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. We develop Bayesian approaches to conduct statistical inference. Efficient Markov chain Monte Carlo algorithms are developed to facilitate the posterior inference. Extensive simulation studies are conducted to assess the performance of the proposed method, and an application to a study of Alzheimer’s disease is presented.

我们考虑了加速故障时间模型，该模型具有容易出错的时间到事件结果。所提出的模型通过允许时间到事件的响应受到测量误差的影响，扩展了传统的加速故障时间模型。我们描述了两个测量误差模型，一个对数变换回归测量误差模型和一个具有正增量的加性误差模型，以描述时间到事件结果中测量误差的可能场景。我们开发了贝叶斯方法来进行统计推断。开发了高效的马尔可夫链蒙特卡罗算法以促进后验推理。进行了广泛的模拟研究以评估所提出方法的性能，并提出了在阿尔茨海默病研究中的应用。