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Uncertainty Computation at Finite Distance in Nonlinear Mixed Effects Models-a New Method Based on
Mélanie Guhl1, Julie Bertrand2, Lucie Fayette2
1Université Paris Cité, Inserm, IAME, F-75018, Paris, France. melanie.guhl@inserm.fr.
The AAPS Journal
|May 9, 2024
Summary
This study introduces a new method for estimating standard errors in nonlinear mixed effect models, improving accuracy at finite distances compared to traditional frequentist approaches. Further calibration is needed for complex scenarios with high variability.
Area of Science:
- Statistics
- Pharmacometrics
- Computational Biology
Background:
- Nonlinear mixed effect models (NLMEM) are crucial for analyzing complex biological and pharmacological data.
- Standard errors (SE) of parameter estimates are typically derived from the inverse Fisher information matrix (FIM).
- The FIM can underestimate SEs in NLMEM, especially at finite distances from asymptotic conditions.
Purpose of the Study:
- To develop and evaluate a novel method for estimating SEs in NLMEM.
- To compare the proposed method with existing frequentist and Bayesian approaches.
- To assess the performance of the new SE estimation technique across various simulation scenarios and a real-world case study.
Main Methods:
- A new method combining the Metropolis-Hastings (MH) algorithm with the stochastic approximation expectation maximization (SAEM) algorithm was developed.
- The SAEM algorithm was implemented using the saemix R package.
- The proposed method was validated through simulation studies and applied to a real case study dataset.
Main Results:
- The developed MH-SAEM method demonstrated improved SE estimation compared to frequentist methods at finite distances.
- The method showed limitations in scenarios characterized by high variability and parameter correlations, as observed in the real case study.
- The study highlights the need for further calibration of the proposed method for complex data structures.
Conclusions:
- The proposed MH-SAEM approach offers a promising alternative for SE estimation in NLMEM, particularly when frequentist methods fall short.
- The method's performance is sensitive to data complexity, indicating areas for future refinement and calibration.
- Further research is warranted to enhance the robustness and applicability of this Bayesian-inspired method in diverse NLMEM contexts.
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