Background Various dose-finding clinical trial designs, including the continual reassessment method (CRM), dichotomize toxicity outcomes based on prespecified dose-limiting toxicity (DLT) criteria. This loss of toxicity information is particularly inefficient due to the small sample sizes in phase I trials, especially when Common Terminology Criteria for Adverse Events (CTCAE v4.0) are an established ordinal toxicity grading classification already used in the clinical practice.
Purpose The purpose of this simulation study is to incorporate ordinal toxicity grades as specified by CTCAE v4.0 using a continuation ratio (CR) model in the likelihood-based CRM.
Methods This simulation study compares the CR model design to the dichotomous CRM as well as an ordinal CRM that implements the proportional odds (PO) model. We compare six scenarios for model performance based on various safety and efficiency criteria and consider a range of dose–toxicity relationship models, including CR models, PO models, and models that violate the PO assumption.
Results The ordinal CRM performs as well as the dichotomous CRM in all scenarios considered, especially in situations where the starting dose is overly toxic, the ordinal designs show slight improvement in the estimation of the maximum tolerated dose (MTD) and fewer median patients exposed to excessively toxic dose levels as compared to the binary CRM. We also find slight discrepancies in the performance between the PO model and CR model; however, the differences were not substantial enough to strongly recommend one model over the other.
Limitations The CR model design does require slightly more input from clinical investigators prior to the start of the trial as compared to the dichotomous CRM. Investigators must specify the distribution of toxicity grades at the expected dose levels for a 10% and 90% DLT rate in this CR design. However, an R package will help with the implementation of this ordinal design.
Conclusions While the ordinal designs did not perform significantly better than the binary counterpart, we were able to incorporate maximal toxicity information available into a feasible dose-finding design without compromising overall design performance.
Get full access to this article
View all access options for this article.
References
1.
PiantadosiSFisherJDGrossmanS. Practical implementation of a modified continual reassessment method for dose-finding trials. Cancer Chemother Pharmacol1998; 41: 429–36.
2.
YuanZChappellRBaileyH. The continual reassessment method for multiple toxicity grades: A Bayesian quasi-likelihood approach. Biometrics2007; 63: 173–79.
3.
BekeleBNThallPF. Dose-finding based on multiple toxicities in a soft tissue sarcoma trial. J Am Stat Assoc2004; 99: 26–36.
4.
CTCAE. Cancer Therapy Evaluation Program, Common Terminology Criteria for Adverse Events, Version 4.0. DCTD, NCI, NIH, DHHS, 2010. Available at: http://ctep.cancer.gov
5.
RosenbergerWHainesL. Competing designs for phase I clinical trials: A review. Stat Med2002; 21: 2757–70.
6.
Garrett-MayerE. The continual reassessment method for dose-finding studies: A tutorial. Clin Trials2006; 3: 57–71.
7.
AhnC. An evaluation of phase I cancer clinical trial designs. Stat Med1998; 17: 1537–49.
8.
HeydJMCarlinBP. Adaptive design improvements in the continual reassessment method for phase I studies. Stat Med1999; 18: 1307–21.
9.
StorerBE. Design and analysis of phase I clinical trials. Biometrics1989; 45: 925–37.
10.
O’QuigleyJPepeMFisherL. Continual reassessment method: A practical design for phase 1 clinical trials in cancer. Biometrics1990; 46: 33–48.
11.
LópezMFDupuyJ-FGonzalezCV. Effectiveness of adaptive designs for phase II cancer trials. Contemp Clin Trials2012; 33: 223–27.
12.
IasonosAZoharSO’QuigleyJ. Incorporating lower grade toxicity information into dose finding designs. Clin Trials2011; 8: 370–79.
13.
WangCChenTTyanI. Designs for phase I cancer clinical trials with differentiation of graded toxicity. Commun Stat A-Theor2000; 29: 975–87.
14.
AnanthCVKleinbaumDG. Regression models for ordinal responses: A review of methods and applications. Int J Epidemiol1997; 26: 1323–33.
15.
AgrestiA. Categorical Data Analysis (2nd edn). John Wiley & Sons, Inc., Hoboken, NJ, 2002.
16.
McCullaghP. Regression models for ordinal data. J Roy Stat Soc Ser B Met1980; 42: 109–42.
17.
HarrellFEJr. Regression Modeling Strategies with Applications to Linear Models, Logistic Regression, and Survival Analysis. Springer, New York, 2001.
18.
GreenlandS. Alternative models for ordinal logistic regression. Stat Med1994; 13: 1665–77.
19.
AndersonJA. Regression and ordered categorical variables. J Roy Stat Soc Ser B Met1984; 46: 1–30.
20.
Van MeterEMGarrett-MayerEBandyopadhyayD. Proportional odds model for dose-finding clinical trial designs with ordinal toxicity grading. Stat Med2011; 30: 2070–80.
21.
ZhangWSargentDJMandrekarS. An adaptive dose-finding design incorporating both toxicity and efficacy. Stat Med2006; 25: 2365–83.
FanSChalonerK. Optimal designs and limiting optimal designs for a trinomial response. Journal of Statistical Planning and Inference2004; 126: 347–60.
24.
R Foundation for Statistical Computing. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2009. ISBN 3-900051-07-0. Available at: http://www.R-project.org.
