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Survival curve estimation for informatively coarsened discrete event‐time data
Author(s) -
Shardell Michelle,
Scharfstein Daniel O.,
Bozzette Samuel A.
Publication year - 2007
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2697
Subject(s) - censoring (clinical trials) , econometrics , bayesian probability , computer science , statistics , event data , event (particle physics) , survival analysis , interval (graph theory) , mathematics , covariate , physics , quantum mechanics , combinatorics
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.