A weighted cohort approach for analysing factors modifying disease risks in carriers of high-risk susceptibility genes
Article first published online: 4 MAY 2005
© 2005 Wiley-Liss, Inc.
Volume 29, Issue 1, pages 1–11, July 2005
How to Cite
Antoniou, A. C., Goldgar, D. E., Andrieu, N., Chang-Claude, J., Brohet, R., Rookus, M. A. and Easton, D. F. (2005), A weighted cohort approach for analysing factors modifying disease risks in carriers of high-risk susceptibility genes. Genet. Epidemiol., 29: 1–11. doi: 10.1002/gepi.20074
- Issue published online: 6 JUN 2005
- Article first published online: 4 MAY 2005
- Manuscript Accepted: 26 JAN 2005
- Manuscript Revised: 1 DEC 2004
- Manuscript Received: 25 OCT 2004
- Cancer Research UK
- NIH. Grant Number: CA81203
- risk modifiers;
- weighted Cox regression;
The authors propose a novel approach to evaluate the effects of risk factors on disease risks in carriers of high-penetrance alleles in disease susceptibility genes. Most studies to date have utilised data collected on carriers identified through ongoing genetic testing programs. The advantage of this approach is that it allows relatively large numbers of affected and unaffected carriers to be identified rapidly. However, genetic testing is targeted at individuals with a strong family history of disease, so that the selection of carriers is not random with respect to disease status. Risk factors are often analysed by standard cohort analysis methods, but these can be biased in retrospective studies if subjects are selected on the basis of phenotype. To overcome this problem, a weighted cohort approach is proposed, under which individuals are weighted according to certain sampling probabilities in order to mimic a true cohort. The method is illustrated by analyses of data from the International BRCA1/2 Carrier Cohort Study (IBCCS). Simulations demonstrate that the method gives rate ratio estimates that are close to unbiased provided that the absolute disease risks are well estimated. The power to detect associations is, however, reduced compared with an unweighted approach. Genet. Epidemiol. © 2005 Wiley-Liss, Inc.