Survival Analysis

Follow this topic Ask about this topic

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.

Random Jump Forests
Oct 23, 2026Martin Bladt, Rasmus Frigaard LemvigRandom Forest Survival Analysis
Department of Mathematical Sciences, University of Copenhagen
Benchmarking Data-Free Continual Model Merging for Whole-Slide Image Survival Analysis
Oct 23, 2026Vu Minh Tran, Doanh C. Bui, Maï K. Nguyen +1Survival Analysis Cancer Survival Prediction
University of Information Technology · Viet Nam National University Ho Chi Minh City · ETIS (UMR 8051), CY Cergy Paris University, ENSEA, CNRS, France
StochIKM: Automated High-Fidelity Reconstruction of Individual Patient Data from Published Kaplan-Meier Curves
Oct 21, 2026Liangze Ke, Daniel Zelterman, Wei Wei +1Survival Analysis
Department of Biostatistics, School of Public Health, Yale University, New Haven, CT 06520, USA · Section of Medical Oncology, Yale School of Medicine and Yale Cancer Center, Yale University, New Haven, CT 06520, USA · Department of Medicine, Yale University School of Medicine and Yale Cancer Center, Yale University, New Haven, CT 06520, USA
Doubly Robust Learning of Heterogeneous Survival Treatment Effects under Time-Varying Treatments
Oct 19, 2026Tomàs Garriga, Munib Mesinovic, Luke De Bretton-Gordon +3Causal Effect Estimation Individualized Treatment Effect Estimation
Novartis · Barcelona Supercomputing Center · Universitat Politècnica de Catalunya +1
Censored Flow Matching for Synthetic Time-to-Event Generation
Oct 16, 2026Jianbin Tan, Xingran Chen, Kevin He +3Synthetic Data Generation Survival Analysis
Department of Biostatistics & Bioinformatics, Duke University · Department of Biostatistics, University of Michigan · Department of Biostatistics, Yale University +1
The Loss or the Architecture? What Matters in Survival Analysis with Competing Risks
Oct 16, 2026Julie Alberge, Gaël Varoquaux, Judith AbécassisSurvival Analysis
SODA Team, Inria Saclay, France
SurvICL: a Foundation Model for in-context Survival Analysis
Oct 15, 2026Julie Alberge, Linus Bleistein, Judith Abécassis +3Survival Analysis Tabular Foundation Models
Inria de Saclay · EPFL · Inria de Saclay, :probabl.
Flexible Doubly Robust Proximal Causal Inference for Censored Outcomes
Oct 14, 2026Dhanajit Brahma, Ruolin Meng, Lawrence Carin +1Proximal Causal Inference Causal Effect Estimation
Duke University
Efficient Learning of Survival Analysis Models Under Outcome-Dependent Truncation and Censoring
Oct 8, 2026Itai Zilberstein, Ioannis Anagnostides, Arman Kilic +1Survival Analysis Statistical Learning Theory
Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA · Department of Surgery, Division of Cardiothoracic Surgery, Medical University of South Carolina, Charleston, SC · Additional affiliations: Strategy Robot, Inc., Strategic Machine, Inc., Optimized Markets, Inc.
DeepAJM: Deep Association Joint Model for Irregularly Sampled data
Oct 5, 2026Barsha Halder, Jeffrey A. ThompsonIrregular Time-Series Modeling Survival Analysis
Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, USA
KESurv: A Kernel Ensemble Method for Patient-Specific Survival Prediction
Oct 5, 2026Rahul GoswamiEnsemble Learning Survival Analysis
Department of Mathematics, Indian Institute of Technology Guwahati
Multimodal Deep Survival Analysis for Sinkhole Susceptibility
Oct 5, 2026Lucas Yuan, Minhee Kim, Zihan Li +4Survival Analysis Multimodal Survival Analysis
Huron High School University of Florida Ann Arbor, MI, USA · Industrial and Systems Engineering University of Florida Gainesville, FL, USA · Forest, Fisheries, and Geomatics Sciences University of Florida Gainesville, FL, USA +1
Distributionally Robust Survival Models under Subpopulation Shift and Outlier Contamination
Oct 2, 2026Seonghwi Kim, Sung Ho Jo, Minwoo ChaeSubgroup Robustness Distribution Shift Robustness
Pohang University of Science and Technology
Scalable Cox Regression via Grouped Risk Sets and Sharper LogSumExp Rates
Sep 30, 2026Elizaveta Iashchinskaia, Egor GladinSurvival Analysis
HSE University
Convergence-Aware Pareto Selection of Covariate Scaling Transformations for Markov Deterioration Hazard Models: Evidence from Bridge Inspection Data
Sep 24, 2026Takato Yasuno, Keita Kobayashi, Ryuta SakaguchiMarkov Models Survival Analysis
Digital Business Promotion Department, Yachiyo Engineering Co., Ltd.
Horizon-Aware Early Event Prediction for Tokamak Disruption Alarms
Sep 21, 2026Takeshi Koshizuka, Takaharu YaguchiEvent Time Prediction Survival Analysis
RIKEN AIP, Japan. · Kyushu University IMI, Fukuoka, Japan.
DynSHAP: Towards Explainable Dynamic Survival Analysis
Sep 11, 2026Nastasya Anokhina, Jonas Jürß, Pietro LiòFeature Attribution SHAP Feature Attribution
University of Cambridge
Adaptation Interfaces for In-Context Tabular Foundation Models in Time-to-Event Prediction
Sep 4, 2026Minh-Khoi Pham, Luca Cotugno, Dan Cernei +8Event Time Prediction Tabular Foundation Models
ADAPT Centre, School of Computing, Dublin City University DCU Glasnevin Campus, Dublin 9, D09 V209, Ireland · Department of Computer Science and Engineering, University of Bologna, Italy · University of Pisa, Italy +2
Timing-Aware Repurchase Prediction for Web-Scale E-Commerce: Survival Models for Multi-Surface Grocery Recommendation
Aug 28, 2026Akshay Kekuda, Shreeranjani Srirangamsridharan, Ishan Bhatt +5Probability Calibration Survival Analysis
Walmart Global Tech · Bellevue, WA, USA · Sunnyvale, CA, USA
Random Hazard Forests
Aug 21, 2026Hemant Ishwaran, Eileen M. Hsich, Udaya B. Kogalur +1Irregular Time-Series Modeling Survival Analysis
Division of Biostatistics, Department of Public Health Sciences, Miller School of Medicine, University of Miami · Heart and Vascular Institute, Cleveland Clinic; Cardiology Division, Intermountain Health · Kogalur & Company, Inc. +1
Non-Crossing Deep Quantile Regression for Distributional Survival Prediction
Aug 17, 2026Shuai Huang, Zhe Qu, Zhaowei Hua +3Survival Analysis Quantile Regression
Department of Biostatistics, University of North Carolina at Chapel Hill · Servier Pharmaceuticals, Boston, MA · The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China +1
CACSurv: Concordance-Aligned Comparative Learning with Large Language Models for Cancer Survival Prediction
Aug 17, 2026Tianqi Xiang, Qixiang Zhang, Xinpeng Ding +2Cancer Survival Prediction Survival Analysis
The Hong Kong University of Science and Technology
MultiSigBERT: Beyond Survival Analysis through Multimodal and Sequential Modeling in Oncology
Aug 17, 2026Paul Minchella, Stéphane Chrétien, Guillaume Metzler +2Multimodal Survival Analysis Cancer Survival Prediction
Université Lumière Lyon 2, 69500 Bron, France · Léon Bérard Center, Lyon 69008, France · EPITA, Lyon 69007, France
Surv-IPTB: An Attention-Based Model for Estimating Individual Probability of Treatment Benefit with Survival Data
Aug 6, 2026Lev V. Utkin, Stanislav K. Kogan, Andrei V. KonstantinovCausal Effect Estimation Survival Analysis
Higher School of Artificial Intelligence Technologies Peter the Great St.Petersburg Polytechnic University, St.Petersburg, Russia
CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment
Aug 4, 2026Jing Dai, Qibin Zhang, Weiwei Zhou +4Cancer Survival Prediction Survival Analysis
Cancer Hospital of Dalian University of Technology, Shenyang, China · School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China · Institute of Pathology, Technical University of Munich, TUM School of Medicine and Health, Munich, Germany +2
The Cost of Binarizing Survival Outcomes in Clinical Prognostic Modeling
Aug 4, 2026Shashank Yadav, David M. Routman, Andrew Y. K. FoongCancer Survival Prediction Bayesian Networks
Department of Radiation Oncology Mayo Clinic, Rochester, MN, USA 55905
Paired Recipient-based Evaluation of Survival Prediction for Deceased Donor Kidney Transplants
Aug 4, 2026Misaki Matsuura, Mohammadreza Nemati, Dulat Bekbolsynov +2Healthcare Survival Analysis
Department of Computer and Data Sciences Case Western Reserve University Cleveland, OH 44106, USA · Department of Medical Microbiology and Immunology University of Toledo Toledo, OH 43614, USA
A reproducible and extensible framework for benchmarking competing risks survival models
Jul 31, 2026Begoña B. Sierra, Colin McLean, Peter S. Hall +2Benchmark Design Survival Analysis
Cancer Research UK Scotland Centre · Institute of Genetics and Cancer · University of Edinburgh +2
Collaborative System Failure Prognostics via Federated Longitudinal-Survival Modeling
Jul 28, 2026Fan Yang, Madelyn Weller, Dimuthu Fernando +2RUL Estimation Survival Analysis
Fowler School of Engineering, Chapman University, Orange, CA 92866 USA · Department of Statistics, Grand Valley State University, Allendale, MI 49401 USA
K-Survival Means
Jul 27, 2026Abdallah AlabdallahSurvival Analysis Clustering
Center for Applied Intelligent Systems Research (CAISR), Halmstad, Sweden

Oct 23, 2026J/K move · Enter open · S save

Random Jump Forests

Martin Bladt, Rasmus Frigaard Lemvig

Department of Mathematical Sciences, University of Copenhagen

Random Forest Survival Analysis

Open paper AskPDF arXiv

Random jump forests are introduced as a nonparametric tool for estimating conditional cumulative transition rates and occupation probabilities in multi-state models. The main idea is that a single tree partitions the covariate space for all transitions simultaneously, using partially observed trajectories, and with Nelson--Aalen estimators at the terminal nodes. Occupation probabilities are recovered through the product integral without requiring the Markov assumption. Uniform consistency and a functional central limit theorem for the estimators of cumulative rates can be established under the assumption that the covariate vector takes finitely many values, and under suitable splitting conditions. In particular, pointwise confidence intervals and simultaneous bands are available in the Markov case. Simulation studies show a predictive accuracy close to the correctly specified benchmark models and marked improvements when benchmarks are misspecified, with further illustration on four biomedical data sets. In the coverage study, confidence bands attain near-nominal coverage in several settings, with pooling of heterogeneous rates being the main source of possible undercoverage. The method, including prediction assessment and variable importance, is implemented in the R package JumpForests.