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Cohort Case—Control Design and Analysis for Clustered Failure‐Time Data
Author(s) -
Lu ShouEn,
Wang MeiCheng
Publication year - 2002
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2002.00764.x
Subject(s) - univariate , estimator , cohort , statistics , proportional hazards model , multivariate statistics , computer science , sample size determination , incidence (geometry) , cohort study , medicine , econometrics , mathematics , geometry
Summary. 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).