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Simultaneous variable selection and estimation for joint models of longitudinal and failure time data with interval censoring

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  • Fengting Yi
  • Niansheng Tang
  • Jianguo Sun

Abstract

This paper discusses variable selection in the context of joint analysis of longitudinal data and failure time data. A large literature has been developed for either variable selection or the joint analysis but there exists only limited literature for variable selection in the context of the joint analysis when failure time data are right censored. Corresponding to this, we will consider the situation where instead of right‐censored data, one observes interval‐censored failure time data, a more general and commonly occurring form of failure time data. For the problem, a class of penalized likelihood‐based procedures will be developed for simultaneous variable selection and estimation of relevant covariate effects for both longitudinal and failure time variables of interest. In particular, a Monte Carlo EM (MCEM) algorithm is presented for the implementation of the proposed approach. The proposed method allows for the number of covariates to be diverging with the sample size and is shown to have the oracle property. An extensive simulation study is conducted to assess the finite sample performance of the proposed approach and indicates that it works well in practical situations. An application is also provided.

Suggested Citation

  • Fengting Yi & Niansheng Tang & Jianguo Sun, 2022. "Simultaneous variable selection and estimation for joint models of longitudinal and failure time data with interval censoring," Biometrics, The International Biometric Society, vol. 78(1), pages 151-164, March.
    • Handle: RePEc:bla:biomet:v:78:y:2022:i:1:p:151-164
    DOI: 10.1111/biom.13387
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    References listed on IDEAS

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    1. Colin Griesbach & Andreas Mayr & Elisabeth Bergherr, 2023. "Variable Selection and Allocation in Joint Models via Gradient Boosting Techniques," Mathematics, MDPI, vol. 11(2), pages 1-16, January.

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