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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 171–180 of 472 papers

TitleStatusHype
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
Discrete Stochastic Models in Continuous Time for Ecology—0
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer—0
Evidential time-to-event prediction with calibrated uncertainty quantification—0
Explainable AI for survival analysis: a median-SHAP approach—0
Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review—0
Federated Survival Analysis with Discrete-Time Cox Models—0
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