Affiliation of authors: Stanford University School of Medicine, Stanford, CA.
Correspondence to: Alice S. Whittemore, Ph.D., Stanford University School of Medicine, Department of Health Research and Policy, Redwood Bldg., Rm. T204, Stanford, CA 943055405 (e-mail: alicesw{at}stanford.edu).
Breast cancer risks in BRCA1 and BRCA2 gene mutation carriers may vary with other modifying genes or personal attributes. Begg (1) noted that such heterogeneity causes upward bias in risk estimates based on cancer occurrence in families of population-based samples of case patients with breast cancer. We argue that this bias is small compared with the standard errors of the estimates. Thus, the large variability in risk estimates across studies does not appear to be caused by their biases but rather by the large standard errors of their estimates. This assertion is supported both by the data reviewed by Begg and by our computer simulations, as we discuss below. Our simulations also show that estimates from multiple-case families are more precise than those from families of population-based case patients.
Table 1 shows results from the four studies reviewed by Begg that give estimates and 95% confidence intervals (CIs) for breast cancer risk among carriers of mutations in BRCA1 or BRCA2. The CIs are wide, reflecting the large standard errors of the risk estimates. We used the CIs in Table 1
to calculate a variance for each risk estimate in the table and then used the estimates and their variances to test for differences in risk across the three studies with data for each gene. We found no evidence that risk differs across studies, which does not support Begg's hypothesis that some are more biased than others.
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Table 2 shows three risk ranges among BRCA mutation carriers corresponding to three sets of assumptions about risk heterogeneity among carriers as a result of the modifying gene. The lower and upper risks in each range are, respectively, the risks among noncarriers and carriers of the modifying gene. We approximated the bias in mean risk among relatives of case patients as half of the bias given by formula 2 of Begg (1), based on the arguments of Wacholder et al. (7). We then used this approximation to calculate the mean risk among the relatives. The calculated bias is small. Even when BRCA mutation penetrances in the population range from 52% to 98%, the bias is only 3.5%. In contrast, the standard deviations of risk estimates across the 50 simulated studies were much larger, giving 95% CIs that span some 2530 percentage points.
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REFERENCES
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2002;94:12216.
2 Thorlacius S, Struewing JP, Hartge P, Olafsdottir GH, Sigvaldason H, Tryggvadottir L, et al. Population-based study of risk of breast cancer in carriers of BRCA2 mutation. Lancet 1998;352:13379.[CrossRef][Medline]
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6 Gong G, Whittemore AS. Optimal designs for estimating penetrance of rare mutations of disease-susceptibility genes. Genet Epidemiol. In press 2003.
7 Wacholder S, Hartge P, Struewing JP, Pee D, McAdams M, Brody L, et al. The kin-cohort study for estimating penetrance. Am J Epidemiol 1998;148:62330.[Abstract]
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