# Deep Neural Network Based Accelerated Failure Time Models using Rank Loss

arXiv: https://arxiv.org/abs/2206.05974 · HTML: https://mlsanity.com/paper/2206.05974/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 57 references that are in the map.
- First posted on arXiv 13 Jun 2022.

## Builds on (most cited first, 12 of 12)

- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Time-to-Event Prediction with Neural Networks and Cox Regression](https://mlsanity.com/paper/1907.00825/) (2019)
- [An overview of deep learning in medical imaging focusing on MRI](https://mlsanity.com/paper/1811.10052/) (2018)
- [Incremental Gradient, Subgradient, and Proximal Methods for Convex Optimization: A Survey](https://mlsanity.com/paper/1507.01030/) (2015)
- [A Statistical Perspective on Algorithmic Leveraging](https://mlsanity.com/paper/1306.5362/) (2013)
- [Adversarial Time-to-Event Modeling](https://mlsanity.com/paper/1804.03184/) (2018)
- [Optimal Subsampling for Large Sample Logistic Regression](https://mlsanity.com/paper/1702.01166/) (2017)
- [A test case for application of convolutional neural networks to spatio-temporal climate data: Re-identifying clustered weather patterns](https://mlsanity.com/paper/1811.04817/) (2018)
- [DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values](https://mlsanity.com/paper/1908.02337/) (2019)
- [Optimal subsampling for quantile regression in big data](https://mlsanity.com/paper/2001.10168/) (2020)
- [Optimal Cox Regression Subsampling Procedure with Rare Events](https://mlsanity.com/paper/2012.02122/) (2020)
- [DeepHazard: neural network for time-varying risks](https://mlsanity.com/paper/2007.13218/) (2020)

## Built on by (most cited first, 1 of 1)

- [Neural Networks for Censored Expectile Regression Based on Data Augmentation](https://mlsanity.com/paper/2510.20344/) (2025)

## Abstract (arXiv)

An accelerated failure time (AFT) model assumes a log-linear relationship between failure times and a set of covariates. In contrast to other popular survival models that work on hazard functions, the effects of covariates are directly on failure times, whose interpretation is intuitive. The semiparametric AFT model that does not specify the error distribution is flexible and robust to departures from the distributional assumption. Owing to the desirable features, this class of models has been considered as a promising alternative to the popular Cox model in the analysis of censored failure time data. However, in these AFT models, a linear predictor for the mean is typically assumed. Little research has addressed the nonlinearity of predictors when modeling the mean. Deep neural networks (DNNs) have received a focal attention over the past decades and have achieved remarkable success in a variety of fields. DNNs have a number of notable advantages and have been shown to be particularly useful in addressing the nonlinearity. By taking advantage of this, we propose to apply DNNs in fitting AFT models using a Gehan-type loss, combined with a sub-sampling technique. Finite sample properties of the proposed DNN and rank based AFT model (DeepR-AFT) are investigated via an extensive stimulation study. DeepR-AFT shows a superior performance over its parametric or semiparametric counterparts when the predictor is nonlinear. For linear predictors, DeepR-AFT performs better when the dimensions of covariates are large. The proposed DeepR-AFT is illustrated using two real datasets, which demonstrates its superiority.