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Zhu YY, Wang WX, Cheuk SK, Feng GR, Li XG, Peng JY, Liu Y, Yu SR, Tang JL, Chow SC, Li JB. A landscape of methodology and implementation of adaptive designs in cancer clinical trials. Crit Rev Oncol Hematol 2024; 200:104402. [PMID: 38848881 DOI: 10.1016/j.critrevonc.2024.104402] [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: 01/23/2024] [Revised: 05/06/2024] [Accepted: 05/23/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND The use of adaptive designs in cancer trials has considerably increased worldwide in recent years, along with the release of various guidelines for their application. This systematic review aims to comprehensively summarize the key methodological and executive features of adaptive designs in cancer clinical trials. METHODS A comprehensive search from PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials was conducted to screen eligible clinical trials that employed adaptive designs and were conducted in cancer patients. The methodological and executive characteristics of adaptive designs were the main measurements extracted. Descriptive analyses, primarily consisting of frequency and percentage, were employed to analyzed and reported the data. RESULTS A total of 180 cancer clinical trials with adaptive designs were identified. The first three most common type of adaptive design was the group sequential design (n=114, 63.3 %), adaptive dose-finding design (n=22, 12.2 %), and adaptive platform design (n=16, 8.9 %). The results showed that 4.4 % (n=8) of trials conducted post hoc modifications, and around 29.4 % (n=53) did not provide the methods for controlling type I errors. Among phase II or above trials, 79.9 % (112/140) applied the surrogate endpoint as the primary outcome in these trials. Importantly, 27.2 % (49/180) of trials did not report clear information on the independent data monitoring committee (iDMC), and 13.3 % (n=24) without clear information on interim analyses. Interim analyses suggested 34.4 % (62/180) of trials being stopped for futility, 10.6 % (n=19) for efficacy, and 2.2 % (n=4) for safety concerns in the early stage. CONCLUSIONS This study emphasizes adaptive designs in cancer trials face significant challenges in their design or strict implementation according to protocol, which might significantly compromise the validity and integrity of trials. It is thus important for researchers, sponsors, and policymakers to actively oversee and guide their application.
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Affiliation(s)
- Ying-Ying Zhu
- Clinical Research Design Division, Clinical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, PR China
| | - Wen-Xuan Wang
- School of Public Health, Sun Yat-sen University, Guangzhou, PR China; Department of Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Shui-Kit Cheuk
- School of Public Health, Sun Yat-sen University, Guangzhou, PR China; Department of Epidemiology and Health Statistics, School of Public Health, Peking University, Beijing, PR China
| | - Guan-Rui Feng
- Department of Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Xing-Ge Li
- School of Public Health, Sun Yat-sen University, Guangzhou, PR China
| | - Jia-Ying Peng
- School of Public Health, Sun Yat-sen University, Guangzhou, PR China
| | - Ying Liu
- School of Public Health, Sun Yat-sen University, Guangzhou, PR China
| | - Shao-Rui Yu
- Department of Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, PR China
| | - Jin-Ling Tang
- Shenzhen Institute of Advanced Technology of the Chinese Academy of Sciences, Shenzhen, PR China
| | - Shein-Chung Chow
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
| | - Ji-Bin Li
- Department of Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, PR China; State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, PR China.
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Edney LC, Pellizzer ML. Adaptive design trials in eating disorder research: A scoping review. Int J Eat Disord 2024; 57:1278-1290. [PMID: 38619362 DOI: 10.1002/eat.24198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 03/01/2024] [Accepted: 03/01/2024] [Indexed: 04/16/2024]
Abstract
OBJECTIVE This scoping review sought to map the breadth of literature on the use of adaptive design trials in eating disorder research. METHOD A systematic literature search was conducted in Medline, Scopus, PsycInfo, Emcare, Econlit, CINAHL and ProQuest Dissertations and Theses. Articles were included if they reported on an intervention targeting any type of eating disorder (including anorexia nervosa, bulimia nervosa, binge-eating disorder, and other specified feeding or eating disorders), and employed the use of an adaptive design trial to evaluate the intervention. Two independent reviewers screened citations for inclusion, and data abstraction was performed by one reviewer and verified by a second. RESULTS We identified five adaptive design trials targeting anorexia nervosa, bulimia nervosa and binge-eating disorder conducted in the USA and Australia. All employed adaptive treatment arm switching based on early response to treatment and identified a priori stopping rules. None of the studies included value of information analysis to guide adaptive design decisions and none included lived experience perspectives. DISCUSSION The limited use of adaptive designs in eating disorder trials represents a missed opportunity to improve enrolment targets, attrition rates, treatment outcomes and trial efficiency. We outline the range of adaptive methodologies, how they could be applied to eating disorder research, and the specific operational and statistical considerations relevant to adaptive design trials. PUBLIC SIGNIFICANCE Adaptive design trials are increasingly employed as flexible, efficient alternatives to fixed trial designs, but they are not often used in eating disorder research. This first scoping review identified five adaptive design trials targeting anorexia nervosa, bulimia nervosa and binge-eating disorder that employed treatment arm switching adaptive methodology. We make recommendations on the use of adaptive design trials for future eating disorder trials.
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Affiliation(s)
- Laura C Edney
- Flinders University Institute for Mental Health and Wellbeing, Flinders University, Adelaide, South Australia, Australia
| | - Mia L Pellizzer
- Flinders University Institute for Mental Health and Wellbeing, Flinders University, Adelaide, South Australia, Australia
- Blackbird Initiative, Flinders University, Adelaide, Australia
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Li W, Cornelius V, Finfer S, Venkatesh B, Billot L. Adaptive designs in critical care trials: a simulation study. BMC Med Res Methodol 2023; 23:236. [PMID: 37853343 PMCID: PMC10585789 DOI: 10.1186/s12874-023-02049-6] [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: 04/18/2023] [Accepted: 09/28/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND Adaptive clinical trials are growing in popularity as they are more flexible, efficient and ethical than traditional fixed designs. However, notwithstanding their increased use in assessing treatments for COVID-19, their use in critical care trials remains limited. A better understanding of the relative benefits of various adaptive designs may increase their use and interpretation. METHODS Using two large critical care trials (ADRENAL. CLINICALTRIALS gov number, NCT01448109. Updated 12-12-2017; NICE-SUGAR. CLINICALTRIALS gov number, NCT00220987. Updated 01-29-2009), we assessed the performance of three frequentist and two bayesian adaptive approaches. We retrospectively re-analysed the trials with one, two, four, and nine equally spaced interims. Using the original hypotheses, we conducted 10,000 simulations to derive error rates, probabilities of making an early correct and incorrect decision, expected sample size and treatment effect estimates under the null scenario (no treatment effect) and alternative scenario (a positive treatment effect). We used a logistic regression model with 90-day mortality as the outcome and the treatment arm as the covariate. The null hypothesis was tested using a two-sided significance level (α) at 0.05. RESULTS Across all approaches, increasing the number of interims led to a decreased expected sample size. Under the null scenario, group sequential approaches provided good control of the type-I error rate; however, the type I error rate inflation was an issue for the Bayesian approaches. The Bayesian Predictive Probability and O'Brien-Fleming approaches showed the highest probability of correctly stopping the trials (around 95%). Under the alternative scenario, the Bayesian approaches showed the highest overall probability of correctly stopping the ADRENAL trial for efficacy (around 91%), whereas the Haybittle-Peto approach achieved the greatest power for the NICE-SUGAR trial. Treatment effect estimates became increasingly underestimated as the number of interims increased. CONCLUSIONS This study confirms the right adaptive design can reach the same conclusion as a fixed design with a much-reduced sample size. The efficiency gain associated with an increased number of interims is highly relevant to late-phase critical care trials with large sample sizes and short follow-up times. Systematically exploring adaptive methods at the trial design stage will aid the choice of the most appropriate method.
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Affiliation(s)
- W Li
- MRC Biostatistics Unit, East Forvie Building, University of Cambridge, Cambridge, CB2 0QY, UK.
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, 90 High Holborn, 2nd Floor, London, WC1V 6LJ, UK.
| | - V Cornelius
- Imperial Clinical Trials Unit, School of Public Health, Imperial College London, Stadium House, 68 Woodlane, London, W12 7RH, UK
| | - S Finfer
- The George Institute for Global Health, 1 King Street, Newtown, NSW, 2042, Australia
- Faculty of Medicine, University of New South Wales, Sydney, NSW, 2052, Australia
- Faculty of Medicine, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
| | - B Venkatesh
- The George Institute for Global Health, 1 King Street, Newtown, NSW, 2042, Australia
- Faculty of Medicine, University of New South Wales, Sydney, NSW, 2052, Australia
| | - L Billot
- The George Institute for Global Health, 1 King Street, Newtown, NSW, 2042, Australia
- Faculty of Medicine, University of New South Wales, Sydney, NSW, 2052, Australia
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Robertson DS, Choodari-Oskooei B, Dimairo M, Flight L, Pallmann P, Jaki T. Point estimation for adaptive trial designs II: Practical considerations and guidance. Stat Med 2023; 42:2496-2520. [PMID: 37021359 PMCID: PMC7614609 DOI: 10.1002/sim.9734] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 01/20/2023] [Accepted: 03/18/2023] [Indexed: 04/07/2023]
Abstract
In adaptive clinical trials, the conventional end-of-trial point estimate of a treatment effect is prone to bias, that is, a systematic tendency to deviate from its true value. As stated in recent FDA guidance on adaptive designs, it is desirable to report estimates of treatment effects that reduce or remove this bias. However, it may be unclear which of the available estimators are preferable, and their use remains rare in practice. This article is the second in a two-part series that studies the issue of bias in point estimation for adaptive trials. Part I provided a methodological review of approaches to remove or reduce the potential bias in point estimation for adaptive designs. In part II, we discuss how bias can affect standard estimators and assess the negative impact this can have. We review current practice for reporting point estimates and illustrate the computation of different estimators using a real adaptive trial example (including code), which we use as a basis for a simulation study. We show that while on average the values of these estimators can be similar, for a particular trial realization they can give noticeably different values for the estimated treatment effect. Finally, we propose guidelines for researchers around the choice of estimators and the reporting of estimates following an adaptive design. The issue of bias should be considered throughout the whole lifecycle of an adaptive design, with the estimation strategy prespecified in the statistical analysis plan. When available, unbiased or bias-reduced estimates are to be preferred.
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Affiliation(s)
| | - Babak Choodari-Oskooei
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Munya Dimairo
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Laura Flight
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | | | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Faculty of Informatics and Data Science, University of Regensburg, Regensburg, Germany
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5
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Wason JMS, Dimairo M, Biggs K, Bowden S, Brown J, Flight L, Hall J, Jaki T, Lowe R, Pallmann P, Pilling MA, Snowdon C, Sydes MR, Villar SS, Weir CJ, Wilson N, Yap C, Hancock H, Maier R. Practical guidance for planning resources required to support publicly-funded adaptive clinical trials. BMC Med 2022; 20:254. [PMID: 35945610 PMCID: PMC9364623 DOI: 10.1186/s12916-022-02445-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 06/20/2022] [Indexed: 11/15/2022] Open
Abstract
Adaptive designs are a class of methods for improving efficiency and patient benefit of clinical trials. Although their use has increased in recent years, research suggests they are not used in many situations where they have potential to bring benefit. One barrier to their more widespread use is a lack of understanding about how the choice to use an adaptive design, rather than a traditional design, affects resources (staff and non-staff) required to set-up, conduct and report a trial. The Costing Adaptive Trials project investigated this issue using quantitative and qualitative research amongst UK Clinical Trials Units. Here, we present guidance that is informed by our research, on considering the appropriate resourcing of adaptive trials. We outline a five-step process to estimate the resources required and provide an accompanying costing tool. The process involves understanding the tasks required to undertake a trial, and how the adaptive design affects them. We identify barriers in the publicly funded landscape and provide recommendations to trial funders that would address them. Although our guidance and recommendations are most relevant to UK non-commercial trials, many aspects are relevant more widely.
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Affiliation(s)
- James M S Wason
- Biostatistics Research Group, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK.
| | - Munyaradzi Dimairo
- School of Health and Related Research, Clinical Trials Research Unit, University of Sheffield, Sheffield, UK
| | - Katie Biggs
- School of Health and Related Research, Clinical Trials Research Unit, University of Sheffield, Sheffield, UK
| | - Sarah Bowden
- Cancer Research UK Clinical Trials Unit (CRCTU), University of Birmingham, Birmingham, UK
| | - Julia Brown
- Cancer Research UK CTU, University of Leeds, Leeds, UK
| | - Laura Flight
- School of Health and Related Research, Health Economics and Decision Science, University of Sheffield, Sheffield, UK
| | - Jamie Hall
- School of Health and Related Research, Clinical Trials Research Unit, University of Sheffield, Sheffield, UK
| | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Rachel Lowe
- Centre for Trials Research, Cardiff University, Cardiff, UK
| | | | - Mark A Pilling
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Claire Snowdon
- The Institute of Cancer Research Clinical Trials & Statistics Unit, London, UK
| | | | - Sofía S Villar
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Christopher J Weir
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Nina Wilson
- Biostatistics Research Group, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Christina Yap
- The Institute of Cancer Research Clinical Trials & Statistics Unit, London, UK
| | - Helen Hancock
- Newcastle Clinical Trials Unit, Newcastle University, Newcastle upon Tyne, UK
| | - Rebecca Maier
- Newcastle Clinical Trials Unit, Newcastle University, Newcastle upon Tyne, UK
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Vervaart M, Strong M, Claxton KP, Welton NJ, Wisløff T, Aas E. An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial. Med Decis Making 2022; 42:612-625. [PMID: 34967237 PMCID: PMC9189722 DOI: 10.1177/0272989x211068019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 11/30/2021] [Indexed: 11/15/2022]
Abstract
BACKGROUND Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we develop new methods for computing the EVSI of extending an existing trial's follow-up, first for an assumed survival model and then extending to capture uncertainty about the true survival model. METHODS We developed a nested Markov Chain Monte Carlo procedure and a nonparametric regression-based method. We compared the methods by computing single-model and model-averaged EVSI for collecting additional follow-up data in 2 synthetic case studies. RESULTS There was good agreement between the 2 methods. The regression-based method was fast and straightforward to implement, and scales easily to include any number of candidate survival models in the model uncertainty case. The nested Monte Carlo procedure, on the other hand, was extremely computationally demanding when we included model uncertainty. CONCLUSIONS We present a straightforward regression-based method for computing the EVSI of extending an existing trial's follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. EVSI for ongoing trials can help decision makers determine whether early patient access to a new technology can be justified on the basis of the current evidence or whether more mature evidence is needed. HIGHLIGHTS Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life-expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial.In this article, we have developed new methods for computing the EVSI of extending a trial's follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. We extend a previously described nonparametric regression-based method for computing EVSI, which we demonstrate in synthetic case studies is fast, straightforward to implement, and scales easily to include any number of candidate survival models in the EVSI calculations.The EVSI methods that we present in this article can quantify the need for collecting additional follow-up data before making an adoption decision given any decision-making context.
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Affiliation(s)
- Mathyn Vervaart
- Department of Health Management and Health Economics, University of Oslo, Oslo, Norway
- Norwegian Medicines Agency, Oslo, Norway
| | - Mark Strong
- School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Karl P. Claxton
- Centre for Health Economics, University of York, York, UK
- Department of Economics and Related Studies, University of York, York, UK
| | - Nicky J. Welton
- Population Health Sciences, University of Bristol, Bristol, UK
| | - Torbjørn Wisløff
- Department of Community Medicine, UiT The Arctic University of Norway, Oslo, Norway
- Norwegian Institute of Public Health, Oslo, Norway
| | - Eline Aas
- Department of Health Management and Health Economics, University of Oslo, Oslo, Norway
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Lauffenburger JC, Choudhry NK, Russo M, Glynn RJ, Ventz S, Trippa L. Designing and conducting adaptive trials to evaluate interventions in health services and implementation research: practical considerations. BMJ MEDICINE 2022; 1:e000158. [PMID: 36386444 PMCID: PMC9650931 DOI: 10.1136/bmjmed-2022-000158] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Randomized controlled clinical trials are widely considered the gold standard for evaluating the efficacy or effectiveness of interventions in health care. Adaptive trials incorporate changes as the study proceeds, such as modifying allocation probabilities or eliminating treatment arms that are likely to be ineffective. These designs have been widely used in drug discovery studies but can also be useful in health services and implementation research and have been minimally used. As motivating examples, we use an ongoing adaptive trial and two completed parallel group studies and highlight potential advantages, disadvantages, and important considerations when using adaptive trial designs in health services and implementation research. In addition, we investigate the impact on power and the study duration if the two completed parallel-group trials had instead been conducted using adaptive principles. Compared with traditional trial designs, adaptive designs can often allow one to evaluate more interventions, adjust participant allocation probabilities (e.g., to achieve covariate balance), and identify participants who are likely to agree to enroll. These features could reduce resources needed to conduct a trial. However, adaptive trials have potential disadvantages and practical aspects that need to be considered, most notably outcomes that can be rapidly measured and extracted (e.g., long-term outcomes that take significant time to measure from data sources can be challenging), minimal missing data, and time trends. In conclusion, adaptive designs are a promising approach to help identify how best to implement evidence-based interventions into real-world practice in health services and implementation research.
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Affiliation(s)
- Julie C Lauffenburger
- Center for Healthcare Delivery Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Boston, MA, USA
| | - Niteesh K Choudhry
- Center for Healthcare Delivery Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Boston, MA, USA
| | - Massimiliano Russo
- Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Boston, MA, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Robert J Glynn
- Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Boston, MA, USA
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Steffen Ventz
- Dana-Farber Cancer Institute Department of Biostatistics and Computational Biology, Boston, MA, USA
| | - Lorenzo Trippa
- Dana-Farber Cancer Institute Department of Biostatistics and Computational Biology, Boston, MA, USA
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8
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Flight L, Julious S, Brennan A, Todd S. Expected Value of Sample Information to Guide the Design of Group Sequential Clinical Trials. Med Decis Making 2021; 42:461-473. [PMID: 34859693 PMCID: PMC9005835 DOI: 10.1177/0272989x211045036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Introduction Adaptive designs allow changes to an ongoing trial based on prespecified early examinations of accrued data. Opportunities are potentially being missed to incorporate health economic considerations into the design of these studies. Methods We describe how to estimate the expected value of sample information for group sequential design adaptive trials. We operationalize this approach in a hypothetical case study using data from a pilot trial. We report the expected value of sample information and expected net benefit of sampling results for 5 design options for the future full-scale trial including the fixed-sample-size design and the group sequential design using either the Pocock stopping rule or the O’Brien-Fleming stopping rule with 2 or 5 analyses. We considered 2 scenarios relating to 1) using the cost-effectiveness model with a traditional approach to the health economic analysis and 2) adjusting the cost-effectiveness analysis to incorporate the bias-adjusted maximum likelihood estimates of trial outcomes to account for the bias that can be generated in adaptive trials. Results The case study demonstrated that the methods developed could be successfully applied in practice. The results showed that the O’Brien-Fleming stopping rule with 2 analyses was the most efficient design with the highest expected net benefit of sampling in the case study. Conclusions Cost-effectiveness considerations are unavoidable in budget-constrained, publicly funded health care systems, and adaptive designs can provide an alternative to costly fixed-sample-size designs. We recommend that when planning a clinical trial, expected value of sample information methods be used to compare possible adaptive and nonadaptive trial designs, with appropriate adjustment, to help justify the choice of design characteristics and ensure the cost-effective use of research funding. Highlights
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Affiliation(s)
- Laura Flight
- Laura Flight, School of Health and Related Research (ScHARR), University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK; ()
| | - Steven Julious
- School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Alan Brennan
- School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Susan Todd
- Department of Mathematics and Statistics, University of Reading, Reading, UK
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Park JJH, Grais RF, Taljaard M, Nakimuli-Mpungu E, Jehan F, Nachega JB, Ford N, Xavier D, Kengne AP, Ashorn P, Socias ME, Bhutta ZA, Mills EJ. Urgently seeking efficiency and sustainability of clinical trials in global health. Lancet Glob Health 2021; 9:e681-e690. [PMID: 33865473 PMCID: PMC8424133 DOI: 10.1016/s2214-109x(20)30539-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 12/07/2020] [Accepted: 12/10/2020] [Indexed: 12/22/2022]
Abstract
This paper shows the scale of global health research and the context in which we frame the subsequent papers in the Series. In this Series paper, we provide a historical perspective on clinical trial research by revisiting the 1948 streptomycin trial for pulmonary tuberculosis, which was the first documented randomised clinical trial in the English language, and we discuss its close connection with global health. We describe the current state of clinical trial research globally by providing an overview of clinical trials that have been registered in the WHO International Clinical Trial Registry since 2010. We discuss challenges with current trial planning and designs that are often used in clinical trial research undertaken in low-income and middle-income countries, as an overview of the global health trials landscape. Finally, we discuss the importance of collaborative work in global health research towards generating sustainable and culturally appropriate research environments.
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Affiliation(s)
- Jay J H Park
- Department of Experimental Medicine, University of British Columbia, Vancouver, BC, Canada
| | | | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute and School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | | | - Fyezah Jehan
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Jean B Nachega
- Department of Medicine and Center for Infectious Diseases, Stellenbosch University, Cape Town, South Africa; Department of Epidemiology and Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Epidemiology and Department of Infectious Diseases and Microbiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA
| | - Nathan Ford
- Centre for Infectious Disease Epidemiology and Research, School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa
| | - Denis Xavier
- Department of Pharmacology and Division of Clinical Research, St John's Medical College, Bangalore, India
| | - Andre P Kengne
- Non-Communicable Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa
| | - Per Ashorn
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Maria Eugenia Socias
- Fundación Huésped, Buenos Aires, Argentina; British Columbia Centre for Substance Use, Vancouver, BC, Canada; Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Zulfiqar A Bhutta
- Centre for Global Child Health, Hospital for Sick Children, Toronto, ON, Canada; Institute of Global Health and Development, and Center of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan
| | - Edward J Mills
- School of Public Health, University of Rwanda, Kigali, Rwanda; Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada; Cytel, Vancouver, BC, Canada.
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10
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Tarricone R, Ciani O, Torbica A, Brouwer W, Chaloutsos G, Drummond MF, Martelli N, Persson U, Leidl R, Levin L, Sampietro-Colom L, Taylor RS. Lifecycle evidence requirements for high-risk implantable medical devices: a European perspective. Expert Rev Med Devices 2020; 17:993-1006. [PMID: 32975149 DOI: 10.1080/17434440.2020.1825074] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
INTRODUCTION The new European Union (EU) Regulations on medical devices and on in vitro diagnostics provide manufacturers and Notified Bodies with new tools to improve pre-market and post-market clinical evidence generation especially for high-risk products but fail to indicate what type of clinical evidence is appropriate at each stage of the whole lifecycle of medical devices. In this paper we address: i) the appropriate level and timing of clinical evidence throughout the lifecycle of high-risk implantable medical devices; and ii) how the clinical evidence generation ecosystem could be adapted to optimize patient access. AREAS COVERED The European regulatory and health technology assessment (HTA) contexts are reviewed, in relation to the lifecycle of high-risk medical devices and clinical evidence generation recommended by international network or endorsed by regulatory and HTA agencies in different jurisdictions. EXPERT OPINION Four stages are relevant for clinical evidence generation: i) pre-clinical, pre-market; ii) clinical, pre-market; iii) diffusion, post-market; and iv) obsolescence & replacement, post-market. Each stage has its own evaluation needs and specific studies are recommended to generate the appropriate evidence. Effective lifecycle planning requires anticipation of what evidence will be needed at each stage.
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Affiliation(s)
- Rosanna Tarricone
- Department of Social and Political Science, Bocconi University , Milan, Italy.,SDA Bocconi School of Management , Milan, Italy.,SDA Bocconi School of Management, Centre for Research on Health and Social Care Management (CERGAS) , Milan, Italy
| | - Oriana Ciani
- SDA Bocconi School of Management, Centre for Research on Health and Social Care Management (CERGAS) , Milan, Italy.,Institute of College and Medicine, University of Exeter, South Cloisters, St Luke's Campus , Exeter, UK
| | - Aleksandra Torbica
- Department of Social and Political Science, Bocconi University , Milan, Italy.,SDA Bocconi School of Management , Milan, Italy
| | - Werner Brouwer
- Erasmus School of Health Policy & Management (ESHPM), Erasmus University Rotterdam , Rotterdam, PA, The Netherlands
| | - Georges Chaloutsos
- Biomedical Engineering Department, Onassis Cardiac Surgery Centre & Director , Athens, Greece
| | - Michael F Drummond
- Professor of Health Economics, Centre for Health Economics, University of York , York, UK
| | - Nicolas Martelli
- Associate Clinical Professor, Hôpital Européen Georges Pompidou , Paris, France
| | - Ulf Persson
- IHE, Swedish Institute for Health Economics , Lund, Sweden
| | - Reiner Leidl
- Institute of Health Economics and Healthcare Management, Helmholtz Zentrum München - German Research Center for Environmental Health (Gmbh) , Neuherberg, Germany
| | - Les Levin
- Chief Executive Officer & Scientific Officer, EXCITE International , Canada
| | - Laura Sampietro-Colom
- Deputy Director of Innovation, Head of Health Technology Assessment Unit at Hospital Clinic Barcelona , Spain
| | - Rod S Taylor
- Institute of Health and Wellbeing, University of Glasgow , Glasgow, UK
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11
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Dimairo M, Pallmann P, Wason J, Todd S, Jaki T, Julious SA, Mander AP, Weir CJ, Koenig F, Walton MK, Nicholl JP, Coates E, Biggs K, Hamasaki T, Proschan MA, Scott JA, Ando Y, Hind D, Altman DG. The adaptive designs CONSORT extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design. Trials 2020; 21:528. [PMID: 32546273 PMCID: PMC7298968 DOI: 10.1186/s13063-020-04334-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised.This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process.The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist comprises seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text.The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits. In order to encourage its wide dissemination this article is freely accessible on the BMJ and Trials journal websites."To maximise the benefit to society, you need to not just do research but do it well" Douglas G Altman.
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Affiliation(s)
- Munyaradzi Dimairo
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK.
| | | | - James Wason
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Institute of Health and Society, Newcastle University, Newcastle, UK
| | - Susan Todd
- Department of Mathematics and Statistics, University of Reading, Reading, UK
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Steven A Julious
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | - Adrian P Mander
- Centre for Trials Research, Cardiff University, Cardiff, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Christopher J Weir
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Franz Koenig
- Centre for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Marc K Walton
- Janssen Pharmaceuticals, Titusville, New Jersey, USA
| | - Jon P Nicholl
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | - Elizabeth Coates
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | - Katie Biggs
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | | | - Michael A Proschan
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, USA
| | - John A Scott
- Division of Biostatistics in the Center for Biologics Evaluation and Research, Food and Drug Administration, Rockville, USA
| | - Yuki Ando
- Pharmaceuticals and Medical Devices Agency, Tokyo, Japan
| | - Daniel Hind
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | - Douglas G Altman
- Centre for Statistics in Medicine, University of Oxford, Oxford, UK
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12
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Dimairo M, Pallmann P, Wason J, Todd S, Jaki T, Julious SA, Mander AP, Weir CJ, Koenig F, Walton MK, Nicholl JP, Coates E, Biggs K, Hamasaki T, Proschan MA, Scott JA, Ando Y, Hind D, Altman DG. The Adaptive designs CONSORT Extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design. BMJ 2020; 369:m115. [PMID: 32554564 PMCID: PMC7298567 DOI: 10.1136/bmj.m115] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/19/2019] [Indexed: 12/11/2022]
Abstract
Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised.This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process.The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist comprises seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text.The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits.
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Affiliation(s)
- Munyaradzi Dimairo
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | | | - James Wason
- MRC Biostatistics Unit, University of Cambridge, UK
- Institute of Health and Society, Newcastle University, UK
| | - Susan Todd
- Department of Mathematics and Statistics, University of Reading, UK
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, UK
| | - Steven A Julious
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | - Adrian P Mander
- Centre for Trials Research, Cardiff University, UK
- MRC Biostatistics Unit, University of Cambridge, UK
| | - Christopher J Weir
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, UK
| | - Franz Koenig
- Centre for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Austria
| | | | - Jon P Nicholl
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | - Elizabeth Coates
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | - Katie Biggs
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | | | - Michael A Proschan
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, USA
| | - John A Scott
- Division of Biostatistics in the Center for Biologics Evaluation and Research, Food and Drug Administration, USA
| | - Yuki Ando
- Pharmaceuticals and Medical Devices Agency, Japan
| | - Daniel Hind
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
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13
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Mitchell JM, Patterson JA. The Inclusion of Economic Endpoints as Outcomes in Clinical Trials Reported to ClinicalTrials.gov. J Manag Care Spec Pharm 2020; 26:386-393. [PMID: 32223593 PMCID: PMC10391117 DOI: 10.18553/jmcp.2020.26.4.386] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
BACKGROUND As medication expenditures rise, payers are increasingly demanding evidence of economic value for new medications. The 2015 Professional Society for Health Economics and Outcomes Research (ISPOR) Task Force on Cost-Effectiveness Analysis Alongside Clinical Trials noted that clinical trials are increasingly including health care utilization endpoints to address this rising interest in economic information. OBJECTIVES To (a) describe the prevalence of economic endpoints in clinical trials submitted to ClinicalTrials.gov and (b) examine associations between trial characteristics and the inclusion of economic endpoints. METHODS This retrospective review of ClinicalTrials.gov data extracted the characteristics of clinical trials that were submitted to ClinicalTrials.gov from January 2004 to December 2018; studied a drug and/or biological; and had a recruitment status of not yet recruiting, recruiting, active but not recruiting, or completed. Studies were classified as containing an economic endpoint based on 2 independent evaluations of the inclusion of endpoints relevant to costs, resource utilization, cost-effectiveness, productivity, absenteeism, presenteeism, or unemployment. Descriptive statistics were used to summarize trial characteristics, and chi-square analyses were used to evaluate differences in characteristics between trials with and without economic endpoints. RESULTS Of the 104,885 trials included in the study, 1,437 (1.37%) included an economic endpoint; among later phase (phase 2/3, 3, 4) trials, 939 (2.54%) included economic endpoints. Compared with studies that did not include economic endpoints, those that did were less often industry funded (48.0% vs. 52.0%, P < 0.001) and were for a high-spend specialty condition (24.1% vs. 27.4%, P < 0.001). The proportion of trials that included economic endpoints increased by a small but significant amount over the time period studied, from 1.2% (2004-2008) to 1.6% (2014-2018; P < 0.001). CONCLUSIONS A small but growing number of clinical trials are including economic endpoints. This finding may reflect continued industry concerns surrounding the cost and logistical challenges of piggybacking economic data collection alongside clinical trials and/or manufacturers' preferences for modeling for value demonstration. Future research is needed to better understand barriers to the inclusion of economic endpoints as well as the degree to which incorporating health care resource utilization collected during clinical trials into early economic modeling may reduce payer concerns about model transparency and bias. DISCLOSURES No outside funding supported this study. Patterson reports past employment by Indivior, unrelated to this study. Mitchell has nothing to disclose. The research included in this study was presented as a nonreviewed student pharmacist poster at AMCP Nexus 2019; October 30-November 1, 2019; National Harbor, MD.
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Affiliation(s)
- Jordan M. Mitchell
- PharmD candidate, Department of Pharmacotherapy & Outcomes Science, Virginia Commonwealth University School of Pharmacy, Richmond
| | - Julie A. Patterson
- Department of Pharmacotherapy & Outcomes Science, Virginia Commonwealth University School of Pharmacy, Richmond
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14
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Flight L, Julious S, Brennan A, Todd S, Hind D. How can health economics be used in the design and analysis of adaptive clinical trials? A qualitative analysis. Trials 2020; 21:252. [PMID: 32143728 PMCID: PMC7060544 DOI: 10.1186/s13063-020-4137-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2019] [Accepted: 02/04/2020] [Indexed: 12/04/2022] Open
Abstract
INTRODUCTION Adaptive designs offer a flexible approach, allowing changes to a trial based on examinations of the data as it progresses. Adaptive clinical trials are becoming a popular choice, as the prudent use of finite research budgets and accurate decision-making are priorities for healthcare providers around the world. The methods of health economics, which aim to maximise the health gained for money spent, could be incorporated into the design and analysis of adaptive clinical trials to make them more efficient. We aimed to understand the perspectives of stakeholders in health technology assessments to inform recommendations for the use of health economics in adaptive clinical trials. METHODS A qualitative study explored the attitudes of key stakeholders-including researchers, decision-makers and members of the public-towards the use of health economics in the design and analysis of adaptive clinical trials. Data were collected using interviews and focus groups (29 participants). A framework analysis was used to identify themes in the transcripts. RESULTS It was considered that answering the clinical research question should be the priority in a clinical trial, notwithstanding the importance of cost-effectiveness for decision-making. Concerns raised by participants included handling the volatile nature of cost data at interim analyses; implementing this approach in global trials; resourcing adaptive trials which are designed and adapted based on health economic outcomes; and training stakeholders in these methods so that they can be implemented and appropriately interpreted. CONCLUSION The use of health economics in the design and analysis of adaptive clinical trials has the potential to increase the efficiency of health technology assessments worldwide. Recommendations are made concerning the development of methods allowing the use of health economics in adaptive clinical trials, and suggestions are given to facilitate their implementation in practice.
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Affiliation(s)
- Laura Flight
- School of Health And Related Research, University of Sheffield, Sheffield, UK
| | - Steven Julious
- School of Health And Related Research, University of Sheffield, Sheffield, UK
| | - Alan Brennan
- School of Health And Related Research, University of Sheffield, Sheffield, UK
| | - Susan Todd
- Department of Mathematics and Statistics, University of Reading, Reading, UK
| | - Daniel Hind
- School of Health And Related Research, University of Sheffield, Sheffield, UK
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