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Case†only approach to identifying markers predicting treatment effects on the relative risk scale

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Listed:
  • James Y. Dai
  • C. Jason Liang
  • Michael LeBlanc
  • Ross L. Prentice
  • Holly Janes

Abstract

Retrospectively measuring markers on stored baseline samples from participants in a randomized controlled trial (RCT) may provide high quality evidence as to the value of the markers for treatment selection. Originally developed for approximating gene†environment interactions in the odds ratio scale, the case†only method has recently been advocated for assessing gene†treatment interactions on rare disease endpoints in randomized clinical trials. In this article, the case†only approach is shown to provide a consistent and efficient estimator of marker by treatment interactions and marker†specific treatment effects on the relative risk scale. The prohibitive rare†disease assumption is no longer needed, broadening the utility of the case†only approach. The case†only method is resource†efficient as markers only need to be measured in cases only. It eliminates the need to model the marker's main effect, and can be used with any parametric or nonparametric learning method. The utility of this approach is illustrated by an application to genetic data in the Women's Health Initiative (WHI) hormone therapy trial.

Suggested Citation

  • James Y. Dai & C. Jason Liang & Michael LeBlanc & Ross L. Prentice & Holly Janes, 2018. "Case†only approach to identifying markers predicting treatment effects on the relative risk scale," Biometrics, The International Biometric Society, vol. 74(2), pages 753-763, June.
  • Handle: RePEc:bla:biomet:v:74:y:2018:i:2:p:753-763
    DOI: 10.1111/biom.12789
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    References listed on IDEAS

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    1. Yingqi Zhao & Donglin Zeng & A. John Rush & Michael R. Kosorok, 2012. "Estimating Individualized Treatment Rules Using Outcome Weighted Learning," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1106-1118, September.
    2. Baqun Zhang & Anastasios A. Tsiatis & Eric B. Laber & Marie Davidian, 2012. "A Robust Method for Estimating Optimal Treatment Regimes," Biometrics, The International Biometric Society, vol. 68(4), pages 1010-1018, December.
    3. James Y. Dai & Charles Kooperberg & Michael Leblanc & Ross L. Prentice, 2012. "Two-stage testing procedures with independent filtering for genome-wide gene-environment interaction," Biometrika, Biometrika Trust, vol. 99(4), pages 929-944.
    4. Roland A. Matsouaka & Junlong Li & Tianxi Cai, 2014. "Evaluating marker-guided treatment selection strategies," Biometrics, The International Biometric Society, vol. 70(3), pages 489-499, September.
    5. Chaeryon Kang & Holly Janes & Ying Huang, 2014. "Rejoinder: Combining biomarkers to optimize patient treatment recommendations," Biometrics, The International Biometric Society, vol. 70(3), pages 719-720, September.
    6. Chaeryon Kang & Holly Janes & Ying Huang, 2014. "Combining biomarkers to optimize patient treatment recommendations," Biometrics, The International Biometric Society, vol. 70(3), pages 695-707, September.
    7. Xiao Song & Margaret Pepe, 2004. "Evaluating Markers for Selecting a Patient's Treatment," UW Biostatistics Working Paper Series 1029, Berkeley Electronic Press.
    8. Vansteelandt, Stijn & VanderWeele, Tyler J. & Tchetgen, Eric J. & Robins, James M., 2008. "Multiply Robust Inference for Statistical Interactions," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1693-1704.
    9. E. Vittinghoff & D. C. Bauer, 2006. "Case-Only Analysis of Treatment–Covariate Interactions in Clinical Trials," Biometrics, The International Biometric Society, vol. 62(3), pages 769-776, September.
    10. Ying Huang & Peter B. Gilbert & Holly Janes, 2012. "Assessing Treatment-Selection Markers using a Potential Outcomes Framework," Biometrics, The International Biometric Society, vol. 68(3), pages 687-696, September.
    11. Xiao Song & Margaret Sullivan Pepe, 2004. "Evaluating Markers for Selecting a Patient's Treatment," Biometrics, The International Biometric Society, vol. 60(4), pages 874-883, December.
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