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Using covariate-specific disease prevalence information to increase the power of case-control studies

Author

Listed:
  • Jing Qin
  • Han Zhang
  • Pengfei Li
  • Demetrius Albanes
  • Kai Yu

Abstract

Public registration databases and large cohort studies provide vital information on disease prevalence at various levels of a risk factor. This auxiliary information can be helpful in conducting statistical inference in a new study. We aim to develop a statistical procedure that improves the efficiency of the logistic regression model for a case-control study by utilizing auxiliary information on covariate-specific disease prevalence via a series of unbiased estimating equations. We adopt empirical likelihood for statistical inference, and demonstrate its advantages through simulation and an application.

Suggested Citation

  • Jing Qin & Han Zhang & Pengfei Li & Demetrius Albanes & Kai Yu, 2015. "Using covariate-specific disease prevalence information to increase the power of case-control studies," Biometrika, Biometrika Trust, vol. 102(1), pages 169-180.
    • Handle: RePEc:oup:biomet:v:102:y:2015:i:1:p:169-180.
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    File URL: http://hdl.handle.net/10.1093/biomet/asu048
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    Citations

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    Cited by:

    1. Jie He & Hui Li & Shumei Zhang & Xiaogang Duan, 2019. "Additive hazards model with auxiliary subgroup survival information," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(1), pages 128-149, January.
    2. Chixiang Chen & Ming Wang & Shuo Chen, 2023. "An efficient data integration scheme for synthesizing information from multiple secondary datasets for the parameter inference of the main analysis," Biometrics, The International Biometric Society, vol. 79(4), pages 2947-2960, December.
    3. Han Zhang & Lu Deng & William Wheeler & Jing Qin & Kai Yu, 2022. "Integrative analysis of multiple case‐control studies," Biometrics, The International Biometric Society, vol. 78(3), pages 1080-1091, September.
    4. Fei Gao & K. C. G. Chan, 2023. "Noniterative adjustment to regression estimators with population‐based auxiliary information for semiparametric models," Biometrics, The International Biometric Society, vol. 79(1), pages 140-150, March.
    5. Prosenjit Kundu & Nilanjan Chatterjee, 2023. "Logistic regression analysis of two‐phase studies using generalized method of moments," Biometrics, The International Biometric Society, vol. 79(1), pages 241-252, March.
    6. Ying Sheng & Yifei Sun & Chiung‐Yu Huang & Mi‐Ok Kim, 2022. "Synthesizing external aggregated information in the presence of population heterogeneity: A penalized empirical likelihood approach," Biometrics, The International Biometric Society, vol. 78(2), pages 679-690, June.
    7. Ziqi Chen & Jing Ning & Yu Shen & Jing Qin, 2021. "Combining primary cohort data with external aggregate information without assuming comparability," Biometrics, The International Biometric Society, vol. 77(3), pages 1024-1036, September.
    8. Wang, Chunlin & Marriott, Paul & Li, Pengfei, 2018. "Semiparametric inference on the means of multiple nonnegative distributions with excess zero observations," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 182-197.
    9. Ruoyu Wang & Qihua Wang & Wang Miao, 2023. "A robust fusion-extraction procedure with summary statistics in the presence of biased sources," Biometrika, Biometrika Trust, vol. 110(4), pages 1023-1040.

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