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Lee H, Hwang S, Jang IJ, Chung JY, Oh J. Adaptive design clinical trials: current status by disease and trial phase in various perspectives. Transl Clin Pharmacol 2023; 31:202-216. [PMID: 38197001 PMCID: PMC10772057 DOI: 10.12793/tcp.2023.31.e21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 10/18/2023] [Accepted: 11/15/2023] [Indexed: 01/11/2024] Open
Abstract
An adaptive design is a clinical trial design that allows for modification of a structured plan in a clinical trial based on data accumulated during pre-planned interim analyses. This flexible approach to clinical trial design improves the success rate of clinical trials while reducing time, cost, and sample size compared to conventional methods. The purpose of this study is to identify the current status of adaptive design and present key considerations for planning an appropriate adaptive design based on specific circumstances. We searched for clinical trials conducted between January 2006 to July 2021 in the Clinical Trials Registry (ClinicalTrials.gov) using keywords specified in the Food and Drug Administration Adaptive Design Clinical Trial Guidelines. In order to analyze the adaptive designs used in selected cases, we classified the results according to the phase of the clinical trial, type of indication, and the specific adaptation method employed. A total of 267 clinical trials were identified on ClinicalTrials.gov. Among them, 236 clinical trials actually applied adaptive designs and were classified according to phase, indication types, and adaptation methods. Adaptive designs were most frequently used in phase 2 clinical trials and oncology research. The most commonly used adaptation method was the adaptive treatment selection design. In the case of coronavirus disease 2019, the most frequently used designs were adaptive platform design and seamless design. Through this study, we expect to provide valuable insights and considerations for the implementation of adaptive design clinical trials in different diseases and stages.
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Affiliation(s)
- Hyunjoon Lee
- Department of Clinical Pharmacology and Therapeutics, Seoul National University Hospital, Seoul National University College of Medicine, Seoul 03080, Korea
- Department of Translational Medicine, Seoul National University College of Medicine, Seoul 03080, Korea
| | - Sejung Hwang
- Department of Clinical Pharmacology and Therapeutics, Seoul National University Hospital, Seoul National University College of Medicine, Seoul 03080, Korea
- Kidney Research Institute, Seoul National University Medical Research Center, Seoul 03080, Korea
| | - In-Jin Jang
- Department of Clinical Pharmacology and Therapeutics, Seoul National University Hospital, Seoul National University College of Medicine, Seoul 03080, Korea
| | - Jae-Yong Chung
- Department of Translational Medicine, Seoul National University College of Medicine, Seoul 03080, Korea
- Department of Clinical Pharmacology and Therapeutics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam 13620, Korea
| | - Jaeseong Oh
- Department of Clinical Pharmacology and Therapeutics, Seoul National University Hospital, Seoul National University College of Medicine, Seoul 03080, Korea
- Department of Pharmacology, Jeju National University College of Medicine, Jeju 63241, Korea
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2
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Adaptive clinical trials and master protocols. HANDBOOK OF CLINICAL NEUROLOGY 2023; 193:313-323. [PMID: 36803819 DOI: 10.1016/b978-0-323-85555-6.00005-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Methodologies for randomized, double-blind, placebo-controlled clinical trials continue to develop in concert with evolving scientific and translational knowledge. Adaptive trial designs, in which data generated during the study are used to modify subsequent study activity (i.e., sample sizes, entry criteria, or outcomes), can optimize flexibility and expedite the safety and efficacy assessments for interventions of interest. This chapter will summarize general designs, advantages, and pitfalls associated with adaptive clinical trials and compare their features with those of conventional trial designs. It will also review novel ways for which seamless designs and master protocols may improve trial efficiency while offering interpretable data.
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3
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Collignon O, Schiel A, Burman C, Rufibach K, Posch M, Bretz F. Estimands and Complex Innovative Designs. Clin Pharmacol Ther 2022; 112:1183-1190. [PMID: 35253205 PMCID: PMC9790227 DOI: 10.1002/cpt.2575] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 03/01/2022] [Indexed: 01/31/2023]
Abstract
Since the release of the ICH E9(R1) (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials) document in 2019, the estimand framework has become a fundamental part of clinical trial protocols. In parallel, complex innovative designs have gained increased popularity in drug development, in particular in early development phases or in difficult experimental situations. While the estimand framework is relevant to any study in which a treatment effect is estimated, experience is lacking as regards its application to these designs. In a basket trial for example, should a different estimand be specified for each subpopulation of interest, defined, for example, by cancer site? Or can a single estimand focusing on the general population (defined, for example, by the positivity to a certain biomarker) be used? In the case of platform trials, should a different estimand be proposed for each drug investigated? In this work we discuss possible ways of implementing the estimand framework for different types of complex innovative designs. We consider trials that allow adding or selecting experimental treatment arms, modifying the control arm or the standard of care, and selecting or pooling populations. We also address the potentially data-driven, adaptive selection of estimands in an ongoing trial and disentangle certain statistical issues that pertain to estimation rather than to estimands, such as the borrowing of nonconcurrent information. We hope this discussion will facilitate the implementation of the estimand framework and its description in the study protocol when the objectives of the trial require complex innovative designs.
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Affiliation(s)
| | | | - Carl‐Fredrik Burman
- Statistical Innovation, Data Science & Artificial IntelligenceAstraZeneca Research & DevelopmentGothenburgSweden
| | - Kaspar Rufibach
- Methods, Collaboration, and Outreach Group, Product Development Data SciencesF.Hoffmann‐La RocheBaselSwitzerland
| | - Martin Posch
- Section for Medical StatisticsCenter for Medical Statistics Informatics, and Intelligent SystemsMedical University of ViennaViennaAustria
| | - Frank Bretz
- Section for Medical StatisticsCenter for Medical Statistics Informatics, and Intelligent SystemsMedical University of ViennaViennaAustria,NovartisBaselSwitzerland
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4
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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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5
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Quan H, Luo X, Zhou T, Zhao PL. Seamless phase II/III/IIIb clinical trial designs with different endpoints for different phases. COMMUN STAT-THEOR M 2020. [DOI: 10.1080/03610926.2019.1618871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Hui Quan
- Biostatistics and Programming, Sanofi, 55C-335A, Bridgewater, New Jersey, USA
| | - Xiaodong Luo
- Biostatistics and Programming, Sanofi, 55C-335A, Bridgewater, New Jersey, USA
| | - Tianyue Zhou
- Biostatistics and Programming, Sanofi, 55C-335A, Bridgewater, New Jersey, USA
| | - Peng-Liang Zhao
- Biostatistics and Programming, Sanofi, 55C-335A, Bridgewater, New Jersey, USA
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6
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Adaptive trial designs for spinal cord injury clinical trials directed to the central nervous system. Spinal Cord 2020; 58:1235-1248. [DOI: 10.1038/s41393-020-00547-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 08/28/2020] [Accepted: 08/31/2020] [Indexed: 02/08/2023]
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Contemporary strategies to improve clinical trial design for critical care research: insights from the First Critical Care Clinical Trialists Workshop. Intensive Care Med 2020; 46:930-942. [PMID: 32072303 PMCID: PMC7224097 DOI: 10.1007/s00134-020-05934-6] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 01/11/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND Conducting research in critically-ill patient populations is challenging, and most randomized trials of critically-ill patients have not achieved pre-specified statistical thresholds to conclude that the intervention being investigated was beneficial. METHODS In 2019, a diverse group of patient representatives, regulators from the USA and European Union, federal grant managers, industry representatives, clinical trialists, epidemiologists, and clinicians convened the First Critical Care Clinical Trialists (3CT) Workshop to discuss challenges and opportunities in conducting and assessing critical care trials. Herein, we present the advantages and disadvantages of available methodologies for clinical trial design, conduct, and analysis, and a series of recommendations to potentially improve future trials in critical care. CONCLUSION The 3CT Workshop participants identified opportunities to improve critical care trials using strategies to optimize sample size calculations, account for patient and disease heterogeneity, increase the efficiency of trial conduct, maximize the use of trial data, and to refine and standardize the collection of patient-centered and patient-informed outcome measures beyond mortality.
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Shi H, Zhang T, Yin G. START: single‐to‐double arm transition design for phase II clinical trials. Pharm Stat 2020; 19:454-467. [DOI: 10.1002/pst.2005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 10/07/2019] [Accepted: 12/09/2019] [Indexed: 01/05/2023]
Affiliation(s)
- Haolun Shi
- Department of Statistics and Actuarial ScienceSimon Fraser University Burnaby British Columbia Canada
| | - Teng Zhang
- Department of Statistics and Actuarial ScienceThe University of Hong Kong Pok Fu Lam Road Hong Kong
| | - Guosheng Yin
- Department of Statistics and Actuarial ScienceThe University of Hong Kong Pok Fu Lam Road Hong Kong
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Ding AA, Wu SS, Dean NE, Zahigian RS. Two-stage adaptive enrichment design for testing an active factor. J Biopharm Stat 2019; 30:18-30. [PMID: 31135263 DOI: 10.1080/10543406.2019.1609015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
We propose an adaptive enrichment approach to test an active factor, which is a factor whose effect is non-zero in at least one subpopulation. We implement a two-stage play-the-winner design where all subjects in the second stage are enrolled from the subpopulation that has the highest observed effect in the first stage. We recommend a weighted Fisher's combination of the most powerful test for each stage, respectively: the first stage Hotelling's test and the second stage noncentral chi-square test. The test is further extended to cover binary outcomes and time-to-event outcomes.
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Affiliation(s)
- A Adam Ding
- Department of Mathematics, Northeastern University, Boston, Massachusetts, USA
| | - Samuel S Wu
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Natalie E Dean
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Rachel S Zahigian
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
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10
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Affiliation(s)
- Lisa M. LaVange
- Department of Biostatistics, University of North Carolina at Chapel Hill, NC
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11
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Lim J, Walley R, Yuan J, Liu J, Dabral A, Best N, Grieve A, Hampson L, Wolfram J, Woodward P, Yong F, Zhang X, Bowen E. Minimizing Patient Burden Through the Use of Historical Subject-Level Data in Innovative Confirmatory Clinical Trials: Review of Methods and Opportunities. Ther Innov Regul Sci 2018; 52:546-559. [DOI: 10.1177/2168479018778282] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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12
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Collignon O, Koenig F, Koch A, Hemmings RJ, Pétavy F, Saint-Raymond A, Papaluca-Amati M, Posch M. Adaptive designs in clinical trials: from scientific advice to marketing authorisation to the European Medicine Agency. Trials 2018; 19:642. [PMID: 30454061 PMCID: PMC6245528 DOI: 10.1186/s13063-018-3012-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 10/21/2018] [Indexed: 12/15/2022] Open
Abstract
Background In recent years, experience on the application of adaptive designs in confirmatory clinical trials has accumulated. Although planning such trials comes at the cost of additional operational complexity, adaptive designs offer the benefit of flexibility to update trial design and objectives as data accrue. In 2007, the European Medicines Agency (EMA) provided guidance on confirmatory clinical trials with adaptive (or flexible) designs. In order to better understand how adaptive trials are implemented in practice and how they may impact medicine approval within the EMA centralised procedure, we followed on 59 medicines for which an adaptive clinical trial had been submitted to the EMA Scientific Advice (SA) and analysed previously in a dedicated EMA survey of scientific advice letters. We scrutinized in particular the submission of the corresponding medicines for a marketing authorisation application (MAA). We also discuss the current regulatory perspective as regards the implementation of adaptive designs in confirmatory clinical trials. Methods Using the internal EMA MAA database, the AdisInsight database and related trial registries, we analysed how many of these 59 trials actually started, the completion status, results, the time to trial start, the adaptive elements finally implemented after SA, their possible influence on the success of the trial and corresponding product approval. Results Overall 31 trials out of 59 (53%) were retrieved. Thirty of them (97%) have been started and 23 (74%) concluded. Nine of these trials (39% out of 23) demonstrated a significant treatment effect on their primary endpoint and 4 (17% out of 23) supported a marketing authorisation (MA). An additional two trials were stopped using pre-defined criteria for futility, efficiently identifying trials on which further resources should not be spent. Median time to trial start after SA letter was given by EMA was 5 months. In the investigated trial registries, at least 18 trial (58% of 31 retrieved trials) designs were implemented with adaptive elements, which were predominantly dose selection, sample size reassessment (SSR) and stopping for futility (SFF). Among the 11 completed trials including adaptive elements, 6 demonstrated a significant treatment effect on their primary endpoint (55%). Conclusions Adaptive designs are now well established in the drug development landscape. If properly pre-planned, adaptations can play a key role in the success of some of these trials, for example to help successfully select the most promising dose regimens for phase II/III trials. Interim analyses can also enable stopping of trials for futility when they do not hold their promises. Type I error rate control, trial integrity and results consistency between the different stages of the analyses are fundamental aspects to be discussed thoroughly. Engaging early dialogue with regulators and implementing the scientific advice received is strongly recommended, since much experience in discussing adaptive designs and assessing their results has been accumulated.
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Affiliation(s)
- Olivier Collignon
- European Medicines Agency, 30 Churchill Place, London, E14 5EU, UK. .,Competence Center for Methodology and Statistics, Luxembourg Institute of Health, 1A-B, rue Thomas Edison, L-1445, Strassen, Luxembourg.
| | - Franz Koenig
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria
| | - Armin Koch
- Institut für Biometrie, Medizinische Hochschule Hannover, OE 8410, 30625, Hanover, Germany
| | - Robert James Hemmings
- Medicines and Healthcare Products Regulatory Agency, 151 Buckingham Palace Road, London, SW1W 9SZ, UK
| | - Frank Pétavy
- European Medicines Agency, 30 Churchill Place, London, E14 5EU, UK
| | | | | | - Martin Posch
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria
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Glimm E, Bezuidenhoudt M, Caputo A, Maurer W. A Testing Strategy With Adaptive Dose Selection and Two Endpoints. Stat Biopharm Res 2018. [DOI: 10.1080/19466315.2018.1497531] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Ekkehard Glimm
- Novartis Pharma AG, Basel, Switzerland
- Otto-von-Guericke-University Magdeburg, Germany
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14
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Sugitani T, Posch M, Bretz F, Koenig F. Flexible alpha allocation strategies for confirmatory adaptive enrichment clinical trials with a prespecified subgroup. Stat Med 2018; 37:3387-3402. [PMID: 29945304 DOI: 10.1002/sim.7851] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Revised: 03/08/2018] [Accepted: 05/25/2018] [Indexed: 02/05/2023]
Abstract
Adaptive enrichment designs have recently received considerable attention as they have the potential to make drug development process for personalized medicine more efficient. Several statistical approaches have been proposed so far in the literature and the operating characteristics of these approaches are extensively investigated using simulation studies. In this paper, we improve on existing adaptive enrichment designs by assigning unequal weights to the significance levels associated with the hypotheses of the overall population and a prespecified subgroup. More specifically, we focus on the standard combination test, a modified combination test, the marginal combination test, and the partial conditional error rate approach and explore the operating characteristics of these approaches by a simulation study. We show that these approaches can lead to power gains, compared to existing approaches, if the weights are chosen carefully.
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Affiliation(s)
- Toshifumi Sugitani
- Biostatistics Group, Astellas Pharma Inc, Tokyo, Japan.,Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Martin Posch
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Frank Bretz
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria.,Statistical Methodology, Novartis Pharma AG, Basel, Switzerland
| | - Franz Koenig
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
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15
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Pallmann P, Bedding AW, Choodari-Oskooei B, Dimairo M, Flight L, Hampson LV, Holmes J, Mander AP, Odondi L, Sydes MR, Villar SS, Wason JMS, Weir CJ, Wheeler GM, Yap C, Jaki T. Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Med 2018; 16:29. [PMID: 29490655 PMCID: PMC5830330 DOI: 10.1186/s12916-018-1017-7] [Citation(s) in RCA: 361] [Impact Index Per Article: 60.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 01/30/2018] [Indexed: 12/11/2022] Open
Abstract
Adaptive designs can make clinical trials more flexible by utilising results accumulating in the trial to modify the trial's course in accordance with pre-specified rules. Trials with an adaptive design are often more efficient, informative and ethical than trials with a traditional fixed design since they often make better use of resources such as time and money, and might require fewer participants. Adaptive designs can be applied across all phases of clinical research, from early-phase dose escalation to confirmatory trials. The pace of the uptake of adaptive designs in clinical research, however, has remained well behind that of the statistical literature introducing new methods and highlighting their potential advantages. We speculate that one factor contributing to this is that the full range of adaptations available to trial designs, as well as their goals, advantages and limitations, remains unfamiliar to many parts of the clinical community. Additionally, the term adaptive design has been misleadingly used as an all-encompassing label to refer to certain methods that could be deemed controversial or that have been inadequately implemented.We believe that even if the planning and analysis of a trial is undertaken by an expert statistician, it is essential that the investigators understand the implications of using an adaptive design, for example, what the practical challenges are, what can (and cannot) be inferred from the results of such a trial, and how to report and communicate the results. This tutorial paper provides guidance on key aspects of adaptive designs that are relevant to clinical triallists. We explain the basic rationale behind adaptive designs, clarify ambiguous terminology and summarise the utility and pitfalls of adaptive designs. We discuss practical aspects around funding, ethical approval, treatment supply and communication with stakeholders and trial participants. Our focus, however, is on the interpretation and reporting of results from adaptive design trials, which we consider vital for anyone involved in medical research. We emphasise the general principles of transparency and reproducibility and suggest how best to put them into practice.
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Affiliation(s)
- Philip Pallmann
- Department of Mathematics & Statistics, Lancaster University, Lancaster, LA1 4YF UK
| | | | - Babak Choodari-Oskooei
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK
| | | | - Laura Flight
- Medical Statistics Group, University of Sheffield, Sheffield, UK
| | - Lisa V. Hampson
- Department of Mathematics & Statistics, Lancaster University, Lancaster, LA1 4YF UK
- Statistical Innovation Group, Advanced Analytics Centre, AstraZeneca, Cambridge, UK
| | - Jane Holmes
- Centre for Statistics in Medicine, University of Oxford, Oxford, UK
| | | | - Lang’o Odondi
- Centre for Statistics in Medicine, University of Oxford, Oxford, UK
| | - Matthew R. Sydes
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Sofía S. Villar
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - James M. S. Wason
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Institute of Health and Society, Newcastle University, Newcastle, UK
| | - Christopher J. Weir
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Graham M. Wheeler
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cancer Research UK & UCL Cancer Trials Centre, University College London, London, UK
| | - Christina Yap
- Cancer Research UK Clinical Trials Unit, University of Birmingham, Birmingham, UK
| | - Thomas Jaki
- Department of Mathematics & Statistics, Lancaster University, Lancaster, LA1 4YF UK
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16
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Maurer W, Jones B, Chen Y. Controlling the type I error rate in two-stage sequential adaptive designs when testing for average bioequivalence. Stat Med 2018; 37:1587-1607. [PMID: 29462835 DOI: 10.1002/sim.7614] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Revised: 10/04/2017] [Accepted: 01/01/2018] [Indexed: 11/09/2022]
Abstract
In a 2×2 crossover trial for establishing average bioequivalence (ABE) of a generic agent and a currently marketed drug, the recommended approach to hypothesis testing is the two one-sided test (TOST) procedure, which depends, among other things, on the estimated within-subject variability. The power of this procedure, and therefore the sample size required to achieve a minimum power, depends on having a good estimate of this variability. When there is uncertainty, it is advisable to plan the design in two stages, with an interim sample size reestimation after the first stage, using an interim estimate of the within-subject variability. One method and 3 variations of doing this were proposed by Potvin et al. Using simulation, the operating characteristics, including the empirical type I error rate, of the 4 variations (called Methods A, B, C, and D) were assessed by Potvin et al and Methods B and C were recommended. However, none of these 4 variations formally controls the type I error rate of falsely claiming ABE, even though the amount of inflation produced by Method C was considered acceptable. A major disadvantage of assessing type I error rate inflation using simulation is that unless all possible scenarios for the intended design and analysis are investigated, it is impossible to be sure that the type I error rate is controlled. Here, we propose an alternative, principled method of sample size reestimation that is guaranteed to control the type I error rate at any given significance level. This method uses a new version of the inverse-normal combination of p-values test, in conjunction with standard group sequential techniques, that is more robust to large deviations in initial assumptions regarding the variability of the pharmacokinetic endpoints. The sample size reestimation step is based on significance levels and power requirements that are conditional on the first-stage results. This necessitates a discussion and exploitation of the peculiar properties of the power curve of the TOST testing procedure. We illustrate our approach with an example based on a real ABE study and compare the operating characteristics of our proposed method with those of Method B of Povin et al.
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Affiliation(s)
- Willi Maurer
- Statistical Methodology and Consulting Center, Novartis Pharma AG, Basel, Switzerland
| | - Byron Jones
- Statistical Methodology and Consulting Center, Novartis Pharma AG, Basel, Switzerland
| | - Ying Chen
- Shanghai University of Finance and Economics, Shanghai, China
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17
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Bauer P, Bretz F, Dragalin V, König F, Wassmer G. Twenty-five years of confirmatory adaptive designs: opportunities and pitfalls. Stat Med 2016; 35:325-47. [PMID: 25778935 PMCID: PMC6680191 DOI: 10.1002/sim.6472] [Citation(s) in RCA: 135] [Impact Index Per Article: 16.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2014] [Revised: 02/03/2015] [Accepted: 02/19/2015] [Indexed: 12/26/2022]
Abstract
'Multistage testing with adaptive designs' was the title of an article by Peter Bauer that appeared 1989 in the German journal Biometrie und Informatik in Medizin und Biologie. The journal does not exist anymore but the methodology found widespread interest in the scientific community over the past 25 years. The use of such multistage adaptive designs raised many controversial discussions from the beginning on, especially after the publication by Bauer and Köhne 1994 in Biometrics: Broad enthusiasm about potential applications of such designs faced critical positions regarding their statistical efficiency. Despite, or possibly because of, this controversy, the methodology and its areas of applications grew steadily over the years, with significant contributions from statisticians working in academia, industry and agencies around the world. In the meantime, such type of adaptive designs have become the subject of two major regulatory guidance documents in the US and Europe and the field is still evolving. Developments are particularly noteworthy in the most important applications of adaptive designs, including sample size reassessment, treatment selection procedures, and population enrichment designs. In this article, we summarize the developments over the past 25 years from different perspectives. We provide a historical overview of the early days, review the key methodological concepts and summarize regulatory and industry perspectives on such designs. Then, we illustrate the application of adaptive designs with three case studies, including unblinded sample size reassessment, adaptive treatment selection, and adaptive endpoint selection. We also discuss the availability of software for evaluating and performing such designs. We conclude with a critical review of how expectations from the beginning were fulfilled, and - if not - discuss potential reasons why this did not happen.
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Affiliation(s)
- Peter Bauer
- Section of Medical StatisticsMedical University of ViennaSpitalgasse 231090 WienAustria
| | - Frank Bretz
- Novartis Pharma AGLichtstrasse 354002BaselSwitzerland
- Shanghai University of Finance and EconomicsChina
| | | | - Franz König
- Section of Medical StatisticsMedical University of ViennaSpitalgasse 231090 WienAustria
| | - Gernot Wassmer
- Aptiv Solutions, an ICON plc companyRobert‐Perthel‐Str. 77a50739KölnGermany
- Institute for Medical Statistics, Informatics and EpidemiologyUniversity of Cologne50924KölnGermany
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18
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Grieve AP. Idle thoughts of a 'well-calibrated' Bayesian in clinical drug development. Pharm Stat 2016; 15:96-108. [PMID: 26799060 DOI: 10.1002/pst.1736] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Revised: 08/20/2015] [Accepted: 12/11/2015] [Indexed: 12/30/2022]
Abstract
The use of Bayesian approaches in the regulated world of pharmaceutical drug development has not been without its difficulties or its critics. The recent Food and Drug Administration regulatory guidance on the use of Bayesian approaches in device submissions has mandated an investigation into the operating characteristics of Bayesian approaches and has suggested how to make adjustments in order that the proposed approaches are in a sense calibrated. In this paper, I present examples of frequentist calibration of Bayesian procedures and argue that we need not necessarily aim for perfect calibration but should be allowed to use procedures, which are well-calibrated, a position supported by the guidance.
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Affiliation(s)
- Andrew P Grieve
- Innovation Centre, Icon PLC, Globeside Business Park, Marlow, Buckinghamshire, SL7 1HZ, UK
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19
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Stallard N, Kunz CU, Todd S, Parsons N, Friede T. Flexible selection of a single treatment incorporating short-term endpoint information in a phase II/III clinical trial. Stat Med 2015; 34:3104-15. [PMID: 26112909 PMCID: PMC4745001 DOI: 10.1002/sim.6567] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Revised: 05/11/2015] [Accepted: 06/01/2015] [Indexed: 11/07/2022]
Abstract
Seamless phase II/III clinical trials in which an experimental treatment is selected at an interim analysis have been the focus of much recent research interest. Many of the methods proposed are based on the group sequential approach. This paper considers designs of this type in which the treatment selection can be based on short-term endpoint information for more patients than have primary endpoint data available. We show that in such a case, the familywise type I error rate may be inflated if previously proposed group sequential methods are used and the treatment selection rule is not specified in advance. A method is proposed to avoid this inflation by considering the treatment selection that maximises the conditional error given the data available at the interim analysis. A simulation study is reported that illustrates the type I error rate inflation and compares the power of the new approach with two other methods: a combination testing approach and a group sequential method that does not use the short-term endpoint data, both of which also strongly control the type I error rate. The new method is also illustrated through application to a study in Alzheimer's disease.
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Affiliation(s)
- Nigel Stallard
- Statistics and Epidemiology, Division of Health SciencesWarwick Medical School, University of WarwickCoventryU.K.
| | - Cornelia Ursula Kunz
- Statistics and Epidemiology, Division of Health SciencesWarwick Medical School, University of WarwickCoventryU.K.
| | - Susan Todd
- Department of Mathematics and StatisticsUniversity of ReadingReadingU.K.
| | - Nicholas Parsons
- Statistics and Epidemiology, Division of Health SciencesWarwick Medical School, University of WarwickCoventryU.K.
| | - Tim Friede
- Department of Medical StatisticsUniversity Medical CenterGöttingenGermany
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20
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Abstract
Adaptive designs have generated a great deal of attention to clinical trial communities. The literature contains many statistical methods to deal with added statistical uncertainties concerning the adaptations. Increasingly encountered in regulatory applications are adaptive statistical information designs that allow modification of sample size or related statistical information and adaptive selection designs that allow selection of doses or patient populations during the course of a clinical trial. For adaptive statistical information designs, a few statistical testing methods are mathematically equivalent, as a number of articles have stipulated, but arguably there are large differences in their practical ramifications. We pinpoint some undesirable features of these methods in this work. For adaptive selection designs, the selection based on biomarker data for testing the correlated clinical endpoints may increase statistical uncertainty in terms of type I error probability, and most importantly the increased statistical uncertainty may be impossible to assess.
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Affiliation(s)
- H M James Hung
- a Division of Biometrics I , Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration , Silver Spring , Maryland , USA
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21
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Multiplicity Adjustment in Seamless Phase II/III Adaptive Trials Using Biomarkers for Dose Selection. ICSA BOOK SERIES IN STATISTICS 2015. [DOI: 10.1007/978-3-319-12694-4_17] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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22
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Klinglmueller F, Posch M, Koenig F. Adaptive graph-based multiple testing procedures. Pharm Stat 2014; 13:345-56. [PMID: 25319733 PMCID: PMC4789493 DOI: 10.1002/pst.1640] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2013] [Revised: 08/13/2014] [Accepted: 08/26/2014] [Indexed: 11/17/2022]
Abstract
Multiple testing procedures defined by directed, weighted graphs have recently been proposed as an intuitive visual tool for constructing multiple testing strategies that reflect the often complex contextual relations between hypotheses in clinical trials. Many well-known sequentially rejective tests, such as (parallel) gatekeeping tests or hierarchical testing procedures are special cases of the graph based tests. We generalize these graph-based multiple testing procedures to adaptive trial designs with an interim analysis. These designs permit mid-trial design modifications based on unblinded interim data as well as external information, while providing strong family wise error rate control. To maintain the familywise error rate, it is not required to prespecify the adaption rule in detail. Because the adaptive test does not require knowledge of the multivariate distribution of test statistics, it is applicable in a wide range of scenarios including trials with multiple treatment comparisons, endpoints or subgroups, or combinations thereof. Examples of adaptations are dropping of treatment arms, selection of subpopulations, and sample size reassessment. If, in the interim analysis, it is decided to continue the trial as planned, the adaptive test reduces to the originally planned multiple testing procedure. Only if adaptations are actually implemented, an adjusted test needs to be applied. The procedure is illustrated with a case study and its operating characteristics are investigated by simulations.
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Affiliation(s)
- Florian Klinglmueller
- Center for Medical Statistics, Informatics, and Intelligent Systems,
Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
| | - Martin Posch
- Center for Medical Statistics, Informatics, and Intelligent Systems,
Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
| | - Franz Koenig
- Center for Medical Statistics, Informatics, and Intelligent Systems,
Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
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23
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Chang M, Wang J. The Add-Arm Design for Unimodal Response Curve with Unknown Mode. J Biopharm Stat 2014; 25:1039-64. [PMID: 25331003 DOI: 10.1080/10543406.2014.971164] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
In a classical drop-loser (or drop-arm) design, patients are randomized into all arms (doses) and at the interim analysis, inferior arms are dropped. Therefore, compared to the traditional dose-finding design, this adaptive design can reduce the sample size by not carrying over all doses to the end of the trial or dropping the losers earlier. However, all the doses have to be explored. For unimodal (including linear or umbrella) response curves, we proposed an effective dose-finding design that allows adding arms at the interim analysis. The trial design starts with two arms, depending on the response of the two arms and the unimodality assumption; we can decide which new arms to be added. This design does not require exploring all arms (doses) to find the best responsive dose; therefore, it can further reduce the sample size from the drop-loser design by as much as 10-20%.
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Affiliation(s)
- Mark Chang
- a AMG Pharmaceuticals, Inc ., Lexington , Massachusetts , USA
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24
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Graf AC, Bauer P, Glimm E, Koenig F. Maximum type 1 error rate inflation in multiarmed clinical trials with adaptive interim sample size modifications. Biom J 2014; 56:614-30. [PMID: 24753160 PMCID: PMC4282114 DOI: 10.1002/bimj.201300153] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2013] [Revised: 01/20/2014] [Accepted: 01/22/2014] [Indexed: 11/24/2022]
Abstract
Sample size modifications in the interim analyses of an adaptive design can inflate the type 1 error rate, if test statistics and critical boundaries are used in the final analysis as if no modification had been made. While this is already true for designs with an overall change of the sample size in a balanced treatment-control comparison, the inflation can be much larger if in addition a modification of allocation ratios is allowed as well. In this paper, we investigate adaptive designs with several treatment arms compared to a single common control group. Regarding modifications, we consider treatment arm selection as well as modifications of overall sample size and allocation ratios. The inflation is quantified for two approaches: a naive procedure that ignores not only all modifications, but also the multiplicity issue arising from the many-to-one comparison, and a Dunnett procedure that ignores modifications, but adjusts for the initially started multiple treatments. The maximum inflation of the type 1 error rate for such types of design can be calculated by searching for the "worst case" scenarios, that are sample size adaptation rules in the interim analysis that lead to the largest conditional type 1 error rate in any point of the sample space. To show the most extreme inflation, we initially assume unconstrained second stage sample size modifications leading to a large inflation of the type 1 error rate. Furthermore, we investigate the inflation when putting constraints on the second stage sample sizes. It turns out that, for example fixing the sample size of the control group, leads to designs controlling the type 1 error rate.
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Affiliation(s)
- Alexandra C Graf
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of ViennaSpitalgasse 23, 1090, Vienna, Austria
- Competence Center for Clinical Trials, University of BremenLinzer Strasse 4, 28359, Bremen, Germany
| | - Peter Bauer
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of ViennaSpitalgasse 23, 1090, Vienna, Austria
| | - Ekkehard Glimm
- Novartis Pharma AG, Novartis Campus4056, Basel, Switzerland
| | - Franz Koenig
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of ViennaSpitalgasse 23, 1090, Vienna, Austria
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25
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Wang* SJ, Brannath* W, Brückner M, James Hung HM, Koch A. Unblinded Adaptive Statistical Information Design Based on Clinical Endpoint or Biomarker. Stat Biopharm Res 2013. [DOI: 10.1080/19466315.2013.791639] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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26
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Sugitani T, Hamasaki T, Hamada C. Partition testing in confirmatory adaptive designs with structured objectives. Biom J 2013; 55:341-59. [PMID: 23576221 DOI: 10.1002/bimj.201200218] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2012] [Revised: 02/12/2013] [Accepted: 03/03/2013] [Indexed: 11/10/2022]
Abstract
In this paper, we propose a partition testing for adaptive clinical trials with structured study objectives. The proposed approach is a combination of the graphical approach with partition testing. The proposed approach enables one to handle many types of structured objectives, to tailor a multiple test procedure to given structured objectives, and to draw sensible conclusions in adaptive clinical trials. In addition, the proposed approach reduces to the Bonferroni-based graphical approaches that can allow adaptations such as treatment selection and sample size reassessment during the course of the trial. Some practical aspects of the proposed approach are investigated via a simulation study.
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Affiliation(s)
- Toshifumi Sugitani
- Department of Biomedical Statistics, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
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27
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Wassmer G. On Sample Size Determination in Multi-Armed Confirmatory Adaptive Designs. J Biopharm Stat 2011; 21:802-17. [DOI: 10.1080/10543406.2011.551336] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Affiliation(s)
- Gernot Wassmer
- a ADDPLAN GmbH, Cologne, Germany, and Institute for Medical Statistics, Informatics, and Epidemiology , University of Cologne , Cologne, Germany
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28
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Gallo P, Fardipour P, Dragalin V, Krams M, Littman GS, Bretz F. Data Monitoring in Adaptive Dose-Ranging Trials. Stat Biopharm Res 2010. [DOI: 10.1198/sbr.2010.09043] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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