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Semiparametric approaches for matched case–control studies with error-in-covariates

Author

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  • Nels G. Johnson

    (University of Tennessee)

  • Inyoung Kim

    (Virginia Tech)

Abstract

The matched case–control study is a popular design in public health, biomedical, and epidemiological research for human, animal, and other subjects for clustered binary outcomes. Often covariates in such studies are measured with error. Not accounting for this error can lead to incorrect inference for all covariates in the model. The methods for assessing and characterizing error-in-covariates in matched case–control studies are quite limited. In this article we propose several approaches for handling error-in-covariates that detect both parametric and nonparametric relationships between the covariates and the binary outcome. We propose a Bayesian approach and two approximate-Bayesian approaches for addressing error-in-covariates that is additive and Gaussian, where the variable measured with error has an unknown, nonlinear relationship with the response. The Bayesian approaches use an approximate latent variable probit model. All methods are developed using the nonparametric method of low-rank thin-plate splines. We assess the performance of each method in terms of mean squared error and mean bias in both simulations and a perturbed example of 1–4 matched case-crossover study.

Suggested Citation

  • Nels G. Johnson & Inyoung Kim, 2019. "Semiparametric approaches for matched case–control studies with error-in-covariates," Computational Statistics, Springer, vol. 34(4), pages 1675-1692, December.
    • Handle: RePEc:spr:compst:v:34:y:2019:i:4:d:10.1007_s00180-019-00888-w
    DOI: 10.1007/s00180-019-00888-w
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    References listed on IDEAS

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    1. Crainiceanu, Ciprian M. & Ruppert, David & Wand, Matthew P., 2005. "Bayesian Analysis for Penalized Spline Regression Using WinBUGS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i14).
    2. Lisa M. McShane & Douglas N. Midthune & Joanne F. Dorgan & Laurence S. Freedman & Raymond J. Carroll, 2001. "Covariate Measurement Error Adjustment for Matched Case–Control Studies," Biometrics, The International Biometric Society, vol. 57(1), pages 62-73, March.
    3. Berry S. M. & Carroll R. J & Ruppert D., 2002. "Bayesian Smoothing and Regression Splines for Measurement Error Problems," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 160-169, March.
    4. Huang Y. & Wang C.Y., 2001. "Consistent Functional Methods for Logistic Regression With Errors in Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1469-1482, December.
    5. Tester, J.M. & Rutherford, G.W. & Wald, Z. & Rutherford, M.W., 2004. "A Matched Case-Control Study Evaluating the Effectiveness of Speed Humps in Reducing Child Pedestrian Injuries," American Journal of Public Health, American Public Health Association, vol. 94(4), pages 646-650.
    6. Samiran Sinha & Bani K. Mallick & Victor Kipnis & Raymond J. Carroll, 2010. "Semiparametric Bayesian Analysis of Nutritional Epidemiology Data in the Presence of Measurement Error," Biometrics, The International Biometric Society, vol. 66(2), pages 444-454, June.
    7. Duchwan Ryu & Erning Li & Bani K. Mallick, 2011. "Bayesian Nonparametric Regression Analysis of Data with Random Effects Covariates from Longitudinal Measurements," Biometrics, The International Biometric Society, vol. 67(2), pages 454-466, June.
    8. Raymond J. Carroll & David Ruppert & Ciprian M. Crainiceanu & Tor D. Tosteson & Margaret R. Karagas, 2004. "Nonlinear and Nonparametric Regression and Instrumental Variables," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 736-750, January.
    9. A. Guolo, 2008. "A Flexible Approach to Measurement Error Correction in Case–Control Studies," Biometrics, The International Biometric Society, vol. 64(4), pages 1207-1214, December.
    10. Buzas, J. S. & Stefanski, L. A., 1996. "A note on corrected-score estimation," Statistics & Probability Letters, Elsevier, vol. 28(1), pages 1-8, June.
    11. Raymond J. Carroll & Kathryn Roeder & Larry Wasserman, 1999. "Flexible Parametric Measurement Error Models," Biometrics, The International Biometric Society, vol. 55(1), pages 44-54, March.
    12. Sinha, Samiran & Mukherjee, Bhramar & Ghosh, Malay & Mallick, Bani K. & Carroll, Raymond J., 2005. "Semiparametric Bayesian Analysis of Matched Case-Control Studies With Missing Exposure," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 591-601, June.
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