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The Analysis of Informatively Coarsened Discrete Time-to-Event Data

 Michelle Shardell, PhD Candidate, Johns Hopkins Department of Biostatistics

In many prospective studies, participants are evaluated for the occurrence of an absorbing event of interest (e.g., HIV seroconversion) at baseline and a common set of pre-specified visit times after enrollment. Since participants often miss scheduled visits, the underlying visit of first detection may be interval censored, or more generally, coarsened. Interval-censored data are usually analyzed assuming non-informative censoring, a special case of coarsening at random (CAR).

We posit a class of models for the event-time distribution that loosen the CAR assumption and use the EM algorithm for estimation. To perform inference on the estimated survivor functions, we propose an extension of the logrank test utilizing the EM-based estimates. We extend this methodology to estimate regression parameters for a discrete-time proportional hazards model with a low-dimensional covariate. Since CAR is usually not testable and often scientifically implausible, estimation is performed by incorporating elicited expert opinion about the relationship between event times and visit compliance into the model. The procedures are illustrated using data from the AIDS Link to the Intravenous Experience (ALIVE) study, an observational study of HIV infection among injection drug users in Baltimore, Maryland. Performance of our method is assessed via simulation studies.

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