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American Journal of Epidemiology Advance Access originally published online on April 8, 2009
American Journal of Epidemiology 2009 169(11):1398-1405; doi:10.1093/aje/kwp055
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American Journal of Epidemiology © The Author 2009. Published by the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

PRACTICE OF EPIDEMIOLOGY

Using the Whole Cohort in the Analysis of Case-Cohort Data

Norman E. Breslow, Thomas Lumley, Christie M. Ballantyne, Lloyd E. Chambless and Michal Kulich

Correspondence to Dr. Norman E. Breslow, Department of Biostatistics, University of Washington, Mail Stop 357232, Seattle, WA 98195-7232 (e-mail: norm{at}u.washington.edu).

Received for publication April 4, 2008. Accepted for publication February 17, 2009.

Case-cohort data analyses often ignore valuable information on cohort members not sampled as cases or controls. The Atherosclerosis Risk in Communities (ARIC) study investigators, for example, typically report data for just the 10%–15% of subjects sampled for substudies of their cohort of 15,972 participants. Remaining subjects contribute to stratified sampling weights only. Analysis methods implemented in the freely available R statistical system (http://cran.r-project.org/) make better use of the data through adjustment of the sampling weights via calibration or estimation. By reanalyzing data from an ARIC study of coronary heart disease and simulations based on data from the National Wilms Tumor Study, the authors demonstrate that such adjustment can dramatically improve the precision of hazard ratios estimated for baseline covariates known for all subjects. Adjustment can also improve precision for partially missing covariates, those known for substudy participants only, when their values may be imputed with reasonable accuracy for the remaining cohort members. Links are provided to software, data sets, and tutorials showing in detail the steps needed to carry out the adjusted analyses. Epidemiologists are encouraged to consider use of these methods to enhance the accuracy of results reported from case-cohort analyses.

calibration; efficiency; observation; proportional hazards models; selection bias

Abbreviations: ARIC, Atherosclerosis Risk in Communities; Lp-PLA2, lipoprotein-associated phospholipase A2; NWTS, National Wilms Tumor Study


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