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Survival Analysis

Survival Analysis is a branch of statistics focused on the study of time-to-event data, usually called survival times. This type of data appears in a wide range of applications such as failure times in mechanical systems, death times of patients in a clinical trial or duration of unemployment in a population. One of the main objectives of Survival Analysis is the estimation of the so-called survival function and the hazard function. If a random variable has density function $f$ and cumulative distribution function $F$, then its survival function $S$ is $1-F$, and its hazard $λ$ is $f/S$.

Source: Gaussian Processes for Survival Analysis

Image: Kvamme et al.

Papers

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Showing 76–100 of 472 papers

TitleStatusHype
DNAMite: Interpretable Calibrated Survival Analysis with Discretized Additive ModelsCode0
FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models—0
Masked Clinical Modelling: A Framework for Synthetic and Augmented Survival Data Generation—0
Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks—0
HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing RisksCode0
Predicting Breast Cancer Survival: A Survival Analysis Approach Using Log Odds and Clinical Variables—0
Global Censored Quantile Random Forest—0
End-Stage Liver Disease Comorbidities in Patients Awaiting Transplantation: Identification and Impact on Liver Transplant Survival—0
Toward a Well-Calibrated Discrimination via Survival Outcome-Aware Contrastive Learning—0
Deep End-to-End Survival Analysis with Temporal Consistency—0
An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event OutcomesCode1
SurvCORN: Survival Analysis with Conditional Ordinal Ranking Neural Network—0
Forecasting Disease Progression with Parallel Hyperplanes in Longitudinal Retinal OCTCode0
Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modelling—0
FPBoost: Fully Parametric Gradient Boosting for Survival Analysis—0
SeqRisk: Transformer-augmented latent variable model for improved survival prediction with longitudinal data—0
Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational PathologyCode2
A Cost-Aware Approach to Adversarial Robustness in Neural Networks—0
Adaptive Transformer Modelling of Density Function for Nonparametric Survival AnalysisCode0
MENSA: A Multi-Event Network for Survival Analysis under Informative CensoringCode0
forester: A Tree-Based AutoML Tool in RCode2
Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression LabelsCode1
CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival AnalysisCode1
End-to-end Multi-source Visual Prompt Tuning for Survival Analysis in Whole Slide Images—0
Estimating Heterogenous Treatment Effects for Survival Data with Doubly Doubly Robust Estimator—0
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