scholarly journals A default method to specify skeletons for Bayesian model averaging continual reassessment method for phase I clinical trials

2016 ◽  
Vol 36 (2) ◽  
pp. 266-279 ◽  
Author(s):  
Haitao Pan ◽  
Ying Yuan
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Phase I Clinical Trials ◽  
Continual Reassessment Method ◽  
Continual Reassessment

Bridging continual reassessment method for phase I clinical trials in different ethnic populations

2015 ◽  
Vol 34 (10) ◽  
pp. 1681-1694 ◽  
Author(s):  
Suyu Liu ◽  
Haitao Pan ◽  
Jielai Xia ◽  
Qin Huang ◽  
Ying Yuan
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Phase I Clinical Trials ◽  
Continual Reassessment Method ◽  
Ethnic Populations ◽  
Continual Reassessment

Longitudinal Design for Phase I Clinical Trials Using the Continual Reassessment Method

2000 ◽  
Vol 21 (6) ◽  
pp. 574-588 ◽  
Author(s):  
Anna T.R Legedza ◽  
Joseph G Ibrahim
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Longitudinal Design ◽  
Phase I Clinical Trials ◽  
Continual Reassessment Method ◽  
Continual Reassessment

scholarly journals A comparison between the continual reassessment method and D-optimum design for dose finding in phase I clinical trials

2016 ◽  
Vol 53 (2) ◽  
pp. 69-82
Author(s):  
M. Iftakhar Alam
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Optimum Design ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Parameter Estimates ◽  
Phase I Clinical Trials ◽  
Continual Reassessment Method ◽  
Continual Reassessment ◽  
Asymptotic Variability

AbstractThe continual reassessment method is a model-based procedure, described in the literature, used to determine the maximum tolerated dose in phase I clinical trials. The maximum tolerated dose can also be found under the framework of D-optimum design, where information is gathered in such a way so that asymptotic variability in the parameter estimates in minimised. This paper investigates the two methods under some realistic settings to explore any potential differences between them. Simulation studies for six plausible dose-response scenarios show that D-optimum design can work well in comparison with the continual reassessment method in many cases. The D-optimum design is also found to allocate doses from the extremes of the design region to the patients in a trial.

scholarly journals Continual reassessment method with regularization in phase I clinical trials

2020 ◽  
pp. 1-15
Author(s):  
Xiang Li ◽  
Anastasia Ivanova ◽  
Hong Tian ◽  
Pilar Lim ◽  
Kevin Liu
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Phase I Clinical Trials ◽  
Continual Reassessment Method ◽  
Continual Reassessment

scholarly journals Modeling adverse event counts in phase I clinical trials of a cytotoxic agent

2018 ◽  
Vol 15 (4) ◽  
pp. 386-397 ◽  
Author(s):  
Daniel G Muenz ◽  
Thomas M Braun ◽  
Jeremy MG Taylor
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Optimal Dose ◽  
Maximum Tolerated Dose ◽  
Cytotoxic Agents ◽  
Phase I Clinical Trials ◽  
Continual Reassessment Method ◽  
Low Level ◽  
Continual Reassessment ◽  
Dose Limiting Toxicity

Background/Aims The goal of phase I clinical trials for cytotoxic agents is to find the maximum dose with an acceptable risk of severe toxicity. The most common designs for these dose-finding trials use a binary outcome indicating whether a patient had a dose-limiting toxicity. However, a patient may experience multiple toxicities, with each toxicity assigned an ordinal severity score. The binary response is then obtained by dichotomizing a patient’s richer set of data. We contribute to the growing literature on new models to exploit this richer toxicity data, with the goal of improving the efficiency in estimating the maximum tolerated dose. Methods We develop three new, related models that make use of the total number of dose-limiting and low-level toxicities a patient experiences. We use these models to estimate the probability of having at least one dose-limiting toxicity as a function of dose. In a simulation study, we evaluate how often our models select the true maximum tolerated dose, and we compare our models with the continual reassessment method, which uses binary data. Results Across a variety of simulation settings, we find that our models compare well against the continual reassessment method in terms of selecting the true optimal dose. In particular, one of our models which uses dose-limiting and low-level toxicity counts beats or ties the other models, including the continual reassessment method, in all scenarios except the one in which the true optimal dose is the highest dose available. We also find that our models, when not selecting the true optimal dose, tend to err by picking lower, safer doses, while the continual reassessment method errs more toward toxic doses. Conclusion Using dose-limiting and low-level toxicity counts, which are easily obtained from data already routinely collected, is a promising way to improve the efficiency in finding the true maximum tolerated dose in phase I trials.

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