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A fast EM algorithm for fitting joint models of a binary response and multiple longitudinal covariates subject to detection limits

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  • Bernhardt, Paul W.
  • Zhang, Daowen
  • Wang, Huixia Judy

Abstract

Joint modeling techniques have become a popular strategy for studying the association between a response and one or more longitudinal covariates. Motivated by the GenIMS study, where it is of interest to model the event of survival using censored longitudinal biomarkers, a joint model is proposed for describing the relationship between a binary outcome and multiple longitudinal covariates subject to detection limits. A fast, approximate EM algorithm is developed that reduces the dimension of integration in the E-step of the algorithm to one, regardless of the number of random effects in the joint model. Numerical studies demonstrate that the proposed approximate EM algorithm leads to satisfactory parameter and variance estimates in situations with and without censoring on the longitudinal covariates. The approximate EM algorithm is applied to analyze the GenIMS data set.

Suggested Citation

  • Bernhardt, Paul W. & Zhang, Daowen & Wang, Huixia Judy, 2015. "A fast EM algorithm for fitting joint models of a binary response and multiple longitudinal covariates subject to detection limits," Computational Statistics & Data Analysis, Elsevier, vol. 85(C), pages 37-53.
    • Handle: RePEc:eee:csdana:v:85:y:2015:i:c:p:37-53
    DOI: 10.1016/j.csda.2014.11.011
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    References listed on IDEAS

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    3. Maire, Florian & Moulines, Eric & Lefebvre, Sidonie, 2017. "Online EM for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 27-47.
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    6. Murray, James & Philipson, Pete, 2022. "A fast approximate EM algorithm for joint models of survival and multivariate longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
    7. Zhang, Zili & Charalambous, Christiana & Foster, Peter, 2023. "A Gaussian copula joint model for longitudinal and time-to-event data with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).

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