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A SAEM algorithm for fused lasso penalized NonLinear Mixed Effect Models: Application to group comparison in pharmacokinetics

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  • Ollier, Edouard
  • Samson, Adeline
  • Delavenne, Xavier
  • Viallon, Vivian

Abstract

Nonlinear mixed effect models are classical tools to analyze nonlinear longitudinal data in many fields such as population pharmacokinetics. Groups of observations are usually compared by introducing the group affiliations as binary covariates with a reference group that is stated among the groups. This approach is relatively limited as it allows only the comparison of the reference group to the others. The proposed method compares groups using a penalized likelihood approach. Groups are described by the same structural model but with parameters that are group specific. The likelihood is penalized with a fused lasso penalty that induces sparsity in the differences between groups for both fixed effects and variances of random effects. A penalized Stochastic Approximation EM algorithm is proposed that is coupled to Alternating Direction Method Multipliers to solve the maximization step. An extensive simulation study illustrates the performance of this algorithm when comparing more than two groups. Then the approach is applied to real data from two pharmacokinetic drug–drug interaction trials.

Suggested Citation

  • Ollier, Edouard & Samson, Adeline & Delavenne, Xavier & Viallon, Vivian, 2016. "A SAEM algorithm for fused lasso penalized NonLinear Mixed Effect Models: Application to group comparison in pharmacokinetics," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 207-221.
    • Handle: RePEc:eee:csdana:v:95:y:2016:i:c:p:207-221
    DOI: 10.1016/j.csda.2015.10.006
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    References listed on IDEAS

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    Cited by:

    1. Ollier, Edouard, 2022. "Fast selection of nonlinear mixed effect models using penalized likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    2. E. Ollier & V. Viallon, 2017. "Regression modelling on stratified data with the lasso," Biometrika, Biometrika Trust, vol. 104(1), pages 83-96.
    3. Lu Tang & Peter X.‐K. Song, 2021. "Poststratification fusion learning in longitudinal data analysis," Biometrics, The International Biometric Society, vol. 77(3), pages 914-928, September.

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