Marginal Models for Clustered Time-to-Event Data with Competing Risks Using Pseudovalues

DOI10.1111/J.1541-0420.2010.01416.XzbMath1216.62175OpenAlexW2057150855WikidataQ33550966 ScholiaQ33550966MaRDI QIDQ3008850

Brent R. Logan, John P. Klein, Mei-Jie Zhang

Publication date: 22 June 2011

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc2902638

zbMATH Keywords

clustered datageneralized estimating equationspseudo-observationsmarginal modelcumulative incidencesandwich variance

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50) Estimation in survival analysis and censored data (62N02)

Related Items (9)

Optimal treatment regimes for competing risk data using doubly robust outcome weighted learning with bi-level variable selection ⋮ Pseudo-value approach for conditional quantile residual lifetime analysis for clustered survival and competing risks data with applications to bone marrow transplant data ⋮ Estimation of the additive hazards model with linear inequality restrictions based on current status data ⋮ Group sequential tests for treatment effect on survival and cumulative incidence at a fixed time point ⋮ Testing for center effects on survival and competing risks outcomes using pseudo-value regression ⋮ Group and within-group variable selection for competing risks data ⋮ Additive gamma frailty models with applications to competing risks in related individuals ⋮ Simple estimation procedures for regression analysis of interval-censored failure time data under the proportional hazards model ⋮ Simple analysis of non-Markov models: A case study on heart transplant data

Uses Software

• cmprsk

Cites Work

• Longitudinal data analysis using generalized linear models
• On pseudo-values for regression analysis in competing risks models
• Analysis of multivariate survival data
• Score test of homogeneity for survival data. (With discussion)
• Predicting cumulative incidence probability by direct binomial regression
• Generalised linear models for correlated pseudo-observations, with applications to multi-state models
• Marginal Regression Models for Multivariate Failure Time Data
• A Proportional Hazards Model for the Subdistribution of a Competing Risk
• The Kaplan–Meier Estimator as an Inverse-Probability-of-Censoring Weighted Average
• Competing Risks Analysis of Correlated Failure Time Data
• Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function

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