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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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2
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Lee SY. A flexible dose-response modeling framework based on continuous toxicity outcomes in phase I cancer clinical trials. Trials 2023; 24:745. [PMID: 37990281 PMCID: PMC10664620 DOI: 10.1186/s13063-023-07793-0] [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] [Received: 08/11/2023] [Accepted: 11/09/2023] [Indexed: 11/23/2023] Open
Abstract
BACKGROUND The past few decades have seen remarkable developments in dose-finding designs for phase I cancer clinical trials. While many of these designs rely on a binary toxicity response, there is an increasing focus on leveraging continuous toxicity responses. A continuous toxicity response pertains to a quantitative measure represented by real numbers. A higher value corresponds not only to an elevated likelihood of side effects for patients but also to an increased probability of treatment efficacy. This relationship between toxicity and dose is often nonlinear, necessitating flexibility in the quest to find an optimal dose. METHODS A flexible, fully Bayesian dose-finding design is proposed to capitalize on continuous toxicity information, operating under the assumption that the true shape of the dose-toxicity curve is nonlinear. RESULTS We conduct simulations of clinical trials across varying scenarios of non-linearity to evaluate the operational characteristics of the proposed design. Additionally, we apply the proposed design to a real-world problem to determine an optimal dose for a molecularly targeted agent. CONCLUSIONS Phase I cancer clinical trials, designed within a fully Bayesian framework with the utilization of continuous toxicity outcomes, offer an alternative approach to finding an optimal dose, providing unique benefits compared to trials designed based on binary toxicity outcomes.
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Affiliation(s)
- Se Yoon Lee
- Department of Statistics, Texas A &M University, 3143 TAMU, College Station, 77843, TX, USA.
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3
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Cho NS, Wong WK, Nghiemphu PL, Cloughesy TF, Ellingson BM. The Future Glioblastoma Clinical Trials Landscape: Early Phase 0, Window of Opportunity, and Adaptive Phase I-III Studies. Curr Oncol Rep 2023; 25:1047-1055. [PMID: 37402043 PMCID: PMC10474988 DOI: 10.1007/s11912-023-01433-1] [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] [Accepted: 05/03/2023] [Indexed: 07/05/2023]
Abstract
PURPOSE OF REVIEW Innovative clinical trial designs for glioblastoma (GBM) are needed to expedite drug discovery. Phase 0, window of opportunity, and adaptive designs have been proposed, but their advanced methodologies and underlying biostatistics are not widely known. This review summarizes phase 0, window of opportunity, and adaptive phase I-III clinical trial designs in GBM tailored to physicians. RECENT FINDINGS Phase 0, window of opportunity, and adaptive trials are now being implemented for GBM. These trials can remove ineffective therapies earlier during drug development and improve trial efficiency. There are two ongoing adaptive platform trials: GBM Adaptive Global Innovative Learning Environment (GBM AGILE) and the INdividualized Screening trial of Innovative GBM Therapy (INSIGhT). The future clinical trials landscape in GBM will increasingly involve phase 0, window of opportunity, and adaptive phase I-III studies. Continued collaboration between physicians and biostatisticians will be critical for implementing these trial designs.
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Affiliation(s)
- Nicholas S Cho
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA, USA
- Medical Scientist Training Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Weng Kee Wong
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
| | - Phioanh L Nghiemphu
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Timothy F Cloughesy
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA, USA.
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
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4
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Pantoja K, Lanke S, Munafo A, Victor A, Habermehl C, Schueler A, Venkatakrishnan K, Girard P, Goteti K. Designing phase I oncology dose escalation using dose-exposure-toxicity models as a complementary approach to model-based dose-toxicity models. CPT Pharmacometrics Syst Pharmacol 2022; 11:1371-1381. [PMID: 35852048 PMCID: PMC9574748 DOI: 10.1002/psp4.12851] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/05/2022] [Accepted: 07/11/2022] [Indexed: 11/29/2022] Open
Abstract
One of the objectives of oncology phase I dose-escalation studies has been to determine the maximum tolerated dose (MTD). Although MTD is no longer set as the dose for further development in contemporary oncology drug development, MTD determination is still important for informing the therapeutic index. Bayesian adaptive model-based designs are becoming mainstream in oncology first-in-human trials. Herein, we illustrate via simulations the use of systemic exposure in Bayesian adaptive dose-toxicity models to estimate MTD. We extend traditional dose-toxicity models to incorporate pharmacokinetic exposure, which provides information on exposure-toxicity relationships. We pursue dose escalation until the maximum tolerated exposure (corresponding to the MTD) is reached. By leveraging pharmacokinetics, dose escalation considers exposure and interindividual variability on a continuous rather than discrete domain, offering additional information for dose-escalation decisions. To demonstrate this, we generated 1000 simulations (starting dose of 1/25th the reference dose and six dose levels) for several different scenarios. Both rule-based and model-based designs were compared using metrics of potential safety, accuracy, and reliability. The mean results over simulations and different toxicity scenarios showed that model-based designs were better than rule-based methods and that exposure-toxicity model-based methods have the potential to valuably complement dose-toxicity model-based methods. Exposure-toxicity model-based methods had decreased underdose risk accompanied by a relatively smaller increase in overdose risk, resulting in improved net reliability. MTD estimation accuracy was compromised when exposure variability was large, emphasizing the importance of appropriate control of pharmacokinetic variability in phase I dose-escalation studies.
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Affiliation(s)
- Kristyn Pantoja
- Department of StatisticsTexas A&M UniversityCollege StationTexasUSA,EMD Serono Research InstituteBillericaMassachusettsUSA
| | - Shankar Lanke
- EMD Serono Research InstituteBillericaMassachusettsUSA
| | - Alain Munafo
- Merck Institute for PharmacometricsLausanneSwitzerland
| | | | | | | | | | - Pascal Girard
- Merck Institute for PharmacometricsLausanneSwitzerland
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5
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Abstract
AbstractTwo major barriers to conducting user studies are the costs involved in recruiting participants and researcher time in performing studies. Typical solutions are to study convenience samples or design studies that can be deployed on crowd-sourcing platforms. Both solutions have benefits but also drawbacks. Even in cases where these approaches make sense, it is still reasonable to ask whether we are using our resources – participants’ and our time – efficiently and whether we can do better. Typically user studies compare randomly-assigned experimental conditions, such that a uniform number of opportunities are assigned to each condition. This sampling approach, as has been demonstrated in clinical trials, is sub-optimal. The goal of many Information Retrieval (IR) user studies is to determine which strategy (e.g., behaviour or system) performs the best. In such a setup, it is not wise to waste participant and researcher time and money on conditions that are obviously inferior. In this work we explore whether Best Arm Identification (BAI) algorithms provide a natural solution to this problem. BAI methods are a class of Multi-armed Bandits (MABs) where the only goal is to output a recommended arm and the algorithms are evaluated by the average payoff of the recommended arm. Using three datasets associated with previously published IR-related user studies and a series of simulations, we test the extent to which the cost required to run user studies can be reduced by employing BAI methods. Our results suggest that some BAI instances (racing algorithms) are promising devices to reduce the cost of user studies. One of the racing algorithms studied, Hoeffding, holds particular promise. This algorithm offered consistent savings across both the real and simulated data sets and only extremely rarely returned a result inconsistent with the result of the full trial. We believe the results can have an important impact on the way research is performed in this field. The results show that the conditions assigned to participants could be dynamically changed, automatically, to make efficient use of participant and experimenter time.
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6
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Ryeznik Y, Sverdlov O, Svensson EM, Montepiedra G, Hooker AC, Wong WK. Pharmacometrics meets statistics-A synergy for modern drug development. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 10:1134-1149. [PMID: 34318621 PMCID: PMC8520751 DOI: 10.1002/psp4.12696] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 05/17/2021] [Accepted: 07/02/2021] [Indexed: 01/20/2023]
Abstract
Modern drug development problems are very complex and require integration of various scientific fields. Traditionally, statistical methods have been the primary tool for design and analysis of clinical trials. Increasingly, pharmacometric approaches using physiology-based drug and disease models are applied in this context. In this paper, we show that statistics and pharmacometrics have more in common than what keeps them apart, and collectively, the synergy from these two quantitative disciplines can provide greater advances in clinical research and development, resulting in novel and more effective medicines to patients with medical need.
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Affiliation(s)
- Yevgen Ryeznik
- BioPharma Early Biometrics and Statistical Innovation, Data Science & AI, R&D Biopharmaceuticals, AstraZeneca, Gothenburg, Sweden
| | - Oleksandr Sverdlov
- Early Development Analytics, Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA
| | - Elin M Svensson
- Department of Pharmacy, Uppsala University, Uppsala, Sweden.,Department of Pharmacy, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Grace Montepiedra
- Center for Biostatistics in AIDS Research, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | | | - Weng Kee Wong
- Department of Biostatistics, University of California Los Angeles, Los Angeles, California, USA
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7
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Günhan BK, Weber S, Seroutou A, Friede T. Phase I dose-escalation oncology trials with sequential multiple schedules. BMC Med Res Methodol 2021; 21:69. [PMID: 33853539 PMCID: PMC8045405 DOI: 10.1186/s12874-021-01218-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 01/26/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Conventional methods for phase I dose-escalation trials in oncology are based on a single treatment schedule only. More recently, however, multiple schedules are more frequently investigated in the same trial. METHODS Here, we consider sequential phase I trials, where the trial proceeds with a new schedule (e.g. daily or weekly dosing) once the dose escalation with another schedule has been completed. The aim is to utilize the information from both the completed and the ongoing schedules to inform decisions on the dose level for the next dose cohort. For this purpose, we adapted the time-to-event pharmacokinetics (TITE-PK) model, which were originally developed for simultaneous investigation of multiple schedules. TITE-PK integrates information from multiple schedules using a pharmacokinetics (PK) model. RESULTS In a simulation study, the developed approach is compared to the bridging continual reassessment method and the Bayesian logistic regression model using a meta-analytic-predictive prior. TITE-PK results in better performance than comparators in terms of recommending acceptable dose and avoiding overly toxic doses for sequential phase I trials in most of the scenarios considered. Furthermore, better performance of TITE-PK is achieved while requiring similar number of patients in the simulated trials. For the scenarios involving one schedule, TITE-PK displays similar performance with alternatives in terms of acceptable dose recommendations. The R and Stan code for the implementation of an illustrative sequential phase I trial example in oncology is publicly available ( https://github.com/gunhanb/TITEPK_sequential ). CONCLUSION In phase I oncology trials with sequential multiple schedules, the use of all relevant information is of great importance. For these trials, the adapted TITE-PK which combines information using PK principles is recommended.
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Affiliation(s)
- Burak Kürsad Günhan
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
| | | | | | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
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Zhang W, Wang X, Muthukumarana S, Yang P. A continual reassessment method without undue risk of toxicity. COMMUN STAT-SIMUL C 2021. [DOI: 10.1080/03610918.2021.1877306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Weijia Zhang
- Chongqing Key Laboratory of Social Economy and Applied Statistics, College of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing, P. R. China
| | - Xikui Wang
- Warren Centre for Actuarial Studies and Research, I.H. Asper School of Business, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Saman Muthukumarana
- Department of Statistics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Po Yang
- Department of Statistics, University of Manitoba, Winnipeg, Manitoba, Canada
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9
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Affiliation(s)
- Nancy Flournoy
- Department of Statistics University of Missouri Columbia MO USA
| | - José Moler
- Departamento de Estadística, Informática y Matemáticas Universidad Pública de Navarra, Campus Arrosadia s/n Pamplona 31006 Spain
| | - Fernando Plo
- Department of Statistical Methods Universidad de Zaragoza Pedro Cerbuna, 12 Zaragoza 50009 Spain
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10
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Yates JWT, Byrne H, Chapman SC, Chen T, Cucurull-Sanchez L, Delgado-SanMartin J, Di Veroli G, Dovedi SJ, Dunlop C, Jena R, Jodrell D, Martin E, Mercier F, Ramos-Montoya A, Struemper H, Vicini P. Opportunities for Quantitative Translational Modeling in Oncology. Clin Pharmacol Ther 2020; 108:447-457. [PMID: 32569424 DOI: 10.1002/cpt.1963] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 06/04/2020] [Indexed: 12/16/2022]
Abstract
A 2-day meeting was held by members of the UK Quantitative Systems Pharmacology Network () in November 2018 on the topic of Translational Challenges in Oncology. Participants from a wide range of backgrounds were invited to discuss current and emerging modeling applications in nonclinical and clinical drug development, and to identify areas for improvement. This resulting perspective explores opportunities for impactful quantitative pharmacology approaches. Four key themes arose from the presentations and discussions that were held, leading to the following recommendations: Evaluate the predictivity and reproducibility of animal cancer models through precompetitive collaboration. Apply mechanism of action (MoA) based mechanistic models derived from nonclinical data to clinical trial data. Apply MoA reflective models across trial data sets to more robustly quantify the natural history of disease and response to differing interventions. Quantify more robustly the dose and concentration dependence of adverse events through mathematical modelling techniques and modified trial design.
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Affiliation(s)
| | | | | | - Tao Chen
- University of Surrey, Surrey, UK
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11
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Sverdlov O, Ryeznik Y, Wong WK. On Optimal Designs for Clinical Trials: An Updated Review. JOURNAL OF STATISTICAL THEORY AND PRACTICE 2019. [DOI: 10.1007/s42519-019-0073-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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12
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Xue X, Foster MC, Ivanova A. Rapid enrollment design for finding the optimal dose in immunotherapy trials with ordered groups. J Biopharm Stat 2019; 29:625-634. [PMID: 31251112 DOI: 10.1080/10543406.2019.1633654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
In immunotherapy dose-finding trials, the optimal dose is usually defined based on both toxicity and response because the relationship between toxicity and response is different than that seen with cytotoxic anti-neoplastic therapies. In immunotherapy trials, toxicity and response often require a longer follow-up time compared to trials with cytotoxic agents. The rapid enrollment design has been proposed for dose-finding trials to find the maximum-tolerated dose where the follow-up for toxicity is long and it is desirable to assign a patient to a dose of a new therapy as soon as the patient is enrolled. We extend the rapid enrollment design to immunotherapy trials to find the optimal dose. We further describe how to use the design in immunotherapy trials with ordered groups where efficacy and safety considerations dictate running dose-finding trials in each group separately as efficacy and toxicity at the same dose can vary across groups. The estimation of the optimal dose in each of the groups can be improved in many, but not all, cases by using the monotonicity of toxicity and response among groups.
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Affiliation(s)
- Xiaoqiang Xue
- a Decision Sciences, Data Science Safety and Regulatory, IQVIA Inc. , Durham , NC , USA
| | - Matthew C Foster
- b Lineberger Comprehensive Cancer Center, University of North Carolina , Chapel Hill , NC , USA
| | - Anastasia Ivanova
- c Department of Biostatistics, UNC at Chapel Hill , Chapel Hill , NC , USA
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13
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Zhang W, Wang X, Yang P. A new design of the continual reassessment method. COMMUN STAT-SIMUL C 2019. [DOI: 10.1080/03610918.2019.1592191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Weijia Zhang
- Department of Statistics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Xikui Wang
- Department of Statistics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Po Yang
- Department of Statistics, University of Manitoba, Winnipeg, Manitoba, Canada
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14
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Lyu J, Curran E, Ji Y. Bayesian Adaptive Design for Finding the Maximum Tolerated Sequence of Doses in Multicycle Dose-Finding Clinical Trials. JCO Precis Oncol 2018; 2:1-19. [DOI: 10.1200/po.18.00020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Purpose Statistical designs for traditional phase I dose-finding trials consider dose-limiting toxicity in the first cycle of treatment. In reality, patients often go through multiple cycles of treatment and may experience toxicity events in more than one cycle. Therefore, it is desirable to identify the maximum tolerated sequence of three doses across three cycles of treatment. Methods Motivated by a three-cycle dose-finding clinical trial for a rare cancer with a JAK inhibitor, we proposed and implemented a simple Bayesian adaptive dose-cycle finding (BaSyc) design that allows intercycle and intrapatient dose modification. Because of the patient-specific dosing strategy over cycles, the BaSyc design is suited as a method in precision oncology. Results BaSyc is simple and transparent because its algorithm can be summarized as two tabulated decision rules before the trial starts, allowing physicians to visually examine these rules. In addition, BaSyc employs a time-saving enrollment scheme that speeds up the trial. Extensive simulation studies show that BaSyc has desirable operating characteristics in identifying the maximum tolerated sequence. Conclusion The BaSyc design provides a first-of-kind multicycle approach for dose finding and will likely lead to better and safer patient care and drug development.
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Affiliation(s)
- Jiaying Lyu
- Jiaying Lyu, School of Public Health, Fudan University, Shanghai, People’s Republic of China; Emily Curran and Yuan Ji, The University of Chicago, Chicago; and Yuan Ji, NorthShore University HealthSystem, Evanston, IL
| | - Emily Curran
- Jiaying Lyu, School of Public Health, Fudan University, Shanghai, People’s Republic of China; Emily Curran and Yuan Ji, The University of Chicago, Chicago; and Yuan Ji, NorthShore University HealthSystem, Evanston, IL
| | - Yuan Ji
- Jiaying Lyu, School of Public Health, Fudan University, Shanghai, People’s Republic of China; Emily Curran and Yuan Ji, The University of Chicago, Chicago; and Yuan Ji, NorthShore University HealthSystem, Evanston, IL
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15
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Lin R, Yin G. Uniformly most powerful Bayesian interval design for phase I dose-finding trials. Pharm Stat 2018; 17:710-724. [PMID: 30066466 DOI: 10.1002/pst.1889] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 05/08/2018] [Accepted: 06/09/2018] [Indexed: 11/10/2022]
Abstract
Interval designs have recently attracted much attention in phase I clinical trials because of their simplicity and desirable finite-sample performance. However, existing interval designs typically cannot converge to the optimal dose level since their intervals do not shrink to the target toxicity probability as the sample size increases. The uniformly most powerful Bayesian test (UMPBT) is an objective Bayesian hypothesis testing procedure, which results in the largest probability that the Bayes factor against null hypothesis exceeds the evidence threshold for all possible values of the data generating parameter. On the basis of the rejection region of UMPBT, we develop the uniformly most powerful Bayesian interval (UMPBI) design for phase I dose-finding trials. The proposed UMPBI design enjoys convergence properties because the induced interval indeed shrinks to the toxicity target and the recommended dose converges to the true maximum tolerated dose as the sample size increases. Moreover, it possesses an optimality property that the probability of incorrect decisions is minimized. We conduct simulation studies to demonstrate the competitive finite-sample operating characteristics of the UMPBI in comparison with other existing interval designs. As an illustration, we apply the UMPBI design to a panitumumab and standard gemcitabine-based chemoradiation combination trial.
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Affiliation(s)
- Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Key Laboratory for Applied Statistics of MOE, Northeast Normal University, Changchun, Jilin, China
| | - Guosheng Yin
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong
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16
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Ananthakrishnan R, Green S, Li D, LaValley M. Extensions of the mTPI and TEQR designs to include non-monotone efficacy in addition to toxicity for optimal dose determination for early phase immunotherapy oncology trials. Contemp Clin Trials Commun 2018; 10:62-76. [PMID: 29696160 PMCID: PMC5898482 DOI: 10.1016/j.conctc.2018.01.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Revised: 01/14/2018] [Accepted: 01/17/2018] [Indexed: 11/20/2022] Open
Abstract
With the emergence of immunotherapy and other novel therapies, the traditional assumption that the efficacy of the study drug increases monotonically with dose levels is not always true. Therefore, dose-finding methods evaluating only toxicity data may not be adequate. In this paper, we have first compared the Modified Toxicity Probability Interval (mTPI) and Toxicity Equivalence Range (TEQR) dose-finding oncology designs for safety with identical stopping rules; we have then extended both designs to include efficacy in addition to safety – we determine the optimal dose for safety and efficacy using these designs by applying isotonic regression to the observed toxicity and efficacy rates, once the early phase trial is completed. We consider multiple types of underlying dose response curves, i.e., monotonically increasing, plateau, or umbrella-shaped. We conduct simulation studies to investigate the operating characteristics of the two proposed designs and compare them to existing designs. We found that the extended mTPI design selects the optimal dose for safety and efficacy more accurately than the other designs for most of the scenarios considered.
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Affiliation(s)
- Revathi Ananthakrishnan
- Department of Biostatistics, Boston University, 801 Massachusetts Avenue 3rd Floor, Boston, MA 02118, USA
- Corresponding author.
| | | | | | - Michael LaValley
- Department of Biostatistics, Boston University, 801 Massachusetts Avenue 3rd Floor, Boston, MA 02118, USA
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17
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Systematic comparison of the statistical operating characteristics of various Phase I oncology designs. Contemp Clin Trials Commun 2016; 5:34-48. [PMID: 29740620 PMCID: PMC5936704 DOI: 10.1016/j.conctc.2016.11.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2016] [Revised: 11/16/2016] [Accepted: 11/22/2016] [Indexed: 11/21/2022] Open
Abstract
Dose finding Phase I oncology designs can be broadly categorized as rule based, such as the 3 + 3 and the accelerated titration designs, or model based, such as the CRM and Eff-Tox designs. This paper systematically reviews and compares through simulations several statistical operating characteristics, including the accuracy of maximum tolerated dose (MTD) selection, the percentage of patients assigned to the MTD, over-dosing, under-dosing, and the trial dose-limiting toxicity (DLT) rate, of eleven rule-based and model-based Phase I oncology designs that target or pre-specify a DLT rate of ∼0.2, for three sets of true DLT probabilities. These DLT probabilities are generated at common dosages from specific linear, logistic, and log-logistic dose-toxicity curves. We find that all the designs examined select the MTD much more accurately when there is a clear separation between the true DLT rate at the MTD and the rates at the dose level immediately above and below it, such as for the DLT rates generated using the chosen logistic dose-toxicity curve; the separations in these true DLT rates depend, in turn, not only on the functional form of the dose-toxicity curve but also on the investigated dose levels and the parameter set-up. The model based mTPI, TEQR, BOIN, CRM and EWOC designs perform well and assign the greatest percentages of patients to the MTD, and also have a reasonably high probability of picking the true MTD across the three dose-toxicity curves examined. Among the rule-based designs studied, the 5 + 5 a design picks the MTD as accurately as the model based designs for the true DLT rates generated using the chosen log-logistic and linear dose-toxicity curves, but requires enrolling a higher number of patients than the other designs. We also find that it is critical to pick a design that is aligned with the true DLT rate of interest. Further, we note that Phase I trials are very small in general and hence may not provide accurate estimates of the MTD. Thus our work provides a map for planning Phase I oncology trials or developing new ones.
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Lin R, Yin G. Nonparametric overdose control with late-onset toxicity in phase I clinical trials. Biostatistics 2016; 18:180-194. [DOI: 10.1093/biostatistics/kxw038] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2016] [Revised: 07/08/2016] [Accepted: 07/11/2016] [Indexed: 11/12/2022] Open
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Ivanova A, Wang Y, Foster MC. The rapid enrollment design for Phase I clinical trials. Stat Med 2016; 35:2516-24. [PMID: 26833922 DOI: 10.1002/sim.6886] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2014] [Revised: 01/01/2016] [Accepted: 01/05/2016] [Indexed: 11/10/2022]
Abstract
We propose a dose-finding design for Phase I oncology trials where each new patient is assigned to the dose most likely to be the target dose given observed data. The main model assumption is that the dose-toxicity curve is non-decreasing. This method is beneficial when it is desirable to assign a patient to a dose as soon as the patient is enrolled into a study. To prevent assignments to doses with limited toxicity information in fast accruing trials we propose a conservative rule that assigns temporary fractional toxicities to patients still in follow-up. We also recommend always using a safety rule in any fast accruing dose-finding trial. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Anastasia Ivanova
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-7420, U.S.A
| | - Yunfei Wang
- Departments of Pediatrics and Biostatistics, George Washington University and Children's National Medical Center, Washington DC, 20010, U.S.A
| | - Matthew C Foster
- University of North Carolina, Lineberger Comprehensive Cancer Center, Chapel Hill, NC, 27599, U.S.A
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Yang S, Wang SJ, Ji Y. An integrated dose-finding tool for phase I trials in oncology. Contemp Clin Trials 2015; 45:426-434. [PMID: 26419937 DOI: 10.1016/j.cct.2015.09.019] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Revised: 09/19/2015] [Accepted: 09/21/2015] [Indexed: 11/16/2022]
Abstract
In the past 25 years, the 3+3 design has been the most popular approach for planning phase I dose-finding trials in oncology. During the same time period, major development of more efficient model-based designs has been made by statistical researchers aiming to improve the clinical practice of dose finding in oncology. Despite the effort, 3+3 is still the most frequently used designs in practice. Part of the reason is due to the lack of software tools that allow comparison of different designs, including 3+3 and other model-based methods, in a head-to-head and easy-to-use fashion. To this end, we introduce NextGen-DF, a next-generation tool for designing oncology dose-finding trials that allows for construction, comparison, and calibration of multiple designs via internet, in real time, and independent of computer operating systems. Through NextGen-DF, we present massive and user-generated comparison results based on over 4 million simulated trials, which clearly indicate the inferiority of 3+3. To our knowledge, the reported crowd-sourcing results are the largest and most objective comparison across major dose-finding methods to date. NextGen-DF is expected to improve patient care and drug development by providing safer and more efficient designs for phase I oncology trials. NextGen-DF is available at www.compgenome.org/NGDF.
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Affiliation(s)
- Shengjie Yang
- Program of Computational Genomics & Medicine, Northshore University HealthSystem, USA
| | - Sue-Jane Wang
- Center for Drug Evaluation and Research, US Food and Drug Administration, USA
| | - Yuan Ji
- Program of Computational Genomics & Medicine, Northshore University HealthSystem, USA; Department of Public Health Sciences, The University of Chicago, Chicago, USA.
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