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 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 On the coherence of model-based dose-finding designs for drug combination trials

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242561
Author(s):  
Yeonhee Park ◽  
Suyu Liu
Keyword(s):  
Phase I ◽  
Drug Combination ◽  
Treated Patient ◽  
Single Agent ◽  
Sufficient Conditions ◽  
Dose Finding ◽  
Phase I Clinical Trials ◽  
Model Based ◽  
Dose Combination ◽  
Theoretical Results

The concept of coherence was proposed for single-agent phase I clinical trials to describe the property that a design never escalates the dose when the most recently treated patient has toxicity and never de-escalates the dose when the most recently treated patient has no toxicity. It provides a useful theoretical tool for investigating the properties of phase I trial designs. In this paper, we generalize the concept of coherence to drug combination trials, which are substantially different and more challenging than single-agent trials. For example, in the dose-combination matrix, each dose has up to 8 neighboring doses as candidates for dose escalation and de-escalation, and the toxicity orders of these doses are only partially known. We derive sufficient conditions for a model-based drug combination trial design to be coherent. Our results are more general and relaxed than the existing results and are applicable to both single-agent and drug combination trials. We illustrate the application of our theoretical results with a number of drug combination dose-finding designs in the literature.

scholarly journals A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations with Ordinal Toxicity Grades

Stats ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 221-238
Author(s):  
Márcio A. Diniz ◽  
Sungjin Kim ◽  
Mourad Tighiouart
Keyword(s):  
Adaptive Design ◽  
Parametric Models ◽  
Model Parameters ◽  
Operating Characteristics ◽  
Bayesian Adaptive Design ◽  
Continual Reassessment ◽  
Proportional Odds Assumption ◽  
Dose Combination ◽  
The One ◽  
The Relationship

We propose a Bayesian adaptive design for early phase drug combination cancer trials incorporating ordinal grade of toxicities. Parametric models are used to describe the relationship between the dose combinations and the probabilities of the ordinal toxicities under the proportional odds assumption. Trial design proceeds by treating cohorts of two patients simultaneously receiving different dose combinations. Specifically, at each stage of the trial, we seek the dose of one agent by minimizing the Bayes risk with respect to a loss function given the current dose of the other agent. We consider two types of loss functions corresponding to the Continual Reassessment Method (CRM) and Escalation with Overdose Control (EWOC). At the end of the trial, we estimate the MTD curve as a function of Bayes estimates of the model parameters. We evaluate design operating characteristics in terms of safety of the trial and percent of dose recommendation at dose combination neighborhoods around the true MTD by comparing this design to the one that uses a binary indicator of DLT. The methodology is further adapted to the case of a pre-specified discrete set of dose combinations.

scholarly journals Using an Interaction Parameter in Model-Based Phase I Trials for Combination Treatments? A Simulation Study

2021 ◽  
Vol 18 (1) ◽  
pp. 345
Author(s):  
Pavel Mozgunov ◽  
Rochelle Knight ◽  
Helen Barnett ◽  
Thomas Jaki
Keyword(s):  
Phase I ◽  
Interaction Parameter ◽  
Simulation Study ◽  
Small Sample Size ◽  
Small Sample ◽  
Dose Finding ◽  
Phase I Trials ◽  
Model Based ◽  
Combination Toxicity ◽  
Toxicity Relationship

There is growing interest in Phase I dose-finding studies studying several doses of more than one agent simultaneously. A number of combination dose-finding designs were recently proposed to guide escalation/de-escalation decisions during the trials. The majority of these proposals are model-based: a parametric combination-toxicity relationship is fitted as data accumulates. Various parameter shapes were considered but the unifying theme for many of these is that typically between 4 and 6 parameters are to be estimated. While more parameters allow for more flexible modelling of the combination-toxicity relationship, this is a challenging estimation problem given the typically small sample size in Phase I trials of between 20 and 60 patients. These concerns gave raise to an ongoing debate whether including more parameters into combination-toxicity model leads to more accurate combination selection. In this work, we extensively study two variants of a 4-parameter logistic model with reduced number of parameters to investigate the effect of modelling assumptions. A framework to calibrate the prior distributions for a given parametric model is proposed to allow for fair comparisons. Via a comprehensive simulation study, we have found that the inclusion of the interaction parameter between two compounds does not provide any benefit in terms of the accuracy of selection, on average, but is found to result in fewer patients allocated to the target combination during the trial.

Informed consent in phase I clinical trials: Implications for trends in design.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14077-e14077
Author(s):  
Paul Henry Frankel ◽  
Susan G. Groshen
Keyword(s):  
Clinical Trials ◽  
Informed Consent ◽  
Phase I ◽  
Dose Finding ◽  
Institutional Review Boards ◽  
Severe Toxicity ◽  
Phase I Clinical Trials ◽  
Traditional Use ◽  
Phase I Studies ◽  
Definition Of

e14077 Background: Informed Consent (IC) is a critical aspect of human subjects protection. Institutional Review Boards are tasked with insuring proper IC as one aspect of protecting participants in clinical trials. Phase I trials in oncology present special issues with IC, as often neither the risks nor the benefits are well-known. This has resulted in carefully worded IC templates for Phase I studies based on the traditional use of dose-finding designs that are geared towards finding the “Maximum Tolerated Dose (MTD)”. As the definition of this term varies by study, the implication for patient risk and informed consent are rarely discussed. Methods: We reviewed Phase I designs to present options for improving the informed consent process for Phase I oncology trials. Results: Phase I studies have seen an increase in designs based on work from the early 1990s seeking a dose that results in a targeted percent of patients experiencing a “Dose Limiting Toxicity (DLT)” to define the MTD. The most common definition of a DLT is a treatment-related toxicity that results in a particularly concerning severe toxicity (grade 3 or higher) in the first cycle of therapy and the most common rate targeted (in designs that define toxicity as a goal) is 25%. In that setting, while lower doses may have a lower likelihood of DLT, higher doses or the expansion cohort are likely to have a 25% chance of DLT if the target is pursued. This information is rarely quantitatively communicated in the informed consent. Conclusions: IRBs and investigators should consider communicating through informed consent the quantitative summary of goals of the study and related risk. For example, transparency suggests conveying when the goal (target) of the study is to find the dose where there is a one in four chance of experiencing a severe adverse event in the first cycle.

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