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The analysis of retrospective family studies

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  • J. Neuhaus

Abstract

Case-control samples allow straightforward calculation of estimates of the association between covariates and disease status by fitting a prospective logistic regression model. In genetic studies of disease, investigators often gather additional information on response and covariate variables from family members of cases and controls. The objective is to model the responses of all the family members in terms of the covariate data. Whittemore (1995) has discussed maximum likelihood methods for fitting a special class of logistic models to family data collected according to a particular design. In the present paper, we show that we can obtain efficient semiparametric maximum likelihood estimates for an arbitrary multivariate binary regression model by fitting a modified prospective model for a wide class of retrospective designs. However, in contrast to the situation with simple case-control studies, the prospective model will differ from the original model even when the model is logistic. Copyright Biometrika Trust 2002, Oxford University Press.

Suggested Citation

  • J. Neuhaus, 2002. "The analysis of retrospective family studies," Biometrika, Biometrika Trust, vol. 89(1), pages 23-37, March.
    • Handle: RePEc:oup:biomet:v:89:y:2002:i:1:p:23-37
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    Cited by:

    1. John M. Neuhaus & Alastair J. Scott & Christopher J. Wild & Yannan Jiang & Charles E. McCulloch & Ross Boylan, 2014. "Likelihood-based analysis of longitudinal data from outcome-related sampling designs," Biometrics, The International Biometric Society, vol. 70(1), pages 44-52, March.
    2. Judith Clarke & Nilanjana Roy & Marsha Courchane, 2009. "On the robustness of racial discrimination findings in mortgage lending studies," Applied Economics, Taylor & Francis Journals, vol. 41(18), pages 2279-2297.
    3. Jonathan S. Schildcrout & Shawn P. Garbett & Patrick J. Heagerty, 2013. "Outcome Vector Dependent Sampling with Longitudinal Continuous Response Data: Stratified Sampling Based on Summary Statistics," Biometrics, The International Biometric Society, vol. 69(2), pages 405-416, June.
    4. Glen McGee & Marianthi‐Anna Kioumourtzoglou & Marc G. Weisskopf & Sebastien Haneuse & Brent A. Coull, 2020. "On the interplay between exposure misclassification and informative cluster size," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1209-1226, November.
    5. Alan Lee & Yuichi Hirose, 2010. "Semi-parametric efficiency bounds for regression models under response-selective sampling: the profile likelihood approach," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(6), pages 1023-1052, December.
    6. Glen McGee & Jonathan Schildcrout & Sharon‐Lise Normand & Sebastien Haneuse, 2020. "Outcome‐dependent sampling in cluster‐correlated data settings with application to hospital profiling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 379-402, January.
    7. Sara Sauer & Bethany Hedt‐Gauthier & Claudia Rivera‐Rodriguez & Sebastien Haneuse, 2022. "Small‐sample inference for cluster‐based outcome‐dependent sampling schemes in resource‐limited settings: Investigating low birthweight in Rwanda," Biometrics, The International Biometric Society, vol. 78(2), pages 701-715, June.
    8. Yingye Zheng & Patrick J. Heagerty & Li Hsu & Polly A. Newcomb, 2010. "On Combining Family-Based and Population-Based Case–Control Data in Association Studies," Biometrics, The International Biometric Society, vol. 66(4), pages 1024-1033, December.
    9. Jonathan S. Schildcrout & Paul J. Rathouz, 2010. "Longitudinal Studies of Binary Response Data Following Case–Control and Stratified Case–Control Sampling: Design and Analysis," Biometrics, The International Biometric Society, vol. 66(2), pages 365-373, June.
    10. J. M. Neuhaus & A. J. Scott & C. J. Wild, 2006. "Family-Specific Approaches to the Analysis of Case–Control Family Data," Biometrics, The International Biometric Society, vol. 62(2), pages 488-494, June.

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