The continual reassessment method in cancer phase i clinical trials: A simulation study

1993 ◽  
Vol 12 (12) ◽  
pp. 1093-1108 ◽  
Author(s):  
Sylvie Chevret

2015 ◽  
Vol 34 (10) ◽  
pp. 1681-1694 ◽  
Author(s):  
Suyu Liu ◽  
Haitao Pan ◽  
Jielai Xia ◽  
Qin Huang ◽  
Ying Yuan




2016 ◽  
Vol 53 (2) ◽  
pp. 69-82
Author(s):  
M. Iftakhar Alam

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.



2019 ◽  
Vol 16 (6) ◽  
pp. 665-672
Author(s):  
Nolan A. Wages ◽  
Evan Bagley

Background: This article studies the notion of irrational dose assignment in Phase I clinical trials. This property was recently defined by Zhou and colleagues as a dose assignment that fails to de-escalate the dose when two out of three, three out of six, or four out of six patients have experienced a dose-limiting toxicity event at the current dose level. The authors claimed that a drawback of the well-known continual reassessment method is that it can result in irrational dose assignments. The aim of this article is to examine this definition of irrationality more closely within the conduct of the continual reassessment method. Methods: Over a broad range of assumed dose-limiting toxicity probability scenarios for six study dose levels and a variety of target dose-limiting toxicity rates, we simulated 2000 trials of n = 36 patients. For each scenario, we counted the number of irrational dose assignments that were made by the continual reassessment method, according to the definitions of Zhou and colleagues. For each of the irrational decisions made, we classified the dose assignment as an underdose assignment, a target dose assignment, or an overdose assignment based on the true dose-limiting toxicity probability at that dose. Results: Across eight dose-toxicity scenarios, there were a total of 181,581 dose assignments made in the simulation study. Of these assignments, 8165 (4.5%) decisions were made when two out of three, three out of six, or four out of six patients had experienced a dose-limiting toxicity at the current dose. Of these 8165 decisions, 1505 (18.4%) recommended staying at the current dose level and would therefore be classified as irrational by Zhou and colleagues. Among the irrational decisions, 41.2% were misclassified, meaning they were made either at the true target dose (17.9%) or at a true underdose (23.3%). The remaining 58.8% were made at a true overdose and therefore truly irrational. Overall, irrational dose assignments comprised <1% of the total dose assignments made during the simulation study. Similar findings are reported in simulations across 100 randomly generated dose-toxicity scenarios from a recently proposed family of curves. Conclusion: Zhou and colleagues argue that the behavior of the continual reassessment method is disturbing due to its ability to make irrational dose assignments. These definitions are based on rules that mimic the popular 3 + 3 design, which should not be the benchmark used to construct guidelines for trial conduct of modern Phase I methods. Our study illustrates that these dose assignments occur very seldom in the continual reassessment method and that even when they do occur, they can often be considered sensible when accounting for all accumulated data in the study.



Author(s):  
Xiang Li ◽  
Anastasia Ivanova ◽  
Hong Tian ◽  
Pilar Lim ◽  
Kevin Liu


2018 ◽  
Vol 15 (4) ◽  
pp. 386-397 ◽  
Author(s):  
Daniel G Muenz ◽  
Thomas M Braun ◽  
Jeremy MG Taylor

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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