In many prospective studies, including AIDS Link to the Intravenous Experience (ALIVE), researchers are interested in comparing event-time distributions (e.g.,for human immunodeficiency virus seroconversion) between a small number of groups (e.g., risk behavior categories). However, these comparisons are complicated by participants missing visits or attending visits off schedule and seroconverting during this absence. Such data are interval-censored, or more generally,coarsened. Most analysis procedures rely on the assumption of non-informative censoring, a special case of coarsening at random that may produce biased results if not valid. Our goal is to perform inference for estimated survival functions across a small number of goups in the presence of informative coarsening. To do so, we propose methods for frequentist and Bayesian inference of ALIVE data utilizing information elicited from ALIVE scientists and an AIDS epidemiology expert about the visit compliance process.



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