A review on statistical and machine learning competing risks methods

Cites Work

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Longitudinal data analysis using generalized linear models
Random survival forests
Nearly unbiased variable selection under minimax concave penalty
Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages \textit{CoxBoost} and \textit{mboost}
Applying competing risks regression models: an overview
A dependent Dirichlet process model for survival data with competing risks
A weakly informative default prior distribution for logistic and other regression models
A class of k-sample tests for comparing the cumulative incidence of a competing risk
Multi-state models: A review
Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
Variable selection for Cox's proportional hazards model and frailty model
Survival analysis. Techniques for censored and truncated data.
Penalized variable selection in competing risks regression
Variable importance in binary regression trees and forests
Nonparametric Bayesian survival analysis using mixtures of Weibull distributions
On semi-competing risks data
Survival Analysis with High-Dimensional Covariates: An Application in Microarray Studies
Kernels for Vector-Valued Functions: A Review
Statistical Analysis of Illness-Death Processes and Semicompeting Risks Data
Time-Dependent Predictive Accuracy in the Presence of Competing Risks
Quantifying the predictive accuracy of time-to-event models in the presence of competing risks
Maximum likelihood estimation via the ECM algorithm: A general framework
Competing Risks
Regression Analysis Based on Semicompeting Risks Data
Predicting cumulative incidence probability by direct binomial regression
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
The identifiability of the competing risks model
A SEMIPARAMETRIC MIXTURE MODEL FOR THE ANALYSIS OF COMPETING RISKS DATA
A nonidentifiability aspect of the problem of competing risks.
Poisson/gamma random field models for spatial statistics
Regression modeling of competing crude failure probabilities
Generalised linear models for correlated pseudo-observations, with applications to multi-state models
Bayesian variable selection for proportional hazards models
A Proportional Hazards Model for the Subdistribution of a Competing Risk
Estimates of marginal survival for dependent competing risks based on an assumed copula
Bayesian Density Estimation and Inference Using Mixtures
Constrained parametric model for simultaneous inference of two cumulative incidence functions
Estimation in the progressive illness‐death model: A nonexhaustive review
Random forest missing data algorithms
High-Dimensional Variable Selection for Survival Data
Competing risks as a multi-state model
Regression Modeling of Semicompeting Risks Data
Super Learner
Adaptive Lasso for Cox's proportional hazards model
Monte Carlo sampling methods using Markov chains and their applications
Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function
Direct Parametric Inference for the Cumulative Incidence Function
Stochastic Estimation of the Maximum of a Regression Function
Business Failures: Another Example of the Analysis of Failure Data
Random forests
The Elements of Statistical Learning
High-dimensional feature selection in competing risks modeling: a stable approach using a split-and-merge ensemble algorithm
Competing risks analysis for discrete time-to-event data
Fine-gray subdistribution hazard models to simultaneously estimate the absolute risk of different event types: cumulative total failure probability may exceed 1
Modeling cumulative incidences of dementia and dementia-free death using a novel three-parameter logistic function

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