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What do we mean by identifiability in mixed effects models?

Marc Lavielle1, Leon Aarons2

  • 1Inria Saclay, Palaiseau, France. Marc.Lavielle@inria.fr.

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|December 15, 2015
PubMed
Summary
This summary is machine-generated.

Nonlinear mixed effects models can be identifiable at the population level, even if not at the individual level. This research clarifies theoretical, structural, and practical identifiability for random effects models, supported by simulations.

Keywords:
Mixed effects modelModel identifiabilityParameter estimationPharmacokineticsPractical identifiabilityStructural identifiability

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Area of Science:

  • Pharmacometrics
  • Statistical Modeling

Background:

  • Limited research exists on identifiability in random effects models compared to fixed effects models.
  • Identifiability is crucial for reliable parameter estimation in complex statistical models.

Purpose of the Study:

  • To investigate model identifiability in nonlinear mixed effects (NLME) models, particularly for random effects.
  • To differentiate between theoretical, structural, and practical identifiability in this context.
  • To explore conditions under which non-identifiable individual-level models become identifiable at the population level.

Main Methods:

  • Distinguishing between theoretical, structural, and practical identifiability.
  • Analyzing pharmacokinetic models known for individual-level non-identifiability.
  • Utilizing simulations to support findings on population-level identifiability.

Main Results:

  • Individual-level non-identifiable pharmacokinetic models can achieve population-level identifiability under specific probabilistic model assumptions.
  • Different probabilistic models lead to distinct likelihoods, even with non-identifiable structural models.
  • Simulations confirm that population-level identifiability can be achieved.

Conclusions:

  • Population-level analysis can overcome individual-level identifiability challenges in NLME models.
  • Assumptions within the probabilistic model are key to achieving identifiability.
  • This work provides a framework for assessing identifiability in random effects models.