Marginal analysis for clustered failure time data.
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.
Lu
Shou-En
SE
Division of Biometrics, School of Public Health, University of Medicine and Dentistry of New Jersey, 683 Hoes Lane West, Piscataway, NJ 08854, USA. lus2@umdnj.edu
Wang
Mei-Cheng
MC
D016000:Cluster Analysis; D004311:Double-Blind Method; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D009390:Nepal; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D014801:Vitamin A / Q000008:administration & dosage*; D014802:Vitamin A Deficiency / Q000401:mortality*
D014801:Vitamin A