IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v112y2017i518p508-520.html
   My bibliography  Save this article

Bayesian Phase I/II Biomarker-Based Dose Finding for Precision Medicine With Molecularly Targeted Agents

Author

Listed:
  • Beibei Guo
  • Ying Yuan

Abstract

The optimal dose for treating patients with a molecularly targeted agent may differ according to the patient's individual characteristics, such as biomarker status. In this article, we propose a Bayesian phase I/II dose-finding design to find the optimal dose that is personalized for each patient according to his/her biomarker status. To overcome the curse of dimensionality caused by the relatively large number of biomarkers and their interactions with the dose, we employ canonical partial least squares (CPLS) to extract a small number of components from the covariate matrix containing the dose, biomarkers, and dose-by-biomarker interactions. Using these components as the covariates, we model the ordinal toxicity and efficacy using the latent-variable approach. Our model accounts for important features of molecularly targeted agents. We quantify the desirability of the dose using a utility function and propose a two-stage dose-finding algorithm to find the personalized optimal dose according to each patient's individual biomarker profile. Simulation studies show that our proposed design has good operating characteristics, with a high probability of identifying the personalized optimal dose. Supplementary materials for this article are available online.

Suggested Citation

  • Beibei Guo & Ying Yuan, 2017. "Bayesian Phase I/II Biomarker-Based Dose Finding for Precision Medicine With Molecularly Targeted Agents," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 508-520, April.
    • Handle: RePEc:taf:jnlasa:v:112:y:2017:i:518:p:508-520
    DOI: 10.1080/01621459.2016.1228534
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2016.1228534
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01621459.2016.1228534?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. Ying Yuan & Guosheng Yin, 2009. "Bayesian dose finding by jointly modelling toxicity and efficacy as time‐to‐event outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(5), pages 719-736, December.
    2. Ick Hoon Jin & Suyu Liu & Peter F. Thall & Ying Yuan, 2014. "Using Data Augmentation to Facilitate Conduct of Phase I-II Clinical Trials With Delayed Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 525-536, June.
    3. Bekele, B. Nebiyou & Thall, Peter F., 2004. "Dose-Finding Based on Multiple Toxicities in a Soft Tissue Sarcoma Trial," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 26-35, January.
    4. Peter F. Thall & Hoang Q. Nguyen & Elihu H. Estey, 2008. "Patient-Specific Dose Finding Based on Bivariate Outcomes and Covariates," Biometrics, The International Biometric Society, vol. 64(4), pages 1126-1136, December.
    5. Yuan, Ying & Yin, Guosheng, 2011. "Robust EM Continual Reassessment Method in Oncology Dose Finding," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 818-831.
    6. M.-K. Riviere & Y. Yuan & F. Dubois & S. Zohar, 2015. "A Bayesian dose finding design for clinical trials combining a cytotoxic agent with a molecularly targeted agent," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(1), pages 215-229, January.
    7. John O'Quigley & Xavier Paoletti, 2003. "Continual Reassessment Method for Ordered Groups," Biometrics, The International Biometric Society, vol. 59(2), pages 430-440, June.
    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. Zhen Li & Jie Chen & Eric Laber & Fang Liu & Richard Baumgartner, 2023. "Optimal Treatment Regimes: A Review and Empirical Comparison," International Statistical Review, International Statistical Institute, vol. 91(3), pages 427-463, December.
    2. Souvik Banerjee & Triparna Bose & Vijay M. Patil & Atanu Bhattacharjee & Kumar Prabhash, 2023. "Bayesian Effective Biological Dose Determination in Immunotherapy Response Trial," Annals of Data Science, Springer, vol. 10(1), pages 209-223, February.
    3. Beibei Guo & Elizabeth Garrett‐Mayer & Suyu Liu, 2021. "A Bayesian phase I/II design for cancer clinical trials combining an immunotherapeutic agent with a chemotherapeutic agent," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1210-1229, November.
    4. Beibei Guo & Ying Yuan, 2023. "DROID: dose‐ranging approach to optimizing dose in oncology drug development," Biometrics, The International Biometric Society, vol. 79(4), pages 2907-2919, December.
    5. Thomas A. Murray & Ying Yuan & Peter F. Thall & Joan H. Elizondo & Wayne L. Hofstetter, 2018. "A utility‐based design for randomized comparative trials with ordinal outcomes and prognostic subgroups," Biometrics, The International Biometric Society, vol. 74(3), pages 1095-1103, September.
    6. Beibei Guo & Rui Zhang, 2018. "Photographic Capture-Recapture for Free-Roaming Dog Population Estimation: Is It Possible to Optimize the Dog Photo-Identification?," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 5(3), pages 88-90, February.

    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. Yifei Zhang & Sha Cao & Chi Zhang & Ick Hoon Jin & Yong Zang, 2021. "A Bayesian adaptive phase I/II clinical trial design with late‐onset competing risk outcomes," Biometrics, The International Biometric Society, vol. 77(3), pages 796-808, September.
    2. Beibei Guo & Ying Yuan, 2023. "DROID: dose‐ranging approach to optimizing dose in oncology drug development," Biometrics, The International Biometric Society, vol. 79(4), pages 2907-2919, December.
    3. Beibei Guo & Rui Zhang, 2018. "Photographic Capture-Recapture for Free-Roaming Dog Population Estimation: Is It Possible to Optimize the Dog Photo-Identification?," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 5(3), pages 88-90, February.
    4. Peter F. Thall & Aniko Szabo & Hoang Q. Nguyen & Catherine M. Amlie-Lefond & Osama O. Zaidat, 2011. "Optimizing the Concentration and Bolus of a Drug Delivered by Continuous Infusion," Biometrics, The International Biometric Society, vol. 67(4), pages 1638-1646, December.
    5. Nadine Houede & Peter F. Thall & Hoang Nguyen & Xavier Paoletti & Andrew Kramar, 2010. "Utility-Based Optimization of Combination Therapy Using Ordinal Toxicity and Efficacy in Phase I/II Trials," Biometrics, The International Biometric Society, vol. 66(2), pages 532-540, June.
    6. Thomas A. Murray & Peter F. Thall & Ying Yuan & Sarah McAvoy & Daniel R. Gomez, 2017. "Robust Treatment Comparison Based on Utilities of Semi-Competing Risks in Non-Small-Cell Lung Cancer," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 11-23, January.
    7. Thomas M. Braun, 2018. "Motivating sample sizes in adaptive Phase I trials via Bayesian posterior credible intervals," Biometrics, The International Biometric Society, vol. 74(3), pages 1065-1071, September.
    8. Chunyan Cai & Ying Yuan & Yuan Ji, 2014. "A Bayesian dose finding design for oncology clinical trials of combinational biological agents," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 159-173, January.
    9. B. Nebiyou Bekele & Yisheng Li & Yuan Ji, 2010. "Risk-Group-Specific Dose Finding Based on an Average Toxicity Score," Biometrics, The International Biometric Society, vol. 66(2), pages 541-548, June.
    10. Beibei Guo & Suyu Liu, 2018. "Optimal Benchmark for Evaluating Drug-Combination Dose-Finding Clinical Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(1), pages 184-201, April.
    11. Xiaobin Yang & Keying Ye, 2012. "A Phase I Dose-_finding Study Based on Polychotomous Toxicity Responses Toxicity issue is always a main concern in phase I study and it is commonly categorized to multiple grades. In this study, the c," Working Papers 0004, College of Business, University of Texas at San Antonio.
    12. Amiri, Mehdi & Izadkhah, Salman & Jamalizadeh, Ahad, 2020. "Linear orderings of the scale mixtures of the multivariate skew-normal distribution," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
    13. Anastasia Ivanova & Se Hee Kim, 2009. "Dose Finding for Continuous and Ordinal Outcomes with a Monotone Objective Function: A Unified Approach," Biometrics, The International Biometric Society, vol. 65(1), pages 307-315, March.
    14. Ying Kuen Cheung, 2014. "Simple benchmark for complex dose finding studies," Biometrics, The International Biometric Society, vol. 70(2), pages 389-397, June.
    15. Xinyang Huang & Jin Xu, 2020. "Estimating individualized treatment rules with risk constraint," Biometrics, The International Biometric Society, vol. 76(4), pages 1310-1318, December.
    16. Laura Deldossi & Silvia Angela Osmetti & Chiara Tommasi, 2019. "Optimal design to discriminate between rival copula models for a bivariate binary response," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 147-165, March.
    17. Danila Azzolina & Paola Berchialla & Dario Gregori & Ileana Baldi, 2021. "Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review," IJERPH, MDPI, vol. 18(4), pages 1-21, February.
    18. Alam, M. Iftakhar & Bogacka, Barbara & Coad, D. Stephen, 2017. "Pharmacokinetically guided optimum adaptive dose selection in early phase clinical trials," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 183-202.
    19. José L. Jiménez & Mourad Tighiouart, 2022. "Combining cytotoxic agents with continuous dose levels in seamless phase I‐II clinical trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1996-2013, November.
    20. Wang, Jixian & Quartey, George, 2013. "A semi-parametric approach to analysis of event duration and prevalence," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 248-257.

    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:taf:jnlasa:v:112:y:2017:i:518:p:508-520. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UASA20 .

    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.