An estimating equation for parametric shared frailty models with marginal additive hazards

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

Standard

An estimating equation for parametric shared frailty models with marginal additive hazards. / Pipper, Christian Bressen; Martinussen, Torben.

In: Journal of The Royal Statistical Society Series B-statistical Methodology, Vol. 66, No. 1, 2004, p. 207-220.

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

Harvard

Pipper, CB & Martinussen, T 2004, 'An estimating equation for parametric shared frailty models with marginal additive hazards', Journal of The Royal Statistical Society Series B-statistical Methodology, vol. 66, no. 1, pp. 207-220. https://doi.org/10.1046/j.1369-7412.2003.05305.x

APA

Pipper, C. B., & Martinussen, T. (2004). An estimating equation for parametric shared frailty models with marginal additive hazards. Journal of The Royal Statistical Society Series B-statistical Methodology, 66(1), 207-220. https://doi.org/10.1046/j.1369-7412.2003.05305.x

Vancouver

Pipper CB, Martinussen T. An estimating equation for parametric shared frailty models with marginal additive hazards. Journal of The Royal Statistical Society Series B-statistical Methodology. 2004;66(1):207-220. https://doi.org/10.1046/j.1369-7412.2003.05305.x

Author

Pipper, Christian Bressen ; Martinussen, Torben. / An estimating equation for parametric shared frailty models with marginal additive hazards. In: Journal of The Royal Statistical Society Series B-statistical Methodology. 2004 ; Vol. 66, No. 1. pp. 207-220.

Bibtex

@article{59af01700e1f11de8478000ea68e967b,
title = "An estimating equation for parametric shared frailty models with marginal additive hazards",
abstract = "Multivariate failure time data arise when data consist of clusters in which the failure times may be dependent. A popular approach to such data is the marginal proportional hazards model with estimation under the working independence assumption. In some contexts, however, it may be more reasonable to use the marginal additive hazards model. We derive asymptotic properties of the Lin and Ying estimators for the marginal additive hazards model for multivariate failure time data. Furthermore we suggest estimating equations for the regression parameters and association parameters in parametric shared frailty models with marginal additive hazards by using the Lin and Ying estimators. We give the large sample properties of the estimators arising from these estimating equations and investigate their small sample properties by Monte Carlo simulation. A real example is provided for illustration",
author = "Pipper, {Christian Bressen} and Torben Martinussen",
year = "2004",
doi = "10.1046/j.1369-7412.2003.05305.x",
language = "English",
volume = "66",
pages = "207--220",
journal = "Journal of the Royal Statistical Society, Series B (Statistical Methodology)",
issn = "1369-7412",
publisher = "Wiley",
number = "1",

}

RIS

TY - JOUR

T1 - An estimating equation for parametric shared frailty models with marginal additive hazards

AU - Pipper, Christian Bressen

AU - Martinussen, Torben

PY - 2004

Y1 - 2004

N2 - Multivariate failure time data arise when data consist of clusters in which the failure times may be dependent. A popular approach to such data is the marginal proportional hazards model with estimation under the working independence assumption. In some contexts, however, it may be more reasonable to use the marginal additive hazards model. We derive asymptotic properties of the Lin and Ying estimators for the marginal additive hazards model for multivariate failure time data. Furthermore we suggest estimating equations for the regression parameters and association parameters in parametric shared frailty models with marginal additive hazards by using the Lin and Ying estimators. We give the large sample properties of the estimators arising from these estimating equations and investigate their small sample properties by Monte Carlo simulation. A real example is provided for illustration

AB - Multivariate failure time data arise when data consist of clusters in which the failure times may be dependent. A popular approach to such data is the marginal proportional hazards model with estimation under the working independence assumption. In some contexts, however, it may be more reasonable to use the marginal additive hazards model. We derive asymptotic properties of the Lin and Ying estimators for the marginal additive hazards model for multivariate failure time data. Furthermore we suggest estimating equations for the regression parameters and association parameters in parametric shared frailty models with marginal additive hazards by using the Lin and Ying estimators. We give the large sample properties of the estimators arising from these estimating equations and investigate their small sample properties by Monte Carlo simulation. A real example is provided for illustration

U2 - 10.1046/j.1369-7412.2003.05305.x

DO - 10.1046/j.1369-7412.2003.05305.x

M3 - Journal article

VL - 66

SP - 207

EP - 220

JO - Journal of the Royal Statistical Society, Series B (Statistical Methodology)

JF - Journal of the Royal Statistical Society, Series B (Statistical Methodology)

SN - 1369-7412

IS - 1

ER -

ID: 11204336