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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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Fast inference for robust nonlinear mixed-effects models.

José Clelto Barros Gomes1, Reiko Aoki2, Victor Hugo Lachos3

  • 1Department of Statistics, Federal University of Amazonas, Manaus, Brazil.

Journal of Applied Statistics
|May 17, 2023
PubMed
Summary
This summary is machine-generated.

Robust nonlinear mixed-effects models using scale mixtures of normal distributions offer alternatives to standard methods. Two estimation techniques were compared, with an approximate method proving faster but potentially introducing bias.

Keywords:
Monte Carlo EMNonlinear mixed-effects modelscomputational efficiencylikelihood-based approximationscale mixture of normal distributions

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Area of Science:

  • Statistics
  • Biostatistics
  • Computational Statistics

Background:

  • Nonlinear mixed-effects models (NLME) are crucial in pharmacokinetics, growth curves, and HIV dynamics.
  • Standard NLME models face challenges with random effects, estimation, and sensitivity to non-normal data.
  • Scale mixture of normal distributions offer heavy-tailed alternatives for robust estimation against outliers.

Purpose of the Study:

  • Compare two estimation methods for NLME models with multivariate scale mixture of normal distributions.
  • Evaluate the performance and robustness of Monte Carlo Expectation-Maximization (MCEM) and an approximate likelihood-based method.
  • Assess the trade-offs between computational speed and estimation accuracy.

Main Methods:

  • Development of a Monte Carlo Expectation-Maximization (MCEM) algorithm.
  • Development of an efficient likelihood-based approximate estimation method.
  • Comparison through simulation studies and analysis of real nonlinear applications.

Main Results:

  • The approximate method significantly outperforms MCEM in terms of speed.
  • Both methods achieve efficient likelihood maximization, with the approximate method showing slightly higher bias in fixed-effects parameters.
  • Scale mixture models demonstrate robustness against outlying observations.

Conclusions:

  • The approximate method provides a computationally efficient alternative for estimating NLME models with scale mixtures of normal distributions.
  • Careful consideration of potential bias is necessary when using the approximate method.
  • The proposed scale mixture models enhance the robustness of NLME analyses in various scientific fields.