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Likelihood methods for missing covariate data in highly stratified studies

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  • Paul J. Rathouz

Abstract

Summary. The paper considers canonical link generalized linear models with stratum‐specific nuisance intercepts and missing covariate data. This family includes the conditional logistic regression model. Existing methods for this problem, each of which uses a conditioning argu‐ ment to eliminate the nuisance intercept, model either the missing covariate data or the missingness process. The paper compares these methods under a common likelihood framework. The semiparametric efficient estimator is identified, and a new estimator, which reduces dependence on the model for the missing covariate, is proposed. A simulation study compares the methods with respect to efficiency and robustness to model misspecification.

Suggested Citation

  • Paul J. Rathouz, 2003. "Likelihood methods for missing covariate data in highly stratified studies," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(3), pages 711-723, August.
    • Handle: RePEc:bla:jorssb:v:65:y:2003:i:3:p:711-723
    DOI: 10.1111/1467-9868.00411
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    Cited by:

    1. 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.
    2. 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.
    3. Jinbo Chen & Carmen Rodriguez, 2007. "Conditional Likelihood Methods for Haplotype-Based Association Analysis Using Matched Case–Control Data," Biometrics, The International Biometric Society, vol. 63(4), pages 1099-1107, December.
    4. 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.
    5. 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.

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