Dose expansion cohorts in Phase I trials

A rapidly increasing number of Phase I dose-finding studies, and in particular those based on the standard 3+3 design, frequently prolong the study and include dose expansion cohorts (DEC) with the goal to better characterize the toxicity profiles of experimental agents and to study disease specific cohorts. These trials consist of two phases: the usual dose escalation phase that aims to establish the maximum tolerated dose (MTD) and the dose expansion phase that accrues additional patients, often with different eligibility criteria, and where additional information is being collected. Current protocols typically do not specify whether the MTD will be updated in light of the new data accumulated from the DEC. In this paper, we propose methodology that allows monitoring of safety in the DEC by re-evaluating the MTD in light of additional information. Our working assumption is that, regardless of the design being used for dose escalation, during the DEC we are experimenting in the neighborhood of a target dose with an acceptable rate of toxicity. We refine our initial estimate of the MTD by continuing experimentation in the immediate vicinity of the initial estimate of the MTD. The auxiliary information provided in this evaluation can include toxicity, pharmacokinetic, efficacy or other endpoints. Weconsider approaches specifically focused on the aims of DEC, that examine efficacy alone or simultaneously with safety and compare the proposed tests via simulations

Variance prior specification for a basket trial design using Bayesian hierarchical modeling

Background: In the era of targeted therapies, clinical trials in oncology are rapidly evolving, wherein patients from multiple diseases are now enrolled and treated according to their genomic mutation(s). In such trials, known as basket trials, the different disease cohorts form the different baskets for inference. Several approaches have been proposed in the literature to efficiently use information from all baskets while simultaneously screening to find individual baskets where the drug works. Most proposed methods are developed in a Bayesian paradigm that requires specifying a prior distribution for a variance parameter, which controls the degree to which information is shared across baskets. Methods: A common method used to capture the correlated endpoints across baskets is Bayesian hierarchical modeling. We evaluate a Bayesian adaptive design in the context of a basket trial and investigate two popular prior specifications: an inverse-gamma prior on the basket-level variance and a uniform prior on the basket-level standard deviation. Results: From our simulation study, we see the inverse-gamma prior is highly sensitive to the input hyperparameters. When the prior mean value of the variance parameter is set to be near zero (\u3c0.5), this can lead to unacceptably high false positive rates (\u3e40%) in some scenarios. Thus, use of this prior requires a fully comprehensive sensitivity analysis before implementation. Alternatively, we see that a prior that moves the mass of the variance parameter away from zero, such as the uniform prior, displays desirable and robust operating characteristics over a wide range of prior specifications, with the caveat that the upper bound of the uniform prior must be larger than 1. Conclusion: Based on our results, we recommend that those involved in designing basket trials that implement hierarchical modeling avoid using a prior distribution that places a large density mass near zero for the variance parameter. Priors with this property force the model to share information regardless of the true efficacy configuration of the baskets. Many commonly used inverse-gamma prior specifications have this undesirable property. We recommend to instead consider the more robust uniform prior on the standard deviation

Combination Early-Phase Trials of Anticancer Agents in Children and Adolescents

PURPOSEThere is an increasing need to evaluate innovative drugs for childhood cancer using combination strategies. Strong biological rationale and clinical experience suggest that multiple agents will be more efficacious than monotherapy for most diseases and may overcome resistance mechanisms and increase synergy. The process to evaluate these combination trials needs to maximize efficiency and should be agreed by all stakeholders.METHODSAfter a review of existing combination trial methodologies, regulatory requirements, and current results, a consensus among stakeholders was achieved.RESULTSCombinations of anticancer therapies should be developed on the basis of mechanism of action and robust preclinical evaluation, and may include data from adult clinical trials. The general principle for combination early-phase studies is that, when possible, clinical trials should be dose- and schedule-confirmatory rather than dose-exploratory, and every effort should be made to optimize doses early. Efficient early-phase combination trials should be seamless, including dose confirmation and randomized expansion. Dose evaluation designs for combinations depend on the extent of previous knowledge. If not previously evaluated, limited evaluation of monotherapy should be included in the same clinical trial as the combination. Randomized evaluation of a new agent plus standard therapy versus standard therapy is the most effective approach to isolate the effect and toxicity of the novel agent. Platform trials may be valuable in the evaluation of combination studies. Patient advocates and regulators should be engaged with investigators early in a proposed clinical development pathway and trial design must consider regulatory requirements.CONCLUSIONAn optimized, agreed approach to the design and evaluation of early-phase pediatric combination trials will accelerate drug development and benefit all stakeholders, most importantly children and adolescents with cancer.</p

Incorporating lower grade toxicity information into dose finding designs

International audienc

Dose Expansion Cohorts in Phase I Trials

<p>A rapidly increasing number of Phase I dose-finding studies, those based on the standard 3+3 design in particular, are being prolonged with the inclusion of dose expansion cohorts (DEC) to better characterize the toxicity profiles of experimental agents and to study disease-specific cohorts. These trials consist of two phases: the usual dose escalation phase that aims to establish the maximum tolerated dose (MTD), and the dose expansion phase that accrues additional patients, often with different eligibility criteria, and where additional information is collected. Currently, not all protocols specify whether and how the MTD will be updated in the light of new data accumulated from the DEC. Here, we propose methods that allow monitoring of safety in the DEC by reevaluating the MTD in light of additional information. Our working assumption is that, regardless of the design being used for dose escalation, during the DEC we are experimenting in the neighborhood of a target dose with an acceptable rate of toxicity. We refine our initial estimate of the MTD by continuing experimentation in the immediate vicinity of the initial estimate of the MTD. The auxiliary information provided by such an evaluation will include toxicity, pharmacokinetic, efficacy, and other endpoints. We consider approaches specifically focused on the aims of DEC that examine efficacy alone or simultaneously with safety. Simulations provide further insight into the behavior of the proposed tests. Supplementary materials for this article are available online.</p

A reconstructed melanoma data set for evaluating differential treatment benefit according to biomarker subgroups

The data presented in this article are related to the research article entitled “Measuring differential treatment benefit across marker specific subgroups: the choice of outcome scale” (Satagopan and Iasonos, 2015) [1]. These data were digitally reconstructed from figures published in Larkin et al. (2015) [2]. This article describes the steps to digitally reconstruct patient-level data on time-to-event outcome and treatment and biomarker groups using published Kaplan-Meier survival curves. The reconstructed data set and the corresponding computer programs are made publicly available to enable further statistical methodology research