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Matched case–control data analyses with missing covariates

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  • M. C. Paik
  • R. L. Sacco

Abstract

We consider methods for analysing matched case–control data when some covariates (W) are completely observed but other covariates (X) are missing for some subjects. In matched case–control studies, the complete‐record analysis discards completely observed subjects if none of their matching cases or controls are completely observed. We investigate an imputation estimate obtained by solving a joint estimating equation for log‐odds ratios of disease and parameters in an imputation model. Imputation estimates for coefficients of W are shown to have smaller bias and mean‐square error than do estimates from the complete‐record analysis.

Suggested Citation

  • M. C. Paik & R. L. Sacco, 2000. "Matched case–control data analyses with missing covariates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(1), pages 145-156.
    • Handle: RePEc:bla:jorssc:v:49:y:2000:i:1:p:145-156
    DOI: 10.1111/1467-9876.00184
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    Cited by:

    1. Samiran Sinha & Tapabrata Maiti, 2008. "Analysis of Matched Case–Control Data in Presence of Nonignorable Missing Exposure," Biometrics, The International Biometric Society, vol. 64(1), pages 106-114, March.
    2. Liu, Tianqing & Yuan, Xiaohui & Li, Zhaohai & Li, Yuanzhang, 2013. "Empirical and weighted conditional likelihoods for matched case-control studies with missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 119(C), pages 185-199.
    3. Inyoung Kim & Noah D. Cohen & Raymond J. Carroll, 2003. "Semiparametric Regression Splines in Matched Case-Control Studies," Biometrics, The International Biometric Society, vol. 59(4), pages 1158-1169, December.
    4. Heng Chen & Daniel F. Heitjan, 2022. "Analysis of local sensitivity to nonignorability with missing outcomes and predictors," Biometrics, The International Biometric Society, vol. 78(4), pages 1342-1352, December.
    5. Mulugeta Gebregziabher & Bryan Langholz, 2010. "A Semiparametric Missing-Data-Induced Intensity Method for Missing Covariate Data in Individually Matched Case–Control Studies," Biometrics, The International Biometric Society, vol. 66(3), pages 845-854, September.
    6. Jaeil Ahn & Bhramar Mukherjee & Stephen B. Gruber & Samiran Sinha, 2011. "Missing Exposure Data in Stereotype Regression Model: Application to Matched Case–Control Study with Disease Subclassification," Biometrics, The International Biometric Society, vol. 67(2), pages 546-558, June.
    7. Myunghee Cho Paik, 2004. "Nonignorable Missingness in Matched Case–Control Data Analyses," Biometrics, The International Biometric Society, vol. 60(2), pages 306-314, June.
    8. Samiran Sinha & Bhramar Mukherjee & Malay Ghosh, 2004. "Bayesian Semiparametric Modeling for Matched Case–Control Studies with Multiple Disease States," Biometrics, The International Biometric Society, vol. 60(1), pages 41-49, March.
    9. Chen, Qixuan & Paik, Myunghee Cho & Kim, Minjin & Wang, Cuiling, 2016. "Using link-preserving imputation for logistic partially linear models with missing covariates," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 174-185.
    10. Glen A. Satten & Raymond J. Carroll, 2000. "Conditional and Unconditional Categorical Regression Models with Missing Covariates," Biometrics, The International Biometric Society, vol. 56(2), pages 384-388, June.
    11. I-Feng Lin & Myunghee Cho Paik, 2001. "Matched Case—Control Data Analysis with Selection Bias," Biometrics, The International Biometric Society, vol. 57(4), pages 1106-1112, December.

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