Applying competing risks regression models: an overview

DOI10.1007/S10985-012-9230-8zbMath1322.62311OpenAlexW2399832552WikidataQ38046436 ScholiaQ38046436MaRDI QIDQ746407

Kurt Ulm, Georg Schmidt, Bernhard Haller

Publication date: 16 October 2015

Published in: Lifetime Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10985-012-9230-8

zbMATH Keywords

mixture modelcompeting riskscause-specific hazardpseudo-observation approachsubdistribution hazardvertical modelling

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Research exposition (monographs, survey articles) pertaining to statistics (62-02) Medical applications (general) (92C50)

Related Items (6)

Composite partial likelihood estimation for length-biased and right-censored data with competing risks ⋮ A multivariate normal regression model for survival data subject to different types of dependent censoring ⋮ An introduction to survival models: in honor of Ross Prentice ⋮ Accelerated failure time models for the analysis of competing risks ⋮ Parametric regression approach for Gompertz survival times with competing risks ⋮ Cause-specific hazard regression estimation for modified Weibull distribution under a class of non-informative priors

Uses Software

• R
• survival
• geepack
• multicore
• Fahrmeir
• cmprsk
• pseudosurv

Cites Work

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• Longitudinal data analysis using generalized linear models
• Flexible competing risks regression modeling and goodness-of-fit
• A class of k-sample tests for comparing the cumulative incidence of a competing risk
• Piecewise exponential models for survival data with covariates
• Survival analysis. Techniques for censored and truncated data.
• Practical methods for competing risks data: A review
• Competing Risks and Multistate Models with R
• Nonparametric Estimation from Incomplete Observations
• Competing Risks
• Testing and Estimation of Time‐Varying Cause‐Specific Hazard Ratios with Covariate Adjustment
• A nonidentifiability aspect of the problem of competing risks.
• The Analysis of Failure Times in the Presence of Competing Risks
• Generalised linear models for correlated pseudo-observations, with applications to multi-state models
• A Proportional Hazards Model for the Subdistribution of a Competing Risk
• Upper Bounds for Proof‐Search in a Sequent Calculus for Relational Equations
• Fitting a Semi-Parametric Mixture Model for Competing Risks in Survival Data
• Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function
• Multivariate statistical modelling based on generalized linear models.

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