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

ABSTRACT

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.

KEYWORDS:

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

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Il Do Ha  http://orcid.org/0000-0001-8434-9506

Additional information

Funding

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).