A copula model for bivariate hybrid censored survival data with application to the MACS study.

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

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

A copula model for bivariate survival data with hybrid censoring is proposed to study the association between survival time of individuals infected with HIV and persistence time of infection with an additional virus. Survival with HIV is right censored and the persistence time of the additional virus is subject to interval censoring case 1. A pseudo-likelihood method is developed to study the association between the two event times under such hybrid censoring. Asymptotic consistency and normality of the pseudo-likelihood estimator are established based on empirical process theory. Simulation studies indicate good performance of the estimator with moderate sample size. The method is applied to a motivating HIV study which investigates the effect of GB virus type C (GBV-C) co-infection on survival time of HIV infected individuals.

Original languageEnglish (US)
Pages (from-to)231-249
Number of pages19
JournalLifetime Data Analysis
Volume16
Issue number2
DOIs
StatePublished - Apr 1 2010
Externally publishedYes

Fingerprint

Copula Models
Censored Survival Data
Viruses
Virus
Pseudo-likelihood
Survival Time
Censoring
Persistence
Infection
HIV
Interval Censoring
Estimator
Likelihood Methods
Empirical Process
Survival Data
Normality
GB virus C
Sample Size
Simulation Study
Coinfection

ASJC Scopus subject areas

  • Applied Mathematics
  • Medicine(all)

Cite this

A copula model for bivariate hybrid censored survival data with application to the MACS study. / Zhang, Suhong; Zhang, Ying; Chaloner, Kathryn; Stapleton, Jack T.

In: Lifetime Data Analysis, Vol. 16, No. 2, 01.04.2010, p. 231-249.

Research output: Contribution to journalArticle

Zhang, Suhong ; Zhang, Ying ; Chaloner, Kathryn ; Stapleton, Jack T. / A copula model for bivariate hybrid censored survival data with application to the MACS study. In: Lifetime Data Analysis. 2010 ; Vol. 16, No. 2. pp. 231-249.
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