IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v63y2007i1p173-179.html
   My bibliography  Save this article

The Continual Reassessment Method for Multiple Toxicity Grades: A Bayesian Quasi-Likelihood Approach

Author

Listed:
  • Z. Yuan
  • R. Chappell
  • H. Bailey

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:biomet:v:63:y:2007:i:1:p:173-179
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2006.00666.x
    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. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Theory," Econometrica, Econometric Society, vol. 52(3), pages 681-700, May.
    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. 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. Cinzia Carota, 2002. "Semiparametric regression for count data," Biometrika, Biometrika Trust, vol. 89(2), pages 265-281, 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. 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.
    2. 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.
    3. Murray Thomas A., 2017. "Ranking ultimate teams using a Bayesian score-augmented win-loss model," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 13(2), pages 63-78, June.
    4. 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.
    5. Alexia Iasonos & John O'Quigley, 2017. "Phase I designs that allow for uncertainty in the attribution of adverse events," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(5), pages 1015-1030, November.
    6. Márcio A. Diniz & Sungjin Kim & Mourad Tighiouart, 2020. "A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations with Ordinal Toxicity Grades," Stats, MDPI, vol. 3(3), pages 1-18, July.
    7. Ying Kuen Cheung, 2014. "Simple benchmark for complex dose finding studies," Biometrics, The International Biometric Society, vol. 70(2), pages 389-397, June.
    8. Guosheng Yin & Ying Yuan, 2009. "A Latent Contingency Table Approach to Dose Finding for Combinations of Two Agents," Biometrics, The International Biometric Society, vol. 65(3), pages 866-875, September.
    9. 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.
    10. 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.

    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. Giuliani, Elisa & Martinelli, Arianna & Rabellotti, Roberta, 2016. "Is Co-Invention Expediting Technological Catch Up? A Study of Collaboration between Emerging Country Firms and EU Inventors," World Development, Elsevier, vol. 77(C), pages 192-205.
    2. Bettina Becker & Martin Theuringer, 2000. "Macroeconomic Determinants of Contingent Protection: The Case of the European Union," IWP Discussion Paper Series 02/2000, Institute for Economic Policy, Cologne, Germany.
    3. 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.
    4. 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.
    5. Silva João M. C. Santos & Tenreyro Silvana & Windmeijer Frank, 2015. "Testing Competing Models for Non-negative Data with Many Zeros," Journal of Econometric Methods, De Gruyter, vol. 4(1), pages 1-18, January.
    6. Federica DeMaria & Sophie Drogue, 2017. "EU Trade Regulation for Baby Food: Protecting Health or Trade?," The World Economy, Wiley Blackwell, vol. 40(7), pages 1430-1453, July.
    7. de Rassenfosse, Gaétan & Schoen, Anja & Wastyn, Annelies, 2014. "Selection bias in innovation studies: A simple test," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 287-299.
    8. Alessandra Cretarola & Gianna Fig`a-Talamanca & Marco Patacca, 2017. "A sentiment-based model for the BitCoin: theory, estimation and option pricing," Papers 1709.08621, arXiv.org.
    9. Song, Jingyu & Delgado, Michael & Preckel, Paul, 2017. "Aggregated Fractional Regression Estimation: Some Monte Carlo Evidence," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258209, Agricultural and Applied Economics Association.
    10. Gary King, 1989. "A Seemingly Unrelated Poisson Regression Model," Sociological Methods & Research, , vol. 17(3), pages 235-255, February.
    11. Emilie Alberola & Julien Chevallier & Benoît Chèze, 2008. "The EU Emissions Trading Scheme : Disentangling the Effects of Industrial Production and CO2 Emissions on Carbon Prices," Working Papers hal-04140795, HAL.
    12. Czarnitzki, Dirk & Doherr, Thorsten & Hussinger, Katrin & Schliessler, Paula & Toole, Andrew A., 2016. "Knowledge Creates Markets: The influence of entrepreneurial support and patent rights on academic entrepreneurship," European Economic Review, Elsevier, vol. 86(C), pages 131-146.
    13. Aknouche, Abdelhakim & Scotto, Manuel, 2022. "A multiplicative thinning-based integer-valued GARCH model," MPRA Paper 112475, University Library of Munich, Germany.
    14. Alvarez, Javier & Arellano, Manuel, 2022. "Robust likelihood estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 226(1), pages 21-61.
    15. Escribano, Álvaro & Licht, Adrian & Blazsek, Szabolcs, 2018. "Seasonal quasi-vector autoregressive models for macroeconomic data," UC3M Working papers. Economics 26316, Universidad Carlos III de Madrid. Departamento de Economía.
    16. Christelis, Dimitris & Georgarakos, Dimitris & Jappelli, Tullio, 2015. "Wealth shocks, unemployment shocks and consumption in the wake of the Great Recession," Journal of Monetary Economics, Elsevier, vol. 72(C), pages 21-41.
    17. Juan Carluccio & Thibault Fally, 2012. "Global Sourcing under Imperfect Capital Markets," The Review of Economics and Statistics, MIT Press, vol. 94(3), pages 740-763, August.
    18. 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.
    19. Blazsek, Szabolcs & Escribano, Alvaro, 2016. "Score-driven dynamic patent count panel data models," Economics Letters, Elsevier, vol. 149(C), pages 116-119.
    20. Irem Guceri & Li Liu, 2019. "Effectiveness of Fiscal Incentives for R&D: Quasi-experimental Evidence," American Economic Journal: Economic Policy, American Economic Association, vol. 11(1), pages 266-291, February.

    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:63:y:2007:i:1:p:173-179. 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.