IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0098147.html

The Continual Reassessment Method for Multiple Toxicity Grades: A Bayesian Model Selection Approach

Author

Listed:
  • Haitao Pan
  • Cailin Zhu
  • Feng Zhang
  • Ying Yuan
  • Shemin Zhang
  • Wenhong Zhang
  • Chanjuan Li
  • Ling Wang
  • Jielai Xia

Abstract

Grade information has been considered in Yuan et al. (2007) wherein they proposed a Quasi-CRM method to incorporate the grade toxicity information in phase I trials. A potential problem with the Quasi-CRM model is that the choice of skeleton may dramatically vary the performance of the CRM model, which results in similar consequences for the Quasi-CRM model. In this paper, we propose a new model by utilizing bayesian model selection approach – Robust Quasi-CRM model – to tackle the above-mentioned pitfall with the Quasi-CRM model. The Robust Quasi-CRM model literally inherits the BMA-CRM model proposed by Yin and Yuan (2009) to consider a parallel of skeletons for Quasi-CRM. The superior performance of Robust Quasi-CRM model was demonstrated by extensive simulation studies. We conclude that the proposed method can be freely used in real practice.

Suggested Citation

  • Haitao Pan & Cailin Zhu & Feng Zhang & Ying Yuan & Shemin Zhang & Wenhong Zhang & Chanjuan Li & Ling Wang & Jielai Xia, 2014. "The Continual Reassessment Method for Multiple Toxicity Grades: A Bayesian Model Selection Approach," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-8, May.
    • Handle: RePEc:plo:pone00:0098147
    DOI: 10.1371/journal.pone.0098147
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0098147
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0098147&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0098147?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
    ---><---

    References listed on IDEAS

    as
    1. Z. Yuan & R. Chappell & H. Bailey, 2007. "The Continual Reassessment Method for Multiple Toxicity Grades: A Bayesian Quasi-Likelihood Approach," Biometrics, The International Biometric Society, vol. 63(1), pages 173-179, March.
    2. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Applications to Poisson Models," Econometrica, Econometric Society, vol. 52(3), pages 701-720, May.
    3. Yin, Guosheng & Yuan, Ying, 2009. "Bayesian Model Averaging Continual Reassessment Method in Phase I Clinical Trials," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 954-968.
    4. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Theory," Econometrica, Econometric Society, vol. 52(3), pages 681-700, May.
    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. Becker Bettina & Theuringer Martin, 2001. "Macroeconomic Determinants of Contingent Protection: The Case of the European Union," Zeitschrift für Wirtschaftspolitik, De Gruyter, vol. 50(3), pages 350-374, December.
    2. Hallin, Marc & La Vecchia, Davide, 2020. "A Simple R-estimation method for semiparametric duration models," Journal of Econometrics, Elsevier, vol. 218(2), pages 736-749.
    3. Barone-Adesi, Giovanni & Fusari, Nicola & Mira, Antonietta & Sala, Carlo, 2020. "Option market trading activity and the estimation of the pricing kernel: A Bayesian approach," Journal of Econometrics, Elsevier, vol. 216(2), pages 430-449.
    4. Agostino Capponi & Zhaonan Qu, 2025. "Handling Sparse Non-negative Data in Finance," Papers 2509.01478, arXiv.org.
    5. Gary King, 1989. "A Seemingly Unrelated Poisson Regression Model," Sociological Methods & Research, , vol. 17(3), pages 235-255, February.
    6. Emilie Alberola & Benoît Chèze & Julien Chevallier, 2008. "The EU Emissions Trading Scheme : Disentangling the Effects of Industrial Production and CO2 Emissions on Carbon Prices," EconomiX Working Papers 2008-12, University of Paris Nanterre, EconomiX.
    7. Blazsek, Szabolcs & Escribano, Álvaro & Licht, Adrian, 2018. "Seasonal quasi-vector autoregressive models for macroeconomic data," UC3M Working papers. Economics 26316, Universidad Carlos III de Madrid. Departamento de Economía.
    8. Stefan Boes & Michael Gerfin, 2016. "Does Full Insurance Increase the Demand for Health Care?," Health Economics, John Wiley & Sons, Ltd., vol. 25(11), pages 1483-1496, November.
    9. Gurmu, Shiferaw & Rilstone, Paul & Stern, Steven, 1998. "Semiparametric estimation of count regression models1," Journal of Econometrics, Elsevier, vol. 88(1), pages 123-150, November.
    10. Pang, Arwin, 2017. "Incorporating the effect of successfully bagging big game into recreational hunting: An examination of deer, moose and elk hunting," Journal of Forest Economics, Elsevier, vol. 28(C), pages 12-17.
    11. Cassiman, Bruno & Veugelers, Reinhilde & Arts, Sam, 2018. "Mind the gap: Capturing value from basic research through combining mobile inventors and partnerships," Research Policy, Elsevier, vol. 47(9), pages 1811-1824.
    12. La Vecchia, Davide & Camponovo, Lorenzo & Ferrari, Davide, 2015. "Robust heart rate variability analysis by generalized entropy minimization," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 137-151.
    13. Cui, Xin & Wen, Lulu & Wang, Chunfeng & Fang, Zhenming & Yao, Shouyu, 2025. "Innovating under pressure: How geopolitical risk exposure drives energy innovation in firms," Energy Economics, Elsevier, vol. 148(C).
    14. Hajivassiliou, Vassilis A. & Ruud, Paul A., 1986. "Classical estimation methods for LDV models using simulation," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 40, pages 2383-2441, Elsevier.
    15. Vasiliki Christou & Konstantinos Fokianos, 2014. "Quasi-Likelihood Inference For Negative Binomial Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(1), pages 55-78, January.
    16. Vânia G. Silva & Esmeralda A. Ramalho & Carlos R. Vieira, 2017. "The Use of Cheques in the European Union: A Cross-Country Analysis," Open Economies Review, Springer, vol. 28(3), pages 581-602, July.
    17. repec:gnv:wpaper:unige:76321 is not listed on IDEAS
    18. Song, Jingyu & Delgado, Michael & Preckel, Paul & Villoria, Nelson, 2016. "Pixel Level Cropland Allocation and Marginal Impacts of Biophysical Factors," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235327, Agricultural and Applied Economics Association.
    19. Vijverberg, Wim P. & Hasebe, Takuya, 2015. "GTL Regression: A Linear Model with Skewed and Thick-Tailed Disturbances," IZA Discussion Papers 8898, Institute of Labor Economics (IZA).
    20. Gouriéroux, Christian & Monfort, Alain & Zakoian, Jean-Michel, 2017. "Pseudo-Maximum Likelihood and Lie Groups of Linear Transformations," MPRA Paper 79623, University Library of Munich, Germany.
    21. Tang, Niansheng & Wang, Wenjun, 2019. "Robust estimation of generalized estimating equations with finite mixture correlation matrices and missing covariates at random for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 640-655.

    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:plo:pone00:0098147. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.