Penalized variable selection in copula survival models for clustered time-to-event data

Sookhee Kwon, Il Do Ha, Jong Min Kim

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


A dependence among individual event times within a cluster can be modelled using a copula. Variable selection methods using a penalized likelihood allowing for several penalty functions have been widely studied in various statistical models. To the best of our knowledge, however, there is no literature on variable selection methods for the copula survival models. In this paper, we propose a variable selection procedure in the copula survival models with a parametric (e.g. Weibull) marginal using a one-stage estimation method based on a penalized likelihood. Here, we consider four penalty functions, i.e. LASSO, adaptive LASSO, SCAD and HL (h-likelihood). The performance of the proposed method is demonstrated via simulation study. The usefulness of the new method is illustrated using two well-known clinical data sets.

Original languageEnglish (US)
Pages (from-to)657-675
Number of pages19
JournalJournal of Statistical Computation and Simulation
Issue number4
StatePublished - Mar 3 2020

Bibliographical note

Funding Information:
This research was supported by the Basic Science Research Programme through the National Research Foundation of Korea (NRF) funded by the Ministry of Science & ICT (No. NRF-2017R1E1A1A03070747).

Publisher Copyright:
© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.


  • Copula survival models
  • frailty models
  • penalized likelihood
  • penalty function
  • variable selection


Dive into the research topics of 'Penalized variable selection in copula survival models for clustered time-to-event data'. Together they form a unique fingerprint.

Cite this