Seminar Details

Seminar Details


Apr 13

3:30 pm

Association and Aggregation Analysis Using Kin-Cohort Designs with Applications to Genotype and Family History Data from the Washington Ashkenazi Study

Nilanjan Chatterjee (Joint with Biostatistics)


National Cancer Institute - Division of Cancer Epidemiology and Genetics - Biostatistics Branch

The Kin-Cohort design is a promising alternative to traditional cohort or case-control designs for estimating penetrance of an identified rare mutation. In this design, a suitably selected sample of probands provide genotype and detailed family history information on the disease of interest.

For estimation of the marginal penetrance, the age-specific cumulative probability of the disease for carriers and non-carriers, we consider using a marginal likelihood approach. The proposed method is computationally simple to implement, more flexible than the original analytic approach proposed by Wacholder et al.(1998) and more robust than the likelihood approach considered by Gail et al.(1999) in presence of residual familial correlation.

We further consider incorporating family history information into penetrance estimation by modeling residual familial correlation. A marginal modeling approach is considered using copula models for bivariate survival data. For estimation, we consider a two stage approach in the spirit of Shih and Louis (1995).

Methods are applied to the data from the Washington Ashkenazi Study (Struewing, 1997) for estimation of the risk of breast cancer associated with specific BRCA1/BRCA2 mutations and the residual effects of family history after accounting for these mutations.