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 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.

scholarly journals A Bayesian dose-finding design for outcomes evaluated with uncertainty

2021 ◽  
pp. 174077452110015
Author(s):  
Matthew J Schipper ◽  
Ying Yuan ◽  
Jeremy MG Taylor ◽  
Randall K Ten Haken ◽  
Christina Tsien ◽  
...  
Keyword(s):  
Phase I ◽  
Phase I Trial ◽  
Data Augmentation ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Continual Reassessment Method ◽  
Number Of Patients ◽  
Continual Reassessment ◽  
Partially Observed ◽  
Dose Limiting Toxicity

Introduction: In some phase I trial settings, there is uncertainty in assessing whether a given patient meets the criteria for dose-limiting toxicity. Methods: We present a design which accommodates dose-limiting toxicity outcomes that are assessed with uncertainty for some patients. Our approach could be utilized in many available phase I trial designs, but we focus on the continual reassessment method due to its popularity. We assume that for some patients, instead of the usual binary dose-limiting toxicity outcome, we observe a physician-assessed probability of dose-limiting toxicity specific to a given patient. Data augmentation is used to estimate the posterior probabilities of dose-limiting toxicity at each dose level based on both the fully observed and partially observed patient outcomes. A simulation study is used to assess the performance of the design relative to using the continual reassessment method on the true dose-limiting toxicity outcomes (available in simulation setting only) and relative to simple thresholding approaches. Results: Among the designs utilizing the partially observed outcomes, our proposed design has the best overall performance in terms of probability of selecting correct maximum tolerated dose and number of patients treated at the maximum tolerated dose. Conclusion: Incorporating uncertainty in dose-limiting toxicity assessment can improve the performance of the continual reassessment method design.

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

scholarly journals Adaptive Dose-Finding Studies: A Review of Model-Guided Phase I Clinical Trials

2014 ◽  
Vol 32 (23) ◽  
pp. 2505-2511 ◽  
Author(s):  
Alexia Iasonos ◽  
John O'Quigley
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Dose Escalation ◽  
Late Onset ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Design Parameters ◽  
Phase I Trials ◽  
Phase I Clinical Trials ◽  
Trial Duration

Purpose We provide a comprehensive review of adaptive phase I clinical trials in oncology that used a statistical model to guide dose escalation to identify the maximum-tolerated dose (MTD). We describe the clinical setting, practical implications, and safety of such applications, with the aim of understanding how these designs work in practice. Methods We identified 53 phase I trials published between January 2003 and September 2013 that used the continual reassessment method (CRM), CRM using escalation with overdose control, or time-to-event CRM for late-onset toxicities. Study characteristics, design parameters, dose-limiting toxicity (DLT) definition, DLT rate, patient-dose allocation, overdose, underdose, sample size, and trial duration were abstracted from each study. In addition, we examined all studies in terms of safety, and we outlined the reasons why escalations occur and under what circumstances. Results On average, trials accrued 25 to 35 patients over a 2-year period and tested five dose levels. The average DLT rate was 18%, which is lower than in previous reports, whereas all levels above the MTD had an average DLT rate of 36%. On average, 39% of patients were treated at the MTD, and 74% were treated at either the MTD or an adjacent level (one level above or below). Conclusion This review of completed phase I studies confirms the safety and generalizability of model-guided, adaptive dose-escalation designs, and it provides an approach for using, interpreting, and understanding such designs to guide dose escalation in phase I trials.

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