covariance.
    • The proposed estimation procedures are straightforward to implement using standard statistical software.

    Conclusions:

    • The presented nonlinear mixed-effects model offers a robust framework for analyzing repeated measurement data.
    • The model's flexibility in handling covariate dependence and intraindividual structures enhances analytical capabilities.
    • The practical implementation using standard software facilitates its application in growth analysis and assay development.