Validating Bayesian Prediction Models: a Case Study in Genetic Susceptibility to Breast Cancer

  • Edwin IversenJr.
  • Giovanni Parmigiani
  • Donald Berry
Conference paper
Part of the Lecture Notes in Statistics book series (LNS, volume 140)

Abstract

A family history of breast cancer has long been recognized to be associated with predisposition to the disease, but only recently have susceptibility genes, BRCA1 and BRCA2, been identified. Though rare, mutation of a gene at either locus is associated with a much increased risk of developing breast as well as ovarian cancer. Understanding this risk is an important element of medical counseling in clinics that serve women who present with a family history. In this paper we discuss validation of a probability model for risk of mutation at BRCA1 or BRCA2. Genetic status is unknown, but of interest, for a sample of individuals. Family histories of breast and ovarian cancer in 1st and 2nd degree relatives are available and enable calculation, via the model of Berry et al. (1997) and Parmigiani et al. (1998b), of a carrier probability score. Results of genetic tests with unknown error rates are available with which to validate carrier probability scores. A model is developed which allows joint assessment of test sensitivity and specificity and carrier score error, treating genetic status as a latent variable. Estimating risk and using receiver operating characteristic (ROC) curves for communicating results to practitioners are discussed.

Keywords

Haas 

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Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Edwin IversenJr.
  • Giovanni Parmigiani
  • Donald Berry

There are no affiliations available

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