Semiparametric regression model for recurrent bacterial infections after hematopoietic stem cell transplantation

Chi Hyun Lee, Chiung Yu Huang, Todd E. DeFor, Claudio G. Brunstein, Daniel J. Weisdorf, Xianghua Luo

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Patients who undergo hematopoietic stem cell transplantation (HSCT) often experience multiple bacterial infections during the early post-transplant period. In this article, we consider a semiparametric regression model that correlates patient- and transplant-related risk factors with inter-infection gap times. Existing regression methods for recurrent gap times are not directly applicable to studies of post-transplant infections because the initiating event (i.e., the transplant) is different to the recurrent events of interest (i.e., post-transplant infections). As a result, the time between a transplant and the first infection and that between consecutive infections have distinct biological meanings and, hence, follow different distributions. Moreover, risk factors may have different effects on these two types of gap times. Therefore, we propose a semiparametric estimation procedure that lets us simultaneously evaluate the covariate effects on the time between a transplant and the first infection and on the gap times between consecutive infections. The proposed estimator accounts for dependent censoring induced by within-subject correlation between recurrent gap times and length bias in the last censored gap time due to intercept sampling. We study the finite sample properties through simulations and apply the proposed method to post-HSCT bacterial infection data collected at the University of Minnesota.

Original languageEnglish (US)
Pages (from-to)1489-1509
Number of pages21
JournalStatistica Sinica
Issue number3
StatePublished - 2019

Bibliographical note

Funding Information:
The authors thank the Associate Editor and two reviewers for their valuable comments, and Dr James Hodges for the enlightening discussions with the authors. The authors also gratefully acknowledge the University of Minnesota Supercomputing Institute and the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing computing resources that have contributed to the research results reported within this paper. This research was supported by the National Institutes of Health grants R01CA193888 to Huang and R03CA187991 to Luo.

Publisher Copyright:
© 2019 Institute of Statistical Science. All rights reserved.


  • Accelerated failure time model
  • Gap times
  • Recurrent events
  • Semiparametric method
  • Weighted risk-set method

PubMed: MeSH publication types

  • Journal Article


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