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Predictions of machine learning with mixed-effects in analyzing longitudinal data under model misspecification

Author

Listed:
  • Shuwen Hu

    (Queensland University of Technology
    CSIRO Agriculture & Food)

  • You-Gan Wang

    (Queensland University of Technology
    Australian Catholic University)

  • Christopher Drovandi

    (Queensland University of Technology)

  • Taoyun Cao

    (Guangdong University of Finance and Economics)

Abstract

We consider predictions in longitudinal studies, and investigate the well known statistical mixed-effects model, piecewise linear mixed-effects model and six different popular machine learning approaches: decision trees, bagging, random forest, boosting, support-vector machine and neural network. In order to consider the correlated data in machine learning, the random effects is combined into the traditional tree methods and random forest. Our focus is the performance of statistical modelling and machine learning especially in the cases of the misspecification of the fixed effects and the random effects. Extensive simulation studies have been carried out to evaluate the performance using a number of criteria. Two real datasets from longitudinal studies are analysed to demonstrate our findings. The R code and dataset are freely available at https://github.com/shuwen92/MEML .

Suggested Citation

  • Shuwen Hu & You-Gan Wang & Christopher Drovandi & Taoyun Cao, 2023. "Predictions of machine learning with mixed-effects in analyzing longitudinal data under model misspecification," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(2), pages 681-711, June.
    • Handle: RePEc:spr:stmapp:v:32:y:2023:i:2:d:10.1007_s10260-022-00658-x
    DOI: 10.1007/s10260-022-00658-x
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    References listed on IDEAS

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