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

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

Research output: Contribution to journalArticle


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)
JournalStatistics in Medicine
StateAccepted/In press - Jan 1 2017
Externally publishedYes



  • Accelerated failure time model
  • Gap times
  • Gehan-type weight
  • Induced smoothing
  • Recurrent events

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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