scholarly journals A Bayesian adaptive design for estimating the maximum tolerated dose curve using drug combinations in cancer phase I clinical trials

2016 ◽  
Vol 36 (2) ◽  
pp. 280-290 ◽  
Author(s):  
Mourad Tighiouart ◽  
Quanlin Li ◽  
André Rogatko
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Adaptive Design ◽  
Maximum Tolerated Dose ◽  
Drug Combinations ◽  
Phase I Clinical Trials ◽  
Bayesian Adaptive Design ◽  
Dose Curve

scholarly journals A Bayesian Adaptive Design for Combination of Three Drugs in Cancer Phase I Clinical Trials

2016 ◽  
Vol 6 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Mourad Tighiouart ◽  
Quanlin Li ◽  
Steven Piantadosi ◽  
Andre Rogatko
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Adaptive Design ◽  
Phase I Clinical Trials ◽  
Bayesian Adaptive Design

scholarly journals A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations in the Presence of a Baseline Covariate

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Márcio Augusto Diniz ◽  
Sungjin Kim ◽  
Mourad Tighiouart
Keyword(s):  
Phase I ◽  
Adaptive Design ◽  
Cytotoxic Agents ◽  
Dose Finding ◽  
Model Parameters ◽  
Baseline Characteristic ◽  
Phase I Clinical Trials ◽  
Bayesian Adaptive Design ◽  
Dose Combination ◽  
Toxicity Relationship

A Bayesian adaptive design for dose finding of a combination of two drugs in cancer phase I clinical trials that takes into account patients heterogeneity thought to be related to treatment susceptibility is described. The estimation of the maximum tolerated dose (MTD) curve is a function of a baseline covariate using two cytotoxic agents. A logistic model is used to describe the relationship between the doses, baseline covariate, and the probability of dose limiting toxicity (DLT). Trial design proceeds by treating cohorts of two patients simultaneously using escalation with overdose control (EWOC), where at each stage of the trial, the next dose combination corresponds to the α quantile of the current posterior distribution of the MTD of one of two agents at the current dose of the other agent and the next patient’s baseline covariate value. The MTD curves are estimated as function of Bayes estimates of the model parameters at the end of trial. Average DLT, pointwise average bias, and percent of dose recommendation at dose combination neighborhoods around the true MTD are compared between the design that uses the covariate and the one that ignores the baseline characteristic. We also examine the performance of the approach under model misspecifications for the true dose-toxicity relationship. The methodology is further illustrated in the case of a prespecified discrete set of dose combinations.

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 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 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 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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