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Lu X, Shan G. Two-stage response adaptive randomization designs for multi-arm trials with binary outcome. J Biopharm Stat 2024; 34:526-538. [PMID: 37452825 PMCID: PMC10788381 DOI: 10.1080/10543406.2023.2234028] [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: 03/02/2023] [Accepted: 07/01/2023] [Indexed: 07/18/2023]
Abstract
In recent years, adaptive randomization methods have gained significant popularity in clinical research and trial design due to their ability to provide both efficiency and flexibility in adjusting the statistical procedures of ongoing clinical trials. For a study to compare multiple treatments, a multi-arm two-stage design could be utilized to select the best treatment from the first stage and further compare that treatment with control in the second stage. The traditional design used equal randomization in both stages. To better utilize the interim results from the first stage, we propose to develop response adaptive randomization two-stage designs for a multi-arm clinical trial with binary outcome. Two allocation methods are considered: (1) an optimal allocation based on a sequential design; (2) the play-the-winner rule. Optimal multi-arm two-stage designs are obtained under three criteria: minimizing the expected number of failures, minimizing the average expected sample size, and minimizing the expected sample size under the null hypothesis. Simulation studies show that the proposed adaptive design based on the play-the-winner rule has good performance. A phase II trial for patients with pancreas adenocarcinoma and a germline BRCA/ PALB2 mutation was used to illustrate the application of the proposed response adaptive randomization designs.
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Affiliation(s)
- Xinlin Lu
- Department of Biostatistics, University of Florida, Gainesville FL, 32611
| | - Guogen Shan
- Department of Biostatistics, University of Florida, Gainesville FL, 32611
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Wang Y, Yao M, Liu J, Liu Y, Ma Y, Luo X, Mei F, Xiang H, Zou K, Li L, Sun X. Adaptive designs were primarily used but inadequately reported in early phase drug trials. BMC Med Res Methodol 2024; 24:130. [PMID: 38840047 PMCID: PMC11151552 DOI: 10.1186/s12874-024-02256-9] [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: 05/26/2023] [Accepted: 05/27/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND Faced with the high cost and limited efficiency of classical randomized controlled trials, researchers are increasingly applying adaptive designs to speed up the development of new drugs. However, the application of adaptive design to drug randomized controlled trials (RCTs) and whether the reporting is adequate are unclear. Thus, this study aimed to summarize the epidemiological characteristics of the relevant trials and assess their reporting quality by the Adaptive designs CONSORT Extension (ACE) checklist. METHODS We searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL) and ClinicalTrials.gov from inception to January 2020. We included drug RCTs that explicitly claimed to be adaptive trials or used any type of adaptative design. We extracted the epidemiological characteristics of included studies to summarize their adaptive design application. We assessed the reporting quality of the trials by Adaptive designs CONSORT Extension (ACE) checklist. Univariable and multivariable linear regression models were used to the association of four prespecified factors with the quality of reporting. RESULTS Our survey included 108 adaptive trials. We found that adaptive design has been increasingly applied over the years, and was commonly used in phase II trials (n = 45, 41.7%). The primary reasons for using adaptive design were to speed the trial and facilitate decision-making (n = 24, 22.2%), maximize the benefit of participants (n = 21, 19.4%), and reduce the total sample size (n = 15, 13.9%). Group sequential design (n = 63, 58.3%) was the most frequently applied method, followed by adaptive randomization design (n = 26, 24.1%), and adaptive dose-finding design (n = 24, 22.2%). The proportion of adherence to the ACE checklist of 26 topics ranged from 7.4 to 99.1%, with eight topics being adequately reported (i.e., level of adherence ≥ 80%), and eight others being poorly reported (i.e., level of adherence ≤ 30%). In addition, among the seven items specific for adaptive trials, three were poorly reported: accessibility to statistical analysis plan (n = 8, 7.4%), measures for confidentiality (n = 14, 13.0%), and assessments of similarity between interim stages (n = 25, 23.1%). The mean score of the ACE checklist was 13.9 (standard deviation [SD], 3.5) out of 26. According to our multivariable regression analysis, later published trials (estimated β = 0.14, p < 0.01) and the multicenter trials (estimated β = 2.22, p < 0.01) were associated with better reporting. CONCLUSION Adaptive design has shown an increasing use over the years, and was primarily applied to early phase drug trials. However, the reporting quality of adaptive trials is suboptimal, and substantial efforts are needed to improve the reporting.
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Affiliation(s)
- Yuning Wang
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Minghong Yao
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Jiali Liu
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Yanmei Liu
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Yu Ma
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Xiaochao Luo
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Fan Mei
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Hunong Xiang
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Kang Zou
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Ling Li
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China.
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China.
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China.
| | - Xin Sun
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China.
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China.
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China.
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Vazquez AR, Wong WK. Mathematical programming tools for randomization purposes in small two-arm clinical trials: A case study with real data. Pharm Stat 2024. [PMID: 38613324 DOI: 10.1002/pst.2388] [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: 06/06/2023] [Revised: 02/06/2024] [Accepted: 03/26/2024] [Indexed: 04/14/2024]
Abstract
Modern randomization methods in clinical trials are invariably adaptive, meaning that the assignment of the next subject to a treatment group uses the accumulated information in the trial. Some of the recent adaptive randomization methods use mathematical programming to construct attractive clinical trials that balance the group features, such as their sizes and covariate distributions of their subjects. We review some of these methods and compare their performance with common covariate-adaptive randomization methods for small clinical trials. We introduce an energy distance measure that compares the discrepancy between the two groups using the joint distribution of the subjects' covariates. This metric is more appealing than evaluating the discrepancy between the groups using their marginal covariate distributions. Using numerical experiments, we demonstrate the advantages of the mathematical programming methods under the new measure. In the supplementary material, we provide R codes to reproduce our study results and facilitate comparisons of different randomization procedures.
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Affiliation(s)
- Alan R Vazquez
- School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Nuevo Leon, Mexico
| | - Weng-Kee Wong
- Department of Biostatistics, University of California, Los Angeles, California, USA
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Wang Y, Yao M, Liu J, Liu Y, Ma Y, Luo X, Mei F, Xiang H, Zou K, Sun X, Li L. A systematic survey of adaptive trials shows substantial improvement in methods is needed. J Clin Epidemiol 2024; 167:111257. [PMID: 38218461 DOI: 10.1016/j.jclinepi.2024.111257] [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: 09/20/2023] [Revised: 12/22/2023] [Accepted: 01/08/2024] [Indexed: 01/15/2024]
Abstract
OBJECTIVES To investigate the design, conduct, and analysis of adaptive trials through a systematic survey and provide recommendations for future adaptive trials. STUDY DESIGN AND SETTING We systematically searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov databases up to January 2020. We included trials that were self-described as adaptive trials or applied adaptive designs. We identified three frequently used adaptive designs and summarized their methodological details in terms of design, conduct, and analysis. Lastly, we provided recommendations for future adaptive trials. RESULTS We included a total of 128 trials in this study. The primary motivations for using adaptive design were to speed up the trials and facilitate decision-making (n = 29, 31.5%). The three most frequently used methods were group sequential design (GSD) (n = 71, 55.5%), adaptive dose-finding design (ADFD) (n = 35, 27.3%), and adaptive randomization design (ARD) (n = 26, 20.3%). The timing and frequency of interim analysis were detailed in three-fourths of the GSD trials (n = 55, 77.5%) and in half of the ADFD trials (n = 19, 54.3%); however, more than half of the ARD trials (n = 15, 57.7%) did not provide this information. Some trials selected a different outcome than the primary outcome for interim analysis (GSD: n = 7, 12.7%; ADFD: n = 8, 27.6%; ARD: n = 7, 50.0%), but the majority of these trials did not provide explicit reasons for this choice (GSD: n = 7, 100.0%; ADFD: n = 7, 87.5%; ARD: n = 5, 71.4%). More than half (n = 76, 59.4%) of trials did not mention the accessibility of supporting documents, and two-thirds (n = 86, 67.2%) did not state the establishment of independent data monitoring committees (IDMCs). Moreover, unplanned adjustments were observed during the conduct of one-sixth adaptive trials (n = 22, 17.2%). Based on our findings, we provide 14 recommendations for improving adaptive trials in the future. CONCLUSION Substantial improvements were needed in methods of adaptive trials, particularly in the areas of interim analysis, the establishment of independent data monitoring committees, and unplanned adjustments. In this study, we offer recommendations from both general and specific aspects for researchers to carefully design, conduct, and analyze adaptive trials.
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Affiliation(s)
- Yuning Wang
- Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, West China Hospital, Sichuan University, Chengdu, 610041, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China; China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Minghong Yao
- Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, West China Hospital, Sichuan University, Chengdu, 610041, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China; China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Jiali Liu
- Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, West China Hospital, Sichuan University, Chengdu, 610041, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China; China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Yanmei Liu
- Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, West China Hospital, Sichuan University, Chengdu, 610041, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China; China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Yu Ma
- Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, West China Hospital, Sichuan University, Chengdu, 610041, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China; China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Xiaochao Luo
- Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, West China Hospital, Sichuan University, Chengdu, 610041, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China; China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Fan Mei
- Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, West China Hospital, Sichuan University, Chengdu, 610041, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China; China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Hunong Xiang
- Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, West China Hospital, Sichuan University, Chengdu, 610041, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China; China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Kang Zou
- Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, West China Hospital, Sichuan University, Chengdu, 610041, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China; China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Xin Sun
- Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, West China Hospital, Sichuan University, Chengdu, 610041, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China; China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China.
| | - Ling Li
- Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, West China Hospital, Sichuan University, Chengdu, 610041, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China; China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China.
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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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Robertson DS, Lee KM, López-Kolkovska BC, Villar SS. Response-adaptive randomization in clinical trials: from myths to practical considerations. Stat Sci 2023; 38:185-208. [PMID: 37324576 PMCID: PMC7614644 DOI: 10.1214/22-sts865] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In that context, patient allocation to treatments is determined by randomization probabilities that change based on the accrued response data in order to achieve experimental goals. RAR has received abundant theoretical attention from the biostatistical literature since the 1930's and has been the subject of numerous debates. In the last decade, it has received renewed consideration from the applied and methodological communities, driven by well-known practical examples and its widespread use in machine learning. Papers on the subject present different views on its usefulness, and these are not easy to reconcile. This work aims to address this gap by providing a unified, broad and fresh review of methodological and practical issues to consider when debating the use of RAR in clinical trials.
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Affiliation(s)
- David S. Robertson
- MRC Biostatistics Unit, University of Cambridge, Forvie Site, Robinson Way, Cambridge CB2 0SR, United Kingdom
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Kaizer AM, Belli HM, Ma Z, Nicklawsky AG, Roberts SC, Wild J, Wogu AF, Xiao M, Sabo RT. Recent innovations in adaptive trial designs: A review of design opportunities in translational research. J Clin Transl Sci 2023; 7:e125. [PMID: 37313381 PMCID: PMC10260347 DOI: 10.1017/cts.2023.537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/29/2023] [Accepted: 04/17/2023] [Indexed: 06/15/2023] Open
Abstract
Clinical trials are constantly evolving in the context of increasingly complex research questions and potentially limited resources. In this review article, we discuss the emergence of "adaptive" clinical trials that allow for the preplanned modification of an ongoing clinical trial based on the accumulating evidence with application across translational research. These modifications may include terminating a trial before completion due to futility or efficacy, re-estimating the needed sample size to ensure adequate power, enriching the target population enrolled in the study, selecting across multiple treatment arms, revising allocation ratios used for randomization, or selecting the most appropriate endpoint. Emerging topics related to borrowing information from historic or supplemental data sources, sequential multiple assignment randomized trials (SMART), master protocol and seamless designs, and phase I dose-finding studies are also presented. Each design element includes a brief overview with an accompanying case study to illustrate the design method in practice. We close with brief discussions relating to the statistical considerations for these contemporary designs.
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Affiliation(s)
- Alexander M. Kaizer
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Hayley M. Belli
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Zhongyang Ma
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Andrew G. Nicklawsky
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Samantha C. Roberts
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Jessica Wild
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Adane F. Wogu
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Mengli Xiao
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Roy T. Sabo
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA
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Correll CU, Solmi M, Cortese S, Fava M, Højlund M, Kraemer HC, McIntyre RS, Pine DS, Schneider LS, Kane JM. The future of psychopharmacology: a critical appraisal of ongoing phase 2/3 trials, and of some current trends aiming to de-risk trial programmes of novel agents. World Psychiatry 2023; 22:48-74. [PMID: 36640403 PMCID: PMC9840514 DOI: 10.1002/wps.21056] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/14/2022] [Indexed: 01/15/2023] Open
Abstract
Despite considerable progress in pharmacotherapy over the past seven decades, many mental disorders remain insufficiently treated. This situation is in part due to the limited knowledge of the pathophysiology of these disorders and the lack of biological markers to stratify and individualize patient selection, but also to a still restricted number of mechanisms of action being targeted in monotherapy or combination/augmentation treatment, as well as to a variety of challenges threatening the successful development and testing of new drugs. In this paper, we first provide an overview of the most promising drugs with innovative mechanisms of action that are undergoing phase 2 or 3 testing for schizophrenia, bipolar disorder, major depressive disorder, anxiety and trauma-related disorders, substance use disorders, and dementia. Promising repurposing of established medications for new psychiatric indications, as well as variations in the modulation of dopamine, noradrenaline and serotonin receptor functioning, are also considered. We then critically discuss the clinical trial parameters that need to be considered in depth when developing and testing new pharmacological agents for the treatment of mental disorders. Hurdles and perils threatening success of new drug development and testing include inadequacy and imprecision of inclusion/exclusion criteria and ratings, sub-optimally suited clinical trial participants, multiple factors contributing to a large/increasing placebo effect, and problems with statistical analyses. This information should be considered in order to de-risk trial programmes of novel agents or known agents for novel psychiatric indications, increasing their chances of success.
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Affiliation(s)
- Christoph U Correll
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Psychiatry, Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Department of Psychiatry and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Marco Solmi
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Psychiatry, University of Ottawa, Ottawa, ON, Canada
- Department of Mental Health, Ottawa Hospital, Ottawa, ON, Canada
- Ottawa Hospital Research Institute (OHRI) Clinical Epidemiology Program, University of Ottawa, Ottawa, ON, Canada
- School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Centre for Innovation in Mental Health, School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK
| | - Samuele Cortese
- Centre for Innovation in Mental Health, School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK
- Clinical and Experimental Sciences (CNS and Psychiatry), Faculty of Medicine, University of Southampton, Southampton, UK
- Solent NHS Trust, Southampton, UK
- Division of Psychiatry and Applied Psychology, School of Medicine, University of Nottingham, Nottingham, UK
- Hassenfeld Children's Hospital at NYU Langone, New York University Child Study Center, New York, NY, USA
| | - Maurizio Fava
- Depression Clinical and Research Program, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mikkel Højlund
- Department of Public Health, Clinical Pharmacology, Pharmacy and Environmental Medicine, University of Southern Denmark, Odense, Denmark
- Mental Health Services in the Region of Southern Denmark, Department of Psychiatry Aabenraa, Aabenraa, Denmark
| | - Helena C Kraemer
- Department of Psychiatry and Behavioral Sciences, Stanford University, Cupertino, CA, USA
| | - Roger S McIntyre
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Canadian Rapid Treatment Center of Excellence, Mississauga, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Pharmacology, University of Toronto, Toronto, ON, Canada
- Brain and Cognition Discovery Foundation, Toronto, ON, Canada
| | - Daniel S Pine
- Section on Developmental Affective Neuroscience, National Institute of Mental Health, Bethesda, MD, USA
| | - Lon S Schneider
- Department of Psychiatry and Behavioral Sciences, and Department of Neurology, Keck School of Medicine, and L. Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - John M Kane
- Department of Psychiatry, Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Department of Psychiatry and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA
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Liu X, Deliu N, Chakraborty B. Microrandomized Trials: Developing Just-in-Time Adaptive Interventions for Better Public Health. Am J Public Health 2023; 113:60-69. [PMID: 36413704 PMCID: PMC9755932 DOI: 10.2105/ajph.2022.307150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Just-in-time adaptive interventions (JITAIs) represent an intervention design that adapts the provision and type of support over time to an individual's changing status and contexts, intending to deliver the right support on the right occasion. As a novel strategy for delivering mobile health interventions, JITAIs have the potential to improve access to quality care in underserved communities and, thus, alleviate health disparities, a significant public health concern. Valid experimental designs and analysis methods are required to inform the development of JITAIs. Here, we briefly review the cutting-edge design of microrandomized trials (MRTs), covering both the classical MRT design and its outcome-adaptive counterpart. Associated statistical challenges related to the design and analysis of MRTs are also discussed. Two case studies are provided to illustrate the aforementioned concepts and designs throughout the article. We hope our work leads to better design and application of JITAIs, advancing public health research and practice. (Am J Public Health. 2023;113(1):60-69. https://doi.org/10.2105/AJPH.2022.307150).
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Affiliation(s)
- Xueqing Liu
- Xueqing Liu is with the Centre for Quantitative Medicine, Duke-National University of Singapore (NUS) Medical School, Singapore. Nina Deliu is with the Medical Research Council Biostatistics Unit, University of Cambridge, UK, and the Department of Methods and Models for Economics, Territory and Finance, Sapienza University of Rome, Italy. Bibhas Chakraborty is with the Centre for Quantitative Medicine and Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore; the Department of Statistics and Data Science, NUS, Singapore; and the Department of Biostatistics and Bioinformatics, Duke University, Durham, NC
| | - Nina Deliu
- Xueqing Liu is with the Centre for Quantitative Medicine, Duke-National University of Singapore (NUS) Medical School, Singapore. Nina Deliu is with the Medical Research Council Biostatistics Unit, University of Cambridge, UK, and the Department of Methods and Models for Economics, Territory and Finance, Sapienza University of Rome, Italy. Bibhas Chakraborty is with the Centre for Quantitative Medicine and Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore; the Department of Statistics and Data Science, NUS, Singapore; and the Department of Biostatistics and Bioinformatics, Duke University, Durham, NC
| | - Bibhas Chakraborty
- Xueqing Liu is with the Centre for Quantitative Medicine, Duke-National University of Singapore (NUS) Medical School, Singapore. Nina Deliu is with the Medical Research Council Biostatistics Unit, University of Cambridge, UK, and the Department of Methods and Models for Economics, Territory and Finance, Sapienza University of Rome, Italy. Bibhas Chakraborty is with the Centre for Quantitative Medicine and Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore; the Department of Statistics and Data Science, NUS, Singapore; and the Department of Biostatistics and Bioinformatics, Duke University, Durham, NC
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10
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Afolabi MO, Kelly LE. Non-static framework for understanding adaptive designs: an ethical justification in paediatric trials. JOURNAL OF MEDICAL ETHICS 2022; 48:825-831. [PMID: 34362828 PMCID: PMC9626916 DOI: 10.1136/medethics-2021-107263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 06/25/2021] [Indexed: 06/13/2023]
Abstract
Many drugs used in paediatric medicine are off-label. There is a rising call for the use of adaptive clinical trial designs (ADs) in responding to the need for safe and effective drugs given their potential to offer efficiency and cost-effective benefits compared with traditional clinical trials. ADs have a strong appeal in paediatric clinical trials given the small number of available participants, limited understanding of age-related variability and the desire to limit exposure to futile or unsafe interventions. Although the ethical value of adaptive trials has increasingly come under scrutiny, there is a paucity of literature on the ethical dilemmas that may be associated with paediatric adaptive designs (PADs). This paper highlights some of these ethical concerns around safety, scientific/social value and caregiver/guardian comprehension of the trial design. Against this background, the paper develops a non-static conceptual lens for understanding PADs. It shows that ADs are epistemically open and reduce some of the knowledge-associated uncertainties inherent in clinical trials as well as fast-track the time to draw conclusions about the value of evaluated drugs/treatments. On this note, the authors argue that PADs are ethically justifiable given they (1) have multiple layers of safety, exposing enrolled children to lesser potential risks, (2) create social/scientific value generally and for paediatric populations in particular, (3) specifically foster the flourishing of paediatric populations and (4) can significantly improve paediatric trial efficiency when properly designed and implemented. However, because PADs are relatively new and their regulatory, ethical and logistical characteristics are yet to be clarified in some jurisdictions, the cooperation of various public and private stakeholders is required to ensure that the interests of children, their caregivers and parents/guardians are best served while exposing paediatric research subjects to the most minimal of risks when they are enrolled in paediatric trials that use ADs.
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Affiliation(s)
- Michael Os Afolabi
- Department of Pediatrics and Child Health, Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Lauren E Kelly
- Department of Pediatrics and Child Health, Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
- Children's Hospital Research Institute of Manitoba, Winnipeg, Manitoba, Canada
- George & Fay Yee Centre for Healthcare Innovation, Winnipeg, Manitoba, Canada
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11
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Lee KM, Robertson DS, Jaki T, Emsley R. The benefits of covariate adjustment for adaptive multi-arm designs. Stat Methods Med Res 2022; 31:2104-2121. [PMID: 35876412 PMCID: PMC7613816 DOI: 10.1177/09622802221114544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Covariate adjustment via a regression approach is known to increase the precision
of statistical inference when fixed trial designs are employed in randomized
controlled studies. When an adaptive multi-arm design is employed with the
ability to select treatments, it is unclear how covariate adjustment affects
various aspects of the study. Consider the design framework that relies on
pre-specified treatment selection rule(s) and a combination test approach for
hypothesis testing. It is our primary goal to evaluate the impact of covariate
adjustment on adaptive multi-arm designs with treatment selection. Our secondary
goal is to show how the Uniformly Minimum Variance Conditionally Unbiased
Estimator can be extended to account for covariate adjustment analytically. We
find that adjustment with different sets of covariates can lead to different
treatment selection outcomes and hence probabilities of rejecting hypotheses.
Nevertheless, we do not see any negative impact on the control of the familywise
error rate when covariates are included in the analysis model. When adjusting
for covariates that are moderately or highly correlated with the outcome, we see
various benefits to the analysis of the design. Conversely, there is negligible
impact when including covariates that are uncorrelated with the outcome.
Overall, pre-specification of covariate adjustment is recommended for the
analysis of adaptive multi-arm design with treatment selection. Having the
statistical analysis plan in place prior to the interim and final analyses is
crucial, especially when a non-collapsible measure of treatment effect is
considered in the trial.
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Affiliation(s)
- Kim May Lee
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - Thomas Jaki
- 47959MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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12
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Mau YL, Su PF. Evaluating response-adaptive randomization procedures for recurrent events and terminal event data using a composite endpoint. Pharm Stat 2022; 21:1167-1184. [PMID: 35853695 DOI: 10.1002/pst.2253] [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: 02/08/2022] [Revised: 05/20/2022] [Accepted: 07/04/2022] [Indexed: 11/12/2022]
Abstract
Recurrent event and terminal event data commonly arise in clinical and observational studies. To evaluate the efficacy of a treatment effect for both types of events, a composite endpoint has been used as a possible assessment, particularly when faced with high costs and a longer follow-up study. To model recurrent event processes complicated by the existence of a terminal event, joint frailty modeling has been typically employed. In this study, the objective was to develop some target-driven response adaptive randomization strategies using a composite endpoint based on joint frailty modeling. We first implemented a balanced randomized design and then investigated the response adaptive randomization. The former is intuitively first adopted while the latter is expected to be desirable and ethical in terms of allocating more subjects to the more effective treatment. The results show that the proposed procedures using a composite endpoint are capable of reducing the number of trial participants who receive inferior treatment while simultaneously reaching a desired optimal target as compared to a balanced randomized design. The R shiny application for calculating the sample size and allocation probabilities is also available. Finally, two clinical trials were used as pilot datasets to introduce the proposed procedures.
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Affiliation(s)
- Yu-Lin Mau
- Department of Statistics, National Cheng Kung University, Tainan, Taiwan
| | - Pei-Fang Su
- Department of Statistics, National Cheng Kung University, Tainan, Taiwan
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13
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Generalisations of a Bayesian decision-theoretic randomisation procedure and the impact of delayed responses. Comput Stat Data Anal 2021; 174:107407. [DOI: 10.1016/j.csda.2021.107407] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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14
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Su PF. Response-adaptive treatment allocation for clinical studies with recurrent event and terminal event data. Stat Med 2021; 41:258-275. [PMID: 34693543 DOI: 10.1002/sim.9235] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 10/04/2021] [Accepted: 10/10/2021] [Indexed: 11/07/2022]
Abstract
In long-term clinical studies, recurrent event data are frequently collected to contrast the efficacy of two different treatments. However, the recurrent event process can be stopped by a terminal event, such as death. For analyzing recurrent event and terminal event data, joint frailty modeling has recently received considerable attention because it makes it possible to study the joint evolution over time of both recurrent and terminal event processes and gives consistent and efficient parameters. For a two-arm clinical trial design based on these data sets, there has been limited research on investigating the balanced design, let alone adaptive treatment allocation. Although equal sample size allocation obtained for both treatments is intuitively first adopted in a trial design, if one treatment is expected to be superior, it may be desirable to allocate more subjects to the effective treatment. In this article, we calculate the required sample size based on restricted randomization and then propose a target response-adaptive randomization procedure for recurrent and terminal event outcomes based on the joint frailty model. A randomization procedure, the doubly adaptive biased coin design that targets some optimal allocations, is implemented. The proposed adaptive treatment allocation schemes have been shown to be capable of reducing the number of trial participants who receive inferior treatment while simultaneously reaching an optimal target, as well as retaining a comparable test power as compared to a restricted randomization design. Finally, two clinical studies, the COAPT trial and the A-HeFT trial, are used to illustrate the advantages of adopting the proposed procedure.
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Affiliation(s)
- Pei-Fang Su
- Department of Statistics, National Cheng Kung University, Tainan, Taiwan
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15
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Das S, Bhattacharya R. An optimal multiarmed response adaptive design for survival outcome with independent censoring. Biom J 2021; 64:165-185. [PMID: 34585751 DOI: 10.1002/bimj.202000089] [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: 03/27/2020] [Revised: 11/25/2020] [Accepted: 03/13/2021] [Indexed: 11/10/2022]
Abstract
Compromising ethics and precision in the context of a multiarmed clinical trial, an optimal order adjusted response adaptive design is proposed for survival outcomes subject to independent random censoring. The operating characteristics of the proposed design and the follow-up inference are studied both theoretically as well as empirically and are compared with those of the competitors. Applicability of the developed design is further illustrated through redesigning a real clinical trial with survival responses.
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Affiliation(s)
- Soumyadeep Das
- Department of Statistics, Bidhannagar Government College, Kolkata, India
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16
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Affiliation(s)
- Ian C. Marschner
- Ian C. Marschner is Professor of Biostatistics, NHMRC Clinical Trials Centre, The University of Sydney, Sydney, New South Wales, Australia
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17
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Lee KM, Brown LC, Jaki T, Stallard N, Wason J. Statistical consideration when adding new arms to ongoing clinical trials: the potentials and the caveats. Trials 2021; 22:203. [PMID: 33691748 PMCID: PMC7944243 DOI: 10.1186/s13063-021-05150-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 02/24/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Platform trials improve the efficiency of the drug development process through flexible features such as adding and dropping arms as evidence emerges. The benefits and practical challenges of implementing novel trial designs have been discussed widely in the literature, yet less consideration has been given to the statistical implications of adding arms. MAIN: We explain different statistical considerations that arise from allowing new research interventions to be added in for ongoing studies. We present recent methodology development on addressing these issues and illustrate design and analysis approaches that might be enhanced to provide robust inference from platform trials. We also discuss the implication of changing the control arm, how patient eligibility for different arms may complicate the trial design and analysis, and how operational bias may arise when revealing some results of the trials. Lastly, we comment on the appropriateness and the application of platform trials in phase II and phase III settings, as well as publicly versus industry-funded trials. CONCLUSION Platform trials provide great opportunities for improving the efficiency of evaluating interventions. Although several statistical issues are present, there are a range of methods available that allow robust and efficient design and analysis of these trials.
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Affiliation(s)
- Kim May Lee
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SR, UK.
- Pragmatic Clinical Trials Unit, Queen Mary University of London, Yvonne Carter Building, 58 Turner Street, London, E1 2AB, UK.
| | - Louise C Brown
- MRC Clinical Trials Unit, University College London, 90 High Holborn 2nd Floor, London, WC1V 6LJ, UK
| | - Thomas Jaki
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SR, UK
- Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Nigel Stallard
- Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK
| | - James Wason
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SR, UK
- Population Health Sciences Institute, Baddiley-Clark Building, Newcastle University, Richardson Road, Newcastle upon Tyne, UK
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18
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Abstract
Adaptive enrichment designs for clinical trials may include rules that use interim data to identify treatment-sensitive patient subgroups, select or compare treatments, or change entry criteria. A common setting is a trial to compare a new biologically targeted agent to standard therapy. An enrichment design's structure depends on its goals, how it accounts for patient heterogeneity and treatment effects, and practical constraints. This article first covers basic concepts, including treatment-biomarker interaction, precision medicine, selection bias, and sequentially adaptive decision making, and briefly describes some different types of enrichment. Numerical illustrations are provided for qualitatively different cases involving treatment-biomarker interactions. Reviews are given of adaptive signature designs; a Bayesian design that uses a random partition to identify treatment-sensitive biomarker subgroups and assign treatments; and designs that enrich superior treatment sample sizes overall or within subgroups, make subgroup-specific decisions, or include outcome-adaptive randomization.
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Affiliation(s)
- Peter F Thall
- Department of Biostatistics, M.D. Anderson Cancer Center, University of Texas, Houston, Texas 77030, USA
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19
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Esmaeili V, Juneau A, Dyer JO, Lamontagne A, Kairy D, Bouyer L, Duclos C. Intense and unpredictable perturbations during gait training improve dynamic balance abilities in chronic hemiparetic individuals: a randomized controlled pilot trial. J Neuroeng Rehabil 2020; 17:79. [PMID: 32552850 PMCID: PMC7298869 DOI: 10.1186/s12984-020-00707-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 06/08/2020] [Indexed: 11/26/2022] Open
Abstract
Background Previous studies have assessed the effects of perturbation training on balance after stroke. However, the perturbations were either applied while standing or were small in amplitude during gait, which is not representative of the most common fall conditions. The perturbations were also combined with other challenges such as progressive increases in treadmill speed. Objective To determine the benefit of treadmill training with intense and unpredictable perturbations compared to treadmill walking-only training for dynamic balance and gait post-stroke. Methods Twenty-one individuals post-stroke with reduced dynamic balance abilities, with or without a history of fall and ability to walk on a treadmill without external support or a walking aid for at least 1 min were allocated to either an unpredictable gait perturbation (Perturb) group or a walking-only (NonPerturb) group through covariate adaptive randomization. Nine training sessions were conducted over 3 weeks. NonPerturb participants only walked on the treadmill but were offered perturbation training after the control intervention. Pre- and post-training evaluations included balance and gait abilities, maximal knee strength, balance confidence and community integration. Six-week phone follow-ups were conducted for balance confidence and community integration. Satisfaction with perturbation training was also assessed. Results With no baseline differences between groups (p > 0.075), perturbation training yielded large improvements in most variables in the Perturb (p < 0.05, Effect Size: ES > .46) group (n = 10) and the NonPerturb (p ≤ .089, ES > .45) group (n = 7 post-crossing), except for maximal strength (p > .23) in the NonPerturb group. Walking-only training in the NonPerturb group (n = 8, pre-crossing) mostly had no effect (p > .292, ES < .26), except on balance confidence (p = .063, ES = .46). The effects of the gait training were still present on balance confidence and community integration at follow-up. Satisfaction with the training program was high. Conclusion Intense and unpredictable gait perturbations have the potential to be an efficient component of training to improve balance abilities and community integration in individuals with chronic stroke. Retrospective registration: ClinicalTrials.gov. March 18th, 2020. Identifier: NCT04314830.
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Affiliation(s)
- Vahid Esmaeili
- School of Rehabilitation, Université de Montréal, P.O. Box 6128, Station Centre-Ville, Montreal, Quebec, H3C 3J7, Canada.,Centre for Interdisciplinary Research in Rehabilitation-Institut Universitaire sur la Réadaptation en Déficience Physique de Montréal, in CIUSSS du Centre-Sud-de-l'ile-de-Montréal, Montreal, Canada
| | - Andréanne Juneau
- Centre for Interdisciplinary Research in Rehabilitation-Institut Universitaire sur la Réadaptation en Déficience Physique de Montréal, in CIUSSS du Centre-Sud-de-l'ile-de-Montréal, Montreal, Canada.,Lethbridge-Layton-MacKay Rehabilitation Centre, Montréal, Canada
| | - Joseph-Omer Dyer
- School of Rehabilitation, Université de Montréal, P.O. Box 6128, Station Centre-Ville, Montreal, Quebec, H3C 3J7, Canada
| | - Anouk Lamontagne
- Centre for Interdisciplinary Research in Rehabilitation-Institut Universitaire sur la Réadaptation en Déficience Physique de Montréal, in CIUSSS du Centre-Sud-de-l'ile-de-Montréal, Montreal, Canada.,School of Physical and Occupationnal Therapy, McGill University, Montréal, Canada
| | - Dahlia Kairy
- School of Rehabilitation, Université de Montréal, P.O. Box 6128, Station Centre-Ville, Montreal, Quebec, H3C 3J7, Canada.,Centre for Interdisciplinary Research in Rehabilitation-Institut Universitaire sur la Réadaptation en Déficience Physique de Montréal, in CIUSSS du Centre-Sud-de-l'ile-de-Montréal, Montreal, Canada
| | - Laurent Bouyer
- Department of Rehabilitation, Faculty of Medicine, Université Laval and Center for Interdisciplinary Research in Rehabilitation and Social Integration, CIUSSS-CN, Quebec City, Canada
| | - Cyril Duclos
- School of Rehabilitation, Université de Montréal, P.O. Box 6128, Station Centre-Ville, Montreal, Quebec, H3C 3J7, Canada. .,Centre for Interdisciplinary Research in Rehabilitation-Institut Universitaire sur la Réadaptation en Déficience Physique de Montréal, in CIUSSS du Centre-Sud-de-l'ile-de-Montréal, Montreal, Canada.
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20
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Viele K, Saville BR, McGlothlin A, Broglio K. Comparison of response adaptive randomization features in multiarm clinical trials with control. Pharm Stat 2020; 19:602-612. [DOI: 10.1002/pst.2015] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Revised: 01/27/2020] [Accepted: 03/02/2020] [Indexed: 12/27/2022]
Affiliation(s)
- Kert Viele
- Berry Consultants Austin Texas USA
- Department of Biostatistics University of Kentucky Lexington Kentucky USA
| | - Benjamin R. Saville
- Berry Consultants Austin Texas USA
- Department of Biostatistics Vanderbilt University Nashville Tennessee USA
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21
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Kuhn J, Sheldrick RC, Broder-Fingert S, Chu A, Fortuna L, Jordan M, Rubin D, Feinberg E. Simulation and minimization: technical advances for factorial experiments designed to optimize clinical interventions. BMC Med Res Methodol 2019; 19:239. [PMID: 31842765 PMCID: PMC6915895 DOI: 10.1186/s12874-019-0883-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 12/05/2019] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND The Multiphase Optimization Strategy (MOST) is designed to maximize the impact of clinical healthcare interventions, which are typically multicomponent and increasingly complex. MOST often relies on factorial experiments to identify which components of an intervention are most effective, efficient, and scalable. When assigning participants to conditions in factorial experiments, researchers must be careful to select the assignment procedure that will result in balanced sample sizes and equivalence of covariates across conditions while maintaining unpredictability. METHODS In the context of a MOST optimization trial with a 2x2x2x2 factorial design, we used computer simulation to empirically test five subject allocation procedures: simple randomization, stratified randomization with permuted blocks, maximum tolerated imbalance (MTI), minimal sufficient balance (MSB), and minimization. We compared these methods across the 16 study cells with respect to sample size balance, equivalence on key covariates, and unpredictability. Leveraging an existing dataset to compare these procedures, we conducted 250 computerized simulations using bootstrap samples of 304 participants. RESULTS Simple randomization, the most unpredictable procedure, generated poor sample balance and equivalence of covariates across the 16 study cells. Stratified randomization with permuted blocks performed well on stratified variables but resulted in poor equivalence on other covariates and poor balance. MTI, MSB, and minimization had higher complexity and cost. MTI resulted in balance close to pre-specified thresholds and a higher degree of unpredictability, but poor equivalence of covariates. MSB had 19.7% deterministic allocations, poor sample balance and improved equivalence on only a few covariates. Minimization was most successful in achieving balanced sample sizes and equivalence across a large number of covariates, but resulted in 34% deterministic allocations. Small differences in proportion of correct guesses were found across the procedures. CONCLUSIONS Based on the computer simulation results and priorities within the study context, minimization with a random element was selected for the planned research study. Minimization with a random element, as well as computer simulation to make an informed randomization procedure choice, are utilized infrequently in randomized experiments but represent important technical advances that researchers implementing multi-arm and factorial studies should consider.
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Affiliation(s)
- Jocelyn Kuhn
- Boston Medical Center, 72 E. Concord St, Boston, MA, USA.
| | | | - Sarabeth Broder-Fingert
- Boston Medical Center, 72 E. Concord St, Boston, MA, USA
- Boston University School of Medicine, 72 E. Concord St, Boston, MA, USA
| | - Andrea Chu
- Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA, USA
| | - Lisa Fortuna
- Boston Medical Center, 72 E. Concord St, Boston, MA, USA
- Boston University School of Medicine, 72 E. Concord St, Boston, MA, USA
| | - Megan Jordan
- DotHouse Health Center, 1353 Dorchester Ave, Dorchester, MA, USA
| | - Dana Rubin
- Boston University School of Medicine, 72 E. Concord St, Boston, MA, USA
- DotHouse Health Center, 1353 Dorchester Ave, Dorchester, MA, USA
| | - Emily Feinberg
- Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA, USA
- Boston University School of Medicine, 72 E. Concord St, Boston, MA, USA
- DotHouse Health Center, 1353 Dorchester Ave, Dorchester, MA, USA
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22
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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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Kim S, Seddon JA, Garcia-Prats AJ, Montepiedra G. Statistical considerations for pediatric multidrug-resistant tuberculosis efficacy trials. Int J Tuberc Lung Dis 2019; 22:34-39. [PMID: 29665951 DOI: 10.5588/ijtld.17.0358] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
The inclusion of newly licensed or repurposed drugs in regimens to treat children for multidrug-resistant tuberculosis (TB) may lead to treatment that is shorter than traditional regimens and composed only of oral medications. As an all-oral regimen may be more acceptable and have a better safety profile than current regimens, demonstrating non-inferiority may be satisfactory. Demonstrating non-inferior efficacy requires setting a non-inferiority margin and safeguarding study assay sensitivity. Multi-arm, multistage designs may currently not be appropriate in pediatric trials because of the lack of sensitive and specific intermediate outcomes. However, including an arm with an agent to ameliorate toxicity would be efficient. Covariates can be used to stratify randomization, define subgroups, and improve efficiency of analysis. Enriching the sample for the confirmed-TB subgroup to ensure that they are well represented may be important. Primary outcomes using a fixed timepoint from randomization for all study arms will result in variations in post-treatment duration, but may be the best choice. While blinding of site personnel and patients may not be possible when regimens differ substantially in drugs and modes of administration, blinding should be maintained for independent endpoint review groups and other personnel. Type I error and family-wise error rates should be tightly controlled.
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Affiliation(s)
- S Kim
- Frontier Science Foundation, Brookline, Massachusetts, USA
| | - J A Seddon
- Centre for International Child Health, Department of Paediatrics, Imperial College London, London, UK
| | - A J Garcia-Prats
- Desmond Tutu TB Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
| | - G Montepiedra
- Center for Biostatistics in AIDS Research and Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, Massachusetts, USA
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24
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Sim J. Outcome-adaptive randomization in clinical trials: issues of participant welfare and autonomy. THEORETICAL MEDICINE AND BIOETHICS 2019; 40:83-101. [PMID: 30778720 PMCID: PMC6478640 DOI: 10.1007/s11017-019-09481-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Outcome-adaptive randomization (OAR) has been proposed as a corrective to certain ethical difficulties inherent in the traditional randomized clinical trial (RCT) using fixed-ratio randomization. In particular, it has been suggested that OAR redresses the balance between individual and collective ethics in favour of the former. In this paper, I examine issues of welfare and autonomy arising in relation to OAR. A central issue in discussions of welfare in OAR is equipoise, and the moral status of OAR is crucially influenced by the way in which this concept is construed. If OAR is based on a model of equipoise that demands strict indifference between competing interventions throughout the trial, such equipoise is disturbed by accruing data favouring one treatment over another; OAR seeks to redress this by weighting randomization to the seemingly superior treatment. However, this is a partial response, as patients continue to be allocated to the inferior therapy. Moreover, it rests upon considerations of aggregate harms and benefits, and does not therefore uphold individual ethics. Issues of fairness also arise, as early and late enrollees are randomized on a different basis. Fixed-ratio randomization represents a fuller and more consistent response to a loss of equipoise, as so construed. With regard to consent, the complexity of OAR poses challenges to adequate disclosure and comprehension. Additionally, OAR does not offer a remedy to the therapeutic misconception-participants' tendency to attribute treatment allocation in an RCT to individual clinical judgments, rather than to scientific considerations-and, if anything, accentuates rather than alleviates this misconception. In relation to these issues, OAR fails to offer ethical advantages over fixed-ratio randomization. More broadly, the ethical basis of OAR can be seen to lie more in collective than in individual ethics, and overall it fares worse in this territory than fixed-ratio randomization.
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Affiliation(s)
- Julius Sim
- Institute for Primary Care and Health Sciences, Keele University, Staffordshire, ST5 5BG, UK.
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Hecksteden A, Faude O, Meyer T, Donath L. How to Construct, Conduct and Analyze an Exercise Training Study? Front Physiol 2018; 9:1007. [PMID: 30140237 PMCID: PMC6094975 DOI: 10.3389/fphys.2018.01007] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Accepted: 07/09/2018] [Indexed: 11/13/2022] Open
Abstract
Randomized controlled trials (RCTs) can be regarded as gold standard in investigating dose-response and causal relationships in exercise science. Recommendations for exercise training routines and efficacy analyses of certain training regimen require valid data derived from robust RCTs. Moreover, meta-analyses rely on RCTs and both RCTs and meta-analyses are considered the highest level of scientific evidence. Beyond general study design a variety of methodological aspects and notable pitfalls has to be considered. Therefore, exercise training studies should be carefully constructed focusing on the consistency of the whole design "package" from an explicit hypothesis or research question over study design and methodology to data analysis and interpretation. The present scoping review covers all main aspects of planning, conducting, and analyzing exercise based RCTs. We aim to focus on relevant aspects regarding study design, statistical power, training planning and documentation as well as traditional and recent statistical approaches. We intend to provide a comprehensive hands-on paper for conceptualizing future exercise training studies and hope to stimulate and encourage researchers to conduct sound and valid RCTs in the field of exercise training.
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Affiliation(s)
- Anne Hecksteden
- Institute of Sports and Preventive Medicine, Saarland University, Saarbrücken, Germany
| | - Oliver Faude
- Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Tim Meyer
- Institute of Sports and Preventive Medicine, Saarland University, Saarbrücken, Germany
| | - Lars Donath
- Department of Intervention Research in Exercise Training, German Sport University Cologne, Cologne, Germany
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Su PF, Cheung SH. Response-adaptive treatment allocation for survival trials with clustered right-censored data. Stat Med 2018; 37:2427-2439. [PMID: 29672881 DOI: 10.1002/sim.7652] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 01/09/2018] [Accepted: 02/10/2018] [Indexed: 11/05/2022]
Abstract
A comparison of 2 treatments with survival outcomes in a clinical study may require treatment randomization on clusters of multiple units with correlated responses. For example, for patients with otitis media in both ears, a specific treatment is normally given to a single patient, and hence, the 2 ears constitute a cluster. Statistical procedures are available for comparison of treatment efficacies. The conventional approach for treatment allocation is the adoption of a balanced design, in which half of the patients are assigned to each treatment arm. However, considering the increasing acceptability of responsive-adaptive designs in recent years because of their desirable features, we have developed a response-adaptive treatment allocation scheme for survival trials with clustered data. The proposed treatment allocation scheme is superior to the balanced design in that it allows more patients to receive the better treatment. At the same time, the test power for comparing treatment efficacies using our treatment allocation scheme remains highly competitive. The advantage of the proposed randomization procedure is supported by a simulation study and the redesign of a clinical study.
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Affiliation(s)
- Pei-Fang Su
- Department of Statistics, National Cheng Kung University, Tainan, 70101, Taiwan
| | - Siu Hung Cheung
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China
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Costa ML, Achten J, Hennings S, Boota N, Griffin J, Petrou S, Maredza M, Dritsaki M, Wood T, Masters J, Pallister I, Lamb SE, Parsons NR. Intramedullary nail fixation versus locking plate fixation for adults with a fracture of the distal tibia: the UK FixDT RCT. Health Technol Assess 2018; 22:1-148. [PMID: 29785926 DOI: 10.3310/hta22250] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND The best treatment for fractures of the distal tibia remains controversial. Most of these fractures require surgical fixation, but the outcomes are unpredictable and complications are common. OBJECTIVES To assess disability, quality of life, complications and resource use in patients treated with intramedullary (IM) nail fixation versus locking plate fixation in the 12 months following a fracture of the distal tibia. DESIGN This was a multicentre randomised trial. SETTING The trial was conducted in 28 UK acute trauma centres from April 2013 to final follow-up in February 2017. PARTICIPANTS In total, 321 adult patients were recruited. Participants were excluded if they had open fractures, fractures involving the ankle joint, contraindication to nailing or inability to complete questionnaires. INTERVENTIONS IM nail fixation (n = 161), in which a metal rod is inserted into the hollow centre of the tibia, versus locking plate fixation (n = 160), in which a plate is attached to the surface of the tibia with fixed-angle screws. MAIN OUTCOME MEASURES The primary outcome measure was the Disability Rating Index (DRI) score, which ranges from 0 points (no disability) to 100 points (complete disability), at 6 months with a minimum clinically important difference of 8 points. The DRI score was also collected at 3 and 12 months. The secondary outcomes were the Olerud-Molander Ankle Score (OMAS), quality of life as measured using EuroQol-5 Dimensions (EQ-5D), complications such as infection, and further surgery. Resource use was collected to inform the health economic evaluation. RESULTS Participants had a mean age of 45 years (standard deviation 16.2 years), were predominantly male (61%, 197/321) and had experienced traumatic injury after a fall (69%, 223/321). There was no statistically significant difference in DRI score at 6 months [IM nail fixation group, mean 29.8 points, 95% confidence interval (CI) 26.1 to 33.7 points; locking plate group, mean 33.8 points, 95% CI 29.7 to 37.9 points; adjusted difference, 4.0 points, 95% CI -1.0 to 9.0 points; p = 0.11]. There was a statistically significant difference in DRI score at 3 months in favour of IM nail fixation (IM nail fixation group, mean 44.2 points, 95% CI 40.8 to 47.6 points; locking plate group, mean 52.6 points, 95% CI 49.3 to 55.9 points; adjusted difference 8.8 points, 95% CI 4.3 to 13.2 points; p < 0.001), but not at 12 months (IM nail fixation group, mean 23.1 points, 95% CI 18.9 to 27.2 points; locking plate group, 24.0 points, 95% CI 19.7 to 28.3 points; adjusted difference 1.9 points, 95% CI -3.2 to 6.9 points; p = 0.47). Secondary outcomes showed the same pattern, including a statistically significant difference in mean OMAS and EQ-5D scores at 3 and 6 months in favour of IM nail fixation. There were no statistically significant differences in complications, including the number of postoperative infections (13% in the locking plate group and 9% in the IM nail fixation group). Further surgery was more common in the locking plate group (12% in locking plate group and 8% in IM nail fixation group at 12 months). The economic evaluation showed that IM nail fixation provided a slightly higher quality of life in the 12 months after injury and at lower cost and, therefore, it was cost-effective compared with locking plate fixation. The probability of cost-effectiveness for IM nail fixation exceeded 90%, regardless of the value of the cost-effectiveness threshold. LIMITATIONS As wound dressings after surgery are clearly visible, it was not possible to blind the patients to their treatment allocation. This evidence does not apply to intra-articular (pilon) fractures of the distal tibia. CONCLUSIONS Among adults with an acute fracture of the distal tibia who were randomised to IM nail fixation or locking plate fixation, there were similar disability ratings at 6 months. However, recovery across all outcomes was faster in the IM nail fixation group and costs were lower. FUTURE WORK The potential benefit of IM nail fixation in several other fractures requires investigation. Research is also required into the role of adjuvant treatment and different rehabilitation strategies to accelerate recovery following a fracture of the tibia and other long-bone fractures in the lower limb. The patients in this trial will remain in longer-term follow-up. TRIAL REGISTRATION Current Controlled Trials ISRCTN99771224 and UKCRN 13761. FUNDING This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 22, No. 25. See the NIHR Journals Library website for further project information.
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Affiliation(s)
- Matthew L Costa
- Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, UK.,Department of Trauma and Orthopaedics, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.,Oxford Trauma, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Juul Achten
- Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, UK.,Oxford Trauma, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Susie Hennings
- Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, UK
| | - Nafisa Boota
- Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, UK
| | - James Griffin
- Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, UK
| | - Stavros Petrou
- Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, UK
| | - Mandy Maredza
- Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, UK
| | - Melina Dritsaki
- Oxford Trauma, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Thomas Wood
- Department of Trauma and Orthopaedics, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
| | - James Masters
- Department of Trauma and Orthopaedics, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.,Oxford Trauma, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | | | - Sarah E Lamb
- Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, UK.,Oxford Trauma, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Nick R Parsons
- Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, UK
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Piccorelli AV, Fraker SA. Balancing statistical and ethical considerations in planning clinical trials: recommendations for response-adaptive randomization urn designs. J Biopharm Stat 2018; 28:1105-1118. [DOI: 10.1080/10543406.2018.1437172] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
| | - Sarah A. Fraker
- Clinical Services Group, HCA Holdings, Inc., Nashville, TN, USA
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Bothwell LE, Kesselheim AS. The Real-World Ethics of Adaptive-Design Clinical Trials. Hastings Cent Rep 2017; 47:27-37. [DOI: 10.1002/hast.783] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Yu Q, Zhu L, Zhu H. A Bayesian sequential design with adaptive randomization for 2-sided hypothesis test. Pharm Stat 2017; 16:451-465. [PMID: 28980435 DOI: 10.1002/pst.1830] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Revised: 07/28/2017] [Accepted: 08/26/2017] [Indexed: 11/09/2022]
Abstract
Bayesian sequential and adaptive randomization designs are gaining popularity in clinical trials thanks to their potentials to reduce the number of required participants and save resources. We propose a Bayesian sequential design with adaptive randomization rates so as to more efficiently attribute newly recruited patients to different treatment arms. In this paper, we consider 2-arm clinical trials. Patients are allocated to the 2 arms with a randomization rate to achieve minimum variance for the test statistic. Algorithms are presented to calculate the optimal randomization rate, critical values, and power for the proposed design. Sensitivity analysis is implemented to check the influence on design by changing the prior distributions. Simulation studies are applied to compare the proposed method and traditional methods in terms of power and actual sample sizes. Simulations show that, when total sample size is fixed, the proposed design can obtain greater power and/or cost smaller actual sample size than the traditional Bayesian sequential design. Finally, we apply the proposed method to a real data set and compare the results with the Bayesian sequential design without adaptive randomization in terms of sample sizes. The proposed method can further reduce required sample size.
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Affiliation(s)
- Qingzhao Yu
- School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA
| | - Lin Zhu
- School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA
| | - Han Zhu
- Pharmaceutical Product Development, LLC, 7551 Metro Center Dr 300, Austin, TX 78744, USA
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Metelkina A, Pronzato L. Information-regret compromise in covariate-adaptive treatment allocation. Ann Stat 2017. [DOI: 10.1214/16-aos1518] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Srivastava A, Srivastava A, Pandey RM. Was RA Fisher Right? Indian J Surg 2017; 79:444-445. [DOI: 10.1007/s12262-017-1679-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Accepted: 08/11/2017] [Indexed: 10/19/2022] Open
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Du Y, Cook JD, Lee JJ. Comparing three regularization methods to avoid extreme allocation probability in response-adaptive randomization. J Biopharm Stat 2017; 28:309-319. [PMID: 28323532 DOI: 10.1080/10543406.2017.1293077] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
We examine three variations of the regularization methods for response-adaptive randomization (RAR) and compare their operating characteristics. A power transformation (PT) is applied to refine the randomization probability. The clip method is used to bound the randomization probability within specified limits. A burn-in period of equal randomization (ER) can be added before adaptive randomization (AR). For each method, more patients are assigned to the superior arm and overall response rate increase as the scheme approximates simple AR, while statistical power increases as it approximates ER. We evaluate the performance of the three methods by varying the tuning parameter to control the extent of AR to achieve the same statistical power. When there is no early stopping rule, PT method generally performed the best in yielding higher proportion to the superior arm and higher overall response rate, but with larger variability. The burn-in method showed smallest variability compared with the clip method and the PT method. With the efficacy early stopping rule, all three methods performed more similarly. The PT and clip methods are better than the burn-in method in achieving higher proportion randomized to the superior arm and higher overall response rate but burn-in method required fewer patients in the trial. By carefully choosing the method and the tuning parameter, RAR methods can be tailored to strike a balance between achieving the desired statistical power and enhancing the overall response rate.
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Affiliation(s)
- Yining Du
- a Department of Biostatistics , Incyte Corporation , Wilmington , Delaware , USA
| | | | - J Jack Lee
- c Department of Biostatistics , The University of Texas MD Anderson Cancer Center , Houston , Texas , USA
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Saville BR, Berry SM. Balanced covariates with response adaptive randomization. Pharm Stat 2017; 16:210-217. [DOI: 10.1002/pst.1803] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Revised: 01/23/2017] [Accepted: 01/31/2017] [Indexed: 11/10/2022]
Affiliation(s)
- Benjamin R. Saville
- Berry Consultants; Austin TX 78746 USA
- Adjunct Faculty, Department of Biostatistics; Vanderbilt University School of Medicine; Nashville TN 37232 USA
| | - Scott M. Berry
- Berry Consultants; Austin TX 78746 USA
- Adjunct Faculty, Department of Biostatistics; University of Kansas Medical Center; Kansas City KS 66160 USA
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Wathen JK, Thall PF. A simulation study of outcome adaptive randomization in multi-arm clinical trials. Clin Trials 2017; 14:432-440. [PMID: 28982263 DOI: 10.1177/1740774517692302] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Randomizing patients among treatments with equal probabilities in clinical trials is the established method to obtain unbiased comparisons. In recent years, motivated by ethical considerations, many authors have proposed outcome adaptive randomization, wherein the randomization probabilities are unbalanced, based on interim data, to favor treatment arms having more favorable outcomes. While there has been substantial controversy regarding the merits and flaws of adaptive versus equal randomization, there has not yet been a systematic simulation study in the multi-arm setting. A simulation study was conducted to evaluate four different Bayesian adaptive randomization methods and compare them to equal randomization in five-arm clinical trials. All adaptive randomization methods included an initial burn-in with equal randomization and some combination of other modifications to avoid extreme randomization probabilities. Trials either with or without a control arm were evaluated, using designs that may terminate arms early for futility and select one or more experimental treatments at the end. The designs were evaluated under a range of scenarios and sample sizes. For trials with a control arm and maximum same size 250 or 500, several commonly used adaptive randomization methods have very low probabilities of correctly selecting a truly superior treatment. Of those studied, the only adaptive randomization method with desirable properties has a burn-in with equal randomization and thereafter randomization probabilities restricted to the interval 0.10-0.90. Compared to equal randomization, this method has a favorable sample size imbalance but lower probability of correctly selecting a superior treatment. In multi-arm trials, compared to equal randomization, several commonly used adaptive randomization methods give much lower probabilities of selecting superior treatments. Aside from randomization method, conducting a multi-arm trial without a control arm may lead to very low probabilities of selecting any superior treatments if differences between the treatment success probabilities are small.
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Affiliation(s)
- J Kyle Wathen
- 1 Model Based Drug Development, Statistical Decision Sciences, Janssen Research & Development, LLC, Titusville, NJ, USA
| | - Peter F Thall
- 2 Department of Biostatistics, MD Anderson Cancer Center, Houston, TX, USA
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Abstract
Individualized treatment rules (ITRs) tailor treatments according to individual patient characteristics. They can significantly improve patient care and are thus becoming increasingly popular. The data collected during randomized clinical trials are often used to estimate the optimal ITRs. However, these trials are generally expensive to run, and, moreover, they are not designed to efficiently estimate ITRs. In this article, we propose a cost-effective estimation method from an active learning perspective. In particular, our method recruits only the "most informative" patients (in terms of learning the optimal ITRs) from an ongoing clinical trial. Simulation studies and real-data examples show that our active clinical trial method significantly improves on competing methods. We derive risk bounds and show that they support these observed empirical advantages. Supplementary materials for this article are available online.
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Abstract
Abstract
In clinical trials with two treatment arms, Efron's biased coin design, Efron (1971), sequentially assigns a patient to the underrepresented arm with probability p > ½. Under this design the proportion of patients in any arm converges to ½, and the convergence rate is n-1, as opposed to n-½ under some other popular designs. The generalization of Efron's design to K ≥ 2 arms and an unequal target allocation ratio (q1, . . ., qK) can be found in some papers, most of which determine the allocation probabilities ps in a heuristic way. Nonetheless, it has been noted that by using inappropriate ps, the proportion of patients in the K arms never converges to the target ratio. We develop a general theory to answer the question of what allocation probabilities ensure that the realized proportions under a generalized design still converge to the target ratio (q1, . . ., qK) with rate n-1.
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Ryeznik Y, Sverdlov O, Wong WK. RARtool: A MATLAB Software Package for Designing Response-Adaptive Randomized Clinical Trials with Time-to-Event Outcomes. J Stat Softw 2015; 66. [PMID: 26997924 DOI: 10.18637/jss.v066.i01] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
Response-adaptive randomization designs are becoming increasingly popular in clinical trial practice. In this paper, we present RARtool, a user interface software developed in MATLAB for designing response-adaptive randomized comparative clinical trials with censored time-to-event outcomes. The RARtool software can compute different types of optimal treatment allocation designs, and it can simulate response-adaptive randomization procedures targeting selected optimal allocations. Through simulations, an investigator can assess design characteristics under a variety of experimental scenarios and select the best procedure for practical implementation. We illustrate the utility of our RARtool software by redesigning a survival trial from the literature.
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Affiliation(s)
- Yevgen Ryeznik
- Department of Mathematics, Uppsala University, Lägerhyddsvägen 1, Hus 1, 5 och 7, Box 480, 751 06, Uppsala, Sweden,
| | - Oleksandr Sverdlov
- R&D Global Biostatistics, EMD Serono, Inc., 45A Middlesex Turnpike, Billerica, MA 01821, United States of America,
| | - Weng Kee Wong
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA 90095, United States of America,
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Affiliation(s)
- J Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Tudur Smith C, Williamson PR, Beresford MW. Methodology of clinical trials for rare diseases. Best Pract Res Clin Rheumatol 2014; 28:247-62. [DOI: 10.1016/j.berh.2014.03.004] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Marchenko O, Fedorov V, Lee JJ, Nolan C, Pinheiro J. Adaptive Clinical Trials: Overview of Early-Phase Designs and Challenges. Ther Innov Regul Sci 2013; 48:20-30. [PMID: 28670507 DOI: 10.1177/2168479013513889] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In this paper, we describe developments in adaptive design methodology and discuss implementation strategies and operational challenges in early phase adaptive clinical trials. The BATTLE trial - the first completed, biomarker-based, Bayesian adaptive randomized study in lung cancer - is presented as a case study to illustrate main ideas and share learnings.
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Affiliation(s)
- Olga Marchenko
- Center for Statistics in Drug Development, Innovation, Quintiles, Durham, NC
| | | | - J Jack Lee
- Department of Biostatistics, Division of Quantitative Sciences, University of Texas MD Anderson Cancer Center, Houston, TX
| | | | - José Pinheiro
- Quantitative Decision Strategies, Janssen Research & Development, LLC
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Senn S. Seven myths of randomisation in clinical trials. Stat Med 2012; 32:1439-50. [PMID: 23255195 DOI: 10.1002/sim.5713] [Citation(s) in RCA: 95] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2011] [Accepted: 11/27/2012] [Indexed: 11/06/2022]
Abstract
I consider seven misunderstandings that may be encountered about the nature, purpose and properties of randomisation in clinical trials. Some concern the practical realities of clinical research on patients. Others are to do with the value and purpose of balance. Still others are to do with a confusion about the role of conditioning in valid statistical inference. I consider a simple game of chance involving two dice to illustrate some points about inference and then consider the seven misunderstandings in turn. I conclude that although one should not make a fetish of randomisation, when proposing alternatives to randomisation in clinical trials, one should be very careful to be precise about the exact nature of the alternative being considered if one is to avoid the danger of underestimating the advantages that randomisation can offer.
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Affiliation(s)
- Stephen Senn
- Competence Centre for Methodology and Statistics, CRP-Santé, L-1445 Strassen, Luxembourg.
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Kairalla JA, Coffey CS, Thomann MA, Muller KE. Adaptive trial designs: a review of barriers and opportunities. Trials 2012; 13:145. [PMID: 22917111 PMCID: PMC3519822 DOI: 10.1186/1745-6215-13-145] [Citation(s) in RCA: 175] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2012] [Accepted: 08/08/2012] [Indexed: 12/13/2022] Open
Abstract
Adaptive designs allow planned modifications based on data accumulating within a study. The promise of greater flexibility and efficiency stimulates increasing interest in adaptive designs from clinical, academic, and regulatory parties. When adaptive designs are used properly, efficiencies can include a smaller sample size, a more efficient treatment development process, and an increased chance of correctly answering the clinical question of interest. However, improper adaptations can lead to biased studies. A broad definition of adaptive designs allows for countless variations, which creates confusion as to the statistical validity and practical feasibility of many designs. Determining properties of a particular adaptive design requires careful consideration of the scientific context and statistical assumptions. We first review several adaptive designs that garner the most current interest. We focus on the design principles and research issues that lead to particular designs being appealing or unappealing in particular applications. We separately discuss exploratory and confirmatory stage designs in order to account for the differences in regulatory concerns. We include adaptive seamless designs, which combine stages in a unified approach. We also highlight a number of applied areas, such as comparative effectiveness research, that would benefit from the use of adaptive designs. Finally, we describe a number of current barriers and provide initial suggestions for overcoming them in order to promote wider use of appropriate adaptive designs. Given the breadth of the coverage all mathematical and most implementation details are omitted for the sake of brevity. However, the interested reader will find that we provide current references to focused reviews and original theoretical sources which lead to details of the current state of the art in theory and practice.
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Affiliation(s)
- John A Kairalla
- Department of Biostatistics, University of Florida, PO Box 117450, Gainesville, FL, 32611-7450, USA
| | - Christopher S Coffey
- Department of Biostatistics, University of Iowa, 2400 University Capitol Centre, Iowa City, IA, 52240-4034, USA
| | - Mitchell A Thomann
- Department of Biostatistics, University of Iowa, 2400 University Capitol Centre, Iowa City, IA, 52240-4034, USA
| | - Keith E Muller
- Department of Health Outcomes and Policy, University of Florida, PO Box 100177, Gainesville, FL, 32610-0177, USA
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