Abstract
Interval-censored, or more generally, coarsened event-time data arise when study participants are observed at irregular time periods and experience the event of interest in between study observations. Such data are often analysed assuming non-informative censoring, which can produce biased results if the assumption is wrong. This paper extends the standard approach for estimating survivor functions to allow informatively interval-censored data by incorporating various assumptions about the censoring mechanism into the model. We include a Bayesian extension in which final estimates are produced by mixing over a distribution of assumed censoring mechanisms. We illustrate these methods with a natural history study of HIV-infected individuals using assumptions elicited from an AIDS expert.
Original language | English (US) |
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Pages (from-to) | 2184-2202 |
Number of pages | 19 |
Journal | Statistics in Medicine |
Volume | 26 |
Issue number | 10 |
DOIs | |
State | Published - May 10 2007 |
Keywords
- Coarsened at random
- Informative censoring
- Interval censoring
- Sensitivity analysis
- Survival
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability