138 results on '"Adaptive Clinical Trials as Topic"'
Search Results
2. Pandemic vaccine testing: Combining conventional and challenge studies.
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Gerhard, Tobias, Strom, Brian L., and Eyal, Nir
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Early into COVID, human challenge trials were considered, but usually as alternatives to conventional randomized controlled trials. Instead, assessment of authorized COVID vaccines, of further COVID vaccines, and of vaccines against future pandemics should combine both designs, in five different ways, including a wholly novel one that we elaborate, Viz., combining data from both designs to answer a single question. [ABSTRACT FROM AUTHOR]
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- 2022
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3. A conceptual model for the development process of confirmatory adaptive clinical trials within an emergency research network
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Mawocha, Samkeliso C, Fetters, Michael D, Legocki, Laurie J, Guetterman, Timothy C, Frederiksen, Shirley, Barsan, William G, Lewis, Roger J, Berry, Donald A, and Meurer, William J
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Biomedical and Clinical Sciences ,Clinical Sciences ,Clinical Research ,Clinical Trials and Supportive Activities ,Adaptive Clinical Trials as Topic ,Biomedical Research ,Communication ,Cooperative Behavior ,Humans ,Models ,Statistical ,Qualitative Research ,Research Design ,Surveys and Questionnaires ,Adaptive clinical trials ,confirmatory-phase clinical trials ,mixed methods ,Strength-Weaknesses-Opportunities-Threats ,qualitative research ,neurology clinical trials ,Strength–Weaknesses–Opportunities–Threats ,Statistics ,Statistics & Probability ,Clinical sciences ,Clinical and health psychology - Abstract
BackgroundAdaptive clinical trials use accumulating data from enrolled subjects to alter trial conduct in pre-specified ways based on quantitative decision rules. In this research, we sought to characterize the perspectives of key stakeholders during the development process of confirmatory-phase adaptive clinical trials within an emergency clinical trials network and to build a model to guide future development of adaptive clinical trials.MethodsWe used an ethnographic, qualitative approach to evaluate key stakeholders' views about the adaptive clinical trial development process. Stakeholders participated in a series of multidisciplinary meetings during the development of five adaptive clinical trials and completed a Strengths-Weaknesses-Opportunities-Threats questionnaire. In the analysis, we elucidated overarching themes across the stakeholders' responses to develop a conceptual model.ResultsFour major overarching themes emerged during the analysis of stakeholders' responses to questioning: the perceived statistical complexity of adaptive clinical trials and the roles of collaboration, communication, and time during the development process. Frequent and open communication and collaboration were viewed by stakeholders as critical during the development process, as were the careful management of time and logistical issues related to the complexity of planning adaptive clinical trials.ConclusionThe Adaptive Design Development Model illustrates how statistical complexity, time, communication, and collaboration are moderating factors in the adaptive design development process. The intensity and iterative nature of this process underscores the need for funding mechanisms for the development of novel trial proposals in academic settings.
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- 2017
4. How Much More Efficient Are Adaptive Platform Trials Than Multiple Stand-Alone Trials? A Comprehensive Simulation Study for Streamlining Drug Development During a Pandemic.
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Sasaki M, Sato H, Uemura Y, Mikami A, Ichihara N, Fujitani S, Kondo M, Doi Y, Morino E, Tokita D, Ohmagari N, Sugiura W, and Hirakawa A
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- Humans, Sample Size, Pandemics, SARS-CoV-2, Clinical Trials as Topic methods, Antiviral Agents therapeutic use, Adaptive Clinical Trials as Topic, Research Design, Drug Development methods, Computer Simulation, COVID-19 Drug Treatment, COVID-19 epidemiology
- Abstract
With the coronavirus disease 2019 (COVID-19) pandemic, there is growing interest in utilizing adaptive platform clinical trials (APTs), in which multiple drugs are compared with a single common control group, such as a placebo or standard-of-care group. APTs evaluate several drugs for one disease and accept additions or exclusions of drugs as the trials progress; however, little is known about the efficiency of APTs over multiple stand-alone trials. In this study, we simulated the total development period, total sample size, and statistical operating characteristics of APTs and multiple stand-alone trials in drug development settings for hospitalized patients with COVID-19. Simulation studies using selected scenarios reconfirmed several findings regarding the efficiency of APTs. The APTs without staggered addition of drugs showed a shorter total development period than stand-alone trials, but the difference rapidly diminished if patient's enrollment was accelerated during the trials owing to the spread of infection. APTs with staggered addition of drugs still have the possibility of reducing the total development period compared with multiple stand-alone trials in some cases. Our study demonstrated that APTs could improve efficiency relative to multiple stand-alone trials regarding the total development period and total sample size without undermining statistical validity; however, this improvement varies depending on the speed of patient enrollment, sample size, presence/absence of family-wise error rate adjustment, allocation ratio between drug and placebo groups, and interval of staggered addition of drugs. Given the complexity of planning and implementing APT, the decision to implement APT during a pandemic must be made carefully., (© 2024 The Authors. Clinical Pharmacology & Therapeutics © 2024 American Society for Clinical Pharmacology and Therapeutics.)
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- 2024
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5. Power Considerations in Designing and Interpreting Adaptive Clinical Trials.
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Wang R and Mehta C
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- Humans, Aspirin therapeutic use, Risk Factors, Adaptive Clinical Trials as Topic, Stroke drug therapy
- Abstract
Adaptive clinical trials allow researchers to make preplanned modifications based on accumulating data from an ongoing trial while preserving the trial's integrity and validity. These modifications may include early termination in cases of successes or lack of efficacy, refining the sample size, altering treatments or doses, or focusing recruitment efforts on individuals most likely to benefit. In this issue of NEJM Evidence , Geisler et al.
1 report results from the Apixaban for Treatment of Embolic Stroke of Undetermined Source (ATTICUS) trial, a multicenter randomized trial of apixaban compared with aspirin in patients with cardioembolism risk factors.- Published
- 2024
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6. Optimizing clinical nutrition research: the role of adaptive and pragmatic trials.
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Orsso CE, Ford KL, Kiss N, Trujillo EB, Spees CK, Hamilton-Reeves JM, and Prado CM
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- Humans, Pragmatic Clinical Trials as Topic, Adaptive Clinical Trials as Topic, Nutritional Status, Research Design
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Evidence-based nutritional recommendations address the health impact of suboptimal nutritional status. Efficacy randomized controlled trials (RCTs) have traditionally been the preferred method for determining the effects of nutritional interventions on health outcomes. Nevertheless, obtaining a holistic understanding of intervention efficacy and effectiveness in real-world settings is stymied by inherent constraints of efficacy RCTs. These limitations are further compounded by the complexity of nutritional interventions and the intricacies of the clinical context. Herein, we explore the advantages and limitations of alternative study designs (e.g., adaptive and pragmatic trials), which can be incorporated into RCTs to optimize the efficacy or effectiveness of interventions in clinical nutrition research. Efficacy RCTs often lack external validity due to their fixed design and restrictive eligibility criteria, leading to efficacy-effectiveness and evidence-practice gaps. Adaptive trials improve the evaluation of nutritional intervention efficacy through planned study modifications, such as recalculating sample sizes or discontinuing a study arm. Pragmatic trials are embedded within clinical practice or conducted in settings that resemble standard of care, enabling a more comprehensive assessment of intervention effectiveness. Pragmatic trials often rely on patient-oriented primary outcomes, acquire outcome data from electronic health records, and employ broader eligibility criteria. Consequently, adaptive and pragmatic trials facilitate the prompt implementation of evidence-based nutritional recommendations into clinical practice. Recognizing the limitations of efficacy RCTs and the potential advantages of alternative trial designs is essential for bridging efficacy-effectiveness and evidence-practice gaps. Ultimately, this awareness will lead to a greater number of patients benefiting from evidence-based nutritional recommendations., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)
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- 2023
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7. Accelerating clinical trial implementation in the context of the COVID-19 pandemic: challenges, lessons learned and recommendations from DisCoVeRy and the EU-SolidAct EU response group
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Alpha Diallo, Marius Trøseid, Victoria Charlotte Simensen, Anaïs Boston, Jacques Demotes, Inge Christoffer Olsen, Florence Chung, José Artur Paiva, Maya Hites, Florence Ader, Jose Ramon Arribas, Andreas Baratt-Due, Øyvind Melien, Evelina Tacconelli, Thèrèse Staub, Richard Greil, Sotirios Tsiodras, Matthias Briel, Hélène Esperou, France Mentré, Joe Eustace, Juliette Saillard, Christelle Delmas, Soizic LeMestre, Marina Dumousseaux, Dominique Costagliola, John-Arne Røttingen, Yazdan Yazdanpanah, Agence Nationale de Recherches sur le Sida et les Hépatites Virales (ANRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Oslo University Hospital [Oslo], European Clinical Research Infrastructures Network [Dusseldorf] (ECRIN), Hospital de São João [Porto], Université libre de Bruxelles (ULB), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, Centre International de Recherche en Infectiologie (CIRI), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), La Paz University Hospital, Norwegian Institute of Public Health [Oslo] (NIPH), Università degli studi di Verona = University of Verona (UNIVR), Centre Hospitalier de Luxembourg [Luxembourg] (CHL), Paracelsus Medizinische Privatuniversität = Paracelsus Medical University (PMU), National and Kapodistrian University of Athens (NKUA), University Hospital Basel [Basel], Pôle de Recherche Clinique [Paris] (PRC), AP-HP - Hôpital Bichat - Claude Bernard [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), University College Cork (UCC), Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), CIC - CHU Bichat, Infection, Anti-microbiens, Modélisation, Evolution (IAME (UMR_S_1137 / U1137)), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Université Sorbonne Paris Nord
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Microbiology (medical) ,Medical education ,Coronavirus disease 2019 (COVID-19) ,Adaptive Clinical Trials as Topic ,EU-RESPOSE ,Financial hurdles ,Legal hurdles ,Platform trials ,Regulatory hurdles ,MEDLINE ,COVID-19 ,Context (language use) ,General Medicine ,Clinical trial ,Infectious Diseases ,Political science ,Pandemic ,Commentary ,Government Regulation ,Humans ,European Union ,Pandemics ,[SDV.MHEP]Life Sciences [q-bio]/Human health and pathology - Published
- 2022
8. Advantages of multi-arm non-randomised sequentially allocated cohort designs for Phase II oncology trials
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James Wason, Michael J. Grayling, Helen Mossop, Ferdia A. Gallagher, Grant D. Stewart, and Sarah J. Welsh
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Cancer Research ,medicine.medical_specialty ,Non-Randomized Controlled Trials as Topic ,Adaptive Clinical Trials as Topic ,Computer science ,Medical Oncology ,Interim analysis ,Phase (combat) ,Article ,Cohort Studies ,Clinical Trials, Phase II as Topic ,Treatment Outcome ,Oncology ,Research Design ,Neoplasms ,Sample Size ,Interim ,Cohort ,Randomized controlled trials ,medicine ,Humans ,Computer Simulation ,Medical physics ,Adaptive clinical trial - Abstract
Background Efficient trial designs are required to prioritise promising drugs within Phase II trials. Adaptive designs are examples of such designs, but their efficiency is reduced if there is a delay in assessing patient responses to treatment. Methods Motivated by the WIRE trial in renal cell carcinoma (NCT03741426), we compare three trial approaches to testing multiple treatment arms: (1) single-arm trials in sequence with interim analyses; (2) a parallel multi-arm multi-stage trial and (3) the design used in WIRE, which we call the Multi-Arm Sequential Trial with Efficient Recruitment (MASTER) design. The MASTER design recruits patients to one arm at a time, pausing recruitment to an arm when it has recruited the required number for an interim analysis. We conduct a simulation study to compare how long the three different trial designs take to evaluate a number of new treatment arms. Results The parallel multi-arm multi-stage and the MASTER design are much more efficient than separate trials. The MASTER design provides extra efficiency when there is endpoint delay, or recruitment is very quick. Conclusions We recommend the MASTER design as an efficient way of testing multiple promising cancer treatments in non-comparative Phase II trials.
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- 2021
9. Adaptive Clinical Trials in Pediatric Critical Care: A Systematic Review.
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Gilholm P, Ergetu E, Gelbart B, Raman S, Festa M, Schlapbach LJ, Long D, and Gibbons KS
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- Child, Humans, Research Design, Adaptive Clinical Trials as Topic, Pediatrics, Critical Care
- Abstract
Objectives: This systematic review investigates the use of adaptive designs in randomized controlled trials (RCTs) in pediatric critical care., Data Sources: PICU RCTs, published between 1986 and 2020, stored in the www.PICUtrials.net database and MEDLINE, EMBASE, CENTRAL, and LILACS databases were searched (March 9, 2022) to identify RCTs published in 2021. PICU RCTs using adaptive designs were identified through an automated full-text screening algorithm., Study Selection: All RCTs involving children (< 18 yr old) cared for in a PICU were included. There were no restrictions to disease cohort, intervention, or outcome. Interim monitoring by a Data and Safety Monitoring Board that was not prespecified to change the trial design or implementation of the study was not considered adaptive., Data Extraction: We extracted the type of adaptive design, the justification for the design, and the stopping rule used. Characteristics of the trial were also extracted, and the results summarized through narrative synthesis. Risk of bias was assessed using the Cochrane Risk of Bias Tool 2., Data Synthesis: Sixteen of 528 PICU RCTs (3%) used adaptive designs with two types of adaptations used; group sequential design and sample size reestimation. Of the 11 trials that used a group sequential adaptive design, seven stopped early due to futility and one stopped early due to efficacy. Of the seven trials that performed a sample size reestimation, the estimated sample size decreased in three trials and increased in one trial., Conclusions: Little evidence of the use of adaptive designs was found, with only 3% of PICU RCTs incorporating an adaptive design and only two types of adaptations used. Identifying the barriers to adoption of more complex adaptive trial designs is needed., Competing Interests: The authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2023 by the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies.)
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- 2023
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10. Considerations for identifying the "right" subgroup in adaptive enrichment trials.
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Simon N
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- Humans, Research Design, Adaptive Clinical Trials as Topic
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Adaptive Enrichment Trials aim to make efficient use of data in a pivotal trial of a new targeted therapy to both (a) more precisely identify who benefits from that therapy and (b) improve the likelihood of successfully concluding that the drug is effective, while controlling the probability of false positives. There are a number of frameworks for conducting such a trial and decisions that must be made regarding how to identify that target subgroup. Among those decisions, one must choose how aggressively to restrict enrollment criteria based on the accumulating evidence in the trial. In this article, we empirically evaluate the impact of aggressive versus conservative enrollment restrictions on the power of the trial to detect an effect of treatment. We identify that, in some cases, a more aggressive strategy can substantially improve power. This additionally raises an important question regarding label indication: To what degree do we need a formal test of the hypothesis of no treatment effect in the exact population implied by the label indication? We discuss this question and evaluate how our answer for adaptive enrichment trials may relate to the answer implied by current practice for broad eligibility trials.
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- 2023
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11. Adapting isotonic dose-finding to a dynamic set of drug combinations with application to a phase I leukemia trial
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Mark R. Conaway, Michael K Keng, Gina R. Petroni, Daniel R Reed, and Nolan A. Wages
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Drug ,Maximum Tolerated Dose ,media_common.quotation_subject ,Pharmacology ,Article ,Set (abstract data type) ,Dose finding ,Phase (matter) ,Isotonic ,medicine ,Humans ,Computer Simulation ,media_common ,Clinical Trials, Phase I as Topic ,Dose-Response Relationship, Drug ,Adaptive Clinical Trials as Topic ,Chemistry ,General Medicine ,medicine.disease ,Vinblastine ,Phase i study ,Drug Combinations ,Leukemia, Myeloid, Acute ,Leukemia ,Research Design ,medicine.drug - Abstract
Background/aims This article describes the proposed design of a phase I study evaluating the safety of ceramide nanoliposome and vinblastine among an initial set of 19 possible dose combinations in patients with relapsed/refractory acute myeloid leukemia and patients with untreated acute myeloid leukemia who are not candidates for intensive induction chemotherapy. Methods Extensive collaboration between statisticians and clinical investigators revealed the need to incorporate several adaptive features into the design, including the flexibility of adding or eliminating certain dose combinations based on safety criteria applied to multiple dose pairs. During the design stage, additional dose levels of vinblastine were added, increasing the dimension of the drug combination space and thus the complexity of the problem. Increased complexity made application of existing drug combination dose-finding methods unsuitable in their current form. Results Our solution to these challenges was to adapt a method based on isotonic regression to meet the research objectives of the study. Application of this adapted method is described herein, and a simulation study of the design’s operating characteristics is conducted. Conclusion The aim of this article is to bring to light examples of novel design applications as a means of augmenting the implementation of innovative designs in the future and to demonstrate the flexibility of adaptive designs in satisfying changing design conditions.
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- 2021
12. A Bayesian response-adaptive dose-finding and comparative effectiveness trial
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Naveen Poonai, Petros Pechlivanoglou, Eleanor Pullenayegum, Terry P. Klassen, Anna Heath, Maryna Yaskina, David Rios, and Martin Offringa
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medicine.medical_specialty ,Bayesian probability ,Bayesian analysis ,03 medical and health sciences ,Dose finding ,0302 clinical medicine ,Humans ,Medicine ,Medical physics ,030212 general & internal medicine ,Child ,Randomized Controlled Trials as Topic ,Pharmacology ,Response-adaptive trial ,non-inferiority trial ,Dose-Response Relationship, Drug ,Adaptive Clinical Trials as Topic ,business.industry ,Clinical study design ,Bayes Theorem ,General Medicine ,clinical trial design ,3. Good health ,Sample Size ,030220 oncology & carcinogenesis ,Non inferiority trial ,Ketamine ,business ,Dexmedetomidine - Abstract
Background/Aims: Combinations of treatments that have already received regulatory approval can offer additional benefit over Each of the treatments individually. However, trials of these combinations are lower priority than those that develop novel therapies, which can restrict funding, timelines and patient availability. This article develops a novel trial design to facilitate the evaluation of New combination therapies. This trial design combines elements of phase II and phase III trials to reduce the burden of evaluating combination therapies, while also maintaining a feasible sample size. This design was developed for a randomised trial that compares the properties of three combination doses of ketamine and dexmedetomidine, given intranasally, to ketamine delivered intravenously for children undergoing a closed reduction for a fracture or dislocation. Methods: This trial design uses response-adaptive randomisation to evaluate different dose combinations and increase the information collected for successful novel drug combinations. The design then uses Bayesian dose-response modelling to undertake a comparative effectiveness analysis for the most successful dose combination against a relevant comparator. We used simulation methods determine the thresholds for adapting the trial and making conclusions. We also used simulations to evaluate the probability of selecting the dose combination with the highest true effectiveness the operating characteristics of the design and its Bayesian predictive power. Results: With 410 participants, five interim updates of the randomisation ratio and a probability of effectiveness of 0.93, 0.88 and 0.83 for the three dose combinations, we have an 83% chance of randomising the largest number of patients to the drug with the highest probability of effectiveness. Based on this adaptive randomisation procedure, the comparative effectiveness analysis has a type I error of less than 5% and a 93% chance of correcting concluding non-inferiority, when the probability of effectiveness for the optimal combination therapy is 0.9. In this case, the trial has a greater than 77% chance of meeting its dual aims of dose-finding and comparative effectiveness. Finally, the Bayesian predictive power of the trial is over 90%. Conclusions: By simultaneously determining the optimal dose and collecting data on the relative effectiveness of an intervention, we can minimise administrative burden and recruitment time for a trial. This will minimise the time required to get effective, safe combination therapies to patients quickly. The proposed trial has high potential to meet the dual study objectives within a feasible overall sample size.
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- 2020
13. A pragmatic adaptive enrichment design for selecting the right target population for cancer immunotherapies
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Claude Berge, Anh Nguyen Duc, Dominik Heinzmann, and Marcel Wolbers
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FOS: Computer and information sciences ,Statistics and Probability ,Oncology ,medicine.medical_specialty ,Target population ,Statistics - Applications ,01 natural sciences ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,Breast cancer ,Neoplasms ,Interim ,Internal medicine ,Pragmatic Clinical Trials as Topic ,medicine ,Humans ,Applications (stat.AP) ,Pharmacology (medical) ,030212 general & internal medicine ,0101 mathematics ,Triple-negative breast cancer ,Selection (genetic algorithm) ,Pharmacology ,Adaptive Clinical Trials as Topic ,business.industry ,Cancer ,medicine.disease ,Interim analysis ,Research Design ,Biomarker (medicine) ,Immunotherapy ,business ,Biomarkers - Abstract
One of the challenges in the design of confirmatory trials is to deal with uncertainties regarding the optimal target population for a novel drug. Adaptive enrichment designs (AED) which allow for a data-driven selection of one or more pre-specified biomarker subpopulations at an interim analysis have been proposed in this setting but practical case studies of AEDs are still relatively rare. We present the design of an AED with a binary endpoint in the highly dynamic setting of cancer immunotherapy. The trial was initiated as a conventional trial in early triple-negative breast cancer but amended to an AED based on emerging data external to the trial suggesting that PD-L1 status could be a predictive biomarker. Operating characteristics are discussed including the concept of a minimal detectable difference, that is, the smallest observed treatment effect that would lead to a statistically significant result in at least one of the target populations at the interim or the final analysis, respectively, in the setting of AED., 10 pages, 1 figures
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- 2020
14. Bayesian Semi-parametric Design (BSD) for adaptive dose-finding with multiple strata
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Rachael Liu, Hongyu Zhao, Mo Li, Jianchang Lin, and Veronica Bunn
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Statistics and Probability ,Computer science ,Bayesian probability ,Antineoplastic Agents ,computer.software_genre ,Neoplasms ,Prior probability ,Humans ,Computer Simulation ,Drug Dosage Calculations ,Pharmacology (medical) ,Event (probability theory) ,Pharmacology ,Models, Statistical ,Dose-Response Relationship, Drug ,Adaptive Clinical Trials as Topic ,Bayes Theorem ,Semiparametric model ,Dirichlet process ,Identification (information) ,Research Design ,Sample size determination ,Data Interpretation, Statistical ,Maximum tolerated dose ,Data mining ,computer - Abstract
In the era of precision medicine, it is of increasing interest to consider multiple strata (e.g. indications, regions, or subgroups) within a single oncology dose-finding study when identifying the maximum tolerated dose (MTD). We propose two Bayesian semi-parametric designs (BSD) for dose-finding with multiple strata to allow for both adaptively dosing patients based on various toxicity profiles and efficient identification of the MTD for each stratum. We develop non-parametric priors based on the Dirichlet process to allow for a flexible prior distribution and negate the need for a pre-specified exchangeability parameter. The two BSD models are built under different prior beliefs of strata heterogeneity and allow for appropriate borrowing of information across similar strata. Simulation studies are performed to evaluate the BSD model performance by comparing it with existing methods, including the fully stratified, exchangeability, and exchangeability-non-exchangeability models. In general, our BSD models outperform the competing methods in correctly identifying the MTD for different strata and necessitate a smaller sample size to determine the MTD. The BSD models are robust to various heterogeneity assumptions and can be easily extended to other binary and time to event endpoints.
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- 2020
15. How can health economics be used in the design and analysis of adaptive clinical trials? A qualitative analysis
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Steven A. Julious, Daniel Hind, Alan Brennan, Laura Flight, and Susan Todd
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Adult ,Male ,Value of information ,Technology Assessment, Biomedical ,Process management ,Cost effectiveness ,Cost-Benefit Analysis ,Medicine (miscellaneous) ,Recommendations ,Interviews as Topic ,03 medical and health sciences ,0302 clinical medicine ,Interim ,Humans ,Medicine ,Pharmacology (medical) ,030212 general & internal medicine ,Qualitative Research ,Cost database ,lcsh:R5-920 ,Health economics ,Adaptive Clinical Trials as Topic ,business.industry ,Research ,030503 health policy & services ,Adaptive design ,Health technology ,Focus Groups ,Middle Aged ,Focus group ,Research Personnel ,United Kingdom ,Clinical trial ,Female ,Cost-effectiveness ,0305 other medical science ,business ,lcsh:Medicine (General) ,Models, Econometric ,Qualitative research - Abstract
IntroductionAdaptive 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.MethodsA 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.ResultsIt 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.ConclusionThe 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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- 2020
16. The Power Derivative Principle, and Its Application to How and When to Perform a One-Shot Unblinded Reassessment Sample Size
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Elodie Blondiaux and Eric Derobert
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One shot ,Adaptive Clinical Trials as Topic ,Computer science ,Public Health, Environmental and Occupational Health ,Derivative ,Power (physics) ,Clinical trial ,Research Design ,Control theory ,Sample size determination ,Sample Size ,Interim ,Pharmacology (medical) ,Pharmacology, Toxicology and Pharmaceutics (miscellaneous) - Abstract
In adaptive two-group clinical trials, a current method is to perform a one-shot unblinded sample size reassessment. Whereas the interim unblinded look of the data is adjusted for inference by using the weighted Cui-Hung-Wang statistics, some questions remain: how and when to reassess the sample size?We define the Power Derivative Principle as follows: a sample size is optimal when the derivative of the power with respect to the sample size has reached some implicit value. Applied to two-group clinical trials, this Power Derivative Principle determines a new one-shot unblinded sample size reassessment rule (including the determination of futility bounds). A full Power Derivative Strategy induces furthermore an optimal information fraction for the interim analysis. The Power Derivative Strategy is then compared to adaptive design methods proposed in the literature and to group sequential strategies. For this comparison, we used, on the one hand, the very common information fraction f = 0.5 and, on the other hand, the information fraction found as being optimal with the Power Derivative Principle.The optimal information fraction depends only on α-and β-risks. For usual values of these risks, the optimal information fraction value is very close to 0.9. Moreover, with this unexpected optimal value, reassessment methods become roughly comparable (it is definitely not the case when f=0.5).Our results suggest that a sample size reassessment is more beneficial when considered close to the planned end of a trial, allowing a study with borderline interim results to be saved.
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- 2020
17. Advantages of multi-arm non-randomised sequentially allocated cohort designs for Phase II oncology trials
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Mossop, Helen, Grayling, Michael J, Gallagher, Ferdia A, Welsh, Sarah J, Stewart, Grant D, Wason, James MS, Grayling, Michael J [0000-0002-0680-6668], Gallagher, Ferdia A [0000-0003-4784-5230], Welsh, Sarah J [0000-0001-5690-2677], Stewart, Grant D [0000-0003-3188-9140], Wason, James MS [0000-0002-4691-126X], and Apollo - University of Cambridge Repository
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Cohort Studies ,Clinical Trials, Phase II as Topic ,Treatment Outcome ,Non-Randomized Controlled Trials as Topic ,Adaptive Clinical Trials as Topic ,Research Design ,Neoplasms ,Sample Size ,Humans ,Computer Simulation ,Medical Oncology - Abstract
Funder: RCUK | MRC | Medical Research Foundation; doi: https://doi.org/10.13039/501100009187, Funder: NIHR Biomedical Research Centre BRC-1215-20014, BACKGROUND: Efficient trial designs are required to prioritise promising drugs within Phase II trials. Adaptive designs are examples of such designs, but their efficiency is reduced if there is a delay in assessing patient responses to treatment. METHODS: Motivated by the WIRE trial in renal cell carcinoma (NCT03741426), we compare three trial approaches to testing multiple treatment arms: (1) single-arm trials in sequence with interim analyses; (2) a parallel multi-arm multi-stage trial and (3) the design used in WIRE, which we call the Multi-Arm Sequential Trial with Efficient Recruitment (MASTER) design. The MASTER design recruits patients to one arm at a time, pausing recruitment to an arm when it has recruited the required number for an interim analysis. We conduct a simulation study to compare how long the three different trial designs take to evaluate a number of new treatment arms. RESULTS: The parallel multi-arm multi-stage and the MASTER design are much more efficient than separate trials. The MASTER design provides extra efficiency when there is endpoint delay, or recruitment is very quick. CONCLUSIONS: We recommend the MASTER design as an efficient way of testing multiple promising cancer treatments in non-comparative Phase II trials.
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- 2022
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18. TITE‐BOIN‐ET: Time‐to‐event Bayesian optimal interval design to accelerate dose‐finding based on both efficacy and toxicity outcomes
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Masataka Taguri, Satoshi Morita, and Kentaro Takeda
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Statistics and Probability ,Time Factors ,Endpoint Determination ,Computer science ,Bayesian probability ,Antineoplastic Agents ,Interval (mathematics) ,Machine learning ,computer.software_genre ,01 natural sciences ,Outcome (game theory) ,010104 statistics & probability ,03 medical and health sciences ,Clinical Trials, Phase II as Topic ,0302 clinical medicine ,Neoplasms ,Humans ,Pharmacology (medical) ,030212 general & internal medicine ,0101 mathematics ,Duration (project management) ,Event (probability theory) ,Pharmacology ,Models, Statistical ,Clinical Trials, Phase I as Topic ,Dose-Response Relationship, Drug ,Adaptive Clinical Trials as Topic ,business.industry ,Nonparametric statistics ,Bayes Theorem ,Clinical trial ,Treatment Outcome ,Drug development ,Research Design ,Data Interpretation, Statistical ,Artificial intelligence ,business ,computer - Abstract
One of the primary purposes of an oncology dose-finding trial is to identify an optimal dose (OD) that is both tolerable and has an indication of therapeutic benefit for subjects in subsequent clinical trials. In addition, it is quite important to accelerate early stage trials to shorten the entire period of drug development. However, it is often challenging to make adaptive decisions of dose escalation and de-escalation in a timely manner because of the fast accrual rate, the difference of outcome evaluation periods for efficacy and toxicity and the late-onset outcomes. To solve these issues, we propose the time-to-event Bayesian optimal interval design to accelerate dose-finding based on cumulative and pending data of both efficacy and toxicity. The new design, named "TITE-BOIN-ET" design, is nonparametric and a model-assisted design. Thus, it is robust, much simpler, and easier to implement in actual oncology dose-finding trials compared with the model-based approaches. These characteristics are quite useful from a practical point of view. A simulation study shows that the TITE-BOIN-ET design has advantages compared with the model-based approaches in both the percentage of correct OD selection and the average number of patients allocated to the ODs across a variety of realistic settings. In addition, the TITE-BOIN-ET design significantly shortens the trial duration compared with the designs without sequential enrollment and therefore has the potential to accelerate early stage dose-finding trials.
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- 2019
19. Efficacy and safety of dexmedetomidine for prevention of withdrawal syndrome in the pediatric intensive care unit: protocol for an adaptive, multicenter, randomized, double-blind, placebo-controlled, non-profit clinical trial
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Aldo Mancino, Marco Daverio, Dario Gregori, Maria Francesca Caligiuri, Francesca Sperotto, Andrea Pettenazzo, Angela Amigoni, Fabio Caramelli, Maria Teresa Cecini, Marco Piastra, Maria Cristina Mondardini, Giorgio Conti, and Francesca Vitale
- Subjects
Male ,Time Factors ,Abstinence syndrome ,Medicine (miscellaneous) ,Study Protocol ,Benzodiazepines ,0302 clinical medicine ,Clinical endpoint ,Adrenergic alpha-2 Receptor Agonists ,Hypnotics and Sedatives ,Multicenter Studies as Topic ,Pharmacology (medical) ,030212 general & internal medicine ,Child ,Infusions, Intravenous ,Randomized Controlled Trials as Topic ,Pediatric intensive care unit ,lcsh:R5-920 ,Adaptive Clinical Trials as Topic ,Age Factors ,Drug Tolerance ,Substance Withdrawal Syndrome ,Analgesics, Opioid ,Treatment Outcome ,Italy ,Anesthesia ,Child, Preschool ,Sedation ,Female ,medicine.symptom ,lcsh:Medicine (General) ,Dexmedetomidine ,medicine.drug ,Adolescent ,Placebo ,Intensive Care Units, Pediatric ,Drug Administration Schedule ,03 medical and health sciences ,Double-Blind Method ,Withdrawal syndrome ,030225 pediatrics ,Settore MED/41 - ANESTESIOLOGIA ,medicine ,Weaning ,Humans ,Adverse effect ,business.industry ,Infant, Newborn ,Infant ,Opioid-Related Disorders ,Clinical trial ,Analgesia ,business - Abstract
Background Prolonged treatment with analgesic and sedative drugs in the pediatric intensive care unit (PICU) may lead to undesirable effects such as dependence and tolerance. Moreover, during analgosedation weaning, patients may develop clinical signs of withdrawal, known as withdrawal syndrome (WS). Some studies indicate that dexmedetomidine, a selective α2-adrenoceptor agonist, may be useful to prevent WS, but no clear evidence supports these data. The aims of the present study are to evaluate the efficacy of dexmedetomidine in reducing the occurrence of WS during analgosedation weaning, and to clearly assess its safety. Methods We will perform an adaptive, multicenter, randomized, double-blind, placebo-controlled trial. Patients aged N = 154 patients). The study was approved by the Ethics Committee of the University-Hospital S.Orsola-Malpighi of Bologna on 22 March 2017. Discussion The present trial will allow us to clearly assess the efficacy of dexmedetomidine in reducing the occurrence of WS during weaning from analgosedation drugs. In addition, the study will provide a unique insight into the safety profile of dexmedetomidine. Trial registration ClinicalTrials.gov, NCT03645603. Registered on 24 August 2018. EudraCT, 2015–002114-80. Retrospectively registered on 2 January 2019.
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- 2019
20. Ensuring the Scientific Value and Feasibility of Clinical Trials: A Qualitative Interview Study.
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Morrell W, Gelinas L, Zarin D, and Bierer BE
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- Humans, Ethics Committees, Research, Feasibility Studies, Qualitative Research, Adaptive Clinical Trials as Topic, Clinical Trials Data Monitoring Committees, Research Design
- Abstract
Background: Ethical and scientific principles require that clinical trials address an important question and have the resources needed to complete the study. However, there are no clear standards for review that would ensure that these principles are upheld., Methods: We conducted semi-structured interviews with a convenience sample of nineteen experts in clinical trial design, conduct, and/or oversight to elucidate current practice and identify areas of need with respect to ensuring the scientific value and feasibility of clinical trials prior to initiation and while ongoing. We used a priori and grounded theory to analyze the data and constant comparative method to induce higher order themes., Results: Interviewees perceived determination of scientific value as the responsibility of the investigator and, secondarily, other parties who review or oversee research. Interviewees reported that ongoing trials are rarely reevaluated due to emerging evidence from external sources, evaluation is complex, and there would be value in the development of standards for monitoring and evaluating evidence systematically. Investigators, IRBs, and/or data monitoring committees (DMCs) could undertake these responsibilities. Feasibility assessments are performed but are typically inadequate; potential solutions are unclear., Conclusions: There are three domains where current approaches are suboptimal and in which further guidance is needed. First, who has the responsibility for conducting scientific review, whether it be the investigator, IRB, and/or DMC is often unclear. Second, the standards for scientific review (e.g., appropriate search terms, data sources, and analytic plan) should be defined. Third, guidance is needed on the evaluation of ongoing studies in light of potentially new and evolving evidence, with particular reference to evidence from outside the trial itself.
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- 2023
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21. Borrowing Concurrent Information from Non-Concurrent Control to Enhance Statistical Efficiency in Platform Trials.
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Liu J, Lu C, Jiang Z, Alemayehu D, Nie L, and Chu H
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- Humans, Research Design, Adaptive Clinical Trials as Topic
- Abstract
A platform trial is a trial involving an innovative adaptive design with a single master protocol to efficiently evaluate multiple interventions. It offers flexible features such as dropping interventions for futility and adding new interventions to be evaluated during the course of a trial. Although there is a consensus that platform trials can identify beneficial interventions with fewer patients, less time, and a higher probability of success than traditional trials, there remains debate on certain issues, one of which is whether (and how) the non-concurrent control (NCC) (i.e., patients in the control group recruited prior to the new interventions) can be combined with the current control (CC) in the analysis, especially if there is a change of standard of care during the trial., Methods: In this paper, considering time-to-event endpoints under the proportional hazard model assumption, we introduce a new concept of NCC concurrent observation time (NCC COT), and propose to borrow NCC COT through left truncation. This assumes that the NCC COT and CC are comparable. If the protocol does not prohibit NCC patients to change the standard of care while on study, NCC COT and CC likely will share the same standard of care. A simulated example is provided to demonstrate the approach., Results: Using exponential distributions, the simulated example assumes that NCC COT and CC have the same hazard, and the treatment group has a lower hazard. The estimated HR comparing treatment to the pooled control group is 0.744 (95% CI 0.575, 0.962), whereas the comparison to the CC group alone is 0.755 (95% CI 0.566, 1.008), with corresponding p -values of 0.024 versus 0.057, respectively. This suggests that borrowing NCC COT can improve statistical efficiency when the exchangeability assumption holds., Conclusion: This article proposes an innovative approach of borrowing NCC COT to enhance statistical inference in platform trials under appropriate scenarios.
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- 2023
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22. Mobile App Interventions to Improve Medication Adherence Among Type 2 Diabetes Mellitus Patients: A Systematic Review of Clinical Trials.
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Shrivastava TP, Goswami S, Gupta R, and Goyal RK
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- Aged, Humans, Middle Aged, Medication Adherence, Research Design, Adaptive Clinical Trials as Topic, Diabetes Mellitus, Type 2 drug therapy, Mobile Applications
- Abstract
Background: Medication adherence in type 2 diabetes mellitus (T2DM) patients is often suboptimal resulting in complications. There has been a growing interest in using mobile apps for improving medication adherence., Objective: The objective of this work was to systematically review the clinical trials that have used mobile app-based interventions in T2DM patients for improving medication adherence., Methodology: A systematic search was performed to identify published clinical trials between January 2008 and December 2020 in databases-PubMed, Cochrane Library, and Google Scholar. All studies were assessed for risk of bias using quality rating tool from the Cochrane Handbook for Systematic Reviews of Interventions., Results: Seven clinical studies having 649 participants were studied. The median sample size was 58 (range = 41-247) and the median age of participants was 53.2 (range = 48-69.4) years. All studies showed improvements in adherence; however, only three studies reported statically significant improvements in adherence measures. Selected studies were deemed as unclear in their risk of bias and the most common source of risk of bias among the studies was the absence of objective outcome assessment., Conclusions: Mobile apps appear to be effective interventions to help improve medication adherence in T2DM patients compared with conventional care strategies. The features of the App to improvise medical adherence cannot be defined based on the meta-analysis because of heterogeneity of study designs and less number of sample size. Systematically planned studies would set up applicability of mobile apps in the clinical management of T2DM.
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- 2023
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23. Inference in response-adaptive clinical trials when the enrolled population varies over time.
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Russo M, Ventz S, Wang V, and Trippa L
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- Humans, Research Design, Adaptive Clinical Trials as Topic
- Abstract
A common assumption of data analysis in clinical trials is that the patient population, as well as treatment effects, do not vary during the course of the study. However, when trials enroll patients over several years, this hypothesis may be violated. Ignoring variations of the outcome distributions over time, under the control and experimental treatments, can lead to biased treatment effect estimates and poor control of false positive results. We propose and compare two procedures that account for possible variations of the outcome distributions over time, to correct treatment effect estimates, and to control type-I error rates. The first procedure models trends of patient outcomes with splines. The second leverages conditional inference principles, which have been introduced to analyze randomized trials when patient prognostic profiles are unbalanced across arms. These two procedures are applicable in response-adaptive clinical trials. We illustrate the consequences of trends in the outcome distributions in response-adaptive designs and in platform trials, and investigate the proposed methods in the analysis of a glioblastoma study., (© 2021 The International Biometric Society.)
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- 2023
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24. Protocol for a cohort study to evaluate the effectiveness and cost-effectiveness of general population screening for cardiovascular disease: the Viborg Screening Programme (VISP).
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Høgh A, Lindholt JS, Søgaard R, Refsgaard J, Svenstrup D, Moeslund NJ, Bredsgaard M, and Dahl M
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- Female, Humans, Male, Cohort Studies, Cost-Benefit Analysis, Adaptive Clinical Trials as Topic, Cardiovascular Diseases diagnosis, Cardiovascular Diseases prevention & control, Hypertension, Peripheral Vascular Diseases
- Abstract
Introduction: The prevalence of cardiovascular disease (CVD) is increasing. Furthermore, asymptomatic individuals may not receive timely preventive initiatives to minimise the risk of further CVD events. Paradoxically, 80% of CVD events are preventable by early detection, followed by prophylactic initiatives. Consequently, we introduced the population-based Viborg Screening Programme (VISP) for subclinical and manifest CVD, focusing on commonly occurring, mainly asymptomatic conditions, followed by prophylactic initiatives.The aim of the VISP was to evaluate the health benefits, harms and cost-effectiveness of the VISP from a healthcare sector perspective. Furthermore, we explored the participants' perspectives., Methods and Analysis: From August 2014 and currently ongoing, approximately 1100 men and women from the Viborg municipality, Denmark, are annually invited to screening for abdominal aortic aneurysm, peripheral arterial disease, carotid plaque, hypertension, diabetes mellitus and cardiac arrhythmia on their 67th birthday. A population from the surrounding municipalities without access to the VISP acts as a control. The VISP invitees and the controls are followed on the individual level by nationwide registries. The primary outcome is all-cause mortality, while costs, hospitalisations and deaths from CVD are the secondary endpoints.Interim evaluations of effectiveness and cost-effectiveness are planned every 5 years using propensity score matching followed by a Cox proportional hazards regression analysis by the 'intention-to-treat' principle. Furthermore, censoring-adjusted incremental costs, life-years and quality-adjusted life-years are estimated. Finally, the participants' perspectives are explored by semistructured face-to-face interviews, with participant selection representing participants with both negative and positive screening results., Ethics and Dissemination: The VISP is not an interventional trial. Therefore, approval from a regional scientific ethical committee is not needed. Data collection from national registries was approved by the Regional Data Protection Agency (record no. 1-16-02-232-15). We ensure patient and public involvement in evaluating the acceptability of VISP by adopting an interviewing approach in the study., Trial Registration Number: NCT03395509., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
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- 2023
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25. Two‐stage enrichment clinical trial design with adjustment for misclassification in predictive biomarkers
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Weichung Shih, Yong Lin, and Shou-En Lu
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Statistics and Probability ,Lung Neoplasms ,Epidemiology ,Computer science ,Biostatistics ,01 natural sciences ,Article ,Cohort Studies ,010104 statistics & probability ,03 medical and health sciences ,Antineoplastic Agents, Immunological ,0302 clinical medicine ,Carcinoma, Non-Small-Cell Lung ,Statistics ,Humans ,030212 general & internal medicine ,0101 mathematics ,Stage (cooking) ,Predictive biomarker ,Models, Statistical ,Adaptive Clinical Trials as Topic ,Clinical study design ,Progression-Free Survival ,Sample Size ,Classification rule ,Adaptive design ,Cohort ,Immunotherapy ,Biomarkers ,Type I and type II errors - Abstract
A two-stage enrichment design is a type of adaptive design, which extends a stratified design with a futility analysis on the marker negative cohort at the first stage, and the second stage can be either a targeted design with only the marker positive stratum, or still the stratified design with both marker strata, depending on the result of the interim futility analysis. In this paper we consider the situation where the marker assay and the classification rule are possibly subject to error. We derive the sequential tests for the global hypothesis as well as the component tests for the overall cohort and the marker-positive cohort. We discuss the power analysis with the control of the type-I error rate and show the adverse impact of the misclassification on the powers. We also show the enhanced power of the two-stage enrichment over the one-stage design, and illustrate with examples of the recent successful development of immunotherapy in non-small-cell lung cancer.
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- 2019
26. An adaptive trial design to optimize dose‐schedule regimes with delayed outcomes
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Peter F. Thall, Ruitao Lin, and Ying Yuan
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Statistics and Probability ,Biometry ,Randomization ,Computer science ,Adaptive randomization ,Outcome (game theory) ,Article ,Drug Administration Schedule ,General Biochemistry, Genetics and Molecular Biology ,Dose schedule ,Clinical Trials, Phase II as Topic ,Outcome Assessment, Health Care ,Statistics ,Humans ,Bayesian hierarchical modeling ,Computer Simulation ,Sensitivity (control systems) ,Decision Making, Computer-Assisted ,Models, Statistical ,Clinical Trials, Phase I as Topic ,Dose-Response Relationship, Drug ,General Immunology and Microbiology ,Adaptive Clinical Trials as Topic ,Applied Mathematics ,Clinical study design ,Bayes Theorem ,General Medicine ,Missing data ,Sample Size ,General Agricultural and Biological Sciences - Abstract
This paper proposes a two-stage phase I-II clinical trial design to optimize dose-schedule regimes of an experimental agent within ordered disease subgroups in terms of the toxicity-efficacy trade-off. The design is motivated by settings where prior biological information indicates it is certain that efficacy will improve with ordinal subgroup level. We formulate a flexible Bayesian hierarchical model to account for associations among subgroups and regimes, and to characterize ordered subgroup effects. Sequentially adaptive decision-making is complicated by the problem, arising from the motivating application, that efficacy is scored on day 90 and toxicity is evaluated within 30 days from the start of therapy, while the patient accrual rate is fast relative to these outcome evaluation intervals. To deal with this in a practical manner, we take a likelihood-based approach that treats unobserved toxicity and efficacy outcomes as missing values, and use elicited utilities that quantify the efficacy-toxicity trade-off as a decision criterion. Adaptive randomization is used to assign patients to regimes while accounting for subgroups, with randomization probabilities depending on the posterior predictive distributions of utilities. A simulation study is presented to evaluate the design's performance under a variety of scenarios, and to assess its sensitivity to the amount of missing data, the prior, and model misspecification.
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- 2019
27. Quantification of prior impact in terms of effective current sample size
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Manuel Wiesenfarth and Silvia Calderazzo
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Statistics and Probability ,Biometry ,Computer science ,Bayesian probability ,Machine learning ,computer.software_genre ,01 natural sciences ,General Biochemistry, Genetics and Molecular Biology ,010104 statistics & probability ,03 medical and health sciences ,Current sample ,Equating ,Prior probability ,Humans ,Computer Simulation ,0101 mathematics ,Prior information ,030304 developmental biology ,0303 health sciences ,Measure (data warehouse) ,Models, Statistical ,General Immunology and Microbiology ,Adaptive Clinical Trials as Topic ,business.industry ,Applied Mathematics ,Bayes Theorem ,General Medicine ,Data model ,Sample size determination ,Data Interpretation, Statistical ,Sample Size ,Artificial intelligence ,General Agricultural and Biological Sciences ,business ,computer - Abstract
Bayesian methods allow borrowing of historical information through prior distributions. The concept of prior effective sample size (prior ESS) facilitates quantification and communication of such prior information by equating it to a sample size. Prior information can arise from historical observations; thus, the traditional approach identifies the ESS with such a historical sample size. However, this measure is independent of newly observed data, and thus would not capture an actual "loss of information" induced by the prior in case of prior-data conflict. We build on a recent work to relate prior impact to the number of (virtual) samples from the current data model and introduce the effective current sample size (ECSS) of a prior, tailored to the application in Bayesian clinical trial designs. Special emphasis is put on robust mixture, power, and commensurate priors. We apply the approach to an adaptive design in which the number of recruited patients is adjusted depending on the effective sample size at an interim analysis. We argue that the ECSS is the appropriate measure in this case, as the aim is to save current (as opposed to historical) patients from recruitment. Furthermore, the ECSS can help overcome lack of consensus in the ESS assessment of mixture priors and can, more broadly, provide further insights into the impact of priors. An R package accompanies the paper.
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- 2019
28. A response‐adaptive randomization procedure for multi‐armed clinical trials with normally distributed outcomes
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S. Faye Williamson and Sofia S. Villar
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Statistics and Probability ,Gittins index ,Mathematical optimization ,Biometry ,Patient Dropouts ,Biometrics ,Computer science ,Endpoint Determination ,Context (language use) ,01 natural sciences ,General Biochemistry, Genetics and Molecular Biology ,010104 statistics & probability ,03 medical and health sciences ,Response adaptive randomization ,missing data ,Clinical Trials, Phase II as Topic ,adaptive designs ,Neoplasms ,Humans ,Computer Simulation ,0101 mathematics ,030304 developmental biology ,Randomized Controlled Trials as Topic ,0303 health sciences ,Models, Statistical ,General Immunology and Microbiology ,Randomization Procedure ,Adaptive Clinical Trials as Topic ,Applied Mathematics ,General Medicine ,Variance (accounting) ,Missing data ,3. Good health ,unknown variance ,Clinical trial ,Treatment Outcome ,dichotomization ,Biometric Methodology ,continuous endpoint ,General Agricultural and Biological Sciences ,Algorithms - Abstract
We propose a novel response‐adaptive randomization procedure for multi‐armed trials with continuous outcomes that are assumed to be normally distributed. Our proposed rule is non‐myopic, and oriented toward a patient benefit objective, yet maintains computational feasibility. We derive our response‐adaptive algorithm based on the Gittins index for the multi‐armed bandit problem, as a modification of the method first introduced in Villar et al. (Biometrics, 71, pp. 969‐978). The resulting procedure can be implemented under the assumption of both known or unknown variance. We illustrate the proposed procedure by simulations in the context of phase II cancer trials. Our results show that, in a multi‐armed setting, there are efficiency and patient benefit gains of using a response‐adaptive allocation procedure with a continuous endpoint instead of a binary one. These gains persist even if an anticipated low rate of missing data due to deaths, dropouts, or complete responses is imputed online through a procedure first introduced in this paper. Additionally, we discuss how there are response‐adaptive designs that outperform the traditional equal randomized design both in terms of efficiency and patient benefit measures in the multi‐armed trial context.
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- 2019
29. Response adaptive randomization procedures in seamless phase II/III clinical trials
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J. Jack Lee, Feifang Hu, Li-Xin Zhang, Jin Piao, and Hongjian Zhu
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Statistics and Probability ,Mathematical optimization ,Computer science ,Process (engineering) ,Asymptotic distribution ,01 natural sciences ,Article ,010104 statistics & probability ,03 medical and health sciences ,Clinical Trials, Phase II as Topic ,0302 clinical medicine ,Frequentist inference ,Statistical inference ,Humans ,Pharmacology (medical) ,030212 general & internal medicine ,0101 mathematics ,Randomized Controlled Trials as Topic ,Pharmacology ,Models, Statistical ,Adaptive Clinical Trials as Topic ,Estimator ,Clinical trial ,Clinical Trials, Phase III as Topic ,Research Design ,Data Interpretation, Statistical ,Martingale (probability theory) ,Type I and type II errors - Abstract
It is desirable to work efficiently and cost effectively to evaluate new therapies in a time-sensitive and ethical manner without compromising the integrity and validity of the development process. The seamless phase II/III clinical trial has been proposed to meet this need, and its efficient, ethical and economic advantages can be strengthened by its combination with innovative response adaptive randomization (RAR) procedures. In particular, well-designed frequentist RAR procedures can target theoretically optimal allocation proportions, and there are explicit asymptotic results. However, there has been little research into seamless phase II/III clinical trials with frequentist RAR because of the difficulty in performing valid statistical inference and controlling the type I error rate. In this paper, we propose the framework for a family of frequentist RAR designs for seamless phase II/III trials, derive the asymptotic distribution of the parameter estimators using martingale processes and offer solutions to control the type I error rate. The numerical studies demonstrate our theoretical findings and the advantages of the proposed methods.
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- 2019
30. Dose optimisation with simultaneous pharmacokinetic estimation in adaptive clinical trials
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Barbara Bogacka, Byron Jones, and Kabir Soeny
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Statistics and Probability ,Drug ,Multiple dose regimen ,Epidemiology ,media_common.quotation_subject ,Pharmacology ,Target concentration ,01 natural sciences ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,Health Information Management ,Pharmacokinetics ,Humans ,Medicine ,Computer Simulation ,030212 general & internal medicine ,0101 mathematics ,Drug toxicity ,media_common ,Dose-Response Relationship, Drug ,Adaptive Clinical Trials as Topic ,business.industry ,Clinical trial ,Research Design ,Dose optimisation ,business - Abstract
Determination of the optimal dose is a critical objective in the drug developmental process. An optimal dose prevents over- and under-exposure to the treatment drug thereby facilitating superior patient experience and reduced costs to the healthcare system. In this paper, we present a method for model-based dose optimisation with simultaneous pharmacokinetic estimation of the model parameters. Multiple doses of the drug are considered and the objective is to maintain the blood concentration of the drug around a pre-decided target concentration. We consider an adaptive setting wherein the model parameters are estimated from the blood samples collected at D-optimal time points from all subjects enrolled so far in the trial. The estimated parameters are then used to determine the optimal dose regimen for the next cohort. This procedure continues until the condition of a pre-decided stopping rule is met. Simulation studies and sensitivity analysis are undertaken to validate the methodology. We also evaluate the performance of the methodology when carried out in a non-adaptive setting. A two-stage design is then presented which combines the advantages of the adaptive as well as the non-adaptive approach. We demonstrate that our methodology enables pharmacokinetic estimation and dose regimen optimisation simultaneously in an ethical and cost-effective manner protecting the subjects from the ill-effects of suboptimal dose regimens and economising the number of subjects required in the trial.
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- 2019
31. Two-stage adaptive enrichment design for testing an active factor
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A. Adam Ding, Samuel S. Wu, Natalie E. Dean, and Rachel S. Zahigian
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Statistics and Probability ,Computer science ,Design for testing ,Catechol O-Methyltransferase ,Polymorphism, Single Nucleotide ,01 natural sciences ,Article ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,Shoulder Pain ,Factor (programming language) ,Humans ,Pharmacology (medical) ,030212 general & internal medicine ,0101 mathematics ,computer.programming_language ,Pharmacology ,Models, Statistical ,Adaptive Clinical Trials as Topic ,Catastrophization ,Reliability engineering ,Research Design ,Data Interpretation, Statistical ,Stage (hydrology) ,computer - 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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- 2019
32. Optimal adaptive single-arm phase II trials under quantified uncertainty
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Meinhard Kieser and Kevin Kunzmann
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Statistics and Probability ,Optimal design ,Mathematical optimization ,Time Factors ,Computer science ,Bayesian probability ,Binary number ,Score ,01 natural sciences ,Phase (combat) ,010104 statistics & probability ,03 medical and health sciences ,Clinical Trials, Phase II as Topic ,0302 clinical medicine ,Humans ,Pharmacology (medical) ,030212 general & internal medicine ,0101 mathematics ,Pharmacology ,Adaptive Clinical Trials as Topic ,Uncertainty ,Bayes Theorem ,Power (physics) ,Treatment Outcome ,Research Design ,Sample size determination ,Data Interpretation, Statistical ,Face (geometry) ,Early Termination of Clinical Trials - Abstract
Single-arm trials with binary endpoint are firmly established in e.g., early clinical oncology. Here, two-stage designs are often employed to allow early termination of the trial in the case of an unexpectedly large or small response rate to the new treatment. Various designs have been proposed over the last few years which usually require strong assumptions about the true response rate during planning. Often, these designs are not robust to deviations from the planning assumptions. In this paper, we define a Bayesian framework for scoring two-stage designs under uncertainty and investigate the characteristics of designs optimizing a commonly employed performance score of Liu et al. The resulting optimal designs are compared with an alternative, utility-based approach incorporating expected power and sample size. We provide insights in the underlying implicit assumptions of using expected power for scoring adaptive designs and relate the global score function to the practice of sample size recalculation based on conditional power. An in-depth comparison of the features of the different performance scores and their respective optimizing designs provides the guidance for practitioners who face the problem of choosing between the various options. A software implementation of the proposed methods is publicly available online.
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- 2019
33. Familywise error control in multi‐armed response‐adaptive trials
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James Wason and David S. Robertson
- Subjects
FOS: Computer and information sciences ,Statistics and Probability ,Mathematical optimization ,Bayesian methods ,Computer science ,Hypercholesterolemia ,Bayesian probability ,01 natural sciences ,Article ,General Biochemistry, Genetics and Molecular Biology ,law.invention ,Methodology (stat.ME) ,Random Allocation ,010104 statistics & probability ,03 medical and health sciences ,symbols.namesake ,Bias ,Randomized controlled trial ,law ,closed testing ,Humans ,Computer Simulation ,0101 mathematics ,Statistics - Methodology ,030304 developmental biology ,Block (data storage) ,multiple comparisons ,0303 health sciences ,Models, Statistical ,type I error ,General Immunology and Microbiology ,Adaptive Clinical Trials as Topic ,Applied Mathematics ,General Medicine ,response-adaptive randomization ,62-07 ,Bonferroni correction ,Research Design ,Multiple comparisons problem ,symbols ,Computerized adaptive testing ,General Agricultural and Biological Sciences ,Error detection and correction ,Type I and type II errors - Abstract
Response-adaptive designs allow the randomization probabilities to change during the course of a trial based on cumulated response data, so that a greater proportion of patients can be allocated to the better performing treatments. A major concern over the use of response-adaptive designs in practice, particularly from a regulatory viewpoint, is controlling the type I error rate. In particular, we show that the naive z-test can have an inflated type I error rate even after applying a Bonferroni correction. Simulation studies have often been used to demonstrate error control, but do not provide a guarantee. In this paper, we present adaptive testing procedures for normally distributed outcomes that ensure strong familywise error control, by iteratively applying the conditional invariance principle. Our approach can be used for fully sequential and block randomized trials, and for a large class of adaptive randomization rules found in the literature. We show there is a high price to pay in terms of power to guarantee familywise error control for randomization schemes with extreme allocation probabilities. However, for proposed Bayesian adaptive randomization schemes in the literature, our adaptive tests maintain or increase the power of the trial compared to the z-test. We illustrate our method using a three-armed trial in primary hypercholesterolemia., 55 pages, 3 figures
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- 2019
34. Outcome-adaptive randomization in clinical trials: issues of participant welfare and autonomy
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Sim, Julius
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Research Subjects ,media_common.quotation_subject ,Psychological intervention ,Redress ,Patient Advocacy ,0603 philosophy, ethics and religion ,Article ,law.invention ,Outcome-adaptive randomization ,Consent ,Random Allocation ,03 medical and health sciences ,Clinical trials ,0302 clinical medicine ,Randomized controlled trial ,law ,Humans ,030212 general & internal medicine ,Equipoise ,Randomized Controlled Trials as Topic ,media_common ,Ethics ,Actuarial science ,Adaptive Clinical Trials as Topic ,06 humanities and the arts ,General Medicine ,R1 ,Clinical trial ,Comprehension ,Issues, ethics and legal aspects ,Research Design ,Philosophy of medicine ,Personal Autonomy ,060301 applied ethics ,Psychology ,RA ,Welfare ,Autonomy - Abstract
Outcome-adaptive randomization (OAR) has been proposed as a corrective to certain ethical difficulties inherent in the traditional randomized clinical trial (RCT) using fixed-ratio randomization. In particular, it has been suggested that OAR redresses the balance between individual and collective ethics in favour of the former. In this paper, I examine issues of welfare and autonomy arising in relation to OAR. A central issue in discussions of welfare in OAR is equipoise, and the moral status of OAR is crucially influenced by the way in which this concept is construed. If OAR is based on a model of equipoise that demands strict indifference between competing interventions throughout the trial, such equipoise is disturbed by accruing data favouring one treatment over another; OAR seeks to redress this by weighting randomization to the seemingly superior treatment. However, this is a partial response, as patients continue to be allocated to the inferior therapy. Moreover, it rests upon considerations of aggregate harms and benefits, and does not therefore uphold individual ethics. Issues of fairness also arise, as early and late enrollees are randomized on a different basis. Fixed-ratio randomization represents a fuller and more consistent response to a loss of equipoise, as so construed. With regard to consent, the complexity of OAR poses challenges to adequate disclosure and comprehension. Additionally, OAR does not offer a remedy to the therapeutic misconception-participants' tendency to attribute treatment allocation in an RCT to individual clinical judgments, rather than to scientific considerations-and, if anything, accentuates rather than alleviates this misconception. In relation to these issues, OAR fails to offer ethical advantages over fixed-ratio randomization. More broadly, the ethical basis of OAR can be seen to lie more in collective than in individual ethics, and overall it fares worse in this territory than fixed-ratio randomization.
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- 2019
35. Adaptive clinical trials and master protocols.
- Author
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McGarry A and Kieburtz K
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- Humans, Research Design, Sample Size, Adaptive Clinical Trials as Topic
- 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., (Copyright © 2023 Elsevier B.V. All rights reserved.)
- Published
- 2023
- Full Text
- View/download PDF
36. Platform trials.
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Roustit M, Demarcq O, Laporte S, Barthélémy P, Chassany O, Cucherat M, Demotes J, Diebolt V, Espérou H, Fouret C, Galaup A, Gambotti L, Gourio C, Guérin A, Labruyère C, Paoletti X, Porcher R, Simon T, and Varoqueaux N
- Subjects
- Humans, COVID-19, Pandemics, SARS-CoV-2, Adaptive Clinical Trials as Topic, Randomized Controlled Trials as Topic
- Abstract
For the past few years, platform trials have experienced a significant increase, recently amplified by the COVID-19 pandemic. The implementation of a platform trial is particularly useful in certain pathologies, particularly when there is a significant number of drug candidates to be assessed, a rapid evolution of the standard of care or in situations of urgent need for evaluation, during which the pooling of protocols and infrastructure optimizes the number of patients to be enrolled, the costs, and the deadlines for carrying out the investigation. However, the specificity of platform trials raises methodological, ethical, and regulatory issues, which have been the subject of the round table and which are presented in this article. The round table was also an opportunity to discuss the complexity of sponsorship and data management related to the multiplicity of partners, funding, and governance of these trials, and the level of acceptability of their findings by the competent authorities., (Copyright © 2022. Published by Elsevier Masson SAS.)
- Published
- 2023
- Full Text
- View/download PDF
37. The Bayesian Design of Adaptive Clinical Trials
- Author
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Alessandra Giovagnoli
- Subjects
predictive power ,target allocation ,Computer science ,Health, Toxicology and Mutagenesis ,media_common.quotation_subject ,Bayesian probability ,lcsh:Medicine ,Adaptive randomization ,Review ,Machine learning ,computer.software_genre ,01 natural sciences ,Bayesian design ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,adaptive designs ,Reading (process) ,Computer Simulation ,0101 mathematics ,media_common ,adaptive randomization ,clinical trials ,business.industry ,Adaptive Clinical Trials as Topic ,lcsh:R ,Perspective (graphical) ,Public Health, Environmental and Occupational Health ,Bayesian designs ,Bayes Theorem ,Clinical trial ,Research Design ,030220 oncology & carcinogenesis ,Adaptive design ,Predictive power ,Artificial intelligence ,business ,computer - Abstract
This paper presents a brief overview of the recent literature on adaptive design of clinical trials from a Bayesian perspective for statistically not so sophisticated readers. Adaptive designs are attracting a keen interest in several disciplines, from a theoretical viewpoint and also—potentially—from a practical one, and Bayesian adaptive designs, in particular, have raised high expectations in clinical trials. The main conceptual tools are highlighted here, with a mention of several trial designs proposed in the literature that use these methods, including some of the registered Bayesian adaptive trials to this date. This review aims at complementing the existing ones on this topic, pointing at further interesting reading material.
- Published
- 2021
38. Ethics of emerging infectious disease outbreak responses: Using Ebola virus disease as a case study of limited resource allocation
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Ariadne A Nichol and Annick Antierens
- Subjects
Male ,Guiding Principles ,Epidemiology ,Maternal Health ,medicine.disease_cause ,Research Ethics ,Communicable Diseases, Emerging ,Disease Outbreaks ,Medical Conditions ,0302 clinical medicine ,Pregnancy ,Informed consent ,Pandemic ,Medicine and Health Sciences ,Medical Personnel ,030212 general & internal medicine ,Research Integrity ,Health Care Rationing ,Multidisciplinary ,Adaptive Clinical Trials as Topic ,Humanitarian aid ,Therapies, Investigational ,Obstetrics and Gynecology ,06 humanities and the arts ,Middle Aged ,Public relations ,Professions ,Africa, Western ,Infectious Diseases ,Emerging infectious disease ,Medicine ,Female ,Research Article ,Adult ,Drug Research and Development ,Science Policy ,Health Personnel ,Science ,Research and Analysis Methods ,0603 philosophy, ethics and religion ,Interviews as Topic ,03 medical and health sciences ,Political science ,medicine ,Humans ,Clinical Trials ,Pharmacology ,Research ethics ,Ebola virus ,business.industry ,Outbreak ,Hemorrhagic Fever, Ebola ,Randomized Controlled Trials ,Emerging Infectious Diseases ,Medical Risk Factors ,People and Places ,Women's Health ,Population Groupings ,060301 applied ethics ,Clinical Medicine ,business - Abstract
Emerging infectious diseases such as Ebola Virus Disease (EVD), Nipah Virus Encephalitis and Lassa fever pose significant epidemic threats. Responses to emerging infectious disease outbreaks frequently occur in resource-constrained regions and under high pressure to quickly contain the outbreak prior to potential spread. As seen in the 2020 EVD outbreaks in the Democratic Republic of Congo and the current COVID-19 pandemic, there is a continued need to evaluate and address the ethical challenges that arise in the high stakes environment of an emerging infectious disease outbreak response. The research presented here provides analysis of the ethical challenges with regard to allocation of limited resources, particularly experimental therapeutics, using the 2013–2016 EVD outbreak in West Africa as a case study. In-depth semi-structured interviews were conducted with senior healthcare personnel (n = 16) from international humanitarian aid organizations intimately engaged in the 2013–2016 EVD outbreak response in West Africa. Interviews were recorded in private setting, transcribed, and iteratively coded using grounded theory methodology. A majority of respondents indicated a clear propensity to adopt an ethical framework of guiding principles for international responses to emerging infectious disease outbreaks. Respondents agreed that prioritization of frontline workers’ access to experimental therapeutics was warranted based on a principle of reciprocity. There was widespread acceptance of adaptive trial designs and greater trial transparency in providing access to experimental therapeutics. Many respondents also emphasized the importance of community engagement in limited resource allocation scheme design and culturally appropriate informed consent procedures. The study results inform a potential ethical framework of guiding principles based on the interview participants’ insights to be adopted by international response organizations and their healthcare workers in the face of allocating limited resources such as experimental therapeutics in future emerging infectious disease outbreaks to ease the moral burden of individual healthcare providers.
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- 2021
39. Adaptive platform trials using multi-arm, multi-stage protocols: getting fast answers in pandemic settings
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Angela M. Crook, Claire L Vale, Nurulamin M Noor, Sarah Pett, Hanif Esmail, Matthew R. Sydes, and Mahesh K. B. Parmar
- Subjects
medicine.medical_specialty ,Computer science ,Psychological intervention ,030204 cardiovascular system & hematology ,General Biochemistry, Genetics and Molecular Biology ,epidemic ,efficient ,multi-stage ,03 medical and health sciences ,0302 clinical medicine ,platform ,Interim ,Health care ,Pandemic ,medicine ,Global health ,Humans ,conduct ,030212 general & internal medicine ,Prospective Studies ,General Pharmacology, Toxicology and Pharmaceutics ,Intensive care medicine ,Pandemics ,General Immunology and Microbiology ,business.industry ,Adaptive Clinical Trials as Topic ,SARS-CoV-2 ,pandemic ,adaptive ,trials ,COVID-19 ,General Medicine ,Articles ,Opinion Article ,FAME ,multi-arm ,Clinical trial ,IPD ,meta-analysis ,Research Design ,Preparedness ,Meta-analysis ,030220 oncology & carcinogenesis ,MAMS ,business - Abstract
Global health pandemics, such as coronavirus disease 2019 (COVID-19), require efficient and well-conducted trials to determine effective interventions, such as treatments and vaccinations. Early work focused on rapid sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), subsequent in-vitro and in-silico work, along with greater understanding of the different clinical phases of the infection, have helped identify a catalogue of potential therapeutic agents requiring assessment. In a pandemic, there is a need to quickly identify efficacious treatments, and reject those that are non-beneficial or even harmful, using randomised clinical trials. Whilst each potential treatment could be investigated across multiple, separate, competing two-arm trials, this is a very inefficient process. Despite the very large numbers of interventional trials for COVID-19, the vast majority have not used efficient trial designs. Well conducted, adaptive platform trials utilising a multi-arm multi-stage (MAMS) approach provide a solution to overcome limitations of traditional designs. The multi-arm element allows multiple different treatments to be investigated simultaneously against a shared, standard-of-care control arm. The multi-stage element uses interim analyses to assess accumulating data from the trial and ensure that only treatments showing promise continue to recruitment during the next stage of the trial. The ability to test many treatments at once and drop insufficiently active interventions significantly speeds up the rate at which answers can be achieved. This article provides an overview of the benefits of MAMS designs and successes of trials, which have used this approach to COVID-19. We also discuss international collaboration between trial teams, including prospective agreement to synthesise trial results, and identify the most effective interventions. We believe that international collaboration will help provide faster answers for patients, clinicians, and health care systems around the world, including for each further wave of COVID-19, and enable preparedness for future global health pandemics.
- Published
- 2020
40. Personalised health education against health damage of COVID-19 epidemic in the elderly hungarian population (PROACTIVE-19): protocol of an adaptive randomised controlled clinical trial
- Author
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Borbála Zsigmond, Márk Félix Juhász, Lajos Szakó, Anikó Nagy, Andrea Sárközi, Nelli Farkas, Eszter Hegyi, Zsolt Szakács, Bálint Erőss, Antal Zemplényi, Péter Hegyi, Dominika Tóth, László Czopf, Gábor Pethő, Attila Márta, András Varró, Zoltán Péterfi, Szabolcs Kiss, Noémi Zádori, Anna Kanjo, Szilárd Váncsa, Richárd Farkas, Dalma Dobszai, Andrea Szentesi, Andrea Párniczky, Erika Pintér, Emőke Miklós, Piroska Pázmány, Fanni Dembrovszky, Zsuzsanna Helyes, Zsolt Molnár, Béla Birkás, Klementina Ocskay, Dóra Czapári, István Kiss, Katalin Márta, Mária Földi, Árpád Csathó, Nóra Vörhendi, and Péter Jenő Hegyi
- Subjects
Male ,Health Knowledge, Attitudes, Practice ,Health Status ,Psychological intervention ,Medicine (miscellaneous) ,030204 cardiovascular system & hematology ,law.invention ,Study Protocol ,0302 clinical medicine ,Randomized controlled trial ,law ,Risk Factors ,Clinical endpoint ,Medicine ,Pharmacology (medical) ,030212 general & internal medicine ,Health Education ,Aged, 80 and over ,Randomised controlled trial ,lcsh:R5-920 ,education.field_of_study ,Public health ,Adaptive Clinical Trials as Topic ,Smoking ,Age Factors ,Middle Aged ,Mental Health ,Host-Pathogen Interactions ,Health education ,Female ,lcsh:Medicine (General) ,Coronavirus Infections ,medicine.medical_specialty ,Alcohol Drinking ,Population ,Pneumonia, Viral ,Risk Assessment ,03 medical and health sciences ,Betacoronavirus ,Pragmatic Clinical Trials as Topic ,Humans ,education ,Exercise ,Pandemics ,Aged ,Hungary ,business.industry ,SARS-CoV-2 ,nCov-2019 ,Prevention ,COVID-19 ,Feeding Behavior ,Protective Factors ,Mental health ,Clinical trial ,Family medicine ,business ,Risk Reduction Behavior - Abstract
Background Early reports indicate that COVID-19 may require intensive care unit (ICU) admission in 5–26% and overall mortality can rise to 11% of the recognised cases, particularly affecting the elderly. There is a lack of evidence-based targeted pharmacological therapy for its prevention and treatment. We aim to compare the effects of a World Health Organization recommendation-based education and a personalised complex preventive lifestyle intervention package (based on the same WHO recommendation) on the outcomes of the COVID-19. Methods PROACTIVE-19 is a pragmatic, randomised controlled clinical trial with adaptive “sample size re-estimation” design. Hungarian population over the age of 60 years without confirmed COVID-19 will be approached to participate in a telephone health assessment and lifestyle counselling voluntarily. Volunteers will be randomised into two groups: (A) general health education and (B) personalised health education. Participants will go through questioning and recommendation in 5 fields: (1) mental health, (2) smoking habits, (3) physical activity, (4) dietary habits, and (5) alcohol consumption. Both groups A and B will receive the same line of questioning to assess habits concerning these topics. Assessment will be done weekly during the first month, every second week in the second month, then monthly. The composite primary endpoint will include the rate of ICU admission, hospital admission (longer than 48 h), and mortality in COVID-19-positive cases. The estimated sample size is 3788 subjects per study arm. The planned duration of the follow-up is a minimum of 1 year. Discussion These interventions may boost the body’s cardiovascular and pulmonary reserve capacities, leading to improved resistance against the damage caused by COVID-19. Consequently, lifestyle changes can reduce the incidence of life-threatening conditions and attenuate the detrimental effects of the pandemic seriously affecting the older population. Trial registration The study has been approved by the Scientific and Research Ethics Committee of the Hungarian Medical Research Council (IV/2428- 2 /2020/EKU) and has been registered at clinicaltrials.gov (NCT04321928) on 25 March 2020.
- Published
- 2020
41. Statistics Commentary Series. Commentary No. 42: Minimization
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David L. Streiner
- Subjects
Psychiatry and Mental health ,Random Allocation ,Series (mathematics) ,Adaptive Clinical Trials as Topic ,Research Design ,Data Interpretation, Statistical ,MEDLINE ,Humans ,Pharmacology (medical) ,Minification ,Psychology ,Mathematical economics ,Randomized Controlled Trials as Topic - Published
- 2020
42. Design Optimization for dose-finding trials: A review
- Author
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Pierre Colin, Bernard Sebastien, Jean Noel Bacro, Gwladys Toulemonde, Loic Darchy, Jihane Aouni, Sanofi Aventis R&D [Chilly-Mazarin], Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Littoral, Environment: MOdels and Numerics (LEMON), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), and Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
Statistics and Probability ,Computer science ,Adaptive trials ,Design optimization ,Plan (drawing) ,Dose selection ,01 natural sciences ,010104 statistics & probability ,03 medical and health sciences ,Dose finding ,0302 clinical medicine ,Double-Blind Method ,Milestone (project management) ,Humans ,Pharmacology (medical) ,Drug Dosage Calculations ,030212 general & internal medicine ,0101 mathematics ,Randomized Controlled Trials as Topic ,Pharmacology ,Models, Statistical ,Dose-Response Relationship, Drug ,Adaptive Clinical Trials as Topic ,Gold standard (test) ,[SDV.SP]Life Sciences [q-bio]/Pharmaceutical sciences ,Patient allocation ,[STAT]Statistics [stat] ,Treatment Outcome ,Risk analysis (engineering) ,Drug development ,Research Design ,Data Interpretation, Statistical ,Multiple comparisons problem ,Utility functions - Abstract
International audience; Dose selection is one of the most difficult and crucial decisions to make during drug development. As a consequence the dose-finding trial is a major milestone in the drug development plan and should be properly designed. This article will review the most recent methodologies for optimizing the design of dose-finding studies: all of them are based on the modeling of the dose-response curve, which is now the gold standard approach for analyzing dose-finding studies instead of the traditional ANOVA/multiple testing approach. We will address the optimization of both fixed and adaptive designs and briefly outline new methodologies currently under investigation, based on utility functions.
- Published
- 2020
43. Modified Goldilocks Design with strict type I error control in confirmatory clinical trials
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Saurabh Mukhopadhyay, Tianyu Zhan, Alan Hartford, and Hongtao Zhang
- Subjects
Pharmacology ,Statistics and Probability ,Models, Statistical ,Time Factors ,Computer science ,Adaptive Clinical Trials as Topic ,Control (management) ,Bayes Theorem ,Clinical trial ,Treatment Outcome ,Order (business) ,Sample size determination ,Research Design ,Data Interpretation, Statistical ,Neoplasms ,Sample Size ,Goldilocks principle ,Humans ,Pharmacology (medical) ,Computer Simulation ,Algorithm ,Type I and type II errors ,Randomized Controlled Trials as Topic - Abstract
Goldilocks Design (GD) utilizes predictive probability to adaptively select a trial's sample size based on accumulating data. In order to control type I error at a desired level for a subset of the null space, extensive simulations at the study design stage are required to choose critical values, which is a challenge for this type of Bayesian adaptive design to be used for confirmatory trials. In this article, we propose a Modified Goldilocks Design (MGD) where type I error is analytically controlled over the entire null space. We do so by applying the conditional invariance principle and a combination test approach on [Formula: see text]-values that are obtained from independent cohorts of subjects. Simulation studies show that despite analytic control of type I error rate, the proposed MGD has similar power when compared with the original GD. We further apply it to an example trial with time-to-event endpoint in oncology.
- Published
- 2020
44. A response-adaptive randomization procedure for multi-armed clinical trials with normally distributed outcomes
- Author
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Williamson, S Faye, Villar, Sofía S, Williamson, S Faye [0000-0002-1800-6625], Villar, Sofía S [0000-0001-7755-2637], and Apollo - University of Cambridge Repository
- Subjects
Biometry ,Models, Statistical ,Patient Dropouts ,Adaptive Clinical Trials as Topic ,Endpoint Determination ,Gittins index ,unknown variance ,missing data ,Clinical Trials, Phase II as Topic ,Treatment Outcome ,adaptive designs ,dichotomization ,Neoplasms ,Humans ,Computer Simulation ,continuous endpoint ,Algorithms ,Randomized Controlled Trials as Topic - Abstract
We propose a novel response-adaptive randomization procedure for multi-armed trials with continuous outcomes that are assumed to be normally distributed. Our proposed rule is non-myopic, and oriented toward a patient benefit objective, yet maintains computational feasibility. We derive our response-adaptive algorithm based on the Gittins index for the multi-armed bandit problem, as a modification of the method first introduced in Villar et al. (Biometrics, 71, pp. 969-978). The resulting procedure can be implemented under the assumption of both known or unknown variance. We illustrate the proposed procedure by simulations in the context of phase II cancer trials. Our results show that, in a multi-armed setting, there are efficiency and patient benefit gains of using a response-adaptive allocation procedure with a continuous endpoint instead of a binary one. These gains persist even if an anticipated low rate of missing data due to deaths, dropouts, or complete responses is imputed online through a procedure first introduced in this paper. Additionally, we discuss how there are response-adaptive designs that outperform the traditional equal randomized design both in terms of efficiency and patient benefit measures in the multi-armed trial context.
- Published
- 2020
45. Bayesian biomarker-driven outcome-adaptive randomization with an imperfect biomarker assay
- Author
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Leandro Garcia Barrado, Tomasz Burzykowski, and Garcia Barrado, Leandro/0000-0003-0793-9881
- Subjects
Randomization ,Bayesian probability ,Adaptive randomization ,Computational biology ,Bayesian statistics ,01 natural sciences ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,Medicine ,Humans ,0101 mathematics ,outcome-adaptive randomization ,Probability ,Randomized Controlled Trials as Topic ,Pharmacology ,business.industry ,Adaptive Clinical Trials as Topic ,biomarkers ,Bayes Theorem ,imperfect assay ,General Medicine ,Outcome (probability) ,Research Design ,030220 oncology & carcinogenesis ,Biomarker (medicine) ,business - Abstract
Objective: We investigate the impact of biomarker assay’s accuracy on the operating characteristics of a Bayesian biomarker-driven outcome-adaptive randomization design. Methods: In a simulation study, we assume a trial with two treatments, two biomarker-based strata, and a binary clinical outcome (response). P bt denotes the probability of response for treatment t ( t = 0 or 1) in biomarker stratum ( b = 0 or 1). Four different scenarios in terms of true underlying response probabilities are considered: a null ( P00 = P01 = 0.25, P10 = P11= 0.25) and consistent ( P00 = P10 = 0.25, P01 = 0.5) treatment effect scenario, as well as a quantitative ( P00 = P01 = P10 = 0.25, P11 = 0.5) and a qualitative ( P00 = P11 = 0.5, P01 = P10 = 0.25) stratum-treatment interaction. For each scenario, we compare the case of a perfect with the case of an imperfect biomarker assay with sensitivity and specificity of 0.8 and 0.7, respectively. In addition, biomarker-positive prevalence values P( B = 1) = 0.2 and 0.5 are investigated. Results: Results show that the use of an imperfect assay affects the operational characteristics of the Bayesian biomarker-based outcome-adaptive randomization design. In particular, the misclassification causes a substantial reduction in power accompanied by a considerable increase in the type-I error probability. The magnitude of these effects depends on the sensitivity and specificity of the assay, as well as on the distribution of the biomarker in the patient population. Conclusion: With an imperfect biomarker assay, the decision to apply a biomarker-based outcome-adaptive randomization design may require careful reflection.
- Published
- 2020
46. From the Valley of Death to the Crossroads of Opportunity: A Discussion of Evolving Benefit/Risk Evaluation Standards
- Author
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Patrick Brady and Peter J. Pitts
- Subjects
media_common.quotation_subject ,Context (language use) ,Risk Assessment ,030226 pharmacology & pharmacy ,01 natural sciences ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,Humans ,Pharmacology (medical) ,Regulatory science ,0101 mathematics ,Drug Approval ,Pharmacology, Toxicology and Pharmaceutics (miscellaneous) ,media_common ,Adaptive clinical trial ,Expediting ,Adaptive Clinical Trials as Topic ,business.industry ,Public Health, Environmental and Occupational Health ,Evidence-based medicine ,Public relations ,Drug development ,Data quality ,Curiosity ,Business ,Patient Participation - Abstract
A series of recent US Food and Drug Administration (FDA) approvals (such as Sarepta's Exondys 51, Merck's Keytruda, and Portola's Bevyxxa) has generated significant interest within the drug development ecosystem. Facilitated regulatory pathways aimed toward expediting medicines to patients suffering from serious and life-threatening conditions are a good thing, even if it raises curiosity and introduces some degree of uncertainty. Over the last 20 years, two key words in drug development have been speed and innovation. Going forward, the patient voice, data quality, and evidence generation must be added to that list. There is a raging debate over the level of evidence expected to first introduce a treatment to patients. Some argue for less data followed by postapproval follow-up, others for more adaptive clinical trial designs and end-point modification driven by patient-focused drug development and use of real-world evidence. The transition in the regulatory framework is happening in front of our eyes. How are these shifts in regulatory science interpreted within the context of 21st-century drug development-and how can these learnings help advance patient care while placing into context the expected uncertainty we find in benefit-risk data?
- Published
- 2018
47. A review of available software for adaptive clinical trial design
- Author
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Graham M. Wheeler and Michael J. Grayling
- Subjects
FOS: Computer and information sciences ,Research design ,Computer science ,Review ,Research & Experimental Medicine ,computer.software_genre ,multi-stage ,Bayes' theorem ,0302 clinical medicine ,Software ,phase II/III ,group sequential ,030212 general & internal medicine ,Computation (stat.CO) ,III ,Adaptive Clinical Trials as Topic ,0104 Statistics ,General Medicine ,sample size re-estimation ,phase II ,Multi stage ,Drug development ,Medicine, Research & Experimental ,Research Design ,030220 oncology & carcinogenesis ,Life Sciences & Biomedicine ,Statistics & Probability ,Machine learning ,Statistics - Computation ,03 medical and health sciences ,phase I/II ,Code (cryptography) ,Humans ,Computer Simulation ,Pharmacology ,Adaptive clinical trial ,Science & Technology ,Dose-Response Relationship, Drug ,business.industry ,Code ,Bayes Theorem ,1103 Clinical Sciences ,phase I ,II ,Sample size determination ,Sample Size ,dose escalation ,Artificial intelligence ,business ,computer ,Biomarkers - Abstract
Background/aims: The increasing cost of the drug development process has seen interest in the use of adaptive trial designs grow substantially. Accordingly, much research has been conducted to identify barriers to increasing the use of adaptive designs in practice. Several articles have argued that the availability of user-friendly software will be an important step in making adaptive designs easier to implement. Therefore, we present a review of the current state of software availability for adaptive trial design. Methods: We review articles from 31 journals published in 2013–2017 that relate to methodology for adaptive trials to assess how often code and software for implementing novel adaptive designs is made available at the time of publication. We contrast our findings against these journals’ policies on code distribution. We also search popular code repositories, such as Comprehensive R Archive Network and GitHub, to identify further existing user-contributed software for adaptive designs. From this, we are able to direct interested parties toward solutions for their problem of interest. Results: Only 30% of included articles made their code available in some form. In many instances, articles published in journals that had mandatory requirements on code provision still did not make code available. There are several areas in which available software is currently limited or saturated. In particular, many packages are available to address group sequential design, but comparatively little code is present in the public domain to determine biomarker-guided adaptive designs. Conclusions: There is much room for improvement in the provision of software alongside adaptive design publications. In addition, while progress has been made, well-established software for various types of trial adaptation remains sparsely available.
- Published
- 2019
48. Maintenance therapy and need for cessation studies in multiple myeloma: Focus on the future
- Author
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C. Ola Landgren, Benjamin Diamond, David J. Chung, Kylee H Maclachlan, and Alexander M. Lesokhin
- Subjects
Boron Compounds ,medicine.medical_specialty ,Neoplasm, Residual ,Clinical Biochemistry ,Glycine ,Context (language use) ,Transplantation, Autologous ,Disease-Free Survival ,Drug Administration Schedule ,Unmet needs ,Maintenance Chemotherapy ,Bortezomib ,03 medical and health sciences ,0302 clinical medicine ,Maintenance therapy ,Induction therapy ,Antineoplastic Combined Chemotherapy Protocols ,Medicine ,Humans ,Intensive care medicine ,Lenalidomide ,Multiple myeloma ,business.industry ,Adaptive Clinical Trials as Topic ,Hematopoietic Stem Cell Transplantation ,Antibodies, Monoclonal ,medicine.disease ,Minimal residual disease ,Thalidomide ,Oncology ,Withholding Treatment ,030220 oncology & carcinogenesis ,business ,Multiple Myeloma ,Oligopeptides ,Median survival ,030215 immunology ,medicine.drug - Abstract
With ten years of follow-up since the advent of the modern paradigm of combination induction therapy, consolidative autologous stem-cell transplant, and lenalidomide maintenance, median survival for multiple myeloma has increased to almost 50% at 10 years. Given this outlook, the overarching goal of maintenance therapy is to spare patients from the toxicities of aggressive or otherwise intrusive therapies while ideally extending survival or, at the least, extending progression-free survival or time until next treatment. This review will focus on the current landscape of maintenance therapies for multiple myeloma. The historical context and evidence for choice of agent, duration of treatment, and current strategies and ongoing trials will be discussed - as well as a focus on unmet needs. The case for studies investigating cessation of therapy and risk and response-adapted approaches will be underscored given that the current paradigm likely results in overtreatment for some patients.
- Published
- 2019
49. The US Food and Drug Administration’s expedited approval programs: Evidentiary standards, regulatory trade-offs, and potential improvements
- Author
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Joshua D. Wallach, Joseph S. Ross, and Huseyin Naci
- Subjects
Male ,Non-Randomized Controlled Trials as Topic ,Design elements and principles ,030204 cardiovascular system & hematology ,law.invention ,Food and drug administration ,03 medical and health sciences ,0302 clinical medicine ,Randomized controlled trial ,law ,RA0421 Public health. Hygiene. Preventive Medicine ,Pragmatic Clinical Trials as Topic ,Product Surveillance, Postmarketing ,Clinical endpoint ,Humans ,030212 general & internal medicine ,Duration (project management) ,Drug Approval ,Pharmacology ,Flexibility (engineering) ,Adaptive Clinical Trials as Topic ,United States Food and Drug Administration ,Patient Selection ,Trade offs ,Authorization ,General Medicine ,United States ,Risk analysis (engineering) ,Female ,Business - Abstract
The US Food and Drug Administration has several regulatory programs and pathways to expedite the development and approval of therapeutic agents aimed at treating serious or life-debilitating conditions. A common feature of these programs is the regulatory flexibility, which allows for a customized approval approach that enables market authorization on the basis of less rigorous evidence, in exchange for requiring postmarket evidence generation. An increasing share of therapeutic agents approved by the Food and Drug Administration in recent years are associated with expedited programs. In this article, we provide an overview of the evidentiary standards required by the Food and Drug Administration’s expedited development and review programs, summarize the findings of the recent academic literature demonstrating some of the limitations of these programs, and outline potential opportunities to address these limitations. Recent evidence suggests that therapeutic agents in the Food and Drug Administration’s expedited programs are approved on the basis of fewer and smaller studies that may lack comparator groups and random allocation, and rather than focusing on clinical outcomes for study endpoints, rely instead on surrogate markers of disease. Once on the market, agents receiving expedited approvals are often quickly incorporated into clinical practice, and evidence generated in the postmarket period may not necessarily address the evidentiary limitations at the time of market entry. Furthermore, not all pathways require additional postmarket studies. Evidence suggests that drugs in expedited approval programs are associated with a greater likelihood that the Food and Drug Administration will take a safety action following market entry. There are several opportunities to improve the timeliness, information value, and validity of the pre- and postmarket studies of therapeutic agents receiving expedited approvals. When use of nonrandomized and uncontrolled studies cannot be avoided prior to market entry, randomized trials should be mandatory in the postmarket period, unless there are strong justifications for not carrying out such studies. In the premarket period, validity of the surrogate markers can be improved by more rigorously evaluating their correlation with patient-relevant clinical outcomes. Opportunities to reduce the duration, complexity, and cost of postmarket randomized trials should not compromise their validity and instead incorporate pragmatic “real-world” design elements. Despite recent enthusiasm for widely using real-world evidence, adaptive designs, and pragmatic trials in the regulatory setting, caution is warranted until large-scale empirical evaluations demonstrate their validity compared to more traditional trial designs.
- Published
- 2018
50. A response adaptive design for ordinal categorical responses
- Author
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Soumyadeep Das, Atanu Biswas, and Rahul Bhattacharya
- Subjects
Statistics and Probability ,medicine.medical_specialty ,Treatment response ,Treatment outcome ,0211 other engineering and technologies ,Phases of clinical research ,02 engineering and technology ,Biostatistics ,01 natural sciences ,Arthritis, Rheumatoid ,010104 statistics & probability ,Humans ,Medicine ,Pharmacology (medical) ,0101 mathematics ,Categorical variable ,Pharmacology ,Models, Statistical ,021103 operations research ,Adaptive Clinical Trials as Topic ,business.industry ,medicine.disease ,Clinical trial ,Treatment Outcome ,Clinical Trials, Phase III as Topic ,Research Design ,Data Interpretation, Statistical ,Rheumatoid arthritis ,Adaptive design ,Physical therapy ,Wounds and Injuries ,business - Abstract
A two treatment response adaptive design is developed for phase III clinical trials with ordinal categorical treatment outcome using Goodman-Kruskal measure of association. Properties of the proposed design are studied both empirically and theoretically and the acceptability is further illustrated using two real data-sets; one from a clinical trial with trauma patients and the other from a trial with patients having rheumatoid arthritis.
- Published
- 2018
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