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Activity Number: 370
Type: Contributed
Date/Time: Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract - #309653
Title: Efficient Estimation of the Regression Parameter in Forward and Backward Recurrence Time Data Using the Accelerated Failure Time Model
Author(s): Pourab Roy*+ and Michael R. Kosorok and Jason Fine
Companies: The University of North Carolina at Chapel Hill and The University of North Carolina-Chapel Hill and The University of North Carolina Chapel Hill
Keywords: length-biased ; accelerated failure time model ; forward recurrence time ; backward recurrence time
Abstract:

In prevalent cohort studies, where subjects are recruited at a cross-section and followed prospectively in time, the observed event times are length-biased and follow a multiplicative censoring scheme. If the associated initiation time is unknown, we only observe the time from sampling to the event of interest. This is the forward recurrence time. In other scenarios, the time of the initiating event may be known, but there is no follow-up. This is the backward recurrence time. In the presence of covariates, the proportional hazards model may not be applicable to forward and backward recurrence time data. However, due to the invariance of the accelerated failure time model under length bias and cross-sectional sampling, it can serve as a useful alternative. Under length-biased sampling, the covariate distribution is dependent on the regression parameter. Thus, a "naive" conditional analysis may result in information loss. However, we show that if the covariate distribution is left completely unspecified, then there is no loss under a conditional analysis. We perform simulation studies to compare our method to existing methods for backward and forward recurrence time data analysis.


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