TY - JOUR
T1 - Marginal analysis for clustered failure time data
AU - Lu, Shou En
AU - Wang, Mei Cheng
N1 - Funding Information:
The authors thank the associate editor and the reviewers for their helpful comments, and thank Drs. Keith West Jr. and Joanne Katz for providing the Vitamin A intervention trial data under Cooperative Agreement No. HRN A-00-97-00015-00 between the Office of Health and Nutrition, US Agency for Interactional Development (USAID), Washington DC and the Center for Human Nutrition, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore MD. This research is also partially supported by UMDNJ foundation grants, RH1619 and RH1728.
PY - 2005/3
Y1 - 2005/3
N2 - Clustered failure time data are commonly encountered in biomedical research where the study subjects from the same cluster (e.g., family) share the common genetic and/or environmental factors such that the failure times within the same cluster are correlated. Two approaches that are commonly used to account for the intra-cluster association are frailty models and marginal models. In this paper, we study the marginal proportional hazards model, where the structure of dependence between individuals within a cluster is unspecified. An estimation procedure is developed based on a pseudo-likelihood approach, and a risk set sampling method is proposed for the formulation of the pseudo-likelihood. The asymptotic properties of the proposed estimators are studied, and the related issues regarding the statistical efficiencies are discussed. The performances of the proposed estimator are demonstrated by the simulation studies. A data example from a child vitamin A supplementation trial in Nepal (Nepal Nutrition Intervention Project-Sarlahi, or NNIPS) is used to illustrate this methodology.
AB - Clustered failure time data are commonly encountered in biomedical research where the study subjects from the same cluster (e.g., family) share the common genetic and/or environmental factors such that the failure times within the same cluster are correlated. Two approaches that are commonly used to account for the intra-cluster association are frailty models and marginal models. In this paper, we study the marginal proportional hazards model, where the structure of dependence between individuals within a cluster is unspecified. An estimation procedure is developed based on a pseudo-likelihood approach, and a risk set sampling method is proposed for the formulation of the pseudo-likelihood. The asymptotic properties of the proposed estimators are studied, and the related issues regarding the statistical efficiencies are discussed. The performances of the proposed estimator are demonstrated by the simulation studies. A data example from a child vitamin A supplementation trial in Nepal (Nepal Nutrition Intervention Project-Sarlahi, or NNIPS) is used to illustrate this methodology.
KW - Clustered failure time data
KW - Marginal proportional hazards model
KW - Pseudo-likelihood function
KW - Risk set sampling method
UR - http://www.scopus.com/inward/record.url?scp=14944367351&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=14944367351&partnerID=8YFLogxK
U2 - 10.1007/s10985-004-5640-6
DO - 10.1007/s10985-004-5640-6
M3 - Article
C2 - 15747590
AN - SCOPUS:14944367351
SN - 1380-7870
VL - 11
SP - 61
EP - 79
JO - Lifetime Data Analysis
JF - Lifetime Data Analysis
IS - 1
ER -