Survival curve estimation for informatively coarsened discrete event-time data.
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.
Shardell
Michelle
M
Department of Epidemiology and Preventive Medicine, University of Maryland, Baltimore, MD 21201-1596, USA. mshardel@epi.umaryland.edu
Scharfstein
Daniel O
DO
Bozzette
Samuel A
SA
D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D015658:HIV Infections; D006801:Humans; D016019:Survival Analysis; D013997:Time Factors; D014481:United States