Survival Analysis

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Survival analysis estimates when an event may occur and how risk changes over time, even when follow-up ends before the event is observed. Methods range from Kaplan–Meier curves, Cox and accelerated failure-time models to random survival forests, neural hazard models, and newer in-context models pretrained on synthetic data. Current work applies these tools to longitudinal, multimodal, and clinical data, including competing risks, personalized treatment decisions, and causal effects. A central challenge is ensuring predictions are not only good at ranking risk but also calibrated, fair, and reliable when censoring or data distributions differ across settings.

Latest papers

106 papers14 papers in the last four weeks, up 250% on the four weeks before. 0.1% of all new papers.

Beyond Consistency: Inference for the Relative risk functional in Deep Nonparametric Cox Models
Mar 25, 2026Sattwik Ghosal, Xuran Meng, Yi LiSurvival Analysis Semiparametric Inference
Department of Biostatistics, University of Michigan
Off-Policy Evaluation and Learning for Survival Outcomes under Censoring
Mar 24, 2026Kohsuke Kubota, Mitsuhiro Takahashi, Yuta SaitoOff-Policy Evaluation Off-Policy RL
NTT DOCOMO, INC. · Hanjuku-kaso, Co, Ltd.
Survival Meets Classification: A Novel Framework for Early Risk Prediction Models of Chronic Diseases
Mar 12, 2026Shaheer Ahmad Khan, Muhammad Usamah Shahid, Muddassar FarooqClinical Prediction Clinical Risk Prediction
CureMD Research, 80 Pine St 21st Floor, New York, NY 10005, United States
An Automated Radiomics Framework for Postoperative Survival Prediction in Colorectal Liver Metastases using Preoperative MRI
Mar 10, 2026Muhammad Alberb, Jianan Chen, Hossam El-rewaidy +5Cancer Survival Prediction Radiomics
Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada · Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada · UCL Cancer Institute, University College London, London, UK +5
SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis
Mar 5, 2026Shahriar Noroozizadeh, Xiaobin Shen, Jeremy C. Weiss +1Individualized Treatment Effect Estimation Survival Analysis
Machine Learning Department & Heinz College, Carnegie Mellon University · Heinz College, Carnegie Mellon University · National Library of Medicine, National Institutes of Health
Identifying and Characterising Response in Clinical Trials: Development and Validation of a Machine Learning Approach in Colorectal Cancer
Feb 28, 2026Adam Marcus, Paul AgapowCancer Drug Response Prediction Individualized Treatment Effect Estimation
Department of Computing Imperial College London London, SW7 2AZ, UK · Oncology R&D ML&AI AstraZeneca Cambridge, CB2 1PQ, UK
Benchmarking Temporal Web3 Intelligence: Lessons from the FinSurvival 2025 Challenge
Feb 26, 2026Oshani Seneviratne, Fernando Spadea, Adrien Pavao +2Survival Analysis
Rensselaer Polytechnic Institute Troy, NY, USA · Codabench, MLChallenges Paris, France · Vassar College Poughkeepsie, NY, USA
Discovering Subgroups with Exceptional Survival Characteristics
Feb 25, 2026Mhd Jawad Al Rahwanji, Sascha Xu, Nils Philipp Walter +1Survival Analysis
CISPA Helmholtz Center for Information Security Saarbrücken, Germany
Multimodal Survival Modeling and Fairness-Aware Clinical Machine Learning for 5-Year Breast Cancer Risk Prediction
Feb 25, 2026Toktam KhatibiCancer Survival Prediction Multimodal Survival Analysis
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
Ensemble Machine Learning and Statistical Procedures for Dynamic Predictions of Time-to-Event Outcomes
Feb 23, 2026Nina van Gerwen, Sten Willemsen, Bettina E. Hansen +9Survival Analysis Ensemble Learning
Department of Biostatistics, Erasmus University Medical Center, the Netherlands · Department of Epidemiology, Erasmus University Medical Center, the Netherlands · Reference Center for Inflammatory Biliary Diseases and Autoimmune Hepatitis, European Reference Network on Hepatological Diseases (ERN Rare-Liver), Saint-Antoine Hospital, Assistance Publique - Hˆopitaux de Paris; Inserm UMR S938, Saint-Antoine Research Center, Sorbonne University, Paris, France +7
Functional Decomposition and Shapley Interactions for Interpreting Survival Models
Feb 18, 2026Sophie Hanna Langbein, Hubert Baniecki, Fabian Fumagalli +3Feature Attribution Feature Interaction Modeling
Leibniz Institute for Prevention Research and Epidemiology – BIPS · Faculty of Mathematics and Computer Science, University of Bremen · University of Warsaw +3
FLEXI-Haz: Semiparametric Deep Survival Inference Beyond Proportional Hazards
Dec 11, 2025Asaf Ben Arie, Malka GorfineSurvival Analysis Semiparametric Inference
Department of Statistics and Operations Research, Tel Aviv University, Israel
Together, Then Apart: Balancing Alignment and Distinctiveness for Multimodal Survival Analysis
Nov 22, 2025Wenjing Liu, Qin Ren, Wen Zhang +2Cancer Survival Prediction Survival Analysis
Stony Brook University, Stony Brook, NY, USA · Johns Hopkins University, Baltimore, MD, USA · Brookhaven National Laboratory, Upton, NY, USA
Survival of the fittest Cox model: Pivotal variable selection for time-to-event data
Oct 22, 2025Maxime van Cutsem, Sylvain SardyModel Selection Survival Analysis
Department of Mathematics, University of Geneva
Overcoming Dependent Censoring in the Evaluation of Survival Models
Feb 26, 2025Christian Marius Lillelund, Shi-ang Qi, Russell GreinerSurvival Analysis
Department of Computing Science, University of Alberta, Edmonton, Canada · Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark · Alberta Machine Intelligence Institute, Edmonton, Canada +1
Deep End-to-End Survival Analysis with Temporal Consistency
Oct 9, 2024Mariana Vargas Vieyra, Pascal FrossardSurvival Analysis Temporal Consistency
EPFL

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Beyond Consistency: Inference for the Relative risk functional in Deep Nonparametric Cox Models

Sattwik Ghosal, Xuran Meng, Yi Li

Department of Biostatistics, University of Michigan

Survival Analysis Semiparametric Inference

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There remain theoretical gaps in deep neural network estimators for the nonparametric Cox proportional hazards model. In particular, it is unclear how gradient-based optimization error propagates to population risk under partial likelihood, how pointwise bias can be controlled to permit valid inference, and how ensemble-based uncertainty quantification behaves under realistic variance decay regimes. We develop an asymptotic distribution theory for deep Cox estimators that addresses these issues. First, we establish nonasymptotic oracle inequalities for general trained networks that link in-sample optimization error to population risk without requiring the exact empirical risk optimizer. We then construct a structured neural parameterization that achieves infinity-norm approximation rates compatible with the oracle bound, yielding control of the pointwise bias. Under these conditions and using the Hajek--Hoeffding projection, we prove pointwise and multivariate asymptotic normality for subsampled ensemble estimators. We derive a range of subsample sizes that balances bias correction with the requirement that the Hajek--Hoeffding projection remain dominant. This range accommodates decay conditions on the single-overlap covariance, which measures how strongly a single shared observation influences the estimator, and is weaker than those imposed in the subsampling literature. An infinitesimal jackknife representation provides analytic covariance estimation and valid Wald-type inference for relative risk contrasts such as log-hazard ratios. Finally, we illustrate the finite-sample implications of the theory through simulations and a real data application.