Nonparametric estimation of the probability of illness in the illness-death model under cross-sectional sampling

M. Mandel*, R. Fluss

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Cross-sectional sampling is an attractive design that saves resources but results in biased data. For proper inference, one should first discover the bias function and then weigh observations appropriately. We consider cross-sectioning of the illness-death model with the aim of estimating the probability of visiting the illness state before death. We develop simple consistent and asymptotically normal estimators under various assumptions on the model and data collection and, in particular, compare designs with and without a follow-up. These designs are common in surveillance of hospital acquired infections, but estimators currently in use do not properly correct the bias.

Original languageAmerican English
Pages (from-to)861-872
Number of pages12
JournalBiometrika
Volume96
Issue number4
DOIs
StatePublished - Dec 2009

Keywords

  • Biased data
  • Disability model
  • Incidence
  • Nosocomial infection
  • Prevalence
  • Semi-competing risks

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