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Score tests for association under response-dependent sampling designs for expensive covariates

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  • Andriy Derkach
  • Jerald F. Lawless
  • Lei Sun

Abstract

Response-dependent sampling is widely used in settings where certain variables are expensive to obtain. Estimation has been thoroughly investigated but recent applications have emphasized tests of association for expensive covariates and a response variable. We consider testing and provide easily implemented likelihood score tests for generalized linear models under a broad range of sampling plans. We show that when there are no additional covariates, the score statistics are identical for conditional and full likelihood approaches, and are of the same form as for ordinary random sampling. Applications in genetics are discussed briefly.

Suggested Citation

  • Andriy Derkach & Jerald F. Lawless & Lei Sun, 2015. "Score tests for association under response-dependent sampling designs for expensive covariates," Biometrika, Biometrika Trust, vol. 102(4), pages 988-994.
    • Handle: RePEc:oup:biomet:v:102:y:2015:i:4:p:988-994.
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    File URL: http://hdl.handle.net/10.1093/biomet/asv038
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    References listed on IDEAS

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    1. Peter J. Diggle & Raquel Menezes & Ting‐li Su, 2010. "Geostatistical inference under preferential sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 191-232, March.
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    Cited by:

    1. J. F. Lawless, 2018. "Two-phase outcome-dependent studies for failure times and testing for effects of expensive covariates," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(1), pages 28-44, January.
    2. Lin Zhang & Lei Sun, 2022. "A generalized robust allele‐based genetic association test," Biometrics, The International Biometric Society, vol. 78(2), pages 487-498, June.
    3. Brady Ryan & Ananthika Nirmalkanna & Candemir Cigsar & Yildiz E. Yilmaz, 2023. "Evaluation of Designs and Estimation Methods Under Response-Dependent Two-Phase Sampling for Genetic Association Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 510-539, July.
    4. Chiara Di Gravio & Ran Tao & Jonathan S. Schildcrout, 2023. "Design and analysis of two‐phase studies with multivariate longitudinal data," Biometrics, The International Biometric Society, vol. 79(2), pages 1420-1432, June.
    5. Ying Wu & Richard J. Cook, 2018. "Variable selection and prediction in biased samples with censored outcomes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(1), pages 72-93, January.

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