On Combining Family-Based and Population-Based Case–Control Data in Association Studies

Authors

  • Yingye Zheng,

    Corresponding author
    1. Biostatistics and Biomathematics Program, Fred Hutchinson Cancer Research Center, Fairview Avenue North, M2-B500, Seattle, Washington 98109, U.S.A.
      email: yzheng@fhcrc.org
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  • Patrick J. Heagerty,

    1. Biostatistics and Biomathematics Program, Fred Hutchinson Cancer Research Center, Fairview Avenue North, M2-B500, Seattle, Washington 98109, U.S.A.
    2. Department of Biostatistics, University of Washington, Seattle, Washington 98109, U.S.A.
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  • Li Hsu,

    1. Biostatistics and Biomathematics Program, Fred Hutchinson Cancer Research Center, Fairview Avenue North, M2-B500, Seattle, Washington 98109, U.S.A.
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  • Polly A. Newcomb

    1. Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, U.S.A.
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email: yzheng@fhcrc.org

Abstract

Summary Combining data collected from different sources can potentially enhance statistical efficiency in estimating effects of environmental or genetic factors or gene–environment interactions. However, combining data across studies becomes complicated when data are collected under different study designs, such as family-based and unrelated individual-based case–control design. In this article, we describe likelihood-based approaches that permit the joint estimation of covariate effects on disease risk under study designs that include cases, relatives of cases, and unrelated individuals. Our methods accommodate familial residual correlation and a variety of ascertainment schemes. Extensive simulation experiments demonstrate that the proposed methods for estimation and inference perform well in realistic settings. Efficiencies of different designs are contrasted in the simulation. We applied the methods to data from the Colorectal Cancer Family Registry.

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