Cohort case-control design and analysis for clustered failure-time data.
Cohort case-control design is an efficient and economical design to study risk factors for disease incidence or mortality in a large cohort. In the last few decades, a variety of cohort case-control designs have been developed and theoretically justified. These designs have been exclusively applied to the analysis of univariate failure-time data. In this work, a cohort case-control design adapted to multivariate failure-time data is developed. A risk set sampling method is proposed to sample controls from nonfailures in a large cohort for each case matched by failure time. This method leads to a pseudolikelihood approach for the estimation of regression parameters in the marginal proportional hazards model (Cox, 1972, Journal of the Royal Statistical Society, Series B 34, 187-220), where the correlation structure between individuals within a cluster is left unspecified. The performance of the proposed estimator is demonstrated by simulation studies. A bootstrap method is proposed for inferential purposes. This methodology is illustrated by a data example from a child vitamin A supplementation trial in Nepal (Nepal Nutrition Intervention Project-Sarlahi, or NNIPS).
Lu
Shou-En
SE
Division of Biometrics, University of Medicine and Dentistry of New Jersey, New Brunswick, New Jersey 08903, USA. lus2@umdnj.edu
Wang
Mei-Cheng
MC
D016022:Case-Control Studies; D002675:Child, Preschool; D016000:Cluster Analysis; D015331:Cohort Studies; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007223:Infant; D007226:Infant Mortality; D016013:Likelihood Functions; D008297:Male; D015999:Multivariate Analysis; D016016:Proportional Hazards Models; D012307:Risk Factors; D014801:Vitamin A / Q000008:administration & dosage
D014801:Vitamin A