Practical designs for Phase I combination studies in oncology

Wages, Nolan A.; Ivanova, Anastasia; Marchenko, Olga

doi:10.1080/10543406.2015.1092029

Cited by 15 publications

(11 citation statements)

References 46 publications

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“…Six orderings provide an appropriate balance between choosing enough orderings so that we include adequate information to account for the uncertainty surrounding partially ordered dose-toxicity curves, without increasing the dimension of the problem so much so that we diminish performance. 14 At any point in the trial, the dose-limiting toxicity data for dose combination d i are of the form Ω i = { ( y i , n i ) : i = 1 , … , I } , with y i equal to the number of observed dose-limiting toxicities and n i equal to the number of participants who have been evaluated for toxicity at combination d i . To model the probability of dose-limiting toxicity π i at each combination, we assumed a beta-binomial model…”

Section: Methodsmentioning

confidence: 99%

Section: Participant Allocationmentioning

confidence: 99%

“…The Conaway-Dunbar-Peddada method has been extensively evaluated and has demonstrated favorable operating characteristics in recent simulation studies among a variety of more complex dose-finding problems. [13][14][15][16][17] Based on these results, as well as its ability to balance simplicity with good statistical properties, we decided to adapt the Conaway-Dunbar-Peddada method to meet the challenges of the phase I leukemia trial. Changes in access to one of the study agents ultimately lead to further design modifications with a more limited scope, but we describe the tailoring of the original design to the dynamic set of combinations herein.…”

Section: Introductionmentioning

confidence: 99%

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Adapting isotonic dose-finding to a dynamic set of drug combinations with application to a phase I leukemia trial

et al. 2021

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Background/aims This article describes the proposed design of a phase I study evaluating the safety of ceramide nanoliposome and vinblastine among an initial set of 19 possible dose combinations in patients with relapsed/refractory acute myeloid leukemia and patients with untreated acute myeloid leukemia who are not candidates for intensive induction chemotherapy. Methods Extensive collaboration between statisticians and clinical investigators revealed the need to incorporate several adaptive features into the design, including the flexibility of adding or eliminating certain dose combinations based on safety criteria applied to multiple dose pairs. During the design stage, additional dose levels of vinblastine were added, increasing the dimension of the drug combination space and thus the complexity of the problem. Increased complexity made application of existing drug combination dose-finding methods unsuitable in their current form. Results Our solution to these challenges was to adapt a method based on isotonic regression to meet the research objectives of the study. Application of this adapted method is described herein, and a simulation study of the design’s operating characteristics is conducted. Conclusion The aim of this article is to bring to light examples of novel design applications as a means of augmenting the implementation of innovative designs in the future and to demonstrate the flexibility of adaptive designs in satisfying changing design conditions.

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Section: Methodsmentioning

confidence: 99%

Section: Participant Allocationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adapting isotonic dose-finding to a dynamic set of drug combinations with application to a phase I leukemia trial

et al. 2021

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“…Several motivating factors lead us to reexamine the operating characteristics of the Conaway–Dunbar–Peddada method in single agents: (1) the aforementioned growing need for available methods that balance simplicity and performance, (2) favorable operating characteristics displayed in recent simulation studies in more complex dose-finding problems, 16–20 and (3) new user-friendly software for simulating and implementing the method. The details of the Conaway–Dunbar–Peddada method are provided elsewhere, 13 so we only briefly recall them here.…”

Section: Methodsmentioning

confidence: 99%

Revisiting isotonic phase I design in the era of model-assisted dose-finding

Wages

Conaway

2018

Clinical Trials

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Background/aims In the conduct of phase I trials, the limited use of innovative model-based designs in practice has led to an introduction of a class of "model-assisted" designs with the aim of effectively balancing the trade-off between design simplicity and performance. Prior to the recent surge of these designs, methods that allocated patients to doses based on isotonic toxicity probability estimates were proposed. Like model-assisted methods, isotonic designs allow investigators to avoid difficulties associated with pre-trial parametric specifications of model-based designs. The aim of this work is to take a fresh look at an isotonic design in light of the current landscape of model-assisted methods. Methods The isotonic phase I method of Conaway, Dunbar, and Peddada was proposed in 2004 and has been regarded primarily as a design for dose-finding in drug combinations. It has largely been overlooked in the single-agent setting. Given its strong simulation performance in application to more complex dose-finding problems, such as drug combinations and patient heterogeneity, as well as the recent development of user-friendly software to accompany the method, we take a fresh look at this design and compare it to a current model-assisted method. We generated operating characteristics of the Conaway-Dunbar-Peddada method using a new web application developed for simulating and implementing the design and compared it to the recently proposed Keyboard design that is based on toxicity probability intervals. Results The Conaway-Dunbar-Peddada method has better performance in terms of accuracy of dose recommendation and safety in patient allocation in 17 of 20 scenarios considered. The Conaway-Dunbar-Peddada method also allocated fewer patients to doses above the maximum tolerated dose than the Keyboard method in many of scenarios studied. Overall, the performance of the Conaway-Dunbar-Peddada method is strong when compared to the Keyboard method, making it a viable simple alternative to the model-assisted methods developed in recent years. Conclusion The Conaway-Dunbar-Peddada method does not rely on the specification and fitting of a parametric model for the entire dose-toxicity curve to estimate toxicity probabilities as other model-based designs do. It relies on a similar set of pre-trial specifications to toxicity probability interval-based methods, yet unlike model-assisted methods, it is able to borrow information across all dose levels, increasing its efficiency. We hope this concise study of the Conaway-Dunbar-Peddada method, and the availability of user-friendly software, will augment its use in practice.

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“…It is perhaps the restrictions associated with model-based designs, such as difficulty of implementation or communication to clinicians, that have made these less commonly used in real trials. Recently, model-free designs have attracted attention due to their practicality [20], although these have not yet been fully evaluated in the literature.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Model-Free Phase I Dose Escalation Designs for Dual-Agent Combination Therapies

Barnett,

George,

Skanji

et al. 2021

Preprint

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It is increasingly common for therapies in oncology to be given in combination. In some cases, patients can benefit from the interaction between two drugs, although often at the risk of higher toxicity. A large number of designs to conduct phase I trials in this setting are available, where the objective is to select the maximum tolerated dose combination (MTC). Recently, a number of model-free (also called model-assisted) designs have provoked interest, providing several practical advantages over the more conventional approaches of rule-based or model-based designs. In this paper, we demonstrate a novel calibration procedure for model-free designs to determine their most desirable parameters.Under the calibration procedure, we compare the behaviour of model-free designs to a model-based approach in a comprehensive simulation study, covering a number of clinically plausible scenarios. It is found that model-free designs are competitive with the modelbased design in terms of the proportion of correct selections of the MTC. However, there are a number of scenarios in which model-free designs offer a safer alternative. This is also illustrated in the application of the designs to a case study using data from a phase I oncology trial.

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Practical designs for Phase I combination studies in oncology

Cited by 15 publications

References 46 publications

Adapting isotonic dose-finding to a dynamic set of drug combinations with application to a phase I leukemia trial

Adapting isotonic dose-finding to a dynamic set of drug combinations with application to a phase I leukemia trial

Revisiting isotonic phase I design in the era of model-assisted dose-finding

A Comparison of Model-Free Phase I Dose Escalation Designs for Dual-Agent Combination Therapies

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