IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v70y2014i3p489-499.html
   My bibliography  Save this article

Evaluating marker-guided treatment selection strategies

Author

Listed:
  • Roland A. Matsouaka
  • Junlong Li
  • Tianxi Cai

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:biomet:v:70:y:2014:i:3:p:489-499
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/biom.12179
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. 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. Imai, Kosuke & Strauss, Aaron, 2011. "Estimation of Heterogeneous Treatment Effects from Randomized Experiments, with Application to the Optimal Planning of the Get-Out-the-Vote Campaign," Political Analysis, Cambridge University Press, vol. 19(1), pages 1-19, January.
    3. Tianxi Cai & Lu Tian & L. J. Wei, 2005. "Semiparametric Box–Cox power transformation models for censored survival observations," Biometrika, Biometrika Trust, vol. 92(3), pages 619-632, September.
    4. Xiao Song & Margaret Pepe, 2004. "Evaluating Markers for Selecting a Patient's Treatment," UW Biostatistics Working Paper Series 1029, Berkeley Electronic Press.
    5. Yuhyun Park, 2003. "Estimating subject-specific survival functions under the accelerated failure time model," Biometrika, Biometrika Trust, vol. 90(3), pages 717-723, September.
    6. Lihui Zhao & Lu Tian & Tianxi Cai & Brian Claggett & L. J. Wei, 2013. "Effectively Selecting a Target Population for a Future Comparative Study," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 527-539, June.
    7. 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.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shi, Chengchun & Wan, Runzhe & Song, Ge & Luo, Shikai & Zhu, Hongtu & Song, Rui, 2023. "A multiagent reinforcement learning framework for off-policy evaluation in two-sided markets," LSE Research Online Documents on Economics 117174, London School of Economics and Political Science, LSE Library.
    2. Baojiang Chen & Ao Yuan & Jing Qin, 2022. "Pool adjacent violators algorithm–assisted learning with application on estimating optimal individualized treatment regimes," Biometrics, The International Biometric Society, vol. 78(4), pages 1475-1488, December.
    3. Yu Zheng & Tianxi Cai, 2017. "Augmented estimation for t‐year survival with censored regression models," Biometrics, The International Biometric Society, vol. 73(4), pages 1169-1178, December.
    4. Dahl, Roy Endré & Lorentzen, Sindre & Oglend, Atle & Osmundsen, Petter, 2017. "Pro-cyclical petroleum investments and cost overruns in Norway," Energy Policy, Elsevier, vol. 100(C), pages 68-78.
    5. Runchao Jiang & Wenbin Lu & Rui Song & Marie Davidian, 2017. "On estimation of optimal treatment regimes for maximizing t-year survival probability," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1165-1185, September.
    6. 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.
    7. Caiyun Fan & Wenbin Lu & Rui Song & Yong Zhou, 2017. "Concordance-assisted learning for estimating optimal individualized treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1565-1582, November.
    8. Barlow, Euan & Tezcaner Öztürk, Diclehan & Revie, Matthew & Akartunalı, Kerem & Day, Alexander H. & Boulougouris, Evangelos, 2018. "A mixed-method optimisation and simulation framework for supporting logistical decisions during offshore wind farm installations," European Journal of Operational Research, Elsevier, vol. 264(3), pages 894-906.

    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Yuanyuan Shen & Tianxi Cai, 2016. "Identifying predictive markers for personalized treatment selection," Biometrics, The International Biometric Society, vol. 72(4), pages 1017-1025, December.
    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. 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.
    15. Yi Zhang & Kosuke Imai, 2023. "Individualized Policy Evaluation and Learning under Clustered Network Interference," Papers 2311.02467, arXiv.org, revised Feb 2024.
    16. Layla Parast & Beth Ann Griffin, 2017. "Landmark estimation of survival and treatment effects in observational studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(2), pages 161-182, April.
    17. Xiaofei Bai & Anastasios A. Tsiatis & Wenbin Lu & Rui Song, 2017. "Optimal treatment regimes for survival endpoints using a locally-efficient doubly-robust estimator from a classification perspective," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 585-604, October.
    18. Layla Parast & Carolyn M. Rutter, 2017. "Discussion of “A risk-based measure of time-varying prognostic discrimination for survival models,” by C. Jason Liang and Patrick J. Heagerty," Biometrics, The International Biometric Society, vol. 73(3), pages 742-744, September.
    19. Shuai Chen & Lu Tian & Tianxi Cai & Menggang Yu, 2017. "A general statistical framework for subgroup identification and comparative treatment scoring," Biometrics, The International Biometric Society, vol. 73(4), pages 1199-1209, December.
    20. Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2022. "Heterogeneous Employment Effects of Job Search Programs: A Machine Learning Approach," Journal of Human Resources, University of Wisconsin Press, vol. 57(2), pages 597-636.

    More about this item

    Statistics

    Access and download statistics

    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:bla:biomet:v:70:y:2014:i:3:p:489-499. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.