Survival Analysis
128 papers with code • 0 benchmarks • 4 datasets
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.
Benchmarks
These leaderboards are used to track progress in Survival Analysis
Libraries
Use these libraries to find Survival Analysis models and implementationsDatasets
Most implemented papers
Deep Landscape Forecasting for Real-time Bidding Advertising
The problem is formulated as to forecast the probability distribution of market price for each ad auction.
The Brier Score under Administrative Censoring: Problems and Solutions
This administrative Brier score does not require estimation of the censoring distribution and is valid even if the censoring times can be identified from the covariates.
Conformalized Survival Analysis
Existing survival analysis techniques heavily rely on strong modelling assumptions and are, therefore, prone to model misspecification errors.
Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data
To this end, we propose a probabilistic model that captures the dependencies between the observed clinical variables and imputes missing ones.
PyDTS: A Python Package for Discrete-Time Survival (Regularized) Regression with Competing Risks
Most methods and software packages for survival regression analysis assume that time is measured on a continuous scale.
FastCPH: Efficient Survival Analysis for Neural Networks
The Cox proportional hazards model is a canonical method in survival analysis for prediction of the life expectancy of a patient given clinical or genetic covariates -- it is a linear model in its original form.
Heterogeneous Datasets for Federated Survival Analysis Simulation
However, the data needed to train survival models are often distributed, incomplete, censored, and confidential.
Discrete-time Competing-Risks Regression with or without Penalization
Additionally, we showcase the utility of our procedure by estimating a survival model for the length of stay of patients hospitalized in the intensive care unit, considering three competing events: discharge to home, transfer to another medical facility, and in-hospital death.
CenTime: Event-Conditional Modelling of Censoring in Survival Analysis
We demonstrate that our approach forms a consistent estimator for the event model parameters, even in the absence of uncensored data.
Learning Genomic Representations to Predict Clinical Outcomes in Cancer
Genomics are rapidly transforming medical practice and basic biomedical research, providing insights into disease mechanisms and improving therapeutic strategies, particularly in cancer.