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Analysis of case-control data with interacting misclassified covariates

Author

Listed:
  • Grace Y. Yi

    (University of Waterloo)

  • Wenqing He

    (University of Western Ontario)

Abstract

Case-control studies are important and useful methods for studying health outcomes and many methods have been developed for analyzing case-control data. Those methods, however, are vulnerable to mismeasurement of variables; biased results are often produced if such a feature is ignored. In this paper, we develop an inference method for handling case-control data with interacting misclassified covariates. We use the prospective logistic regression model to feature the development of the disease. To characterize the misclassification process, we consider a practical situation where replicated measurements of error-prone covariates are available. Our work is motivated in part by a breast cancer case-control study where two binary covariates are subject to misclassification. Extensions to other settings are outlined.

Suggested Citation

  • Grace Y. Yi & Wenqing He, 2017. "Analysis of case-control data with interacting misclassified covariates," Journal of Statistical Distributions and Applications, Springer, vol. 4(1), pages 1-16, December.
    • Handle: RePEc:spr:jstada:v:4:y:2017:i:1:d:10.1186_s40488-017-0069-0
    DOI: 10.1186/s40488-017-0069-0
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    References listed on IDEAS

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    1. Robert H. Lyles, 2002. "A Note on Estimating Crude Odds Ratios in Case–Control Studies with Differentially Misclassified Exposure," Biometrics, The International Biometric Society, vol. 58(4), pages 1034-1036, December.
    2. Mary J. Morrissey & Donna Spiegelman, 1999. "Matrix Methods for Estimating Odds Ratios with Misclassified Exposure Data: Extensions and Comparisons," Biometrics, The International Biometric Society, vol. 55(2), pages 338-344, June.
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    Cited by:

    1. Li‐Pang Chen & Grace Y. Yi, 2021. "Analysis of noisy survival data with graphical proportional hazards measurement error models," Biometrics, The International Biometric Society, vol. 77(3), pages 956-969, September.
    2. Liqun Diao & Grace Y. Yi, 2023. "Classification Trees with Mismeasured Responses," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 168-191, April.

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