Imputation methods for doubly censored HIV data

Wei Zhang, Ying Zhang, Kathryn Chaloner, Jack T. Stapleton

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

In medical research, it is common to have doubly censored survival data: origin time and event time are both subject to censoring. In this paper, we review simple and probability-based methods that are used to impute interval censored origin time and compare the performance of these methods through extensive simulations in the one-sample problem, two-sample problem and Cox regression model problem. The use of a bootstrap procedure for inference is demonstrated.

Original languageEnglish (US)
Pages (from-to)1245-1257
Number of pages13
JournalJournal of Statistical Computation and Simulation
Volume79
Issue number10
DOIs
StatePublished - Oct 1 2009
Externally publishedYes

Fingerprint

Imputation
Cox Regression Model
Censored Survival Data
Two-sample Problem
Censoring
Bootstrap
Interval
Simulation

Keywords

  • Bootstrap
  • Cox regression model
  • Interval censoring
  • Kaplan-Meier curve
  • Logrank test

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Cite this

Imputation methods for doubly censored HIV data. / Zhang, Wei; Zhang, Ying; Chaloner, Kathryn; Stapleton, Jack T.

In: Journal of Statistical Computation and Simulation, Vol. 79, No. 10, 01.10.2009, p. 1245-1257.

Research output: Contribution to journalArticle

Zhang, Wei ; Zhang, Ying ; Chaloner, Kathryn ; Stapleton, Jack T. / Imputation methods for doubly censored HIV data. In: Journal of Statistical Computation and Simulation. 2009 ; Vol. 79, No. 10. pp. 1245-1257.
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