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Sampling Distributions of Critical Illness Insurance Premium Rates: Breast and Ovarian Cancer

Published online by Cambridge University Press:  17 April 2015

Li Lu ,
Angus Macdonald  and
Howard Waters
Angus Macdonald
Affiliation:
Department of Actuarial Mathematics and Statistics and the Maxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, U.K., Tel: +44(0)131-451-3209, Fax: +44(0)131-451-3249, E-mail: [email protected]
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Abstract

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Evaluating the risk of disorders in long-term insurance often relies on rates of onset estimated from quite small epidemiological studies. These estimates can carry considerable uncertainty, hence so may functions of them, such as a premium rate. In the case of genetic disorders, where it may be required to demonstrate the reliability of genetic information as a risk factor, such uncertainty may be material. Epidemiological studies publish their results in a variety of forms and it is rarely easy to estimate the sampling distribution of a premium rate without access to the original data. We found a large study of breast and ovarian cancer that cited relative risks of breast and ovarian cancer onset, with confidence intervals, in 10-year age groups. We obtained critical illness premium rates and their sampling distributions by parametric bootstrapping, and investigated the effect of possible patterns of sampling correlations. We found that this study provides ample statistical evidence that known BRCA1 or BRCA2 mutations, or a typical family history of breast or ovarian cancer, are reliable risk factors, but the sampling covariances of the relative risks could be important at some ages and terms. Studies that cite only standard errors of parameter estimates erect a small but awkward barrier between the models they describe, and some important actuarial questions.

Keywords

Breast CancerCritical Illness InsuranceFamily HistoryGeneticsOvarian CancerParametric BootstrappingSampling Distributions
Type
Articles
Information
ASTIN Bulletin: The Journal of the IAA , Volume 38 , Issue 2 , November 2008 , pp. 527 - 542
Copyright
Copyright © ASTIN Bulletin 2008

References

Antoniou, A.C., Pharoah, P.D.P., Mcmullan, G., Day, N.E., Stratton, M.R., Peto, J., Ponder, B.J. and Easton, D.F (2002) A comprehensive model for familial breast cancer incorporating BRCA1, BRCA2 and other genes. British Journal of Cancer 86, 76–83.Google ScholarPubMed
Antoniou, A.C., Pharoah, P.D.P., Narod, S., Risch, H.A., Eyfjord, J.E., Hopper, J.L., Loman, N., Olsson, H., Johannsson, O., Borg, A., Pasini, B., Radice, P., Manoukian, S., Eccles, D.M., Tang, N., Olah, E., Anton-Culver, H., Warner, E., Lubinski, J., Gronwald, J., Gorski, B., Tulinius, H., Thorlacius, S., Eerola, H., Nevanlinna, H., Syrjäkoski, K., Kallioniemi, O.-P., Thompson, D., Evans, C., Peto, J., Lalloo, F., Evans, D.G. and Easton, D.F. (2003) Average risks of breast and ovarian cancer associated with mutations in BRCA1 or BRCA2 detected in case series unselected for family history: A combined analysis of 22 studies. American Journal of Human Genetics 72, 1117–1130.CrossRefGoogle ScholarPubMed
Daykin, C.D., Akers, D.A., Macdonald, A.S., Mcgleenan, T., Paul, D. and Turvey, P.J. (2003) Genetics and insurance – some social policy issues (with discussions). British Actuarial Journal 9, 787–874.CrossRefGoogle Scholar
Easton, D.F., Bishop, D.T., Ford, D., Crockford, G.P. and The Breast Cancer Linkage Consortium (1993) Genetic linkage analysis in familial breast and ovarian cancer. American Journal of Human Genetics 52, 678–701.Google ScholarPubMed
Easton, D.F., Ford, D., Bishop, D.T. and The Breast Cancer Linkage Consortium (1995) Breast and ovarian cancer incidence in BRCA1-mutation carriers. American Journal of Human Genetics 56, 265–271.Google ScholarPubMed
Glasserman, P. (2004) Monte Carlo Methods in Financial Engineering. Springer-Verlag, New York.Google Scholar
Gui, E.H., Lu, B., Macdonald, A.S., Waters, H.R. and Wekwete, C.T. (2006) The genetics of breast and ovarian cancer III: A new model of family history. Scandinavian Actuarial Journal 2006, 338–367.Google Scholar
GutiÉrrez, M.C. and Macdonald, A.S. (2003) Adult polycystic kidney disease and critical illness insurance. North American Actuarial Journal 7(2), 93–115.CrossRefGoogle Scholar
Lu, L. (2006) Some actuarial and statistical investigations into topics on genetics and insurance. Ph.D. dissertation, Heriot-Watt University, Edinburgh.Google Scholar
Lu, L., Macdonald, A.S. and Waters, H.R. (2007) A dynamic family history model of hereditary nonpolyposis colorectal cancer. To appear in Annals of Actuarial Science.CrossRefGoogle Scholar
Lu, L., Macdonald, A.S. and Wekwete, C.T. (2007) Premium rates based on genetic studies: How reliable are they? Insurance: Mathematics & Economics 42, 319–331.Google Scholar
Macdonald, A.S., Waters, H.R. and Wekwete, C.T. (2003) The genetics of breast and ovarian cancer I: A model of family history. Scandinavian Actuarial Journal 2003, 1–27.CrossRefGoogle Scholar