Pseudo-observations for competing risks with covariate dependent censoring

DOI10.1007/s10985-013-9247-7zbMath1322.62009OpenAlexW2004318142WikidataQ36069484 ScholiaQ36069484MaRDI QIDQ746626

Thomas A. Gerds, Per Kragh Andersen, Nadine Binder

Publication date: 16 October 2015

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

Full work available at URL: http://europepmc.org/articles/pmc4573528

zbMATH Keywords

competing riskspseudo-observationscovariate-dependent censoringcumulative incidence

Mathematics Subject Classification ID

Related Items (12)

Pseudo-value approach for conditional quantile residual lifetime analysis for clustered survival and competing risks data with applications to bone marrow transplant data ⋮ A new approach to regression analysis of censored competing-risks data ⋮ Propensity Score Weighting Analysis of Survival Outcomes Using Pseudo-Observations ⋮ Temporal prediction of future state occupation in a multistate model from high-dimensional baseline covariates via pseudo-value regression ⋮ Regression models for censored time-to-event data using infinitesimal Jack-knife pseudo-observations, with applications to left-truncation ⋮ Modeling restricted mean survival time under general censoring mechanisms ⋮ Bivariate pseudo-observations for recurrent event analysis with terminal events ⋮ Cumulative risk regression in case-cohort studies using pseudo-observations ⋮ Events per variable for risk differences and relative risks using pseudo-observations ⋮ Comparison of stopped Cox regression with direct methods such as pseudo-values and binomial regression ⋮ Modeling marginal features in studies of recurrent events in the presence of a terminal event ⋮ Pseudo-observations under covariate-dependent censoring

Uses Software

• cmprsk

Cites Work

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• Generalised linear models for correlated pseudo-observations, with applications to multi-state models
• A Proportional Hazards Model for the Subdistribution of a Competing Risk
• Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function
• Statistical models based on counting processes

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