Penalized survival models for the analysis of alternating recurrent event data

Biometrics. 2020 Jun;76(2):448-459. doi: 10.1111/biom.13153. Epub 2019 Nov 11.

Abstract

Recurrent event data are widely encountered in clinical and observational studies. Most methods for recurrent events treat the outcome as a point process and, as such, neglect any associated event duration. This generally leads to a less informative and potentially biased analysis. We propose a joint model for the recurrent event rate (of incidence) and duration. The two processes are linked through a bivariate normal frailty. For example, when the event is hospitalization, we can treat the time to admission and length-of-stay as two alternating recurrent events. In our method, the regression parameters are estimated through a penalized partial likelihood, and the variance-covariance matrix of the frailty is estimated through a recursive estimating formula. Moreover, we develop a likelihood ratio test to assess the dependence between the incidence and duration processes. Simulation results demonstrate that our method provides accurate parameter estimation, with a relatively fast computation time. We illustrate the methods through an analysis of hospitalizations among end-stage renal disease patients.

Keywords: alternating recurrent events; correlated frailty model; end-stage renal disease; penalized partial likelihood.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't