Abstract: Aiming at  the challenges of high-dimensional medical image data in survival analysis, we proposed an accelerated failure trace regression model. The regression parameters were estimated by using Kaplan-Meier weighting and the Peaceman-Rachford algorithm. The numerical simulation results show that the estimation performance of the accelerated failure trace regression model is better than that of the traditional Lasso regression model. We apply the model to Alzheimer’s disease image data to further verify its effectiveness and practical value.

Key words: accelerated failure trace regression model, high-dimensional right censored data, Kaplan-Meier weighting, Peaceman-Rachford splitting algorithm, Alzheimer’s disease image data