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
SN - 1369-7412
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
IS - 1
ER -