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Semiparametric inference in matched case-control studies with missing covariate data

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  • Paul J. Rathouz

Abstract

We consider the problem of matched studies with a binary outcome that are analysed using conditional logistic regression, and for which data on some covariates are missing for some study participants. Methods for this problem involve either modelling the distribution of missing covariates or modelling the probability of data being missing. For this second approach, the previously proposed method did not make use of data for those persons with missing covariate data except in the model for the missingness. We propose a new class of estimators that use outcome and available covariate data for all study participants, and show that a particular member of this class always has better efficiency than the previously proposed estimator. We illustrate the efficiency gains that are possible with our approach using simulated data. Copyright Biometrika Trust 2002, Oxford University Press.

Suggested Citation

  • Paul J. Rathouz, 2002. "Semiparametric inference in matched case-control studies with missing covariate data," Biometrika, Biometrika Trust, vol. 89(4), pages 905-916, December.
    • Handle: RePEc:oup:biomet:v:89:y:2002:i:4:p:905-916
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    Cited by:

    1. 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.
    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. Haibo Zhou & Guoyou Qin & Matthew P. Longnecker, 2011. "A Partial Linear Model in the Outcome-Dependent Sampling Setting to Evaluate the Effect of Prenatal PCB Exposure on Cognitive Function in Children," Biometrics, The International Biometric Society, vol. 67(3), pages 876-885, September.
    4. 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.
    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.

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