Bayesian analysis under accelerated failure time models with error-prone time-to-event outcomes.

We consider accelerated failure time models with error-prone time-to-event outcomes. The proposed models extend the conventional accelerated failure time model by allowing time-to-event responses to be subject to measurement errors. We describe two measurement error models, a logarithm transformation regression measurement error model and an additive error model with a positive increment, to delineate possible scenarios of measurement error in time-to-event outcomes.