scholarly journals A dynamic stopping rule for phase I clinical trials

2018 ◽  
Vol 55 (1) ◽  
pp. 17-30 ◽  
Author(s):  
M. Iftakhar Alam ◽  
Mohaimen Mansur
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Dose Response ◽  
Parameter Estimate ◽  
Maximum Tolerated Dose ◽  
Stopping Rule ◽  
Bayesian Posterior Probability ◽  
Phase I Clinical Trials ◽  
Response Data ◽  
Number Of Patients

Summary This paper investigates a stopping rule to be utilised in phase I clinical trials. The motivation is to develop a dynamic rule so that a trial stops early if the maximum tolerated dose lies towards the beginning of a dose region. Also, it will employ many patients if the maximum tolerated dose lies towards the end of a dose region. A two-parameter logistic model is assumed for the dose-response data. A trial is stopped early before reaching the maximum number of patients when the width of the Bayesian posterior probability interval of the slope parameter meets a desired value. Instead of setting a pre-specified width to stop at, we determine it based on the parameter estimate obtained after a reasonable number of steps in a trial. Simulation studies of six plausible dose-response scenarios show that the proposed stopping rule is capable of limiting the number of patients to be recruited depending on the underlying scenario. Although the rule is applied to a D-optimum design here, it will be equally applicable to other model-based designs.

scholarly journals Number of Patients per Cohort and Sample Size Considerations Using Dose Escalation with Overdose Control

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Mourad Tighiouart ◽  
André Rogatko
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Phase I ◽  
Sample Size ◽  
Dose Escalation ◽  
Experimental Treatment ◽  
Maximum Tolerated Dose ◽  
Phase I Clinical Trials ◽  
Number Of Patients ◽  
Therapeutic Doses

The main objective of cancer phase I clinical trials is to determine a maximum tolerated dose (MTD) of a new experimental treatment. In practice, most of these trials are designed so that three patients per cohort are treated at the same dose level. In this paper, we compare the safety and efficiency of trials using the escalation with overdose control (EWOC) scheme designed with three or only one patient per cohort. We show through simulations that the number of patients per cohort does not impact the proportion of patients given therapeutic doses, safety of the trial, and efficiency of the estimate of the MTD. Additionally, we present guidelines and tabulated values on the number of patients needed to design a phase I cancer clinical trial using EWOC to achieve a given accuracy of the estimate of the MTD.

scholarly journals Estimating Similarity of Dose–Response Relationships in Phase I Clinical Trials—Case Study in Bridging Data Package

2021 ◽  
Vol 18 (4) ◽  
pp. 1639
Author(s):  
Adrien Ollier ◽  
Sarah Zohar ◽  
Satoshi Morita ◽  
Moreno Ursino
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Dose Response ◽  
Clinical Trial Data ◽  
Japanese Patients ◽  
Maximum Tolerated Dose ◽  
Phase I Clinical Trials ◽  
Response Curves ◽  
Statistical Tool ◽  
Two Populations

Bridging studies are designed to fill the gap between two populations in terms of clinical trial data, such as toxicity, efficacy, comorbidities and doses. According to ICH-E5 guidelines, clinical data can be extrapolated from one region to another if dose–reponse curves are similar between two populations. For instance, in Japan, Phase I clinical trials are often repeated due to this physiological/metabolic paradigm: the maximum tolerated dose (MTD) for Japanese patients is assumed to be lower than that for Caucasian patients, but not necessarily for all molecules. Therefore, proposing a statistical tool evaluating the similarity between two populations dose–response curves is of most interest. The aim of our work is to propose several indicators to evaluate the distance and the similarity of dose–toxicity curves and MTD distributions at the end of some of the Phase I trials, conducted on two populations or regions. For this purpose, we extended and adapted the commensurability criterion, initially proposed by Ollier et al. (2019), in the setting of completed phase I clinical trials. We evaluated their performance using three synthetic sets, built as examples, and six case studies found in the literature. Visualization plots and guidelines on the way to interpret the results are proposed.

scholarly journals Incorporating a Patient Dichotomous Characteristic in Cancer Phase I Clinical Trials Using Escalation with Overdose Control

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Mourad Tighiouart ◽  
Galen Cook-Wiens ◽  
André Rogatko
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Maximum Tolerated Dose ◽  
The Other ◽  
Phase I Clinical Trials ◽  
Other Hand ◽  
Number Of Patients

We describe a design for cancer phase I clinical trials that takes into account patients heterogeneity thought to be related to treatment susceptibility. The goal is to estimate the maximum tolerated dose (MTD) given patient’s specific dichotomous covariate value. The design is Bayesian adaptive and is an extension of escalation with overdose control (EWOC). We will assess the performance of this method by comparing the following designs via extensive simulations: (1) design using a covariate; patients are accrued to the trial sequentially and the dose given to a patient depends on his/her baseline covariate value, (2) design ignoring the covariate; patients are accrued to the trial sequentially and the dose given to a patient does not depend on his/her baseline covariate value, and (3) design using separate trials; in each group, patients are accrued to the trial sequentially and EWOC is implemented in each group. These designs are compared with respect to safety of the trial and efficiency of the estimates of the MTDs via extensive simulations. We found that ignoring a significant baseline binary covariate in the model results in a substantial number of patients being overdosed. On the other hand, accounting for a nonsignificant covariate in the model has practically no effect on the safety of the trial and efficiency of the estimates of the MTDs.

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.

scholarly journals Dose–Response Relationship in Phase I Clinical Trials: A European Drug Development Network (EDDN) Collaboration Study

2014 ◽  
Vol 20 (22) ◽  
pp. 5663-5671 ◽  
Author(s):  
Victor Moreno García ◽  
David Olmos ◽  
Carlos Gomez-Roca ◽  
Philippe A. Cassier ◽  
Rafael Morales-Barrera ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Drug Development ◽  
Phase I ◽  
Dose Response ◽  
Response Relationship ◽  
Phase I Clinical Trials ◽  
Dose Response Relationship ◽  
Development Network

scholarly journals Isotonic Design for Phase I Clinical Trials: Can We Improve Further?

2019 ◽  
Vol 48 (5) ◽  
pp. 34-44
Author(s):  
M. Iftakhar Alam ◽  
Jafrin Sultana
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Appreciable Effect ◽  
Cohort Size ◽  
Phase I Clinical Trials ◽  
Toxic Dose ◽  
Number Of Patients ◽  
Monotonicity Assumption ◽  
Toxicity Relationship ◽  
The Impact

One of the most challenging tasks in clinical trials is finding the maximum tolerated dose (MTD) to be tested in the next phase. An assurance for the safety of the patients and recommendation of a suitable dose for phase II are the main objectives of a phase I trial. The MTD can be identified through various approaches. A non-parametric approach, known as the isotonic design, has been explored in our study. The design relies on the monotonicity assumption of the dose-toxicity relationship. Usually the number of patients in a trial have an impact on the adequacy of dose recommendation. This paper is a humble attempt to see the impact of cohort size and total cohorts on the isotonic design. It investigates the possibility of improving the current algorithm of the isotonic design for escalation and de-escalation. Also, the paper proposes a stopping rule to avoid any severely toxic dose as the MTD. The simulation study shows that along with total cohort, cohort size also has an appreciable effect on the MTD selection. The proposed modification of the algorithm has also been found to work satisfactorily in majority of the cases.

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.

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