IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v58y2009i2p225-236.html
   My bibliography  Save this article

Parametric non‐mixture cure models for schedule finding of therapeutic agents

Author

Listed:
  • Changying A. Liu
  • Thomas M. Braun

Abstract

Summary. We propose a phase I clinical trial design that seeks to determine the cumulative safety of a series of administrations of a fixed dose of an investigational agent. In contrast with traditional phase I trials that are designed solely to find the maximum tolerated dose of the agent, our design instead identifies a maximum tolerated schedule that includes a maximum tolerated dose as well as a vector of recommended administration times. Our model is based on a non‐mixture cure model that constrains the probability of dose limiting toxicity for all patients to increase monotonically with both dose and the number of administrations received. We assume a specific parametric hazard function for each administration and compute the total hazard of dose limiting toxicity for a schedule as a sum of individual administration hazards. Throughout a variety of settings motivated by an actual study in allogeneic bone marrow transplant recipients, we demonstrate that our approach has excellent operating characteristics and performs as well as the only other currently published design for schedule finding studies. We also present arguments for the preference of our non‐mixture cure model over the existing model.

Suggested Citation

  • Changying A. Liu & Thomas M. Braun, 2009. "Parametric non‐mixture cure models for schedule finding of therapeutic agents," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(2), pages 225-236, May.
  • Handle: RePEc:bla:jorssc:v:58:y:2009:i:2:p:225-236
    DOI: 10.1111/j.1467-9876.2008.00660.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9876.2008.00660.x
    Download Restriction: no

    File URL: https://libkey.io/10.1111/j.1467-9876.2008.00660.x?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. Tsodikov A.D. & Ibrahim J.G. & Yakovlev A.Y., 2003. "Estimating Cure Rates From Survival Data: An Alternative to Two-Component Mixture Models," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 1063-1078, January.
    2. Ying Kuen Cheung & Rick Chappell, 2000. "Sequential Designs for Phase I Clinical Trials with Late-Onset Toxicities," Biometrics, The International Biometric Society, vol. 56(4), pages 1177-1182, 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. Emma Gerard & Sarah Zohar & Hoai‐Thu Thai & Christelle Lorenzato & Marie‐Karelle Riviere & Moreno Ursino, 2022. "Bayesian dose regimen assessment in early phase oncology incorporating pharmacokinetics and pharmacodynamics," Biometrics, The International Biometric Society, vol. 78(1), pages 300-312, March.

    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. Erica Brittain & Dean Follmann & Song Yang, 2008. "Dynamic Comparison of Kaplan–Meier Proportions: Monitoring a Randomized Clinical Trial with a Long-Term Binary Endpoint," Biometrics, The International Biometric Society, vol. 64(1), pages 189-197, March.
    2. 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.
    3. Weiyu Li & Valentin Patilea, 2018. "A dimension reduction approach for conditional Kaplan–Meier estimators," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(2), pages 295-315, June.
    4. 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.
    5. Chen, Chyong-Mei & Lu, Tai-Fang C., 2012. "Marginal analysis of multivariate failure time data with a surviving fraction based on semiparametric transformation cure models," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 645-655.
    6. Portier, Francois & El Ghouch, Anouar & Van Keilegom, Ingrid, 2015. "Efficiency and Bootstrap in the Promotion Time Cure Model," LIDAM Discussion Papers ISBA 2015012, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    7. Mohamed Elamin Abdallah Mohamed Elamin Omer & Mohd Rizam Abu Bakar & Mohd Bakri Adam & Mohd Shafie Mustafa, 2020. "Cure Models with Exponentiated Weibull Exponential Distribution for the Analysis of Melanoma Patients," Mathematics, MDPI, vol. 8(11), pages 1-15, November.
    8. Lajmi Lakhal-Chaieb & Thierry Duchesne, 2017. "Association measures for bivariate failure times in the presence of a cure fraction," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 517-532, October.
    9. Vicente G. Cancho & Márcia A. C. Macera & Adriano K. Suzuki & Francisco Louzada & Katherine E. C. Zavaleta, 2020. "A new long-term survival model with dispersion induced by discrete frailty," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(2), pages 221-244, April.
    10. Guoqing Diao & Ao Yuan, 2019. "A class of semiparametric cure models with current status data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(1), pages 26-51, January.
    11. Vicente G. Cancho & Gladys D. C. Barriga & Gauss M. Cordeiro & Edwin M. M. Ortega & Adriano K. Suzuki, 2021. "Bayesian survival model induced by frailty for lifetime with long‐term survivors," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(3), pages 299-323, August.
    12. 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.
    13. Elizabeth Hashimoto & Gauss Cordeiro & Edwin Ortega, 2013. "The new Neyman type A beta Weibull model with long-term survivors," Computational Statistics, Springer, vol. 28(3), pages 933-954, June.
    14. 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.
    15. repec:syb:wpbsba:03/2013 is not listed on IDEAS
    16. Ying Kuen Cheung & Peter F. Thall, 2002. "Monitoring the Rates of Composite Events with Censored Data in Phase II Clinical Trials," Biometrics, The International Biometric Society, vol. 58(1), pages 89-97, March.
    17. Guosheng Yin, 2005. "Bayesian Cure Rate Frailty Models with Application to a Root Canal Therapy Study," Biometrics, The International Biometric Society, vol. 61(2), pages 552-558, June.
    18. Hu, Tao & Xiang, Liming, 2016. "Partially linear transformation cure models for interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 257-269.
    19. Alessandra Giovagnoli, 2021. "The Bayesian Design of Adaptive Clinical Trials," IJERPH, MDPI, vol. 18(2), pages 1-15, January.
    20. 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.
    21. Guosheng Yin & Joseph G. Ibrahim, 2005. "A General Class of Bayesian Survival Models with Zero and Nonzero Cure Fractions," Biometrics, The International Biometric Society, vol. 61(2), pages 403-412, June.

    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:jorssc:v:58:y:2009:i:2:p:225-236. 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: https://edirc.repec.org/data/rssssea.html .

    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.