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Semiparametric mixed-effects model for analysis of non-invasive longitudinal hemodynamic responses during bone graft healing

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  • Sami Leon
  • Jingxuan Ren
  • Regine Choe
  • Tong Tong Wu

Abstract

When dealing with longitudinal data, linear mixed-effects models (LMMs) are often used by researchers. However, LMMs are not always the most adequate models, especially if we expect a nonlinear relationship between the outcome and a continuous covariate. To allow for more flexibility, we propose the use of a semiparametric mixed-effects model to evaluate the overall treatment effect on the hemodynamic responses during bone graft healing and build a prediction model for the healing process. The model relies on a closed-form expectation–maximization algorithm, where the unknown nonlinear function is estimated using a Lasso-type procedure. Using this model, we were able to estimate the effect of time for individual mice in each group in a nonparametric fashion and the effect of the treatment while accounting for correlation between observations due to the repeated measurements. The treatment effect was found to be statistically significant, with the autograft group having higher total hemoglobin concentration than the allograft group.

Suggested Citation

  • Sami Leon & Jingxuan Ren & Regine Choe & Tong Tong Wu, 2022. "Semiparametric mixed-effects model for analysis of non-invasive longitudinal hemodynamic responses during bone graft healing," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-13, April.
    • Handle: RePEc:plo:pone00:0265471
    DOI: 10.1371/journal.pone.0265471
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    References listed on IDEAS

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    1. Ana Arribas-Gil & Rolando De la Cruz & Emilie Lebarbier & Cristian Meza, 2015. "Classification of longitudinal data through a semiparametric mixed-effects model based on lasso-type estimators," Biometrics, The International Biometric Society, vol. 71(2), pages 333-343, June.
    2. Songfeng Han & Ashley R Proctor & Jingxuan Ren & Danielle S W Benoit & Regine Choe, 2018. "Temporal blood flow changes measured by diffuse correlation tomography predict murine femoral graft healing," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-15, May.
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