IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v10y2014i1p23n4.html
   My bibliography  Save this article

An Approach to Evaluating and Comparing Biomarkers for Patient Treatment Selection

Author

Listed:
  • Janes Holly

    (Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N M2 C200, Seattle, WA 98109, USA)

  • Brown Marshall D.

    (Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N M2 C200, Seattle, WA 98109, USA)

  • Huang Ying

    (Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N M2 C200, Seattle, WA 98109, USA)

  • Pepe Margaret S.

    (Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N M2 B500, Seattle, WA 98109, USA University of Washington, Seattle, WA, USA)

Abstract

Despite the heightened interest in developing biomarkers predicting treatment response that are used to optimize patient treatment decisions, there has been relatively little development of statistical methodology to evaluate these markers. There is currently no unified statistical framework for marker evaluation. This paper proposes a suite of descriptive and inferential methods designed to evaluate individual markers and to compare candidate markers. An R software package has been developed which implements these methods. Their utility is illustrated in the breast cancer treatment context, where candidate markers are evaluated for their ability to identify a subset of women who do not benefit from adjuvant chemotherapy and can therefore avoid its toxicity.

Suggested Citation

  • Janes Holly & Brown Marshall D. & Huang Ying & Pepe Margaret S., 2014. "An Approach to Evaluating and Comparing Biomarkers for Patient Treatment Selection," The International Journal of Biostatistics, De Gruyter, vol. 10(1), pages 99-121, May.
    • Handle: RePEc:bpj:ijbist:v:10:y:2014:i:1:p:23:n:4
    DOI: 10.1515/ijb-2012-0052
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/ijb-2012-0052
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/ijb-2012-0052?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. Ying Huang & Margaret Sullivan Pepe & Ziding Feng, 2007. "Evaluating the Predictiveness of a Continuous Marker," Biometrics, The International Biometric Society, vol. 63(4), pages 1181-1188, December.
    5. Xiao Song & Margaret Pepe, 2004. "Evaluating Markers for Selecting a Patient's Treatment," UW Biostatistics Working Paper Series 1029, Berkeley Electronic Press.
    6. Stuart G. Baker & Barnett S. Kramer, 2005. "Statistics for weighing benefits and harms in a proposed genetic substudy of a randomized cancer prevention trial," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(5), pages 941-954, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ying Huang & Eric Laber, 2016. "Personalized Evaluation of Biomarker Value: A Cost-Benefit Perspective," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(1), pages 43-65, June.
    2. 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.
    3. 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.
    4. 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.
    5. Ying Huang & Juhee Cho & Youyi Fong, 2021. "Threshold‐based subgroup testing in logistic regression models in two‐phase sampling designs," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 291-311, March.
    6. Veronika Skrivankova & Patrick J. Heagerty, 2018. "Single index methods for evaluation of marker†guided treatment rules based on multivariate marker panels," Biometrics, The International Biometric Society, vol. 74(2), pages 663-672, June.
    7. Ying Huang & Youyi Fong, 2014. "Identifying optimal biomarker combinations for treatment selection via a robust kernel method," Biometrics, The International Biometric Society, vol. 70(4), pages 891-901, December.
    8. Ailin Fan & Rui Song & Wenbin Lu, 2017. "Change-Plane Analysis for Subgroup Detection and Sample Size Calculation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 769-778, April.
    9. Juan Shen & Xuming He, 2015. "Inference for Subgroup Analysis With a Structured Logistic-Normal Mixture Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 303-312, March.
    10. Ruo-fan Wu & Ming Zheng & Wen Yu, 2016. "Subgroup Analysis with Time-to-Event Data Under a Logistic-Cox Mixture Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(3), pages 863-878, September.
    11. 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.
    12. Peng Jin & Wenbin Lu & Yu Chen & Mengling Liu, 2023. "Change‐plane analysis for subgroup detection with a continuous treatment," Biometrics, The International Biometric Society, vol. 79(3), pages 1920-1933, September.
    13. Shengli An & Peter Zhang & Hong-Bin Fang, 2023. "Subgroup Identification in Survival Outcome Data Based on Concordance Probability Measurement," Mathematics, MDPI, vol. 11(13), pages 1-10, June.
    14. Zhang Zhiwei & Ma Shujie & Nie Lei & Soon Guoxing, 2017. "A Quantitative Concordance Measure for Comparing and Combining Treatment Selection Markers," The International Journal of Biostatistics, De Gruyter, vol. 13(1), pages 1-24, May.
    15. Xu Gao & Weining Shen & Jing Ning & Ziding Feng & Jianhua Hu, 2022. "Addressing patient heterogeneity in disease predictive model development," Biometrics, The International Biometric Society, vol. 78(3), pages 1045-1055, September.
    16. Q. Clairon & R. Henderson & N. J. Young & E. D. Wilson & C. J. Taylor, 2021. "Adaptive treatment and robust control," Biometrics, The International Biometric Society, vol. 77(1), pages 223-236, March.
    17. Y. Huang & M. S. Pepe, 2009. "A Parametric ROC Model-Based Approach for Evaluating the Predictiveness of Continuous Markers in Case–Control Studies," Biometrics, The International Biometric Society, vol. 65(4), pages 1133-1144, December.
    18. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
    19. Xin Qiu & Donglin Zeng & Yuanjia Wang, 2018. "Estimation and evaluation of linear individualized treatment rules to guarantee performance," Biometrics, The International Biometric Society, vol. 74(2), pages 517-528, June.
    20. Crystal T. Nguyen & Daniel J. Luckett & Anna R. Kahkoska & Grace E. Shearrer & Donna Spruijt‐Metz & Jaimie N. Davis & Michael R. Kosorok, 2020. "Estimating individualized treatment regimes from crossover designs," Biometrics, The International Biometric Society, vol. 76(3), pages 778-788, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:ijbist:v:10:y:2014:i:1:p:23:n:4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.