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A semiparametric efficient estimator in case-control studies for gene–environment independent models

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  • Liang, Liang
  • Ma, Yanyuan
  • Carroll, Raymond J.

Abstract

Case-controls studies are popular epidemiological designs for detecting gene–environment interactions in the etiology of complex diseases, where the genetic susceptibility and environmental exposures may often be reasonably assumed independent in the source population. Various papers have presented analytical methods exploiting gene–environment independence to achieve better efficiency, all of which require either a rare disease assumption or a distributional assumption on the genetic variables. We relax both assumptions. We construct a semiparametric estimator in case-control studies exploiting gene–environment independence, while the distributions of genetic susceptibility and environmental exposures are both unspecified and the disease rate is assumed unknown and is not required to be close to zero. The resulting estimator is semiparametric efficient and its superiority over prospective logistic regression, the usual analysis in case-control studies, is demonstrated in various numerical illustrations.

Suggested Citation

  • Liang, Liang & Ma, Yanyuan & Carroll, Raymond J., 2019. "A semiparametric efficient estimator in case-control studies for gene–environment independent models," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 38-50.
    • Handle: RePEc:eee:jmvana:v:173:y:2019:i:c:p:38-50
    DOI: 10.1016/j.jmva.2019.01.006
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    References listed on IDEAS

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    1. Frank Dudbridge, 2013. "Power and Predictive Accuracy of Polygenic Risk Scores," PLOS Genetics, Public Library of Science, vol. 9(3), pages 1-17, March.
    2. Nilanjan Chatterjee & Raymond J. Carroll, 2005. "Semiparametric maximum likelihood estimation exploiting gene-environment independence in case-control studies," Biometrika, Biometrika Trust, vol. 92(2), pages 399-418, June.
    3. Chen, Yi-Hau & Chatterjee, Nilanjan & Carroll, Raymond J., 2009. "Shrinkage Estimators for Robust and Efficient Inference in Haplotype-Based Case-Control Studies," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 220-233.
    4. Anastasios A. Tsiatis & Yanyuan Ma, 2004. "Locally efficient semiparametric estimators for functional measurement error models," Biometrika, Biometrika Trust, vol. 91(4), pages 835-848, December.
    5. Summer S. Han & Philip S. Rosenberg & Arpita Ghosh & Maria Teresa Landi & Neil E. Caporaso & Nilanjan Chatterjee, 2015. "An exposure‐weighted score test for genetic associations integrating environmental risk factors," Biometrics, The International Biometric Society, vol. 71(3), pages 596-605, September.
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