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Improving the estimation of parameter uncertainty distributions in nonlinear mixed effects models using sampling

Anne-Gaëlle Dosne1, Martin Bergstrand2, Kajsa Harling2

  • 1Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24, Uppsala, Sweden. annegaelle.dosne@farmbio.uu.se.

Journal of Pharmacokinetics and Pharmacodynamics
|October 13, 2016
PubMed
Summary

Sampling Importance Resampling (SIR) offers a robust method for assessing parameter uncertainty in nonlinear mixed-effects models (NLMEM). This approach overcomes limitations of current methods, providing reliable confidence intervals for drug development decisions.

Keywords:
Asymptotic covariance matrixBootstrapConfidence intervalsNonlinear mixed-effects modelsParameter uncertaintySampling importance resampling

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Area of Science:

  • Pharmacometrics
  • Statistical modeling
  • Computational statistics

Background:

  • Assessing parameter uncertainty is crucial for drug development decisions in nonlinear mixed-effects modeling (NLMEM).
  • Existing methods for parameter uncertainty assessment in NLMEM have limitations and lack diagnostic tools.
  • There is a need for reliable methods to evaluate uncertainty, especially with complex models or limited data.

Purpose of the Study:

  • To propose and evaluate a novel method, Sampling Importance Resampling (SIR), for assessing parameter uncertainty in NLMEM.
  • To demonstrate the applicability and performance of SIR across simulation and real-world datasets.
  • To provide guidance on implementing the SIR workflow for NLMEM.

Main Methods:

  • Developed a non-parametric approach using SIR, which avoids distributional assumptions and repeated parameter estimation.
  • Simulated parameter vectors from a proposal distribution and calculated importance ratios to approximate likelihood.
  • Obtained non-parametric uncertainty distributions through resampling based on importance ratios.

Main Results:

  • SIR successfully recovered true parameter uncertainty in simulation studies.
  • SIR-derived confidence intervals (CIs) generally agreed with covariance matrix, bootstrap, and log-likelihood profiling for symmetric CIs.
  • For asymmetric CIs, SIR CIs closely matched log-likelihood profiling, outperforming suboptimal bootstrap methods in tested examples.

Conclusions:

  • SIR is a promising and versatile method for parameter uncertainty assessment in NLMEM, applicable where other methods falter.
  • SIR demonstrates robust performance with small datasets, nonlinear models, and in meta-analysis.
  • The study provides practical guidance and diagnostics for the SIR workflow in NLMEM.