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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 181–190 of 472 papers

TitleStatusHype
Discrete Stochastic Models in Continuous Time for Ecology—0
Exploring novel prognostic biomarkers and biologic processes involved in NASH, cirrhosis and HCC based on survival analysis using systems biology approach—0
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer—0
From Non-Paying to Premium: Predicting User Conversion in Video Games with Ensemble Learning—0
Factor-Augmented Regularized Model for Hazard Regression—0
Fairness in Survival Analysis: A Novel Conditional Mutual Information Augmentation Approach—0
Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework—0
Graph Domain Adaptation with Dual-branch Encoder and Two-level Alignment for Whole Slide Image-based Survival Prediction—0
FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models—0
High-Dimensional False Discovery Rate Control for Dependent Variables—0
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