Marginal Analysis for Clustered Failure Time Data

Shou-En Lu, University of Medicine & Dentistry of New Jersey, Division of Biometrics, School of Public Health
Mei-Cheng Wang, Johns Hopkins Bloomberg School of Public Health

Accepted for publication in Lifetime Data Analysis.


Clustered failure time data are commonly encountered in biomedical research where study subjects from the same cluster (e.g., family) share common genetic and/or environmental factors such that the failure times within the same cluster are correlated. Two commonly used approaches to account for 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 left 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 as well. The performances of the proposed estimator are demonstrated by 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.