1
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Deng Q, Zhu L, Weiss B, Aanur P, Gao L. Strategies for successful dose optimization in oncology drug development: a practical guide. J Biopharm Stat 2024:1-15. [PMID: 39127994 DOI: 10.1080/10543406.2024.2387364] [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: 09/28/2023] [Accepted: 07/27/2024] [Indexed: 08/12/2024]
Abstract
Dose optimization is a critical challenge in drug development. Historically, dose determination in oncology has followed a divergent path from other non-oncology therapeutic areas due to the unique characteristics and requirements in Oncology. However, with the emergence of new drug modalities and mechanisms of drugs in oncology, such as immune therapies, radiopharmaceuticals, targeted therapies, cytostatic agents, and others, the dose-response relationship for efficacy and toxicity could be vastly varied compared to the cytotoxic chemotherapies. The doses below the MTD may demonstrate similar efficacy to the MTD with an improved tolerability profile, resembling what is commonly observed in non-oncology treatments. Hence, alternate strategies for dose optimization are required for new modalities in oncology drug development. This paper delves into the historical evolution of dose finding methods from non-oncology to oncology, highlighting examples and summarizing the underlying drivers of change. Subsequently, a practical framework and guidance are provided to illustrate how dose optimization can be incorporated into various stages of the development program. We provide the following general recommendations: 1) The objective for phase I is to identify a dose range rather than a single MTD dose for subsequent development to better characterize the safety and tolerability profile within the dose range. 2) At least two doses separable by PK are recommended for dose optimization in phase II. 3) Ideally, dose optimization should be performed before launching the confirmatory study. Nevertheless, innovative designs such as seamless II/III design can be implemented for dose selection and may accelerate the drug development program.
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Affiliation(s)
- Qiqi Deng
- Biostatistics and Programming, Moderna Inc., Cambridge, MA, USA
| | - Lili Zhu
- Biostatistics and Programming, Moderna Inc., Cambridge, MA, USA
| | - Brendan Weiss
- Clinical Development Oncology, Moderna Inc., Cambridge, MA, USA
| | - Praveen Aanur
- Clinical Development Oncology, Moderna Inc., Cambridge, MA, USA
| | - Lei Gao
- Biostatistics and Programming, Moderna Inc., Cambridge, MA, USA
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2
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Wu J, Li Y. Group sequential multi-arm multi-stage survival trial design with treatment selection. J Biopharm Stat 2024; 34:453-468. [PMID: 37455424 DOI: 10.1080/10543406.2023.2235409] [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: 09/27/2022] [Accepted: 07/06/2023] [Indexed: 07/18/2023]
Abstract
Multi-arm trials are increasingly of interest because for many diseases; there are multiple experimental treatments available for testing efficacy. Several novel multi-arm multi-stage (MAMS) clinical trial designs have been proposed. However, a major hurdle to adopting the group sequential MAMS routinely is the computational effort of obtaining stopping boundaries. For example, the method of Jaki and Magirr for time-to-event endpoint, implemented in R package MAMS, requires complicated computational efforts to obtain stopping boundaries. In this study, we develop a group sequential MAMS survival trial design based on the sequential conditional probability ratio test. The proposed method is an improvement of the Jaki and Magirr's method in the following three directions. First, the proposed method provides explicit solutions for both futility and efficacy boundaries to an arbitrary number of stages and arms. Thus, it avoids complicated computational efforts for the trial design. Second, the proposed method provides an accurate number of events for the fixed sample and group sequential designs. Third, the proposed method uses a new procedure for interim analysis which preserves the study power.
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Affiliation(s)
- Jianrong Wu
- Biostatistics Shared Resource Facility, University of New Mexico Comprehensive Cancer Center, Albuquerque, New Mexico, United States
| | - Yimei Li
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee, United States
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3
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Gao P, Li Y. Adaptive Multiple Comparison Sequential Design (AMCSD) for clinical trials. J Biopharm Stat 2024; 34:424-440. [PMID: 37526434 DOI: 10.1080/10543406.2023.2233590] [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: 10/06/2022] [Accepted: 07/01/2023] [Indexed: 08/02/2023]
Abstract
We propose an adaptive sequential testing procedure for clinical trials that test the efficacy of multiple treatment options, such as dose/regimen, different drugs, sub-populations, endpoints, or a mixture of them in one trial. At any interim analyses, sample size re-estimation can be conducted, and any option can be dropped for lack of efficacy or unsatisfactory safety profile. Inference after the trial, including p-value, conservative point estimate and confidence intervals, are provided.
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Affiliation(s)
- Ping Gao
- Innovatio Statistics, Inc., Bridgewater, New Jersey, USA
| | - Yingqiu Li
- School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, Hunan, China
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4
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Wang Z, Wang X, Xu W, Li Y, Lai R, Qiu X, Chen X, Chen Z, Mi B, Wu M, Wang J. Translational Challenges and Prospective Solutions in the Implementation of Biomimetic Delivery Systems. Pharmaceutics 2023; 15:2623. [PMID: 38004601 PMCID: PMC10674763 DOI: 10.3390/pharmaceutics15112623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 11/03/2023] [Accepted: 11/09/2023] [Indexed: 11/26/2023] Open
Abstract
Biomimetic delivery systems (BDSs), inspired by the intricate designs of biological systems, have emerged as a groundbreaking paradigm in nanomedicine, offering unparalleled advantages in therapeutic delivery. These systems, encompassing platforms such as liposomes, protein-based nanoparticles, extracellular vesicles, and polysaccharides, are lauded for their targeted delivery, minimized side effects, and enhanced therapeutic outcomes. However, the translation of BDSs from research settings to clinical applications is fraught with challenges, including reproducibility concerns, physiological stability, and rigorous efficacy and safety evaluations. Furthermore, the innovative nature of BDSs demands the reevaluation and evolution of existing regulatory and ethical frameworks. This review provides an overview of BDSs and delves into the multifaceted translational challenges and present emerging solutions, underscored by real-world case studies. Emphasizing the potential of BDSs to redefine healthcare, we advocate for sustained interdisciplinary collaboration and research. As our understanding of biological systems deepens, the future of BDSs in clinical translation appears promising, with a focus on personalized medicine and refined patient-specific delivery systems.
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Affiliation(s)
- Zhe Wang
- Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China; (Z.W.); (R.L.)
| | - Xinpei Wang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; (X.W.); (W.X.); (Y.L.); (X.Q.); (X.C.); (Z.C.)
| | - Wanting Xu
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; (X.W.); (W.X.); (Y.L.); (X.Q.); (X.C.); (Z.C.)
| | - Yongxiao Li
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; (X.W.); (W.X.); (Y.L.); (X.Q.); (X.C.); (Z.C.)
| | - Ruizhi Lai
- Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China; (Z.W.); (R.L.)
| | - Xiaohui Qiu
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; (X.W.); (W.X.); (Y.L.); (X.Q.); (X.C.); (Z.C.)
| | - Xu Chen
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; (X.W.); (W.X.); (Y.L.); (X.Q.); (X.C.); (Z.C.)
| | - Zhidong Chen
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; (X.W.); (W.X.); (Y.L.); (X.Q.); (X.C.); (Z.C.)
| | - Bobin Mi
- Department of Orthopaedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China;
- Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, Wuhan 430022, China
| | - Meiying Wu
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; (X.W.); (W.X.); (Y.L.); (X.Q.); (X.C.); (Z.C.)
| | - Junqing Wang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China; (X.W.); (W.X.); (Y.L.); (X.Q.); (X.C.); (Z.C.)
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5
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Wu J, Li Y, Zhu L. Group sequential multi-arm multi-stage trial design with treatment selection. Stat Med 2023; 42:1480-1491. [PMID: 36808736 DOI: 10.1002/sim.9682] [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: 07/18/2022] [Revised: 12/26/2022] [Accepted: 01/30/2023] [Indexed: 02/22/2023]
Abstract
A multi-arm trial allows simultaneous comparison of multiple experimental treatments with a common control and provides a substantial efficiency advantage compared to the traditional randomized controlled trial. Many novel multi-arm multi-stage (MAMS) clinical trial designs have been proposed. However, a major hurdle to adopting the group sequential MAMS routinely is the computational effort of obtaining total sample size and sequential stopping boundaries. In this paper, we develop a group sequential MAMS trial design based on the sequential conditional probability ratio test. The proposed method provides analytical solutions for futility and efficacy boundaries to an arbitrary number of stages and arms. Thus, it avoids complicated computational effort for the methods proposed by Magirr et al. Simulation results showed that the proposed method has several advantages compared to the methods implemented in R package MAMS by Magirr et al.
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Affiliation(s)
- Jianrong Wu
- Division of Epidemiology, Biostatistics, and Preventive Medicine University of New Mexico, Albuquerque, New Mexico, USA
| | - Yimei Li
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Liang Zhu
- Internal Medicine, University of Texas Health Science Center, Houston, Texas, USA
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6
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Johnson S, Gerding DN, Li X, Reda DJ, Donskey CJ, Gupta K, Goetz MB, Climo MW, Gordin FM, Ringer R, Johnson N, Johnson M, Calais LA, Goldberg AM, Ge L, Haegerich T. Defining optimal treatment for recurrent Clostridioides difficile infection (OpTION study): A randomized, double-blind comparison of three antibiotic regimens for patients with a first or second recurrence. Contemp Clin Trials 2022; 116:106756. [DOI: 10.1016/j.cct.2022.106756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 04/01/2022] [Accepted: 04/04/2022] [Indexed: 11/26/2022]
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7
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Abbas R, Wason J, Michiels S, Teuff GL. Role of peer support in a hepatitis C elimination programme. J Viral Hepat 2022; 29:43-51. [PMID: 34664352 PMCID: PMC7613915 DOI: 10.1111/jvh.13626] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/18/2021] [Accepted: 09/27/2021] [Indexed: 01/06/2023]
Abstract
Many people with chronic hepatitis C infection don't engage in treatment. To eliminate hepatitis C and avoid health inequalities therapy must be provided to everyone. In other diseases peers with lived experience of the condition have improved care but, for hepatitis C, studies have not shown unequivocal benefit. We completed a retrospective analysis of the English National Health Service treatment registry comparing treatment networks with and without peers using Bayesian Poisson (for count outcomes) or Bayesian Binomial (for proportion outcomes) mixed effects models with time fixed effects. For each outcome, we estimated relative ratio (RR-Poisson model) or odds ratio (Odds Ratio (OR)-Binomial model) between peer and non-peer networks. We analysed 30,729 patients within 20 operational delivery networks. In networks with peers there was an increase in the number of people initiating therapy (RR 1.12 95%, credible interval 1.02-1.21) and an increase in the proportion completing therapy (OR 2.45 95%, credible interval 1.49-3.84). However, we saw no change in proportions of people using drugs who initiated therapy nor any significant change in virological response (OR 1.14 95% credible interval 0.979-1.36). We repeated the analysis looking at the impact of peers two months after they had been introduced, when they had established networks of contacts, and saw an increase in the proportion of people treated in addiction services. In treating patients with chronic hepatitis C infection the inclusion of peer supporters may increase the number of people who initiate and complete antiviral therapy.
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Affiliation(s)
- Rachid Abbas
- Biostatistics and Epidemiology Department, Gustave Roussy, Villejuif, France
- Oncostat U1018, Inserm, Université Paris-Saclay, Ligue contre le Cancer, Villejuif, France
| | - James Wason
- Population Health Sciences Institute, Newcastle University, Newcastle Upon Tyne, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Stefan Michiels
- Biostatistics and Epidemiology Department, Gustave Roussy, Villejuif, France
- Oncostat U1018, Inserm, Université Paris-Saclay, Ligue contre le Cancer, Villejuif, France
| | - Gwénaёl Le Teuff
- Biostatistics and Epidemiology Department, Gustave Roussy, Villejuif, France
- Oncostat U1018, Inserm, Université Paris-Saclay, Ligue contre le Cancer, Villejuif, France
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8
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Abbas R, Wason J, Michiels S, Le Teuff G. A two-stage drop-the-losers design for time-to-event outcome using a historical control arm. Pharm Stat 2021; 21:268-288. [PMID: 34496117 DOI: 10.1002/pst.2168] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 07/31/2021] [Accepted: 08/22/2021] [Indexed: 11/10/2022]
Abstract
Phase II immuno-oncology clinical trials screen for efficacy an increasing number of treatments. In rare cancers, using historical control data is a pragmatic approach for speeding up clinical trials. The drop-the-losers design allows dropping off ineffective arms at interim analyses. We extended the original drop-the-losers design for a time-to-event outcome using a historical control through the one-sample log-rank statistic. Simulated trials featured three arms at the first stage, one at the second stage, nine scenarios, eight sample sizes with 5%- and 10%- nominal family-wise error rate (FWER). A numerical algorithm is provided to solve power calculations at the design stage. Our design was compared with a group of three independent single-arm trials (fixed design) with and without correction for multiplicity. Our design allowed strict control of the FWER at nominal levels while the misspecification of survival distribution and fixed design inflated the FWER up to three times the nominal level. The empirical power of our design increased with the sample size, the treatment effect and the number of effective treatments and dropped when more patients were recruited at the second stage. The fixed design with correction showed comparable power, while our design advantageously included more patients to the most promising arm. Recommendations for future applications are given. By taking advantage of the use of historical control data and a time-to-event outcome, the drop-the-losers design is a promising tool to meet the challenge of improving phase II clinical trials in immuno-oncology.
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Affiliation(s)
- Rachid Abbas
- Biostatistics and Epidemiology Department, Gustave Roussy, Villejuif, France.,Oncostat U1018, Inserm, Université Paris-Saclay, Ligue contre le Cancer, Villejuif, France
| | - James Wason
- Population Health Sciences Institute, Newcastle University, Newcastle Upon Tyne, UK.,MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Stefan Michiels
- Biostatistics and Epidemiology Department, Gustave Roussy, Villejuif, France.,Oncostat U1018, Inserm, Université Paris-Saclay, Ligue contre le Cancer, Villejuif, France
| | - Gwénaël Le Teuff
- Biostatistics and Epidemiology Department, Gustave Roussy, Villejuif, France.,Oncostat U1018, Inserm, Université Paris-Saclay, Ligue contre le Cancer, Villejuif, France
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9
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Krystal JH, Chow B, Vessicchio J, Henrie AM, Neylan TC, Krystal AD, Marx BP, Xu K, Jindal RD, Davis LL, Schnurr PP, Stein MB, Thase ME, Ventura B, Huang GD, Shih MC. Design of the National Adaptive Trial for PTSD-related Insomnia (NAP Study), VA Cooperative Study Program (CSP) #2016. Contemp Clin Trials 2021; 109:106540. [PMID: 34416369 DOI: 10.1016/j.cct.2021.106540] [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: 06/03/2021] [Revised: 08/09/2021] [Accepted: 08/10/2021] [Indexed: 11/15/2022]
Abstract
There are currently no validated pharmacotherapies for posttraumatic stress disorder (PTSD)-related insomnia. The purpose of the National Adaptive Trial for PTSD-Related Insomnia (NAP Study) is to efficiently compare to placebo the effects of three insomnia medications with different mechanisms of action that are already prescribed widely to veterans diagnosed with PTSD within U.S. Department of Veterans Affairs (VA) Medical Centers. This study plans to enroll 1224 patients from 34 VA Medical Centers into a 12- week prospective, randomized placebo-controlled clinical trial comparing trazodone, eszopiclone, and gabapentin. The primary outcome measure is insomnia, assessed with the Insomnia Severity Index. A novel aspect of this study is its adaptive design. At the recruitment midpoint, an interim analysis will be conducted to inform a decision to close recruitment to any "futile" arms (i.e. arms where further recruitment is very unlikely to yield a significant result) while maintaining the overall study recruitment target. This step could result in the enrichment of the remaining study arms, enhancing statistical power for the remaining comparisons to placebo. This study will also explore clinical, actigraphic, and biochemical predictors of treatment response that may guide future biomarker development. Lastly, due to the COVID-19 pandemic, this study will allow the consenting process and follow-up visits to be conducted via video or phone contact if in-person meetings are not possible. Overall, this study aims to identify at least one effective pharmacotherapy for PTSD-related insomnia, and, perhaps, to generate definitive negative data to reduce the use of ineffective insomnia medications. NATIONAL CLINICAL TRIAL (NCT) IDENTIFIED NUMBER: NCT03668041.
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Affiliation(s)
- John H Krystal
- Clinical Neuroscience Division, National Center for PTSD, VA Connecticut Healthcare System, West Haven, CT, United States of America; Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States of America; Departments of Neuroscience and Psychology, Yale University, New Haven, CT, United States of America.
| | - Bruce Chow
- Cooperative Studies Program Coordinating Center (CSPCC), VA Palo Alto Healthcare System, Palo Alto, CA, United States of America
| | - Jennifer Vessicchio
- Clinical Neuroscience Division, National Center for PTSD, VA Connecticut Healthcare System, West Haven, CT, United States of America; Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States of America
| | - Adam M Henrie
- Cooperative Studies Program, Clinical Research Pharmacy Coordinating Center (CSPCRPCC), U.S. Department of Veterans Affairs, Albuquerque, NM, United States of America
| | - Thomas C Neylan
- Department of Psychiatry and UCSF Weill Institute for Neurosciences, School of Medicine, University of California, San Francisco, CA; VA San Francisco Healthcare System, San Francisco, CA, United States of America
| | - Andrew D Krystal
- Department of Psychiatry and UCSF Weill Institute for Neurosciences, School of Medicine, University of California, San Francisco, CA
| | - Brian P Marx
- Behavioral Sciences Division, National Center for PTSD, VA Boston Healthcare System, Boston, MA, Department of Psychiatry, Boston University School of Medicine, Boston, MA, United States of America
| | - Ke Xu
- Clinical Neuroscience Division, National Center for PTSD, VA Connecticut Healthcare System, West Haven, CT, United States of America; Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States of America
| | - Ripu D Jindal
- Department of Psychiatry, Birmingham VA Medical Center, Departments of Neurology and Psychiatry, University of Alabama at Birmingham, Birmingham, AL, United States of America
| | - Lori L Davis
- Tuscaloosa VA Medical Center, Tuscaloosa, AL, United States of America; Department of Psychiatry, University of Alabama School of Medicine, Birmingham, AL, United States of America
| | - Paula P Schnurr
- Executive Division, National Center for PTSD, White River Junction, VT, Department of Psychiatry, Geisel School of Medicine at Dartmouth, Hanover, NH, United States of America
| | - Murray B Stein
- VA San Diego Healthcare System, San Diego, CA, Departments of Psychiatry, Family Medicine, and Public Health, University of California, San Diego, CA, United States of America
| | - Michael E Thase
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States of America
| | - Beverly Ventura
- Cooperative Studies Program Coordinating Center (CSPCC), VA Palo Alto Healthcare System, Palo Alto, CA, United States of America
| | - Grant D Huang
- Cooperative Studies Program, Office of Research and Development, U.S. Department of Veterans Affairs, Washington, DC, United States of America
| | - Mei-Chiung Shih
- Cooperative Studies Program Coordinating Center (CSPCC), VA Palo Alto Healthcare System, Palo Alto, CA, United States of America; Department of Biomedical Data Science, Stanford School of Medicine, Stanford, CA, United States of America
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10
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Jin M, Zhang P. A Seamless Adaptive 2-in-1 Design Expanding a Phase 2 Trial for Treatment or Dose Selection Into a Phase 3 Trial. Stat Biopharm Res 2021. [DOI: 10.1080/19466315.2021.1914717] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Man Jin
- Data and Statistical Sciences, AbbVie Inc., North Chicago, IL
| | - Pingye Zhang
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ
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11
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Wang L, Luo X, Zheng C. A simulation-free group sequential design with max-combo tests in the presence of non-proportional hazards. Pharm Stat 2021; 20:879-897. [PMID: 33759337 DOI: 10.1002/pst.2116] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 01/06/2021] [Accepted: 02/23/2021] [Indexed: 11/08/2022]
Abstract
Non-proportional hazards (NPH) have been observed in many immuno-oncology clinical trials. Weighted log-rank tests (WLRT) with suitable weights can be used to improve the power of detecting the difference between survival curves in the presence of NPH. However, it is not easy to choose a proper WLRT in practice. A versatile max-combo test was proposed to achieve the balance of robustness and efficiency, and has received increasing attention recently. Survival trials often warrant interim analyses due to their high cost and long durations. The integration and implementation of max-combo tests in interim analyses often require extensive simulation studies. In this report, we propose a simulation-free approach for group sequential designs with the max-combo test in survival trials. The simulation results support that the proposed method can successfully control the type I error rate and offer excellent accuracy and flexibility in estimating sample sizes, with light computation burden. Notably, our method displays strong robustness towards various model misspecifications and has been implemented in an R package.
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Affiliation(s)
- Lili Wang
- Department of Biotatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Xiaodong Luo
- Department of Biostatistics and Programming, Research and Development, Sanofi US, Bridgewater, New Jersey, USA
| | - Cheng Zheng
- Department of Biostatistics and Programming, Research and Development, Sanofi US, Bridgewater, New Jersey, USA
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12
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Chen TY, Zhao J, Sun L, Anderson KM. Multiplicity for a group sequential trial with biomarker subpopulations. Contemp Clin Trials 2020; 101:106249. [PMID: 33338648 DOI: 10.1016/j.cct.2020.106249] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 08/02/2020] [Accepted: 12/07/2020] [Indexed: 10/22/2022]
Abstract
Biomarker subpopulations have become increasingly important for drug development in targeted therapies. The use of biomarkers has the potential to facilitate more effective outcomes by guiding patient selection appropriately, thus enhancing the benefit-risk profile and improving trial power. Studying a broad population simultaneously with a more targeted one allows the trial to determine the population for which a treatment is effective and allows a goal of making approved regulatory labeling as inclusive as is appropriate. We examine new methods accounting for the complete correlation structure in group sequential designs with hypotheses in nested subgroups. The designs provide full control of family-wise Type I error rate. This extension of previous methods accounting for either group sequential design or correlation between subgroups improves efficiency (power or sample size) over a typical Bonferroni approach for testing nested populations.
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Affiliation(s)
- Ting-Yu Chen
- The University of Texas Health Science Center at Houston, TX, USA
| | - Jing Zhao
- Merck & Co., Inc., Kenilworth, NJ, USA
| | - Linda Sun
- Merck & Co., Inc., Kenilworth, NJ, USA
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13
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Millen GC, Yap C. Adaptive trial designs: what are multiarm, multistage trials? Arch Dis Child Educ Pract Ed 2020; 105:376-378. [PMID: 31662314 DOI: 10.1136/archdischild-2019-317826] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 10/11/2019] [Accepted: 10/18/2019] [Indexed: 02/04/2023]
Affiliation(s)
- Gerard Cathal Millen
- Paediatric Oncology, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| | - Christina Yap
- Cancer Research UK Clinical Trials Unit, Institute of Cancer and Genomic Sciences, University of Birmingham College of Medical and Dental Sciences, Birmingham, Birmingham, UK
- Clinical Trials and Statistics Unit, Institute of Cancer Research, London, UK
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14
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Bassi A, Berkhof J, de Jong D, van de Ven PM. Bayesian adaptive decision-theoretic designs for multi-arm multi-stage clinical trials. Stat Methods Med Res 2020; 30:717-730. [PMID: 33243087 PMCID: PMC8008394 DOI: 10.1177/0962280220973697] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Multi-arm multi-stage clinical trials in which more than two drugs are simultaneously investigated provide gains over separate single- or two-arm trials. In this paper we propose a generic Bayesian adaptive decision-theoretic design for multi-arm multi-stage clinical trials with K (K≥2) arms. The basic idea is that after each stage a decision about continuation of the trial and accrual of patients for an additional stage is made on the basis of the expected reduction in loss. For this purpose, we define a loss function that incorporates the patient accrual costs as well as costs associated with an incorrect decision at the end of the trial. An attractive feature of our loss function is that its estimation is computationally undemanding, also when K > 2. We evaluate the frequentist operating characteristics for settings with a binary outcome and multiple experimental arms. We consider both the situation with and without a control arm. In a simulation study, we show that our design increases the probability of making a correct decision at the end of the trial as compared to nonadaptive designs and adaptive two-stage designs.
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Affiliation(s)
- Andrea Bassi
- Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Johannes Berkhof
- Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Daphne de Jong
- Department of Pathology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Peter M van de Ven
- Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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15
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Follmann D, Proschan M. Two Stage Designs for Phase III Clinical Trials. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.07.29.20164525. [PMID: 32793927 PMCID: PMC7418751 DOI: 10.1101/2020.07.29.20164525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Phase III platform trials are increasingly used to evaluate a sequence of treatments for a specific disease. Traditional approaches to structure such trials tend to focus on the sequential questions rather than the performance of the entire enterprise. We consider two-stage trials where an early evaluation is used to determine whether to continue with an individual study. To evaluate performance, we use the ratio of expected wins (RW), that is, the expected number of reported efficacious treatments using a two-stage approach compared to that using standard phase III trials. We approximate the test statistics during the course of a single trial using Brownian Motion and determine the optimal stage 1 time and type I error rate to maximize RW for fixed power. At times, a surrogate or intermediate endpoint may provide a quicker read on potential efficacy than use of the primary endpoint at stage 1. We generalize our approach to the surrogate endpoint setting and show improved performance, provided a good quality and powerful surrogate is available. We apply our methods to the design of a platform trial to evaluate treatments for COVID-19 disease.
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Affiliation(s)
- Dean Follmann
- Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, 5601 Fishers Lane, Bethesda MD 20892, U.S.A
| | - Michael Proschan
- Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, 5601 Fishers Lane, Bethesda MD 20892, U.S.A
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16
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The Evolution of Master Protocol Clinical Trial Designs: A Systematic Literature Review. Clin Ther 2020; 42:1330-1360. [DOI: 10.1016/j.clinthera.2020.05.010] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 04/10/2020] [Accepted: 05/11/2020] [Indexed: 02/07/2023]
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17
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Dimairo M, Pallmann P, Wason J, Todd S, Jaki T, Julious SA, Mander AP, Weir CJ, Koenig F, Walton MK, Nicholl JP, Coates E, Biggs K, Hamasaki T, Proschan MA, Scott JA, Ando Y, Hind D, Altman DG. The adaptive designs CONSORT extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design. Trials 2020; 21:528. [PMID: 32546273 PMCID: PMC7298968 DOI: 10.1186/s13063-020-04334-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised.This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process.The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist comprises seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text.The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits. In order to encourage its wide dissemination this article is freely accessible on the BMJ and Trials journal websites."To maximise the benefit to society, you need to not just do research but do it well" Douglas G Altman.
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Affiliation(s)
- Munyaradzi Dimairo
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK.
| | | | - James Wason
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Institute of Health and Society, Newcastle University, Newcastle, UK
| | - Susan Todd
- Department of Mathematics and Statistics, University of Reading, Reading, UK
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Steven A Julious
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | - Adrian P Mander
- Centre for Trials Research, Cardiff University, Cardiff, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Christopher J Weir
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Franz Koenig
- Centre for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Marc K Walton
- Janssen Pharmaceuticals, Titusville, New Jersey, USA
| | - Jon P Nicholl
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | - Elizabeth Coates
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | - Katie Biggs
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | | | - Michael A Proschan
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, USA
| | - John A Scott
- Division of Biostatistics in the Center for Biologics Evaluation and Research, Food and Drug Administration, Rockville, USA
| | - Yuki Ando
- Pharmaceuticals and Medical Devices Agency, Tokyo, Japan
| | - Daniel Hind
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | - Douglas G Altman
- Centre for Statistics in Medicine, University of Oxford, Oxford, UK
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18
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Dimairo M, Pallmann P, Wason J, Todd S, Jaki T, Julious SA, Mander AP, Weir CJ, Koenig F, Walton MK, Nicholl JP, Coates E, Biggs K, Hamasaki T, Proschan MA, Scott JA, Ando Y, Hind D, Altman DG. The Adaptive designs CONSORT Extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design. BMJ 2020; 369:m115. [PMID: 32554564 PMCID: PMC7298567 DOI: 10.1136/bmj.m115] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/19/2019] [Indexed: 12/11/2022]
Abstract
Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised.This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process.The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist comprises seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text.The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits.
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Affiliation(s)
- Munyaradzi Dimairo
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | | | - James Wason
- MRC Biostatistics Unit, University of Cambridge, UK
- Institute of Health and Society, Newcastle University, UK
| | - Susan Todd
- Department of Mathematics and Statistics, University of Reading, UK
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, UK
| | - Steven A Julious
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | - Adrian P Mander
- Centre for Trials Research, Cardiff University, UK
- MRC Biostatistics Unit, University of Cambridge, UK
| | - Christopher J Weir
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, UK
| | - Franz Koenig
- Centre for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Austria
| | | | - Jon P Nicholl
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | - Elizabeth Coates
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | - Katie Biggs
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | | | - Michael A Proschan
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, USA
| | - John A Scott
- Division of Biostatistics in the Center for Biologics Evaluation and Research, Food and Drug Administration, USA
| | - Yuki Ando
- Pharmaceuticals and Medical Devices Agency, Japan
| | - Daniel Hind
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
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19
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McCabe L, White IR, Chau NVV, Barnes E, Pett SL, Cooke GS, Walker AS. The design and statistical aspects of VIETNARMS: a strategic post-licensing trial of multiple oral direct-acting antiviral hepatitis C treatment strategies in Vietnam. Trials 2020; 21:413. [PMID: 32423467 PMCID: PMC7236096 DOI: 10.1186/s13063-020-04350-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 04/25/2020] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Eliminating hepatitis C is hampered by the costs of direct-acting antiviral treatment and the need to treat hard-to-reach populations. Access could be widened by shortening or simplifying treatment, but limited research means it is unclear which approaches could achieve sufficiently high cure rates to be acceptable. We present the statistical aspects of a multi-arm trial designed to test multiple strategies simultaneously and a monitoring mechanism to detect and stop individual randomly assigned groups with unacceptably low cure rates quickly. METHODS The VIETNARMS trial will factorially randomly assign patients to two drug regimens, three treatment-shortening strategies or control, and adjunctive ribavirin or no adjunctive ribavirin with shortening strategies (14 randomly assigned groups). We will use Bayesian monitoring at interim analyses to detect and stop recruitment into unsuccessful strategies, defined by more than 0.95 posterior probability that the true cure rate is less than 90% for the individual randomly assigned group (non-comparative). Final comparisons will be non-inferiority for regimens (margin 5%) and strategies (margin 10%) and superiority for adjunctive ribavirin. Here, we tested the operating characteristics of the stopping guideline for individual randomly assigned groups, planned interim analysis timings and explored power at the final analysis. RESULTS A beta (4.5, 0.5) prior for the true cure rate produces less than 0.05 probability of incorrectly stopping an individual randomly assigned group with a true cure rate of more than 90%. Groups with very low cure rates (<60%) are very likely (>0.9 probability) to stop after about 25% of patients are recruited. Groups with moderately low cure rates (80%) are likely to stop (0.7 probability) before overall recruitment finishes. Interim analyses 7, 10, 13 and 18 months after recruitment commences provide good probabilities of stopping inferior individual randomly assigned groups. For an overall true cure rate of 95%, power is more than 90% to confirm non-inferiority in the regimen and strategy comparisons, regardless of the control cure rate, and to detect a 5% absolute difference in the ribavirin comparison. CONCLUSIONS The operating characteristics of the stopping guideline are appropriate, and interim analyses can be timed to detect individual randomly assigned groups that are highly likely to have suboptimal performance at various stages. Therefore, our design is suitable for evaluating treatment-shortening or -simplifying strategies. TRIAL REGISTRATION ISRCTN registry: ISRCTN61522291. Registered on 4 October 2019.
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Affiliation(s)
- Leanne McCabe
- Medical Research Council Clinical Trials Unit at University College London, 90 High Holborn, WC1V 6LJ London, UK
| | - Ian R. White
- Medical Research Council Clinical Trials Unit at University College London, 90 High Holborn, WC1V 6LJ London, UK
| | | | | | - Sarah L. Pett
- Medical Research Council Clinical Trials Unit at University College London, 90 High Holborn, WC1V 6LJ London, UK
| | | | - A. Sarah Walker
- Medical Research Council Clinical Trials Unit at University College London, 90 High Holborn, WC1V 6LJ London, UK
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20
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Ghosh P, Liu L, Mehta C. Adaptive multiarm multistage clinical trials. Stat Med 2020; 39:1084-1102. [PMID: 32048313 PMCID: PMC7065228 DOI: 10.1002/sim.8464] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 11/04/2019] [Accepted: 12/12/2019] [Indexed: 11/07/2022]
Abstract
Two methods for designing adaptive multiarm multistage (MAMS) clinical trials, originating from conceptually different group sequential frameworks are presented, and their operating characteristics are compared. In both methods pairwise comparisons are made, stage-by-stage, between each treatment arm and a common control arm with the goal of identifying active treatments and dropping inactive ones. At any stage one may alter the future course of the trial through adaptive changes to the prespecified decision rules for treatment selection and sample size reestimation, and notwithstanding such changes, both methods guarantee strong control of the family-wise error rate. The stage-wise MAMS approach was historically the first to be developed and remains the standard method for designing inferentially seamless phase 2-3 clinical trials. In this approach, at each stage, the data from each treatment comparison are summarized by a single multiplicity adjusted P-value. These stage-wise P-values are combined by a prespecified combination function and the resultant test statistic is monitored with respect to the classical two-arm group sequential efficacy boundaries. The cumulative MAMS approach is a more recent development in which a separate test statistic is constructed for each treatment comparison from the cumulative data at each stage. These statistics are then monitored with respect to multiplicity adjusted group sequential efficacy boundaries. We compared the powers of the two methods for designs with two and three active treatment arms, under commonly utilized decision rules for treatment selection, sample size reestimation and early stopping. In our investigations, which were carried out over a reasonably exhaustive exploration of the parameter space, the cumulative MAMS designs were more powerful than the stage-wise MAMS designs, except for the homogeneous case of equal treatment effects, where a small power advantage was discernable for the stage-wise MAMS designs.
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Affiliation(s)
| | | | - Cyrus Mehta
- Cytel Inc, Cambridge, Massachusetts.,Harvard T.H. Chan School of Public Health, Boston, Massachusetts
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21
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Placzek M, Friede T. A conditional error function approach for adaptive enrichment designs with continuous endpoints. Stat Med 2019; 38:3105-3122. [PMID: 31066093 DOI: 10.1002/sim.8154] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 02/22/2019] [Accepted: 03/09/2019] [Indexed: 12/15/2022]
Abstract
Adaptive enrichment designs offer an efficient and flexible way to demonstrate the efficacy of a treatment in a clinically defined full population or in, eg, biomarker-defined subpopulations while controlling the family-wise Type I error rate in the strong sense. Frequently used testing strategies in designs with two or more stages include the combination test and the conditional error function approach. Here, we focus on the latter and present some extensions. In contrast to previous work, we allow for multiple subgroups rather than one subgroup only. For nested as well as nonoverlapping subgroups with normally distributed endpoints, we explore the effect of estimating the variances in the subpopulations. Instead of using a normal approximation, we derive new t-distribution-based methods for two different scenarios. First, in the case of equal variances across the subpopulations, we present exact results using a multivariate t-distribution. Second, in the case of potentially varying variances across subgroups, we provide some improved approximations compared to the normal approximation. The performance of the proposed conditional error function approaches is assessed and compared to the combination test in a simulation study. The proposed methods are motivated by an example in pulmonary arterial hypertension.
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Affiliation(s)
- Marius Placzek
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.,DZHK (German Center for Cardiovascular Research), Partner Site Göttingen, Göttingen, Germany
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22
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Scotina AD, Gutman R. Matching algorithms for causal inference with multiple treatments. Stat Med 2019; 38:3139-3167. [DOI: 10.1002/sim.8147] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 02/11/2019] [Accepted: 02/25/2019] [Indexed: 01/02/2023]
Affiliation(s)
- Anthony D. Scotina
- Department of Mathematics and StatisticsSimmons University Boston Massachusetts
| | - Roee Gutman
- Department of BiostatisticsBrown University Providence Rhode Island
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23
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Leifer ES, Geller NL. Discussion of "A Hybrid Phase I-II/III Clinical Trial Design Allowing Dose Re-Optimization in Phase III" by Andrew G. Chapple and Peter F. Thall. Biometrics 2019; 75:382-384. [PMID: 30945259 DOI: 10.1111/biom.12993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Eric S Leifer
- Office of Biostatistics Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland
| | - Nancy L Geller
- Office of Biostatistics Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland
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24
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Elicin O, Cihoric N, Vlaskou Badra E, Ozsahin M. Emerging patient-specific treatment modalities in head and neck cancer - a systematic review. Expert Opin Investig Drugs 2019; 28:365-376. [PMID: 30760055 DOI: 10.1080/13543784.2019.1582642] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 02/11/2019] [Indexed: 02/08/2023]
Abstract
INTRODUCTION Head and neck cancer (HNC) is an immunosuppressive disease that demonstrates heterogeneous molecular characteristics and features of tumor-host interaction. Beside radiotherapy and surgery, the current standard of care in systemic treatment involves the use of cytotoxic chemotherapy, monoclonal antibodies (mAbs), and tyrosine kinase inhibitors (TKIs). There are also other modalities being developed under the category of immunotherapy, but they are overshadowed by the recent advancements of immune checkpoint inhibitors. AREAS COVERED This systematic review covers recent advancements in 'patient-specific' treatment modalities, which can be only administered to a given patient. EXPERT OPINION Currently, patient-specific treatment modalities in HNC mainly consist of active immunotherapy using adoptive cell therapies and/or gene engineered vectors. Despite the slow pace of development, the interest continues in these treatment modalities. The future of HNC treatment is expected to be guided by biomarkers and personalized approaches with tailored combinations of local treatments (radiotherapy, surgery), systemic agents and immune system modulation. Systematic research is required to generate robust data and obtain a high-level of evidence for the effectiveness of such treatment modalities.
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Affiliation(s)
- Olgun Elicin
- a Department of Radiation Oncology , Inselspital, Bern University Hospital and University of Bern , Bern , Switzerland
| | - Nikola Cihoric
- a Department of Radiation Oncology , Inselspital, Bern University Hospital and University of Bern , Bern , Switzerland
| | - Eugenia Vlaskou Badra
- a Department of Radiation Oncology , Inselspital, Bern University Hospital and University of Bern , Bern , Switzerland
| | - Mahmut Ozsahin
- b Department of Radiation Oncology , University of Lausanne, Centre Hospitalier Universitaire Vaudois (CHUV) , Lausanne , Switzerland
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25
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Blenkinsop A, Parmar MK, Choodari-Oskooei B. Assessing the impact of efficacy stopping rules on the error rates under the multi-arm multi-stage framework. Clin Trials 2019; 16:132-141. [PMID: 30648428 PMCID: PMC6442021 DOI: 10.1177/1740774518823551] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND The multi-arm multi-stage framework uses intermediate outcomes to assess lack-of-benefit of research arms at interim stages in randomised trials with time-to-event outcomes. However, the design lacks formal methods to evaluate early evidence of overwhelming efficacy on the definitive outcome measure. We explore the operating characteristics of this extension to the multi-arm multi-stage design and how to control the pairwise and familywise type I error rate. Using real examples and the updated nstage program, we demonstrate how such a design can be developed in practice. METHODS We used the Dunnett approach for assessing treatment arms when conducting comprehensive simulation studies to evaluate the familywise error rate, with and without interim efficacy looks on the definitive outcome measure, at the same time as the planned lack-of-benefit interim analyses on the intermediate outcome measure. We studied the effect of the timing of interim analyses, allocation ratio, lack-of-benefit boundaries, efficacy rule, number of stages and research arms on the operating characteristics of the design when efficacy stopping boundaries are incorporated. Methods for controlling the familywise error rate with efficacy looks were also addressed. RESULTS Incorporating Haybittle-Peto stopping boundaries on the definitive outcome at the interim analyses will not inflate the familywise error rate in a multi-arm design with two stages. However, this rule is conservative; in general, more liberal stopping boundaries can be used with minimal impact on the familywise error rate. Efficacy bounds in trials with three or more stages using an intermediate outcome may inflate the familywise error rate, but we show how to maintain strong control. CONCLUSION The multi-arm multi-stage design allows stopping for both lack-of-benefit on the intermediate outcome and efficacy on the definitive outcome at the interim stages. We provide guidelines on how to control the familywise error rate when efficacy boundaries are implemented in practice.
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26
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Hirakawa A, Asano J, Sato H, Teramukai S. Master protocol trials in oncology: Review and new trial designs. Contemp Clin Trials Commun 2018; 12:1-8. [PMID: 30182068 PMCID: PMC6120722 DOI: 10.1016/j.conctc.2018.08.009] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 08/10/2018] [Accepted: 08/23/2018] [Indexed: 01/08/2023] Open
Abstract
In oncology, next generation sequencing and comprehensive genomic profiling have enabled the detailed classification of tumors using molecular biology. However, it is unrealistic to conduct phase I-III trials according to each sub-population based on patient molecular subtypes. Common protocols that assess the combination of several molecular markers and their targeted therapies by means of multiple sub-studies are required. These protocols are called "master protocols," and are drawing attention as a next-generation clinical trial design. Recently, several reviews of clinical trials based on the master protocol design have been published, but their definitions of these such trials, including basket, umbrella, and platform trials, were not consistent. Concurrently, the acceleration of the development of new statistical designs for master protocol trials has been underway. This article provides an overview of recent reviews for master protocols, including their statistical design methodologies in Oncology. We also introduce several examples of previous and on-going master protocol trials along with their classifications by some recent studies.
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Affiliation(s)
- Akihiro Hirakawa
- Department of Biostatistics and Bioinformatics, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8654, Japan
| | - Junichi Asano
- Biostatistics Group, Center for Product Evaluation, Pharmaceuticals and Medical Devices Agency, Tokyo, 100-0013, Japan
| | - Hiroyuki Sato
- Biostatistics Group, Center for Product Evaluation, Pharmaceuticals and Medical Devices Agency, Tokyo, 100-0013, Japan
| | - Satoshi Teramukai
- Department of Biostatistics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, 602-8566, Japan
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27
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Brueckner M, Titman A, Jaki T, Rojek A, Horby P. Performance of different clinical trial designs to evaluate treatments during an epidemic. PLoS One 2018; 13:e0203387. [PMID: 30204799 PMCID: PMC6133355 DOI: 10.1371/journal.pone.0203387] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Accepted: 08/20/2018] [Indexed: 12/14/2022] Open
Abstract
In the 2013-2016 west Africa outbreak of Ebola Virus Disease (EVD), most of the planned clinical trials failed to reach a conclusion within the time frame of the epidemic. The performance of clinical trial designs for the evaluation of one or more experimental treatments in the specific context of an ongoing epidemic with changing case fatality rates (CFR) and unpredictable case numbers is unclear. We conduct a comprehensive evaluation of commonly used two- and multi-arm clinical trial designs based on real data, which was recorded during the 2013-16 EVD epidemic in west Africa. The primary endpoint is death within 14 days of hospitalization. The impact of the recruitment start times relative to the time course of the epidemic on the operating characteristics of the clinical trials is analysed. Designs with frequent interim analyses with the possibility of early stopping are shown to outperform designs with only a single analysis not only in terms of average time to conclusion and average sample size, but also in terms of the probability of reaching any conclusion at all. Historic control designs almost always result in substantially inflated false positive rates, when the case fatality rate changes over time. Response-adaptive randomization may be a compromise between the goal of scientific validity and the ethical goal of minimizing the number of patients allocated to ineffective treatments.
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Affiliation(s)
- Matthias Brueckner
- Department of Mathematics and Statistics, Lancaster University, Lancaster, United Kingdom
- * E-mail:
| | - Andrew Titman
- Department of Mathematics and Statistics, Lancaster University, Lancaster, United Kingdom
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, Lancaster, United Kingdom
| | - Amanda Rojek
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
| | - Peter Horby
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
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28
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Chiu YD, Koenig F, Posch M, Jaki T. Design and estimation in clinical trials with subpopulation selection. Stat Med 2018; 37:4335-4352. [PMID: 30088280 PMCID: PMC6282861 DOI: 10.1002/sim.7925] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 05/23/2018] [Accepted: 07/06/2018] [Indexed: 11/10/2022]
Abstract
Population heterogeneity is frequently observed among patients' treatment responses in clinical trials because of various factors such as clinical background, environmental, and genetic factors. Different subpopulations defined by those baseline factors can lead to differences in the benefit or safety profile of a therapeutic intervention. Ignoring heterogeneity between subpopulations can substantially impact on medical practice. One approach to address heterogeneity necessitates designs and analysis of clinical trials with subpopulation selection. Several types of designs have been proposed for different circumstances. In this work, we discuss a class of designs that allow selection of a predefined subgroup. Using the selection based on the maximum test statistics as the worst‐case scenario, we then investigate the precision and accuracy of the maximum likelihood estimator at the end of the study via simulations. We find that the required sample size is chiefly determined by the subgroup prevalence and show in simulations that the maximum likelihood estimator for these designs can be substantially biased.
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Affiliation(s)
- Yi-Da Chiu
- Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, Lancashire, UK
| | - Franz Koenig
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Martin Posch
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Thomas Jaki
- Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, Lancashire, UK
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Love R, Adams J, van Sluijs EMF, Foster C, Humphreys D. A cumulative meta-analysis of the effects of individual physical activity interventions targeting healthy adults. Obes Rev 2018; 19:1164-1172. [PMID: 29701299 PMCID: PMC6099338 DOI: 10.1111/obr.12690] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Revised: 01/22/2018] [Accepted: 02/13/2018] [Indexed: 01/22/2023]
Abstract
Despite a large and increasing evidence base on physical activity interventions, the high rates of physical inactivity and associated chronic diseases are continuing to increase globally. The purpose of this cumulative meta-analysis was to investigate the evolution of randomized controlled trial evidence of individual-level physical activity interventions to asses if new trials are contributing novel evidence to the field. Through a two-staged search process, primary studies examining the effects of interventions targeted at increasing physical activity within healthy adult populations were pooled and selected from eligible systematic reviews. Cumulative meta-analyses were performed on effect sizes immediately post-intervention (n = 62), and for long-term behaviour change (≥12-month post-baseline; n = 27). Sufficiency and stability of the evidence was assessed through application of pre-published indicators. Meta-analyses suggest overall positive intervention effects on physical activity. The evidence base for effectiveness immediately post-intervention reached levels of sufficiency and stability in 2007; and for long-term follow-up in 2011. In the time since, intervention effectiveness has not substantially changed, and further trials are unlikely to change the direction and magnitude of effect. Substantial evidence exists demonstrating that physical activity interventions can modify individual behaviour in controlled settings. Researchers are urged to shift focus towards investigating the optimization, implementation, sustainability and cost-effectiveness of interventions.
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Affiliation(s)
- R Love
- Department of Social Policy and Intervention, University of Oxford, Oxford, UK.,Centre for Diet and Activity Research (CEDAR), MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - J Adams
- Centre for Diet and Activity Research (CEDAR), MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - E M F van Sluijs
- Centre for Diet and Activity Research (CEDAR), MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - C Foster
- Centre for Exercise, Nutrition and Health Sciences, University of Bristol, Bristol, UK
| | - D Humphreys
- Department of Social Policy and Intervention, University of Oxford, Oxford, UK
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Curtin F, Heritier S. The role of adaptive trial designs in drug development. Expert Rev Clin Pharmacol 2017; 10:727-736. [DOI: 10.1080/17512433.2017.1321985] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- François Curtin
- Division of Clinical Pharmacology and Toxicology, University of Geneva, Geneva, Switzerland
- Research Center for Statistics, Geneva School of Economics and Management, University of Geneva, Geneva, Switzerland
- Geneuro SA, Geneva, Switzerland
| | - Stephane Heritier
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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