1
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Wages NA, Dillon PM, Portell CA, Slingluff CL, Petroni GR. Applications of the partial-order continual reassessment method in the early development of treatment combinations. Clin Trials 2024; 21:331-339. [PMID: 38554038 DOI: 10.1177/17407745241234634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/01/2024]
Abstract
Combination therapy is increasingly being explored as a promising approach for improving cancer treatment outcomes. However, identifying effective dose combinations in early oncology drug development is challenging due to limited sample sizes in early-phase clinical trials. This task becomes even more complex when multiple agents are being escalated simultaneously, potentially leading to a loss of monotonic toxicity order with respect to the dose. Traditional single-agent trial designs are insufficient for this multi-dimensional problem, necessitating the development and implementation of dose-finding methods specifically designed for drug combinations. While, in practice, approaches to this problem have focused on preselecting combinations with a known toxicity order and applying single-agent designs, this limits the number of combinations considered and may miss promising dose combinations. In recent years, several novel designs have been proposed for exploring partially ordered drug combination spaces with the goal of identifying a maximum tolerated dose combination, based on safety, or an optimal dose combination, based on toxicity and efficacy. However, their implementation in clinical practice remains limited. In this article, we describe the application of the partial order continual reassessment method and its extensions for combination therapies in early-phase clinical trials. We present completed trials that use safety endpoints to identify maximum tolerated dose combinations and adaptively use both safety and efficacy endpoints to determine optimal treatment strategies. We discuss the effectiveness of the partial-order continual reassessment method and its extensions in identifying optimal treatment strategies and provide our experience with executing these novel adaptive designs in practice. By utilizing innovative dose-finding methods, researchers and clinicians can more effectively navigate the challenges of combination therapy development, ultimately improving patient outcomes in the treatment of cancer.
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Affiliation(s)
- Nolan A Wages
- Department of Biostatistics, School of Population Health, Virginia Commonwealth University, Richmond, VA, USA
| | - Patrick M Dillon
- Division of Hematology & Oncology, Department of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Craig A Portell
- Division of Hematology & Oncology, Department of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Craig L Slingluff
- Division of Surgical Oncology, Department of Surgery, University of Virginia, Charlottesville, VA, USA
| | - Gina R Petroni
- Division of Translational Research & Applied Statistics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
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2
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Xiao J, Zhang W. A new function for drug combination dose finding trials. Sci Rep 2024; 14:3483. [PMID: 38346971 PMCID: PMC10861533 DOI: 10.1038/s41598-024-53155-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 01/29/2024] [Indexed: 02/15/2024] Open
Abstract
Combination drugs play an essential role in treating cancers. The challenging part of the combination drugs are to specify the dose-toxicity ordering, which means the sequences of dose escalation and de-escalation in process of dose findings should be pre-determined. In the paper, we extend a novel function of the continual reassessment method based on the combination of the normal distribution for drug-combination dose-finding trials and systematically evaluate its performance using a template of four performance measures EARS (Efficiency, Accuracy, Reliability, Selection). Dose escalation and deescalation rules are based on the nearest neighborhood continual reassessment method for a combination drug, and we specify all possible dose-toxicity orderings in the trial. Simulation demonstrates that the new design is efficient, accurate and reasonably reliable.
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Affiliation(s)
- Jiacheng Xiao
- Department of Financial and Actuarial Mathematics, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, China
| | - Weijia Zhang
- Department of Financial and Actuarial Mathematics, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, China.
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3
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Zhang J, Yan F, Wages NA, Lin R. Local continual reassessment methods for dose finding and optimization in drug-combination trials. Stat Methods Med Res 2023; 32:2049-2063. [PMID: 37593951 PMCID: PMC10563380 DOI: 10.1177/09622802231192955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
Due to the limited sample size and large dose exploration space, obtaining a desirable dose combination is a challenging task in the early development of combination treatments for cancer patients. Most existing designs for optimizing the dose combination are model-based, requiring significant efforts to elicit parameters or prior distributions. Model-based designs also rely on intensive model calibration and may yield unstable performance in the case of model misspecification or sparse data. We propose to employ local, underparameterized models for dose exploration to reduce the hurdle of model calibration and enhance the design robustness. Building upon the framework of the partial ordering continual reassessment method, we develop local data-based continual reassessment method designs for identifying the maximum tolerated dose combination, using toxicity only, and the optimal biological dose combination, using both toxicity and efficacy, respectively. The local data-based continual reassessment method designs only model the local data from neighboring dose combinations. Therefore, they are flexible in estimating the local space and circumventing unstable characterization of the entire dose-exploration surface. Our simulation studies show that our approach has competitive performance compared to widely used methods for finding maximum tolerated dose combination, and it has advantages over existing model-based methods for optimizing optimal biological dose combination.
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Affiliation(s)
- Jingyi Zhang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Nolan A Wages
- Department of Biostatistics, Massey Cancer Center, Virginia Commonwealth University, Richmond, VA , USA
| | - Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX USA
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4
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O'Connell NS, Wages NA, Garrett-Mayer E. Quasi-partial order continual reassessment method: Applying toxicity scores to cancer dose-finding drug combination trials. Contemp Clin Trials 2023; 125:107050. [PMID: 36529437 DOI: 10.1016/j.cct.2022.107050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/08/2022] [Accepted: 12/09/2022] [Indexed: 12/23/2022]
Abstract
The primary endpoint of most dose-finding cancer trials is patient toxicity, and the primary goal is to identify the maximum tolerated dose (MTD), that is, the highest dose that falls below or within a pre-specified toxicity tolerability threshold. Conventionally, dose-finding methods have utilized a binary toxicity endpoint based on whether or not a patient experiences a dose limiting-toxicity (DLT). Improving upon this, in recent years several methods have been developed for modeling toxicity scores, a novel continuous endpoint designed to more precisely estimate patient toxicity burden. Separately, drug-combination trials have become increasingly prevalent, and due to added complexities regarding estimating 'true' dose ordering and potential for more complex patient toxicity profiles, provide an ideal setting which may benefit from the improved precision of toxicity scores. In this paper, we merge two frameworks based on the Continual Reassessment Method (CRM) - the Quasi-CRM and the Partial Order CRM (POCRM) - to propose a novel approach for modeling toxicity scores in a combination-trial setting. We demonstrate that utilizing toxicity scores has the potential to greatly improve correct dose-selection over a variety of trial scenarios. We further present a simple adaptation to the toxicity-score model to control for potential over-dosing issues such that it adheres to the conventional DLT definition and will, at worst, perform equivalently to that of the traditional binary DLT framework. We demonstrate that extending toxicity scores to the combination-trial setting offers potential for improvement over the conventional binary endpoint models.
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Affiliation(s)
- Nathaniel S O'Connell
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston Salem, NC, USA.
| | - Nolan A Wages
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA
| | - Elizabeth Garrett-Mayer
- Center for Research and Analytics, American Society for Clinical Oncology, Alexandria, VA, USA
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5
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Mozgunov P, Jaki T, Gounaris I, Goddemeier T, Victor A, Grinberg M. Practical implementation of the partial ordering continual reassessment method in a Phase I combination-schedule dose-finding trial. Stat Med 2022; 41:5789-5809. [PMID: 36428217 PMCID: PMC10100035 DOI: 10.1002/sim.9594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 07/29/2022] [Accepted: 10/04/2022] [Indexed: 11/27/2022]
Abstract
There is a growing medical interest in combining several agents and optimizing their dosing schedules in a single trial in order to optimize the treatment for patients. Evaluating at doses of several drugs and their scheduling in a single Phase I trial simultaneously possess a number of statistical challenges, and specialized methods to tackle these have been proposed in the literature. However, the uptake of these methods is slow and implementation examples of such advanced methods are still sparse to date. In this work, we share our experience of proposing a model-based partial ordering continual reassessment method (POCRM) design for three-dimensional dose-finding in an oncology trial. In the trial, doses of two agents and the dosing schedule of one of them can be escalated/de-escalated. We provide a step-by-step summary on how the POCRM design was implemented and communicated to the trial team. We proposed an approach to specify toxicity orderings and their a-priori probabilities, and developed a number of visualization tools to communicate the statistical properties of the design. The design evaluation included both a comprehensive simulation study and considerations of the individual trial behavior. The study is now enrolling patients. We hope that sharing our experience of the successful implementation of an advanced design in practice that went through evaluations of several health authorities will facilitate a better uptake of more efficient methods in practice.
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Affiliation(s)
- Pavel Mozgunov
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.,Computational Statistics Group, University of Regensburg, Regensburg, Germany
| | | | - Thomas Goddemeier
- Biostatistics, Epidemiology & Medical Writing, Merck Healthcare KGaA, Darmstadt, Germany
| | - Anja Victor
- Biostatistics, Epidemiology & Medical Writing, Merck Healthcare KGaA, Darmstadt, Germany
| | - Marianna Grinberg
- Biostatistics, Epidemiology & Medical Writing, Merck Healthcare KGaA, Darmstadt, Germany.,Marianna Grinberg, Statistical Sciences and Innovation, UCB, Monheim, Germany
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6
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Hashizume K, Tshuchida J, Sozu T. Flexible use of copula-type model for dose-finding in drug combination clinical trials. Biometrics 2022; 78:1651-1661. [PMID: 34181760 PMCID: PMC10393268 DOI: 10.1111/biom.13510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 05/03/2021] [Accepted: 06/17/2021] [Indexed: 12/30/2022]
Abstract
Identification of the maximum tolerated dose combination (MTDC) of cancer drugs is an important objective in phase I oncology trials. Numerous dose-finding designs for drug combination have been proposed over the years. Copula-type models exhibit distinctive advantages in this task over other models used in existing competitive designs. For example, their application enables the consideration of dose-limiting toxicities attributable to one of two agents. However, if a particular combination therapy demonstrates extremely synergistic toxicity, copula-type models are liable to induce biases in toxicity probability estimators due to the associated Fréchet-Hoeffding bounds. Consequently, the dose-finding performance may be worse than those of other competitive designs. The objective of this study is to improve the performance of dose-finding designs based on copula-type models while maintaining their advantageous properties. We propose an extension of the parameter space of the interaction term in copula-type models. This releases the Fréchet-Hoeffding bounds, making the estimation of toxicity probabilities more flexible. Numerical examples in various scenarios demonstrate that the performance (e.g., the percentage of correct MTDC selection) of the proposed method is better than those exhibited by existing copula-type models and comparable with those of other competitive designs, irrespective of the existence of extreme synergistic toxicity. The results obtained in this study could motivate the real-world application of the proposed method in cases requiring the utilization of the properties of copula-type models.
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Affiliation(s)
- Koichi Hashizume
- Department of Information and Computer Technology, Graduate School of Engineering, Tokyo University of Science, Tokyo, Japan.,Global Biometrics and Data Science, Bristol Myers Squibb K.K, Tokyo, Japan
| | - Jun Tshuchida
- Department of Culture and Information Science, Faculty of Culture and Information Science, Doshisha University, Kyoto, Japan
| | - Takashi Sozu
- Department of Information and Computer Technology, Faculty of Engineering, Tokyo University of Science, Tokyo, Japan
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7
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Wang S, Sayour E, Lee JH. Evaluation of phase I clinical trial designs for combinational agents along with guidance based on simulation studies. J Appl Stat 2022. [DOI: 10.1080/02664763.2022.2105827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Affiliation(s)
- Shu Wang
- Division of Quantitative Sciences, UF Health, Gainesville, FL, USA
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - Elias Sayour
- Department of Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Ji-Hyun Lee
- Division of Quantitative Sciences, UF Health, Gainesville, FL, USA
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
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8
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Takahashi A, Suzuki T. Bayesian optimization design for finding a maximum tolerated dose combination in phase I clinical trials. Int J Biostat 2021; 18:39-56. [PMID: 33818029 DOI: 10.1515/ijb-2020-0147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 03/17/2021] [Indexed: 11/15/2022]
Abstract
The development of combination therapies has become commonplace because potential synergistic benefits are expected for resistant patients of single-agent treatment. In phase I clinical trials, the underlying premise is toxicity increases monotonically with increasing dose levels. This assumption cannot be applied in drug combination trials, however, as there are complex drug-drug interactions. Although many parametric model-based designs have been developed, strong assumptions may be inappropriate owing to little information available about dose-toxicity relationships. No standard solution for finding a maximum tolerated dose combination has been established. With these considerations, we propose a Bayesian optimization design for identifying a single maximum tolerated dose combination. Our proposed design utilizing Bayesian optimization guides the next dose by a balance of information between exploration and exploitation on the nonparametrically estimated dose-toxicity function, thereby allowing us to reach a global optimum with fewer evaluations. We evaluate the proposed design by comparing it with a Bayesian optimal interval design and with the partial-ordering continual reassessment method. The simulation results suggest that the proposed design works well in terms of correct selection probabilities and dose allocations. The proposed design has high potential as a powerful tool for use in finding a maximum tolerated dose combination.
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Affiliation(s)
- Ami Takahashi
- Tokyo Institute of Technology, School of Computing, Meguro-ku, Tokyo, Japan
| | - Taiji Suzuki
- The University of Tokyo, Bunkyo-ku, Tokyo, Japan
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9
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Saha PT, Fine JP, Ivanova A. Consistency of the CRM when the dose-toxicity curve is not monotone and its application to the POCRM. Stat Med 2021; 40:2073-2082. [PMID: 33588519 DOI: 10.1002/sim.8892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 11/02/2020] [Accepted: 01/10/2021] [Indexed: 11/12/2022]
Abstract
The continual reassessment method (CRM) is a well-known design for dose-finding trials with the goal of estimating the maximum tolerated dose (MTD), the dose with a given probability of toxicity. The standard assumption is that the probability of toxicity monotonically increases with dose. We show that the CRM can still be consistent and correctly identify the MTD even when the dose-toxicity curve is not monotone as long as there is monotonicity of the true toxicity probabilities right below and right above the true MTD. In the case of multiple therapies, where it is unclear how to order combinations of dose levels of multiple therapies, our findings provide insight into the performance of the partial order CRM (POCRM). To select the correct dose combination at the end of a trial, the POCRM does not have to select a monotone ordering of drug combinations. We illustrate the connection between our results for the CRM with a nonmonotone dose-toxicity curve and the POCRM via simulations.
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Affiliation(s)
- Pooja T Saha
- Department of Biostatistics, CB #7420, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Jason P Fine
- Department of Biostatistics, CB #7420, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Anastasia Ivanova
- Department of Biostatistics, CB #7420, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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10
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Mozgunov P, Paoletti X, Jaki T. A benchmark for dose-finding studies with unknown ordering. Biostatistics 2021; 23:721-737. [PMID: 33409536 PMCID: PMC9291639 DOI: 10.1093/biostatistics/kxaa054] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 09/25/2020] [Accepted: 11/09/2020] [Indexed: 01/31/2023] Open
Abstract
An important tool to evaluate the performance of a dose-finding design is the nonparametric optimal benchmark that provides an upper bound on the performance of a design under a given scenario. A fundamental assumption of the benchmark is that the investigator can arrange doses in a monotonically increasing toxicity order. While the benchmark can be still applied to combination studies in which not all dose combinations can be ordered, it does not account for the uncertainty in the ordering. In this article, we propose a generalization of the benchmark that accounts for this uncertainty and, as a result, provides a sharper upper bound on the performance. The benchmark assesses how probable the occurrence of each ordering is, given the complete information about each patient. The proposed approach can be applied to trials with an arbitrary number of endpoints with discrete or continuous distributions. We illustrate the utility of the benchmark using recently proposed dose-finding designs for Phase I combination trials with a binary toxicity endpoint and Phase I/II combination trials with binary toxicity and continuous efficacy.
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Affiliation(s)
- Pavel Mozgunov
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Xavier Paoletti
- Université Versailles St Quentin & INSERM U900 STAMPM, Institut Curie, Paris, France
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK and MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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11
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Mozgunov P, Gasparini M, Jaki T. A surface-free design for phase I dual-agent combination trials. Stat Methods Med Res 2020; 29:3093-3109. [PMID: 32338145 DOI: 10.1177/0962280220919450] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In oncology, there is a growing number of therapies given in combination. Recently, several dose-finding designs for Phase I dose-escalation trials for combinations were proposed. The majority of novel designs use a pre-specified parametric model restricting the search of the target combination to a surface of a particular form. In this work, we propose a novel model-free design for combination studies, which is based on the assumption of monotonicity within each agent only. Specifically, we parametrise the ratios between each neighbouring combination by independent Beta distributions. As a result, the design does not require the specification of any particular parametric model or knowledge about increasing orderings of toxicity. We compare the performance of the proposed design to the model-based continual reassessment method for partial ordering and to another model-free alternative, the product of independent beta design. In an extensive simulation study, we show that the proposed design leads to comparable or better proportions of correct selections of the target combination while leading to the same or fewer average number of toxic responses in a trial.
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Affiliation(s)
- Pavel Mozgunov
- Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Mauro Gasparini
- Dipartimento di Scienze Matematiche (DISMA) Giuseppe Luigi Lagrange, Politecnico di Torino, Torino, Italy
| | - Thomas Jaki
- Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
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12
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Yap C, Billingham LJ, Cheung YK, Craddock C, O'Quigley J. Dose Transition Pathways: The Missing Link Between Complex Dose-Finding Designs and Simple Decision-Making. Clin Cancer Res 2017; 23:7440-7447. [PMID: 28733440 DOI: 10.1158/1078-0432.ccr-17-0582] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2017] [Revised: 04/30/2017] [Accepted: 07/17/2017] [Indexed: 11/16/2022]
Abstract
The ever-increasing pace of development of novel therapies mandates efficient methodologies for assessment of their tolerability and activity. Evidence increasingly support the merits of model-based dose-finding designs in identifying the recommended phase II dose compared with conventional rule-based designs such as the 3 + 3 but despite this, their use remains limited. Here, we propose a useful tool, dose transition pathways (DTP), which helps overcome several commonly faced practical and methodologic challenges in the implementation of model-based designs. DTP projects in advance the doses recommended by a model-based design for subsequent patients (stay, escalate, de-escalate, or stop early), using all the accumulated information. After specifying a model with favorable statistical properties, we utilize the DTP to fine-tune the model to tailor it to the trial's specific requirements that reflect important clinical judgments. In particular, it can help to determine how stringent the stopping rules should be if the investigated therapy is too toxic. Its use to design and implement a modified continual reassessment method is illustrated in an acute myeloid leukemia trial. DTP removes the fears of model-based designs as unknown, complex systems and can serve as a handbook, guiding decision-making for each dose update. In the illustrated trial, the seamless, clear transition for each dose recommendation aided the investigators' understanding of the design and facilitated decision-making to enable finer calibration of a tailored model. We advocate the use of the DTP as an integral procedure in the co-development and successful implementation of practical model-based designs by statisticians and investigators. Clin Cancer Res; 23(24); 7440-7. ©2017 AACR.
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Affiliation(s)
- Christina Yap
- Cancer Research UK Clinical Trials Unit, University of Birmingham, Birmingham, United Kingdom.
| | - Lucinda J Billingham
- Cancer Research UK Clinical Trials Unit, University of Birmingham, Birmingham, United Kingdom
| | - Ying Kuen Cheung
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York
| | - Charlie Craddock
- Centre for Clinical Haematology, Queen Elizabeth Hospital, Birmingham, United Kingdom
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13
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Conaway MR. A design for phase I trials in completely or partially ordered groups. Stat Med 2017; 36:2323-2332. [PMID: 28384843 DOI: 10.1002/sim.7295] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Revised: 03/03/2017] [Accepted: 03/03/2017] [Indexed: 11/10/2022]
Abstract
We propose a design for dose finding for cytotoxic agents in completely or partially ordered groups of patients. By completely ordered groups, we mean that prior to the study, there is clinical information that would indicate that for a given dose, the groups can be ordered with respect to the probability of toxicity at that dose. With partially ordered groups, at a given dose, only some of the groups can be ordered with respect to the probability of toxicity at that dose. The method we propose includes elements of the parametric model used in the continual reassessment method combined with the Hwang-Peddada order-restricted estimation procedure. We evaluate the operating characteristics of these designs in a family of dose-toxicity curves representing complete and partial orders. Copyright © 2017 John Wiley & Sons, Ltd.
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Affiliation(s)
- Mark R Conaway
- Department of Public Health Sciences, University of Virginia Health System, P.O. Box 800717, Charlottesville, 22908, VA, U.S.A
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14
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Conaway MR, Wages NA. Designs for phase I trials in ordered groups. Stat Med 2017; 36:254-265. [PMID: 27624880 DOI: 10.1002/sim.7133] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Revised: 08/12/2016] [Accepted: 08/23/2016] [Indexed: 01/27/2023]
Abstract
We propose a new design for dose finding for cytotoxic agents in two ordered groups of patients. By ordered groups, we mean that prior to the study there is clinical information that would indicate that for a given dose one group would be more susceptible to toxicities than patients in the other group. The designs are evaluated relative to two previously proposed designs for ordered groups over a range of scenarios generated randomly from a family of dose-toxicity curves. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Mark R Conaway
- Division of Translational Research and Applied Statistics, Department of Public Health Sciences, The University of Virginia, 22908, CharlottesvilleVA, U.S.A
| | - Nolan A Wages
- Division of Translational Research and Applied Statistics, Department of Public Health Sciences, The University of Virginia, 22908, CharlottesvilleVA, U.S.A
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15
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Wages NA. Comments on 'competing designs for drug combination in phase I dose-finding clinical trials' by M-K. Riviere, F. Dubois, S. Zohar. Stat Med 2016; 34:18-22. [PMID: 25492616 DOI: 10.1002/sim.6336] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2014] [Accepted: 09/29/2014] [Indexed: 11/07/2022]
Affiliation(s)
- Nolan A Wages
- Translational Research and Applied Statistics, Public Health Sciences, University of Virginia, Charlottesville, VA 22908, U.S.A
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16
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Hirakawa A, Wages NA, Sato H, Matsui S. A comparative study of adaptive dose-finding designs for phase I oncology trials of combination therapies. Stat Med 2015; 34:3194-213. [PMID: 25974405 PMCID: PMC4806394 DOI: 10.1002/sim.6533] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2014] [Revised: 03/10/2015] [Accepted: 04/29/2015] [Indexed: 11/06/2022]
Abstract
Little is known about the relative performance of competing model-based dose-finding methods for combination phase I trials. In this study, we focused on five model-based dose-finding methods that have been recently developed. We compared the recommendation rates for true maximum-tolerated dose combinations (MTDCs) and over-dose combinations among these methods under 16 scenarios for 3 × 3, 4 × 4, 2 × 4, and 3 × 5 dose combination matrices. We found that performance of the model-based dose-finding methods varied depending on (1) whether the dose combination matrix is square or not; (2) whether the true MTDCs exist within the same group along the diagonals of the dose combination matrix; and (3) the number of true MTDCs. We discuss the details of the operating characteristics and the advantages and disadvantages of the five methods compared.
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Affiliation(s)
- Akihiro Hirakawa
- Center for Advanced Medicine and Clinical Research, Nagoya University Graduate School of Medicine, Nagoya, 466-8550, Japan
| | - Nolan A Wages
- Department of Public Health Sciences, University of Virginia, Charlottesville, 22904, Virginia, U.S.A
| | - Hiroyuki Sato
- Biostatistics Group, Office of New Drug V, Pharmaceuticals and Medical Devices Agency, Tokyo, 100-0013, Japan
| | - Shigeyuki Matsui
- Department of Biostatistics, Nagoya University Graduate School of Medicine, Nagoya, 466-8550, Japan
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17
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Wages NA, Ivanova A, Marchenko O. Practical designs for Phase I combination studies in oncology. J Biopharm Stat 2015; 26:150-66. [PMID: 26379085 DOI: 10.1080/10543406.2015.1092029] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Phase I trials evaluating the safety of multidrug combinations are becoming more common in oncology. Despite the emergence of novel methodology in the area, it is rare that innovative approaches are used in practice. In this article, we review three methods for Phase I combination studies that are easy to understand and straightforward to implement. We demonstrate the operating characteristics of the designs through illustration in a single trial, as well as through extensive simulation studies, with the aim of increasing the use of novel approaches in Phase I combination studies. Design specifications and software capabilities are also discussed.
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Affiliation(s)
- Nolan A Wages
- a Division of Translational Research & Applied Statistics, Department of Public Health Sciences , University of Virginia , Charlottesville , Virginia , USA
| | - Anastasia Ivanova
- b Department of Biostatistics , The University of North Carolina at Chapel Hill , Chapel Hill , North Carolina , USA
| | - Olga Marchenko
- c Quantitative Decision Strategies and Analytics, Advisory Services, Quintiles Inc. , Durham , North Carolina , USA
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Lin R, Yin G. Bayesian optimal interval design for dose finding in drug-combination trials. Stat Methods Med Res 2015; 26:2155-2167. [PMID: 26178591 DOI: 10.1177/0962280215594494] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Interval designs have recently attracted enormous attention due to their simplicity and desirable properties. We develop a Bayesian optimal interval design for dose finding in drug-combination trials. To determine the next dose combination based on the cumulative data, we propose an allocation rule by maximizing the posterior probability that the toxicity rate of the next dose falls inside a prespecified probability interval. The entire dose-finding procedure is nonparametric (model-free), which is thus robust and also does not require the typical "nonparametric" prephase used in model-based designs for drug-combination trials. The proposed two-dimensional interval design enjoys convergence properties for large samples. We conduct simulation studies to demonstrate the finite-sample performance of the proposed method under various scenarios and further make a modication to estimate toxicity contours by parallel dose-finding paths. Simulation results show that on average the performance of the proposed design is comparable with model-based designs, but it is much easier to implement.
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Affiliation(s)
- Ruitao Lin
- Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong
| | - Guosheng Yin
- Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong
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Wages NA, Conaway MR, Slingluff CL, Williams ME, Portell CA, Hwu P, Petroni GR. Recent developments in the implementation of novel designs for early-phase combination studies. Ann Oncol 2015; 26:1036-1037. [PMID: 25697216 DOI: 10.1093/annonc/mdv075] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- N A Wages
- Division of Translational Research & Applied Statistics, Department of Public Health Sciences, University of Virginia, Charlottesville.
| | - M R Conaway
- Division of Translational Research & Applied Statistics, Department of Public Health Sciences, University of Virginia, Charlottesville
| | - C L Slingluff
- Division of Surgical Oncology, Department of Surgery, University of Virginia, Charlottesville
| | - M E Williams
- Division of Hematology/Oncology, University of Virginia, Charlottesville
| | - C A Portell
- Division of Hematology/Oncology, University of Virginia, Charlottesville
| | - P Hwu
- Department of Melanoma Medical Oncology, Division of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, USA
| | - G R Petroni
- Division of Translational Research & Applied Statistics, Department of Public Health Sciences, University of Virginia, Charlottesville
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Riviere MK, Dubois F, Zohar S. Response to comments on 'competing designs for drug combination in phase I dose-finding clinical trials' by G. Yin, R. Lin and N. Wages. Stat Med 2015; 34:23-6. [PMID: 25492617 DOI: 10.1002/sim.6332] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2014] [Revised: 09/26/2014] [Accepted: 09/29/2014] [Indexed: 11/12/2022]
Affiliation(s)
- Marie-Karelle Riviere
- INSERM, U1138, Equipe 22Centre de Recherche des Cordeliers, Université Paris 5Université Paris 6, Paris, France; IRIS (Institut de Recherches Internationales Servier), Suresnes, France
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Wages NA, Conaway MR, O'Quigley J. Comments on 'A dose-finding approach based on shrunken predictive probability for combinations of two agents in phase I trials' by Akihiro Hirakawa, Chikuma Hamada, and Shigeyuki Matsui. Stat Med 2014; 33:2156-8. [PMID: 24797319 DOI: 10.1002/sim.5934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2013] [Accepted: 07/09/2013] [Indexed: 11/07/2022]
Affiliation(s)
- Nolan A Wages
- Division of Translational Research & Applied Statistics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, U.S.A
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Wages NA, Conaway MR. Phase I/II adaptive design for drug combination oncology trials. Stat Med 2014; 33:1990-2003. [PMID: 24470329 DOI: 10.1002/sim.6097] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2013] [Revised: 11/25/2013] [Accepted: 01/02/2014] [Indexed: 12/28/2022]
Abstract
Existing statistical methodology on dose finding for combination chemotherapies has focused on toxicity considerations alone in finding a maximum tolerated dose combination to recommend for further testing of efficacy in a phase II setting. Recently, there has been increasing interest in integrating phase I and phase II trials in order to facilitate drug development. In this article, we propose a new adaptive phase I/II method for dual-agent combinations that takes into account both toxicity and efficacy after each cohort inclusion. The primary objective, both within and at the conclusion of the trial, becomes finding a single dose combination with an acceptable level of toxicity that maximizes efficacious response. We assume that there exist monotone dose-toxicity and dose-efficacy relationships among doses of one agent when the dose of other agent is fixed. We perform extensive simulation studies that demonstrate the operating characteristics of our proposed approach, and we compare simulated results to existing methodology in phase I/II design for combinations of agents.
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Affiliation(s)
- Nolan A Wages
- Translational Research and Applied Statistics, Public Health Sciences, University of Virginia, Charlottesville, VA 22908, U.S.A
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Riviere MK, Dubois F, Zohar S. Competing designs for drug combination in phase I dose-finding clinical trials. Stat Med 2014; 34:1-12. [DOI: 10.1002/sim.6094] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2012] [Revised: 12/20/2013] [Accepted: 12/23/2013] [Indexed: 11/11/2022]
Affiliation(s)
- M.-K. Riviere
- INSERM, U1138, Equipe 22, Centre de Recherche des Cordeliers; Université Paris 5, Université Paris 6; Paris France
- IRIS (Institut de Recherches Internationales Servier); Suresnes France
| | - F. Dubois
- IRIS (Institut de Recherches Internationales Servier); Suresnes France
| | - S. Zohar
- INSERM, U1138, Equipe 22, Centre de Recherche des Cordeliers; Université Paris 5, Université Paris 6; Paris France
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24
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Sverdlov O, Wong WK, Ryeznik Y. Adaptive clinical trial designs for phase I cancer studies. STATISTICS SURVEYS 2014. [DOI: 10.1214/14-ss106] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Wages NA, Varhegyi N. pocrm: an R-package for phase I trials of combinations of agents. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 112:211-218. [PMID: 23871691 PMCID: PMC3775989 DOI: 10.1016/j.cmpb.2013.05.020] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2013] [Revised: 04/23/2013] [Accepted: 05/26/2013] [Indexed: 06/02/2023]
Abstract
This paper presents the R package pocrm for implementing and simulating the partial order continual reassessment method (PO-CRM; [1,2]) in Phase I trials of combinations of agents. The aim of this article is to illustrate, through examples of the pocrm package, how the PO-CRM works and how its operating characteristics can inform clinical trial investigators. This should promote the use of the PO-CRM in designing and conducting dose-finding Phase I trials of combinations of agents.
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Affiliation(s)
- Nolan A Wages
- Division of Translational Research and Applied Statistics, Department of Public Health Sciences University of Virginia, Charlottesville, VA 22908, USA.
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Wages NA, O'Quigley J, Conaway MR. Phase I design for completely or partially ordered treatment schedules. Stat Med 2013; 33:569-79. [PMID: 24114957 DOI: 10.1002/sim.5998] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2012] [Revised: 08/22/2013] [Accepted: 09/11/2013] [Indexed: 11/12/2022]
Abstract
The majority of methods for the design of phase I trials in oncology are based upon a single course of therapy, yet in actual practice, it may be the case that there is more than one treatment schedule for any given dose. Therefore, the probability of observing a dose-limiting toxicity may depend upon both the total amount of the dose given, as well as the frequency with which it is administered. The objective of the study then becomes to find an acceptable combination of both dose and schedule. Past literature on designing these trials has entailed the assumption that toxicity increases monotonically with both dose and schedule. In this article, we relax this assumption for schedules and present a dose-schedule finding design that can be generalized to situations in which we know the ordering between all schedules and those in which we do not. We present simulation results that compare our method with other suggested dose-schedule finding methodology.
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Affiliation(s)
- Nolan A Wages
- Translational Research & Applied Statistics, Public Health Sciences, University of Virginia, Charlottesville, VA 22908, U.S.A
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