# SurvivalGAN: Generating Time-to-Event Data for Survival Analysis

arXiv: https://arxiv.org/abs/2302.12749 · HTML: https://mlsanity.com/paper/2302.12749/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 86 references that are in the map.
- First posted on arXiv 24 Feb 2023.

## Builds on (most cited first, 30 of 30)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Normalizing Flows for Probabilistic Modeling and Inference](https://mlsanity.com/paper/1912.02762/) (2019)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [NVAE: A Deep Hierarchical Variational Autoencoder](https://mlsanity.com/paper/2007.03898/) (2020)
- [Score-based Generative Modeling in Latent Space](https://mlsanity.com/paper/2106.05931/) (2021)
- [Reliable Fidelity and Diversity Metrics for Generative Models](https://mlsanity.com/paper/2002.09797/) (2020)
- [Assessing Generative Models via Precision and Recall](https://mlsanity.com/paper/1806.00035/) (2018)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models](https://mlsanity.com/paper/2102.08921/) (2021)
- [DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks](https://mlsanity.com/paper/2110.12884/) (2021)
- [Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy](https://mlsanity.com/paper/1611.04488/) (2016)
- [Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data](https://mlsanity.com/paper/1810.10093/) (2018)
- [Synthesizing Tabular Data using Generative Adversarial Networks](https://mlsanity.com/paper/1811.11264/) (2018)
- [Synthetic Data -- what, why and how?](https://mlsanity.com/paper/2205.03257/) (2022)
- [Deep Survival Analysis](https://mlsanity.com/paper/1608.02158/) (2016)
- [Survival regression with accelerated failure time model in XGBoost](https://mlsanity.com/paper/2006.04920/) (2020)
- [TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks](https://mlsanity.com/paper/2109.00666/) (2021)
- [Adversarial Time-to-Event Modeling](https://mlsanity.com/paper/1804.03184/) (2018)
- [An Annotation Saved is an Annotation Earned: Using Fully Synthetic Training for Object Instance Detection](https://mlsanity.com/paper/1902.09967/) (2019)
- [AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning](https://mlsanity.com/paper/2210.12090/) (2022)

## Built on by (most cited first, 17 of 17)

- [Can I trust my fake data -- A comprehensive quality assessment framework for synthetic tabular data in healthcare](https://mlsanity.com/paper/2401.13716/) (2024)
- [Position: Stop Chasing the C-index when Evaluating Survival Analysis Models](https://mlsanity.com/paper/2506.02075/) (2025)
- [Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks](https://mlsanity.com/paper/2603.29475/) (2026)
- [Generating Survival Interpretable Trajectories and Data](https://mlsanity.com/paper/2402.12331/) (2024)
- [Generating Accurate Synthetic Survival Data by Conditioning on Outcomes](https://mlsanity.com/paper/2405.17333/) (2024)
- [Masked Clinical Modelling: A Framework for Synthetic and Augmented Survival Data Generation](https://mlsanity.com/paper/2410.16811/) (2024)
- [Attention-Based Synthetic Data Generation for Calibration-Enhanced Survival Analysis: A Case Study for Chronic Kidney Disease Using Electronic Health Records](https://mlsanity.com/paper/2503.06096/) (2025)
- [SurvDiff: A Diffusion Model for Generating Synthetic Data in Survival Analysis](https://mlsanity.com/paper/2509.22352/) (2025)
- [SurvTimeSurvival: Survival Analysis On The Patient With Multiple Visits/Records](https://mlsanity.com/paper/2311.09854/) (2023)
- [Pareto-Optimal Estimation and Policy Learning on Short-term and Long-term Treatment Effects](https://mlsanity.com/paper/2403.02624/) (2024)
- [Permissioned Blockchain-based Framework for Ranking Synthetic Data Generators](https://mlsanity.com/paper/2405.07196/) (2024)
- [Generating Reliable Synthetic Clinical Trial Data: The Role of Hyperparameter Optimization and Domain Constraints](https://mlsanity.com/paper/2505.05019/) (2025)
- [Synthetic Survival Data Generation for Heart Failure Prognosis Using Deep Generative Models](https://mlsanity.com/paper/2509.04245/) (2025)
- [Toward Valid Generative Clinical Trial Data with Survival Endpoints](https://mlsanity.com/paper/2511.16551/) (2025)
- [Proper Scoring Rules for Right-Censored Survival Data](https://mlsanity.com/paper/2606.06393/) (2026)
- [A Filtered Mixture-of-Generators for Fully Synthetic Survival Training](https://mlsanity.com/paper/2607.00127/) (2026)
- [Large Language Models are Approximate Survival Estimators](https://mlsanity.com/paper/2609.38181/) (2026)

## Abstract (arXiv)

Synthetic data is becoming an increasingly promising technology, and successful applications can improve privacy, fairness, and data democratization. While there are many methods for generating synthetic tabular data, the task remains non-trivial and unexplored for specific scenarios. One such scenario is survival data. Here, the key difficulty is censoring: for some instances, we are not aware of the time of event, or if one even occurred. Imbalances in censoring and time horizons cause generative models to experience three new failure modes specific to survival analysis: (1) generating too few at-risk members; (2) generating too many at-risk members; and (3) censoring too early. We formalize these failure modes and provide three new generative metrics to quantify them. Following this, we propose SurvivalGAN, a generative model that handles survival data firstly by addressing the imbalance in the censoring and event horizons, and secondly by using a dedicated mechanism for approximating time-to-event/censoring. We evaluate this method via extensive experiments on medical datasets. SurvivalGAN outperforms multiple baselines at generating survival data, and in particular addresses the failure modes as measured by the new metrics, in addition to improving downstream performance of survival models trained on the synthetic data.