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A Marginal Likelihood Approach for Estimating Penetrance from Kin‐Cohort Designs

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  • Nilanjan Chatterjee
  • Sholom Wacholder

Abstract

Summary. The kin‐cohort design is a promising alternative to traditional cohort or case‐control designs for estimating penetrance of an identified rare autosomal mutation. In this design, a suitably selected sample of participants provides genotype and detailed family history information on the disease of interest. To estimate penetrance of the mutation, we consider a marginal likelihood approach that is computationally simple to implement, more flexible than the original analytic approach proposed by Wacholder et al. (1998, American Journal of Epidemiology148, 623–629), and more robust than the likelihood approach considered by Gail et al. (1999, Genetic Epidemiology16, 15–39) to presence of residual familial correlation. We study the trade‐off between robustness and efficiency using simulation experiments. The method is illustrated by analysis of the data from the Washington Ashkenazi Study.

Suggested Citation

  • Nilanjan Chatterjee & Sholom Wacholder, 2001. "A Marginal Likelihood Approach for Estimating Penetrance from Kin‐Cohort Designs," Biometrics, The International Biometric Society, vol. 57(1), pages 245-252, March.
    • Handle: RePEc:bla:biomet:v:57:y:2001:i:1:p:245-252
    DOI: 10.1111/j.0006-341X.2001.00245.x
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    Cited by:

    1. Lu Chen & Li Hsu & Kathleen Malone, 2009. "A Frailty-Model-Based Approach to Estimating the Age-Dependent Penetrance Function of Candidate Genes Using Population-Based Case-Control Study Designs: An Application to Data on the BRCA1 Gene," Biometrics, The International Biometric Society, vol. 65(4), pages 1105-1114, December.
    2. Nilanjan Chatterjee & Zeynep Kalaylioglu & Joanna H. Shih & Mitchell H. Gail, 2006. "Case–Control and Case-Only Designs with Genotype and Family History Data: Estimating Relative Risk, Residual Familial Aggregation, and Cumulative Risk," Biometrics, The International Biometric Society, vol. 62(1), pages 36-48, March.
    3. S. Mandal & J. Qin & R.M. Pfeiffer, 2023. "Non‐parametric estimation of the age‐at‐onset distribution from a cross‐sectional sample," Biometrics, The International Biometric Society, vol. 79(3), pages 1701-1712, September.
    4. Yuanjia Wang & Tanya P. Garcia & Yanyuan Ma, 2012. "Nonparametric Estimation for Censored Mixture Data With Application to the Cooperative Huntington’s Observational Research Trial," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1324-1338, December.
    5. Yanyuan Ma & Yuanjia Wang, 2014. "Estimating disease onset distribution functions in mutation carriers with censored mixture data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 1-23, January.

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