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A sample size computation method for non-linear mixed effects models with applications to pharmacokinetics models.

Dongwoo Kang1, Janice B Schwartz, Davide Verotta

  • 1Department of Biopharmaceutical Sciences, University of California San Francisco, San Francisco, CA 94132-0446, USA.

Statistics in Medicine
|August 3, 2004
PubMed
Summary

We developed a straightforward method for calculating sample size in population pharmacokinetic (PK) studies using non-linear mixed effects models. This approach reduces computation time and optimizes subject numbers based on sampling design.

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Area of Science:

  • Pharmacokinetics
  • Statistical Modeling
  • Clinical Trial Design

Background:

  • Population pharmacokinetic (PK) studies are crucial for understanding drug behavior in diverse patient groups.
  • Accurate sample size determination is essential for the statistical power and efficiency of these studies.
  • Existing methods for sample size calculation in mixed-effects models have limitations, especially for non-linear models.

Purpose of the Study:

  • To propose a simple and generalizable method for computing sample size in population PK studies analyzed with non-linear mixed-effects models.
  • To provide a computationally efficient alternative to simulation-based sample size estimation methods.
  • To illustrate the impact of sampling design on required sample size.

Main Methods:

  • The proposed method utilizes first-order linearization of non-linear mixed-effects models.

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  • It employs the Wald chi(2) test statistic for sample size computation.
  • The method accommodates arbitrary non-linear models and random effects distributions, capturing inter- and intra-individual variability.

    • Main Results:

    • The method provides accurate sample size calculations, validated by Monte Carlo simulations demonstrating desired statistical power.
    • Tables of minimum sample sizes are presented, highlighting the influence of sampling strategies.
    • Optimal or frequent sampling designs require fewer subjects compared to sparse sampling.

    Conclusions:

    • The developed method offers a computationally efficient and flexible approach for sample size determination in population PK studies.
    • It enables precise sample size calculations for complex non-linear mixed-effects models.
    • Understanding the impact of sampling design is key to optimizing subject numbers and study efficiency.