Key Laboratory of Advanced Theory and Application in Statistics and Data Science, MOE, School of Statistics, East China Normal University, Shanghai, China.
Department of Statistics, The Chinese University of Hong Kong, Hong Kong, People's Republic of China.
Lifetime Data Anal. 2022 Jan;28(1):139-168. doi: 10.1007/s10985-021-09543-3. Epub 2022 Jan 9.
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.
我们考虑带有易出错的生存时间结局的加速失效时间模型。所提出的模型通过允许生存时间响应受到测量误差的影响，扩展了传统的加速失效时间模型。我们描述了两种测量误差模型，即对数变换回归测量误差模型和具有正增量的加法误差模型，以描绘生存时间结局中测量误差的可能情况。我们开发了贝叶斯方法来进行统计推断。开发了有效的马尔可夫链蒙特卡罗算法来促进后验推断。进行了广泛的模拟研究来评估所提出方法的性能，并展示了一个阿尔茨海默病研究的应用。
