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Statistical Methods for Selective Biomarker Testing

Author

Listed:
  • A. Adam Ding

    (Northeastern University)

  • Natalie DelRocco

    (University of Florida)

  • Samuel S. Wu

    (University of Florida)

Abstract

Biomarkers are critically important tools in modern clinical diagnosis, prognosis, and classification/prediction. However, there are fiscal and analytical barriers to biomarker research. Selective Genotyping is an approach to increasing study power and efficiency where individuals with the most extreme phenotype (response) are chosen for genotyping (exposure) in order to maximize the information in the sample. In this article, we describe an analogous procedure in the biomarker testing landscape where both response and biomarker (exposure) are continuous. We propose an intuitive reverse-regression least squares estimator for the parameters relating biomarker value to response. An expression for robust standard error and corresponding confidence interval are derived. A simulation study is used to demonstrate that this method is unbiased and efficient relative to estimates from random sampling when the joint normal distribution assumption is met, and to compare the estimator to an alternative under a related sampling design. We illustrate application of proposed methods on data from a chronic pain clinical trial.

Suggested Citation

  • A. Adam Ding & Natalie DelRocco & Samuel S. Wu, 2024. "Statistical Methods for Selective Biomarker Testing," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(3), pages 693-722, December.
    • Handle: RePEc:spr:stabio:v:16:y:2024:i:3:d:10.1007_s12561-023-09416-3
    DOI: 10.1007/s12561-023-09416-3
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    References listed on IDEAS

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    1. Jaya M. Satagopan & E. S. Venkatraman & Colin B. Begg, 2004. "Two-Stage Designs for Gene–Disease Association Studies with Sample Size Constraints," Biometrics, The International Biometric Society, vol. 60(3), pages 589-597, September.
    2. Jichang Yu & Haibo Zhou & Jianwen Cai, 2021. "Accelerated failure time model for data from outcome-dependent sampling," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(1), pages 15-37, January.
    3. Jaya M. Satagopan & David A. Verbel & E. S. Venkatraman & Kenneth E. Offit & Colin B. Begg, 2002. "Two-Stage Designs for Gene–Disease Association Studies," Biometrics, The International Biometric Society, vol. 58(1), pages 163-170, March.
    4. Haibo Zhou & M. A. Weaver & J. Qin & M. P. Longnecker & M. C. Wang, 2002. "A Semiparametric Empirical Likelihood Method for Data from an Outcome-Dependent Sampling Scheme with a Continuous Outcome," Biometrics, The International Biometric Society, vol. 58(2), pages 413-421, June.
    5. Xiaofei Wang & Haibo Zhou, 2006. "A Semiparametric Empirical Likelihood Method for Biased Sampling Schemes with Auxiliary Covariates," Biometrics, The International Biometric Society, vol. 62(4), pages 1149-1160, December.
    6. Rui Song & Haibo Zhou & Michael R. Kosorok, 2009. "A note on semiparametric efficient inference for two-stage outcome-dependent sampling with a continuous outcome," Biometrika, Biometrika Trust, vol. 96(1), pages 221-228.
    7. J. F. Lawless & J. D. Kalbfleisch & C. J. Wild, 1999. "Semiparametric methods for response‐selective and missing data problems in regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 413-438, April.
    8. Haibo Zhou & Rui Song & Yuanshan Wu & Jing Qin, 2011. "Statistical Inference for a Two-Stage Outcome-Dependent Sampling Design with a Continuous Outcome," Biometrics, The International Biometric Society, vol. 67(1), pages 194-202, March.
    9. Weaver, Mark A. & Zhou, Haibo, 2005. "An Estimated Likelihood Method for Continuous Outcome Regression Models With Outcome-Dependent Sampling," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 459-469, June.
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