Statistical issues in survival analysis (deep AFT interval-censored)
August 12, 2026 The authors, Qiang et al (2026) proposed a semiparametric modeling framework for intervalcensored data using a deep generalized accelerated hazards model (DGAHM) which uses Cox proportional hazard regression or accelerated failure time (AFT) regression along with neural networks and monotone splines. They used B-splines to enforce monotonicity of the baseline hazard function. They used deep neural network (DNNs) to estimate function of the pooled covariates and monotone splines to estimate the baseline hazard function. They called this method nonparametric deep generalized accelerated hazards model (DGAHM-Non). In simulations, the authors compared their DGAHM method to the extended hazard model (EHM) from Chen and Jewell (2001). They also compared all of these to a modified version of their method at the midpoint, DGAHM-Mid. The performance of the proposed estimator became