Pseudo-observations for competing risks with covariate dependent censoring

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Standard

Pseudo-observations for competing risks with covariate dependent censoring. / Binder, Nadine; Gerds, Thomas A; Andersen, Per Kragh.

In: Lifetime Data Analysis, Vol. 20, No. 2, 04.2014, p. 303-15.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Binder, N, Gerds, TA & Andersen, PK 2014, 'Pseudo-observations for competing risks with covariate dependent censoring', Lifetime Data Analysis, vol. 20, no. 2, pp. 303-15. https://doi.org/10.1007/s10985-013-9247-7

APA

Binder, N., Gerds, T. A., & Andersen, P. K. (2014). Pseudo-observations for competing risks with covariate dependent censoring. Lifetime Data Analysis, 20(2), 303-15. https://doi.org/10.1007/s10985-013-9247-7

Vancouver

Binder N, Gerds TA, Andersen PK. Pseudo-observations for competing risks with covariate dependent censoring. Lifetime Data Analysis. 2014 Apr;20(2):303-15. https://doi.org/10.1007/s10985-013-9247-7

Author

Binder, Nadine ; Gerds, Thomas A ; Andersen, Per Kragh. / Pseudo-observations for competing risks with covariate dependent censoring. In: Lifetime Data Analysis. 2014 ; Vol. 20, No. 2. pp. 303-15.

Bibtex

@article{98434cb5b93f40d3911a6a6a8d5be637,
title = "Pseudo-observations for competing risks with covariate dependent censoring",
abstract = "Regression analysis for competing risks data can be based on generalized estimating equations. For the case with right censored data, pseudo-values were proposed to solve the estimating equations. In this article we investigate robustness of the pseudo-values against violation of the assumption that the probability of not being lost to follow-up (un-censored) is independent of the covariates. Modified pseudo-values are proposed which rely on a correctly specified regression model for the censoring times. Bias and efficiency of these methods are compared in a simulation study. Further illustration of the differences is obtained in an application to bone marrow transplantation data and a corresponding sensitivity analysis.",
author = "Nadine Binder and Gerds, {Thomas A} and Andersen, {Per Kragh}",
year = "2014",
month = apr,
doi = "10.1007/s10985-013-9247-7",
language = "English",
volume = "20",
pages = "303--15",
journal = "Lifetime Data Analysis",
issn = "1380-7870",
publisher = "Springer",
number = "2",

}

RIS

TY - JOUR

T1 - Pseudo-observations for competing risks with covariate dependent censoring

AU - Binder, Nadine

AU - Gerds, Thomas A

AU - Andersen, Per Kragh

PY - 2014/4

Y1 - 2014/4

N2 - Regression analysis for competing risks data can be based on generalized estimating equations. For the case with right censored data, pseudo-values were proposed to solve the estimating equations. In this article we investigate robustness of the pseudo-values against violation of the assumption that the probability of not being lost to follow-up (un-censored) is independent of the covariates. Modified pseudo-values are proposed which rely on a correctly specified regression model for the censoring times. Bias and efficiency of these methods are compared in a simulation study. Further illustration of the differences is obtained in an application to bone marrow transplantation data and a corresponding sensitivity analysis.

AB - Regression analysis for competing risks data can be based on generalized estimating equations. For the case with right censored data, pseudo-values were proposed to solve the estimating equations. In this article we investigate robustness of the pseudo-values against violation of the assumption that the probability of not being lost to follow-up (un-censored) is independent of the covariates. Modified pseudo-values are proposed which rely on a correctly specified regression model for the censoring times. Bias and efficiency of these methods are compared in a simulation study. Further illustration of the differences is obtained in an application to bone marrow transplantation data and a corresponding sensitivity analysis.

U2 - 10.1007/s10985-013-9247-7

DO - 10.1007/s10985-013-9247-7

M3 - Journal article

C2 - 23430270

VL - 20

SP - 303

EP - 315

JO - Lifetime Data Analysis

JF - Lifetime Data Analysis

SN - 1380-7870

IS - 2

ER -

ID: 134781594