Variable selection for joint models with time-varying coefficients

Yujing Xie, Zangdong He, Wanzhu Tu, Zhangsheng Yu

Research output: Contribution to journalArticle


Many clinical studies collect longitudinal and survival data concurrently. Joint models combining these two types of outcomes through shared random effects are frequently used in practical data analysis. The standard joint models assume that the coefficients for the longitudinal and survival components are time-invariant. In many applications, the assumption is overly restrictive. In this research, we extend the standard joint model to include time-varying coefficients, in both longitudinal and survival components, and we present a data-driven method for variable selection. Specifically, we use a B-spline decomposition and penalized likelihood with adaptive group LASSO to select the relevant independent variables and to distinguish the time-varying and time-invariant effects for the two model components. We use Gaussian-Legendre and Gaussian-Hermite quadratures to approximate the integrals in the absence of closed-form solutions. Simulation studies show good selection and estimation performance. Finally, we use the proposed procedure to analyze data generated by a study of primary biliary cirrhosis.

Original languageEnglish (US)
Pages (from-to)309-322
Number of pages14
JournalStatistical Methods in Medical Research
Issue number1
StatePublished - Jan 1 2020


  • B-spline
  • Gaussian quadrature
  • adaptive LASSO

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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