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 $f/S$.
Image: Kvamme et al.
Survival analysis is a fundamental tool in medical research to identify predictors of adverse events and develop systems for clinical decision support.
Tick is a statistical learning library for Python~3, with a particular emphasis on time-dependent models, such as point processes, and tools for generalized linear models and survival analysis.
We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient's covariates and treatment effectiveness in order to provide personalized treatment recommendations.
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.
Application of discrete-time survival methods for continuous-time survival prediction is considered.
New methods for time-to-event prediction are proposed by extending the Cox proportional hazards model with neural networks.
It is important for predictive models to be able to use survival data, where each patient has a known follow-up time and event/censoring indicator.
Survival analysis/time-to-event models are extremely useful as they can help companies predict when a customer will buy a product, churn or default on a loan, and therefore help them improve their ROI.
By capturing the time dependency through modeling the conditional probability of the event for each sample, our method predicts the likelihood of the true event occurrence and estimates the survival rate over time, i. e., the probability of the non-occurrence of the event, for the censored data.