Induced smoothing for rank-based regression with recurrent gap time data

Tianmeng Lyu, Xianghua Luo, Gongjun Xu, Chiung Yu Huang

Research output: Contribution to journalArticlepeer-review


Various semiparametric regression models have recently been proposed for the analysis of gap times between consecutive recurrent events. Among them, the semiparametric accelerated failure time (AFT) model is especially appealing owing to its direct interpretation of covariate effects on the gap times. In general, estimation of the semiparametric AFT model is challenging because the rank-based estimating function is a nonsmooth step function. As a result, solutions to the estimating equations do not necessarily exist. Moreover, the popular resampling-based variance estimation for the AFT model requires solving rank-based estimating equations repeatedly and hence can be computationally cumbersome and unstable. In this paper, we extend the induced smoothing approach to the AFT model for recurrent gap time data. Our proposed smooth estimating function permits the application of standard numerical methods for both the regression coefficients estimation and the standard error estimation. Large-sample properties and an asymptotic variance estimator are provided for the proposed method. Simulation studies show that the proposed method outperforms the existing nonsmooth rank-based estimating function methods in both point estimation and variance estimation. The proposed method is applied to the data analysis of repeated hospitalizations for patients in the Danish Psychiatric Center Register.

Original languageEnglish (US)
Pages (from-to)1086-1100
Number of pages15
JournalStatistics in Medicine
Issue number7
StatePublished - Mar 30 2018

Bibliographical note

Funding Information:
The authors thank the University of Minnesota Supercomputing Institute for providing computing resources. This research was supported by National Institutes of Health/National Cancer Institute (NIH/NCI) R03CA187991 and National Institute of Mental Health R03MH112895 to Luo, National Science Foundation (NSF) SES-1659328 and National Security Agency (NSA) H98230-17-1-0308 to Xu, and National Cancer Institute R01CA193888 to Huang.

Publisher Copyright:
Copyright © 2017 John Wiley & Sons, Ltd.


  • Gehan-type weight
  • accelerated failure time model
  • gap times
  • induced smoothing
  • recurrent events


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