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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 281–290 of 472 papers

TitleStatusHype
Enhancing Collaborative Filtering-Based Course Recommendations by Exploiting Time-to-Event Information with Survival Analysis—0
Enhancing External Validity of Experiments with Ongoing Sampling—0
Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare—0
Ensemble Survival Analysis for Preclinical Cognitive Decline Prediction in Alzheimer's Disease Using Longitudinal Biomarkers—0
EPIC-Survival: End-to-end Part Inferred Clustering for Survival Analysis, Featuring Prognostic Stratification Boosting—0
Estimating and interpreting secondary attack risk: Binomial considered harmful—0
Estimating Heterogenous Treatment Effects for Survival Data with Doubly Doubly Robust Estimator—0
Estimation of conditional mixture Weibull distribution with right-censored data using neural network for time-to-event analysis—0
Estimation of Time-to-Total Knee Replacement Surgery—0
EsurvFusion: An evidential multimodal survival fusion model based on Gaussian random fuzzy numbers—0
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