Buckley–James estimation of generalized additive accelerated lifetime model with ultrahigh‐dimensional data

High‐dimensional covariates in lifetime data is a challenge in survival analysis, especially in gene expression profile. The objective of this paper is to propose an efficient algorithm to extend the generalized additive model to survival data with high‐dimensional covariates. The algorithm is combined of generalized additive (GAM) model and Buckley–James estimation, which makes a nonparametric extension to the nonlinear model, where the GAM is exploited to illustrate the nonlinear effect of the covariates and the Buckley–James estimation is used to address the regression model with right‐censored response. In addition, we use maximal‐information‐coefficient (MIC)‐type variable screening and weighted p‐value to reduce dimension in high‐dimensional situations. The performance of the proposed algorithm is compared with the three benchmark models: Cox proportional hazards regression model, random survival forest, and BJ‐AFT on a simulated dataset and two real survival datasets. The results, evaluated by concordance index (C‐index) as well as modified mean squared error (mMSE), illustrated the superiority of the proposed algorithm.