A flexible semiparametric modeling approach for doubly censored data with an application to prostate cancer

Seungbong Han*, Adin Cristian Andrei, Kam Wah Tsui, Andrew B. Lawson, Duncan Lee, Ying MacNab

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Doubly censored data often arise in medical studies of disease progression involving two related events for which both an originating and a terminating event are interval-censored. Although regression modeling for such doubly censored data may be complicated, we propose a simple semiparametric regression modeling strategy based on jackknife pseudo-observations obtained using nonparametric estimators of the survival function. Inference is carried out via generalized estimating equations. Simulations studies show that the proposed method produces virtually unbiased covariate effect estimates, even for moderate sample sizes. A prostate cancer study example illustrates the practical advantages of the proposed approach.

Original languageEnglish (US)
Pages (from-to)1718-1735
Number of pages18
JournalStatistical Methods in Medical Research
Volume25
Issue number4
DOIs
StatePublished - Aug 1 2016

Keywords

  • doubly censored data
  • pseudo-observations
  • regression
  • semiparametric
  • survival analysis

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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