Survival curve estimation for informatively coarsened discrete event‐time data

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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. Copyright © 2006 John Wiley & Sons, Ltd.

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