Correspondence to: C. I. Amos, Ph.D., Department of Epidemiology, Box 189, The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030 (e-mail: camos{at}mdanderson.org).
Begg (1) recently pointed out limitations of estimating absolute risk from studies of case patients and their relatives. In particular, he points out that risk estimates from the case patients are likely to be inflated because risk coaggregates in the relatives of the case patients. The particular overestimation that he notes should be reduced if ascertainment corrections are used. It has been well noted in the genetic epidemiology literature that the sampling of families is a form of biased sampling. Methods to incorporate mutational information from some individuals and to simultaneously adjust for the ascertainment process have been available from standard statistical genetic software such as the Pedigree Analysis Package (http://hasstedt.genetics.utah.edu/download/pap50manual.pdf) or Mendel [Lange et al. (2)] but have not been widely used. Antoniou et al. (3) developed elegant adaptations of Mendel software to incorporate an ascertainment correction along with effects of additional genetic factors beyond BRCA1 and BRCA2. The statistical model they developed should adjust for biased sampling and so obtain reliable results, subject to the limited sample size of their study. As an alternative to the genetic epidemiologic approaches, which can be difficult to implement, standard cohort or newer kincohort approaches [Wacholder et al. (4)] have been applied. These approaches have the advantage of readily incorporating a broader range of survivorship modeling approaches than are available from genetic epidemiologic software, but their use may lead to biased results, as shown by Begg (1). Historical cohort approaches are valuable in genetic epidemiologic studies of cancer because they permit the study of multiple cancer outcomes.
An important issue not considered in the recent analysis by Begg is the effect that underreporting of cancers can have on the risk estimates from a population-based study. The sensitivity of reporting of breast cancer in first-degree relatives of probands has varied among studies and ranges from 83% [Kerber et al. (5)] to 94% [Love et al. (6)]. The specificity of reporting of breast cancer in first-degree relatives has rarely been reported because of the difficulty in obtaining breast cancer information about individuals who have not been reported to have cancer. However, Anton-Culver et al. (7) found that reporting specificity was more than 99% in a very large study from a population-based registry. The sensitivity of reporting for other cancers is generally lower, as low as 0% for liver cancer [Love et al. (6)].
If we define the penetrance to be the probability that an individual develops the disease (cancer), given that the person has a mutation (M), then we want to obtain
= P(D/M), where D is the event that a person has the disease and P denotes probability. Let
represent the observed penetrance,
represent the event that a person does not have the disease, and
represent the event that a person is reported to have the disease. Ordinarily, the mutation status of the first-degree relatives of individuals is inferred. What we observe, in the absence of any corrections and by assuming the mutation status has been inferred in relatives, is
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The latter term is negligible because the false-positive rate is less than 1%. In the kincohort approach, the penetrance is approximately twice the risk for the relatives of the case patients who test positive for the mutation minus the risk for the relatives who test negative for the mutation [Wacholder et al. (4)], which leads to the estimated penetrance being attenuated by approximately the sensitivity in reporting of the disease in the relatives (as noted above). The population-based cohort studies reviewed by Begg (1) have not ensured that all cases of breast cancer have been reported by the probands. Thus, all of the cohort studies are likely to have underestimated, to some extent, the risks associated with carrying a mutation in BRCA1 and/or BRCA2. Family-based studies that include contact with multiple relatives are difficult to conduct but minimize concerns about reporting of cancers in relatives. Further development of models that accurately include estimates of underreporting of cancer are needed before population-based approaches can be accurately interpreted. Concerns about the sensitivity of reporting are even more important for evaluating results of other cancers that may be even less reliably reported than breast cancer.
NOTES
Supported by Public Health Service grants CA78142 and CA34936 from the National Cancer Institute, National Institutes of Health, Department of Health and Human Services.
REFERENCES
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