Survival Analysis Paper List

A curated reading list of papers and resources in survival analysis, with an emphasis on machine learning, deep learning, calibration, evaluation, and applied time-to-event modeling.

Some papers may belong to multiple categories. I have organized each entry by what I view as its most significant contribution.

Since June 2026, I have also used LLM agents as research assistants to help discover, triage, and maintain survival-analysis papers while keeping the final curation human-reviewed.

Suggestions are welcome—please open an issue or pull request if you would like to add a paper or resource.

Table of Contents

Topic	Topic
Tutorials/Surveys	Perspective & Opinion
ML/DL Survival Models	Objective Functions
Time-varying Covariates Models	Explainable Survival Models
Competing Risks & Multi-Event Models	Generalized Survival Analysis Methods
Evaluation Metrics	Causal Inference
Fairness	Out-Of-Distribution
Dependent Censoring	Synthetic Data Generation
Temporal Point Processes	Applications

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Tutorials and Surveys

Title	Publisher	Date	Code	Notes
Machine Learning for Survival Analysis: A Survey	ACM Computing Surveys	2019.02		Slides
Calibration: the Achilles heel of predictive analytics	BMC Medicine	2019.12
A tutorial on calibration measurements and calibration models for clinical prediction models	JAMIA	2020.02	R
Survival analysis—time-to-event data and censoring	Nature Methods	2022.04
Survey: Strategies for Loss-Based Discrete-Time Hazard and Survival Function Estimation	ICTC	2022.10
Regression modeling of time-to-event data with censoring	Nature Methods	2022.11
Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures	Bioinformatics	2022.09
Factors influencing clinician and patient interaction with machine learning-based risk prediction models: a systematic review	Lancet Digital Health	2024.02
Deep learning for survival analysis: a review	Artificial Intelligence Review	2024.02
Review of Language Models for Survival Analysis	AAAI Spring Symposium on Clinical FMs	2024.05	PyTorch
An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomes	Foundation and Trends in Machine Learning	2024.10	PyTorch
Beyond Cox Models: Assessing the Performance of Machine-Learning Methods in Non-Proportional Hazards and Non-Linear Survival Analysis	ArXiv	2025.04	PyTorch

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Perspective and Opinion

Title	Publisher	Date
Why Test for Proportional Hazards	JAMA	2020.03
Stop Chasing the C-index: This Is How We Should Evaluate Our Survival Models	Arxiv	2025.06

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ML and DL for Survival Analysis

Keyword	Title	Publisher	Date	Code	Notes
GBMCOX	The State of Boosting	Computing Science and Statistics	2008	R
RSF	Random Survival Forest	The Annals of Applied Statistics	2008	R
MTLR	Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors	NeurIPS	2011	R	Poster
DSA	Deep Survival Analysis	ML4HC	2016
N-MTLR	Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework	Arxiv	2018.01	Python
DeepSurv	DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network	BMC Medical Research Methodology	2018.02	Python
DATE & DRAFT	Adversarial Time-to-Event Modeling	ICML	2018.07	TensorFlow
CoxTime / CoxCC	Time-to-Event Prediction with Neural Networks and Cox Regression	JMLR	2019.08	PyTorch	PyCox 1-3
PCHazard / LogisticHazard	Continuous and Discrete-Time Survival Prediction with Neural Networks	Arxiv	2019.10	PyTorch	PyCox 2-3
SurvivalQuilts	Temporal Quilting for Survival Analysis	AISTATS	2020.04	Python
SCA	Survival Cluster Analysis	ACM CHIL	2020.04	TensorFlow
VAECox	Improved survival analysis by learning shared genomic information from pan-cancer data	Bioinformatics	2020.07	Pytorch
DCM	Deep Cox Mixtures for Survival Regression	NeurIPS Machine Learning for Health Workshop	2021.01	TensorFlow
DHBN	Using Discrete Hazard Bayesian Networks to Identify which Features are Relevant at each Time in a Survival Prediction Model	AAAI Spring Symposium (SP-ACA)	2021.03	R
TDSA	Transformer-Based Deep Survival Analysis	AAAI Spring Symposium (SP-ACA)	2021.03
DeepQuantreg	Deep learning for quantile regression under right censoring: DeepQuantreg	Computational Statistics and Data Analysis	2021.07	TensorFlow
IWSG	Inverse-Weighted Survival Games	NeurIPS	2021.12	PyTorch
DeepEH	Deep Extended Hazard Models for Survival Analysis	NeurIPS	2021.12
VaDeSC	A Deep Variational Approach to Clustering Survival Data	ICLR	2022.03	TensorFlow
ODE-Cox	Survival Analysis via Ordinary Differential Equations	JASA	2022
DeSurv	Derivative-Based Neural Modelling of Cumulative Distribution Functions for Survival Analysis	AIStats	2022.05	PyTorch
Survival MDN	Survival Mixture Density Networks	ML4HC	2022.05	PyTorch
SODEN	SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks	JMLR	2022	PyTorch
DCS	Deep Learning-Based Discrete Calibrated Survival Prediction	ICDH	2022.08	PyTorch
CQRNN	Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis	NeurIPS	2022.11	PyTorth	Poster
MSSDA	Multi-Source Survival Domain Adaptation	AAAI	2023
DH-MNN	Metaparametric Neural Networks for Survival Analysis	TNNLS	2023.08
NSOTree	Neural Survival Oblique Tree	Arxiv	2023.09	Python
NNCDE	Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data	JMLR	2023.12	PyTorch
NFM	Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions	NeurIPS	2023.12	PyTorch
Diffsurv	Differentiable sorting for censored time-to-event data	NeurIPS	2023.12	PyTorch
OSST	Optimal Sparse Survival Trees	AIStats	2024.01	Python
Survival Kernets	Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee	JMLR	2024.02	Pytorch
deepAFT	deepAFT: A nonlinear accelerated failure time model with artificial neural network	Statistics in Medicine	2024.06	R
SurvReLU	Inherently Interpretable Survival Analysis via Deep ReLU Networks	CIKM	2024.07	Code
FastSurvival	FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models	NeurIPS	2024.10
ConSurv	Toward a Well-Calibrated Discrimination via Survival Outcome-Aware Contrastive Learning	NeurIPS	2024.10	PyTorch
L2Boost-CUT / L2Boost-IMP	Boosting Methods for Interval-censored Data with Regression and Classification	ICLR	2025.02	R
CDVI	Censor Dependent Variational Inference	ICML	2025.05	Pytorch
ALD	Learning Survival Distributions with the Asymmetric Laplace Distribution	ICML	2025.05	Pytorch

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Objective Functions

Keyword	Title	Publisher	Date	Code	Notes
GBMCI	A Gradient Boosting Algorithm for Survival Analysis via Direct Optimization of Concordance Index	Computational and Mathematical Methods in Medicine	2013.09	R
Survival-CRPS	Countdown Regression: Sharp and Calibrated Survival Predictions	UAI	2019	PyTorch
	Bias in Cross-Entropy-Based Training of Deep Survival Networks	TPAMI	2020.03
SFM	Calibration and Uncertainty in Neural Time-to-Event Modeling	TNNLS	2020.09	TensorFlow
X-CAL	X-CAL: Explicit Calibration for Survival Analysis	NeurIPS	2020	PyTorch	Poster
Discrete-RPS	Estimating Calibrated Individualized Survival Curves with Deep Learning	AAAI	2021.02	PyTorch
KL-Calibration	Simpler Calibration for Survival Analysis	ICLR OpenReview	2021.10
SuMo-net	Survival regression with proper scoring rules and monotonic neural networks	AIStats	2022.03	PyTorch
DQS	Proper Scoring Rules for Survival Analysis	ICML	2023.06	PyTorch	Poster

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Time-varying Covariates Models

Keyword	Title	Publisher	Date	Code	Notes
SPH	Survival Prediction by an Integrated Learning Criterion on Intermittently Varying Healthcare Data	AAAI	2016.02
Time-varying Cox	Time-varying covariates and coefficients in Cox regression models	Annals of Translational Medicine	2018.01
DRSA	Deep Recurrent Survival Analysis	AAAI	2019.02	TensorFlow
TSNN	Time-Dependent Survival Neural Network for Remaining Useful Life Prediction	PAKDD	2019.03
Dynamic-DeepHit	Dynamic-DeepHit: A Deep Learning Approach for Dynamic Survival Analysis With Competing Risks Based on Longitudinal Data	TBME	2019.04	TensorFlow
TSNN & RSNN	Survival neural networks for time-to-event prediction in longitudinal study	Knowledge and Information System	2020.05		Extend Paper
DRSM	Deep Parametric Time-to-Event Regression with Time-Varying Covariates	AAAI Spring Symposium (SP-ACA)	2021.03	PyTorch
SubLign	Clustering Interval-Censored Time-Series for Disease Phenotyping	AAAI	2022.02
TCSA	Temporally-Consistent Survival Analysis	NeurIPS	2022.11	Python	Poster
SurvPP	Survival Permanental Processes for Survival Analysis with Time-Varying Covariates	NeurIPS	2023.12	Python
CoxSig	Dynamic Survival Analysis with Controlled Latent States	ICML	2024.01	PyTorch
DSPS	Support vector machine for dynamic survival prediction with time-dependent covariates	Ann. Appl. Stat.	2024.09	R

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Explainable Survival Models

Keyword	Title	Publisher	Date	Code	Notes
SPIE	Simultaneous Prediction Intervals for Patient-Specific Survival Curves	IJCAI	2019	Python
SurvLIME	SurvLIME: A method for explaining machine learning survival models	Knowledge-Based Systems	2020.09	Python
AutoScore-Survival	AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data	Journal of Biomedical Informatics	2022.01	R
EXCEL	Explainable Censored Learning: Finding Critical Features with Long Term Prognostic Values for Survival Prediction	Arxiv	2022.09
BNN-ISD	Using Bayesian Neural Networks to Select Features and Compute Credible Intervals for Personalized Survival Prediction	IEEE TBME	2023.07	PyTorch

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Competing Risks and Multi-Event Models

Keyword	Title	Publisher	Date	Code	Notes
	On pseudo-values for regression analysis in competing risks models	Lifetime Data Analysis	2009.06
	The Survival Filter: Joint Survival Analysis with a Latent Time Series	UAI	2015
DMGP	Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks	NeurIPS	2017.12
DeepHit	DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks	AAAI	2018.02	TensorFlow
DSM	Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data With Competing Risks	IEEE JBHI	2021.01	PyTorch
HSA	A Hierarchical Approach to Multi-Event Survival Analysis	AAAI	2021.05	PyTorch
SurvTRACE	SurvTRACE: Transformers for Survival Analysis with Competing Events	Arxiv	2021.10	Pytorch
Deep-CSA	Deep-CSA: Deep Contrastive Learning for Dynamic Survival Analysis with Competing Risks	IEEE JBHI	2022.04
DeepPseudo	DeepPseudo: Pseudo Value Based Deep Learning Models for Competing Risk Analysis	KDD DSHealth Workshop	2022.08
SurvivalBoost	Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks	AIStats	2025.02
MENSA	MENSA: A Multi-Event Network for Survival Analysis with Trajectory-based Likelihood Estimation	ML4H	2026	PyTorch

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Generalized Survival Analysis Methods

	Title	Publisher	Date	Code	Notes
Pseudo-observations	Pseudo-observations in survival analysis	Statistical Methods in Medical Research	2010
	A doubly robust censoring unbiased transformation	The International Journal of Biostatistics	2007.03
	Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting	Journal of Biomedical Informatics	2016.03	R
	A General Machine Learning Framework for Survival Analysis	ECML	2020.06	R
Parametrics Pseudo-observations	Regression models using parametric pseudo-observations	Statistics in Medicine	2020.06
	A Kernel Log-Rank Test of Independence for Right-Censored Data	JASA	2021.09	Python
CSA	Conformalized survival analysis	JRSS: Series B	2023.01	R
anchor-vis	A General Framework for Visualizing Embedding Spaces of Neural Survival Analysis Models Based on Angular Information	CHIL	2023.07	Python
Adaptive-CSA	Conformalized survival analysis with adaptive cut-offs	Biometrika	2023.12	R
CSD	Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration	ICML	2024.05	Python	Poster
CiPOT	Toward Conditional Distribution Calibration in Survival Prediction	NeurIPS	2024.10	Python	Poster
Fused	Conformalized Survival Analysis for General Right-Censored Data	ICLR	2025.02

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Evaluation Metrics

Keyword	Title	Publisher	Date	Code	Notes
Brier Score Decomposition	The Comparison and Evaluation of Forecasters	The Statistician	1983
CRPS Decomposition	Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems	Weather and Forecasting	2000.03
IPCW Brier Score	Assessment and Comparison of Prognostic Classification Schemes for Survival Data	Statistics in Medicine	1999.09
Administrative Brier Score	The Brier Score under Administrative Censoring: Problems and Solutions	JMLR	2019.12	PyTorch	PyCox 3-3
Graphical-Cal	Graphical calibration curves and the integrated calibration index (ICI) for survival models	Statistics in Medicine	2019.11	Python
D-Calibration	Effective Ways to Build and Evaluate Individual Survival Distributions	JMLR	2020.06	R
KSD	Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data	ICML	2020.08
	Scoring rules in survival analysis	Arxiv	2022.12
MAE-PO	An Effective Meaningful Way to Evaluate Survival Models	ICML	2023.06	PyTorch	Poster

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Causal Inference

Keyword	Title	Publisher	Date	Code	Notes
	Causal inference in survival analysis using pseudo-observations	Statistics in Medicine	2017.03
CausalTree	Causal Inference for Survival Analysis	Arxiv	2018.03	R
CSA	Enabling Counterfactual Survival Analysis with Balanced Representations	ACM CHIL	2021.03	Python
SurvITE	SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data	NeurIPS	2021.10	TensorFlow
CMHE	Counterfactual Phenotyping with Censored Time-to-Events	KDD	2022.02	PyTorch
DNMC	Disentangling Whether from When in a Neural Mixture Cure Model for Failure Time Data	AISTATS	2022.03	TensorFlow
compCATE	Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data	AISTATS	2023.02	Python
PCI2S	Regression-based proximal causal inference for right-censored time-to-event data	Arxiv	2024.09	R
	Doubly protected estimation for survival outcomes utilizing external controls for randomized clinical trials	ICML	2025.05	R?

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Fairness

Keyword	Title	Publisher	Date	Code	Notes
FSRF	Longitudinal Fairness with Censorship	AAAI	2022.03
FISA	Fair and Interpretable Models for Survival Analysis	KDD	2022.08		Video
IFS	Censored Fairness through Awareness	AAAI	2023.03
	Fairness-Aware Processing Techniques in Survival Analysis: Promoting Equitable Predictions	ECML-PKDD	2023.09
DRO-Cox	Fairness in Survival Analysis with Distributionally Robust Optimization	JMLR	2024.08	PyTorch
FairFSA	Fair Federated Survival Analysis	AAAI	2025.04

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Out-Of-Distribution

Keyword	Title	Publisher	Date	Code	Notes
	Evaluating Domain Generalization for Survival Analysis in Clinical Studies	CHIL	2022.08
Stable-Cox	Stable Cox regression for survival analysis under distribution shifts	Nature Machine Intelligence	2024.12	PyTorch

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Dependent Censoring and Competing Risks

Keyword	Title	Publisher	Date	Code	Notes
	Copula Based Cox Proportional Hazards Models for Dependent Censoring	JASA	2023.03	R
CopulaDeepSurvival	Copula-Based Deep Survival Models for Dependent Censoring	UAI	2023.06	PyTorch
DCSurvival	Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability Guarantees	AAAI	2023.12	PyTorch
PSA	Proximal survival analysis to handle dependent right censoring	JRSS: Series B	2024.05
HACSurv	HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks	AIStats	2025.02	PyTorch
SC-Net	Survival Analysis via Density Estimation	ICML	2025.02	PyTorch

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Synthetic Data Generation

Keyword	Title	Publisher	Date	Code	Notes
SurvivalGAN	SurvivalGAN: Generating Time-to-Event Data for Survival Analysis	AIStats	2023.02	PyTorch
	SYNDSURV: A simple framework for survival analysis with data distributed across multiple institutions	Computers in Biology and Medicine	2024.04	PyTorch
	Conditioning on Time is All You Need for Synthetic Survival Data Generation	Arxiv	2024.05	PyTorch

Temporal Time Process

Title	Publisher	Date	Code	Notes
Lecture Notes: Temporal Point Processes and the Conditional Intensity Function	Arxiv	2018.06
Temporal Point Processes	Course Material	2019.01
Recent Advance in Temporal Point Process: from Machine Learning Perspective		2019
Wavelet Reconstruction Networks for Marked Point Processes	AAAI Spring Symposium (SP-ACA)	2021.03	Python
Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations	ICLR	2024.01

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Applied Survival Analysis

Title	Publisher	Date	Code	Notes
Empirical comparisons between Kaplan-Meier and Nelson-Aalen survival function estimators	Journal of Statistical Computation and Simulation	2002
Machine-Learning Approaches in COVID-19 Survival Analysis and Discharge-Time Likelihood Prediction Using Clinical Data	Patterns	2020.08	Python
Application of a novel machine learning framework for predicting non-metastatic prostate cancer-specific mortality in men using the Surveillance, Epidemiology, and End Results (SEER) database	Lancet Digital Health	2021.03	Python
Learning accurate personalized survival models for predicting hospital discharge and mortality of COVID-19 patients	Scientific Report	2022.03	R
Predicting Time-to-conversion for Dementia of Alzheimer's Type using Multi-modal Deep Survival Analysis	Arxiv	2022.05
Time-to-event modeling for hospital length of stay prediction for COVID-19 patients	Machine Learning with Applications	2022.09
Metabolomic profiles predict individual multidisease outcomes	Nature Medicine	2022.09	PyTorch
Personalized breast cancer onset prediction from lifestyle and health history information	PLOS One	2022.12
SurProGenes: Survival Risk-Ordered Representation of Cancer Patients and Genes for the Identification of Prognostic Genes	ICML	2023.06	TensorFlow	Poster
Semi-Parametric Contextual Pricing Algorithm using Cox Proportional Hazards Model	ICML	2023.06	R	Poster
Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records	CIKM	2023.10	Python
Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Prediction	ICLR	2024.01	PyTorch
MOTOR: A Time-to-Event Foundation Model For Structured Medical Records	ICLR	2024.01	PyTorch
Multimodal Prototyping for cancer survival prediction	ICML	2024.05	PyTorch
Predicting Long-Term Allograft Survival in Liver Transplant Recipients	ML4HC	2024.08	PyTorch
Leveraging Tumor Heterogeneity: Heterogeneous Graph Representation Learning for Cancer Survival Prediction in Whole Slide Images	NeurIPS	2024.12	PyTorch	Poster
Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathology	ICLR	2025.02	PyTorch
VAE-Surv: A novel approach for genetic-based clustering and prognosis prediction in myelodysplastic syndromes	Computer Methods and Programs in Biomedicine	2025.03	PyTorch
From Representation Space to Prognostic Insights: Whole Slide Image Generation with Hierarchical Diffusion Model for Survival Prediction	AAAI	2025.04