Harvard T.H. Chan School of Public Health, FXB 651 Huntington Ave. 5th Floor, Boston, MA, 02115, USA.
Massachusetts General Hospital Biostatistics Center, 50 Staniford St. Suite 560, Boston, MA, 02114, USA.
Lifetime Data Anal. 2020 Jan;26(1):1-20. doi: 10.1007/s10985-018-9452-5. Epub 2018 Nov 1.
The accelerated failure time (AFT) model is a common method for estimating the effect of a covariate directly on a patient's survival time. In some cases, death is the final (absorbing) state of a progressive multi-state process, however when the survival time for a subject is censored, traditional AFT models ignore the intermediate information from the subject's most recent disease state despite its relevance to the mortality process. We propose a method to estimate an AFT model for survival time to the absorbing state that uses the additional data on intermediate state transition times as auxiliary information when a patient is right censored. The method extends the Gehan AFT estimating equation by conditioning on each patient's censoring time and their disease state at their censoring time. With simulation studies, we demonstrate that the estimator is empirically unbiased, and can improve efficiency over commonly used estimators that ignore the intermediate states.
加速失效时间（AFT）模型是一种常用的方法，用于直接估计协变量对患者生存时间的影响。在某些情况下，死亡是渐进多状态过程的最终（吸收）状态，但是当受试者的生存时间被删失时，传统的 AFT 模型忽略了受试者最近疾病状态的中间信息，尽管这些信息与死亡率过程有关。我们提出了一种方法，用于估计吸收状态的 AFT 模型，该方法在患者右删失时使用中间状态转移时间的附加数据作为辅助信息。该方法通过对每个患者的删失时间和他们在删失时间的疾病状态进行条件化，扩展了 Gehan AFT 估计方程。通过模拟研究，我们证明了估计量在经验上是无偏的，并且可以提高效率，而常用的估计量忽略了中间状态。
