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Barrett JS, Lasater K, Russell S, McCune S, Miller TM, Sibbald D. Bringing platform trials closer to reality by enabling with digital research environment (DRE) connectivity. Contemp Clin Trials 2024; 142:107559. [PMID: 38714286 DOI: 10.1016/j.cct.2024.107559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 02/28/2024] [Accepted: 04/30/2024] [Indexed: 05/09/2024]
Abstract
Platform trials are generally regarded as an innovative approach to address clinical valuation of early stage candidates, regardless of modality as the evidence evolves. As a type of randomized clinical trial (RCT) design construct in which multiple interventions are evaluated concurrently against a common control group allowing new interventions to be added and the control group to be updated throughout the trial, they provide a dynamic and efficient mechanism to compare and potentially discriminate new treatment candidates. Their recent use in the evaluation of new therapies for COVID-19 has spurred new interest in the approach. The paucity of platform trials is less influenced by the novelty and operational requirements as opposed to concerns regarding the sharing of intellectual property (IP) and the lack of infrastructure to operationalize the conduct in the context of IP and data sharing. We provide a mechanism how this can be accomplished through the use of a digital research environment (DRE) providing a safe and secure platform for clinical researchers, quantitative and physician scientists to analyze and develop tools (e.g., models) on sensitive data with the confidence that the data and models developed are protected. A DRE, in this context, expands on the concept of a trusted research environment (TRE) by providing remote access to data alongside tools for analysis in a securely controlled workspace, while allowing data and tools to be findable, accessible, interoperable, and reusable (FAIR), version-controlled, and dynamically grow in size or quality as a result of each treatment evaluated in the trial.
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Affiliation(s)
| | - Kara Lasater
- Aridhia Digital Research Environment, Glasgow, United Kingdom
| | - Scott Russell
- Aridhia Digital Research Environment, Glasgow, United Kingdom
| | - Susan McCune
- PPD Clinical Research Business, Thermo Fisher Scientific, Wilmington, NC, USA
| | - Timothy M Miller
- Enterprise Science and Innovation Partnerships, Thermo Fisher Scientific, Wilmington, NC, USA
| | - David Sibbald
- Aridhia Digital Research Environment, Glasgow, United Kingdom
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2
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Burnett T, König F, Jaki T. Adding experimental treatment arms to multi-arm multi-stage platform trials in progress. Stat Med 2024. [PMID: 38852991 DOI: 10.1002/sim.10090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 01/16/2024] [Accepted: 04/15/2024] [Indexed: 06/11/2024]
Abstract
Multi-arm multi-stage (MAMS) platform trials efficiently compare several treatments with a common control arm. Crucially MAMS designs allow for adjustment for multiplicity if required. If for example, the active treatment arms in a clinical trial relate to different dose levels or different routes of administration of a drug, the strict control of the family-wise error rate (FWER) is paramount. Suppose a further treatment becomes available, it is desirable to add this to the trial already in progress; to access both the practical and statistical benefits of the MAMS design. In any setting where control of the error rate is required, we must add corresponding hypotheses without compromising the validity of the testing procedure.To strongly control the FWER, MAMS designs use pre-planned decision rules that determine the recruitment of the next stage of the trial based on the available data. The addition of a treatment arm presents an unplanned change to the design that we must account for in the testing procedure. We demonstrate the use of the conditional error approach to add hypotheses to any testing procedure that strongly controls the FWER. We use this framework to add treatments to a MAMS trial in progress. Simulations illustrate the possible characteristics of such procedures.
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Affiliation(s)
- Thomas Burnett
- Department of Mathematical Sciences, University of Bath, Bath, UK
| | - Franz König
- Center for Medical Data Science, Medical University of Vienna, Vienna, Austria
| | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Faculty of Computer Science and Data Science, University of Regensburg, Regensburg, Germany
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Arsène S, Parès Y, Tixier E, Granjeon-Noriot S, Martin B, Bruezière L, Couty C, Courcelles E, Kahoul R, Pitrat J, Go N, Monteiro C, Kleine-Schultjann J, Jemai S, Pham E, Boissel JP, Kulesza A. In Silico Clinical Trials: Is It Possible? Methods Mol Biol 2024; 2716:51-99. [PMID: 37702936 DOI: 10.1007/978-1-0716-3449-3_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
Modeling and simulation (M&S), including in silico (clinical) trials, helps accelerate drug research and development and reduce costs and have coined the term "model-informed drug development (MIDD)." Data-driven, inferential approaches are now becoming increasingly complemented by emerging complex physiologically and knowledge-based disease (and drug) models, but differ in setup, bottlenecks, data requirements, and applications (also reminiscent of the different scientific communities they arose from). At the same time, and within the MIDD landscape, regulators and drug developers start to embrace in silico trials as a potential tool to refine, reduce, and ultimately replace clinical trials. Effectively, silos between the historically distinct modeling approaches start to break down. Widespread adoption of in silico trials still needs more collaboration between different stakeholders and established precedence use cases in key applications, which is currently impeded by a shattered collection of tools and practices. In order to address these key challenges, efforts to establish best practice workflows need to be undertaken and new collaborative M&S tools devised, and an attempt to provide a coherent set of solutions is provided in this chapter. First, a dedicated workflow for in silico clinical trial (development) life cycle is provided, which takes up general ideas from the systems biology and quantitative systems pharmacology space and which implements specific steps toward regulatory qualification. Then, key characteristics of an in silico trial software platform implementation are given on the example of jinkō.ai (nova's end-to-end in silico clinical trial platform). Considering these enabling scientific and technological advances, future applications of in silico trials to refine, reduce, and replace clinical research are indicated, ranging from synthetic control strategies and digital twins, which overall shows promise to begin a new era of more efficient drug development.
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Wathen JK, Jagannatha S, Ness S, Bangerter A, Pandina G. A platform trial approach to proof-of-concept (POC) studies in autism spectrum disorder: Autism spectrum POC initiative (ASPI). Contemp Clin Trials Commun 2023; 32:101061. [PMID: 36949847 PMCID: PMC10025278 DOI: 10.1016/j.conctc.2023.101061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 11/29/2022] [Accepted: 01/14/2023] [Indexed: 01/18/2023] Open
Abstract
Background Over the past decade, autism spectrum disorder (ASD) research has blossomed, and multiple clinical trials have tested potential interventions, with varying results and no clear demonstration of efficacy. Lack of clarity concerning appropriate biological mechanisms to target and lack of sensitive, objective tools to identify subgroups and measure symptom changes have hampered the efforts to develop treatments. A platform trial for proof-of-concept studies in ASD could help address these issues. A major goal of a platform trial is to find the best treatment in the most expeditious manner, by simultaneously investigating multiple treatments, using specialized statistical tools for allocation and analysis. We describe the setup of a platform trial and perform simulations to evaluate the operating characteristics under several scenarios. We use the Autism Behavior Inventory (ABI), a psychometrically validated web-based rating scale to measure the change in ASD core and associated symptoms. Methods Detailed description of the setup, conduct, and decision-making rules of a platform trial are explained. Simulations of a virtual platform trial for several scenarios are performed to compare operating characteristics. The success and futility criteria for treatments are based on a Bayesian posterior probability model. Results Overall, simulation results show the potential gain in terms of statistical properties especially for improved decision-making ability, while careful planning is needed due to the complexities of a platform trial. Conclusions Autism research, shaped particularly by its heterogeneity, may benefit from the platform trial approach for POC clinical studies.
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Affiliation(s)
| | - Shyla Jagannatha
- Corresponding author. Janssen Research & Development, LLC 1125 Trenton-Harbourton Road Titusville NJ 08560, USA.
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Meyer EL, Mesenbrink P, Di Prospero NA, Pericàs JM, Glimm E, Ratziu V, Sena E, König F. Designing an exploratory phase 2b platform trial in NASH with correlated, co-primary binary endpoints. PLoS One 2023; 18:e0281674. [PMID: 36893087 PMCID: PMC9997886 DOI: 10.1371/journal.pone.0281674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 01/28/2023] [Indexed: 03/10/2023] Open
Abstract
Non-alcoholic steatohepatitis (NASH) is the progressive form of nonalcoholic fatty liver disease (NAFLD) and a disease with high unmet medical need. Platform trials provide great benefits for sponsors and trial participants in terms of accelerating drug development programs. In this article, we describe some of the activities of the EU-PEARL consortium (EU Patient-cEntric clinicAl tRial pLatforms) regarding the use of platform trials in NASH, in particular the proposed trial design, decision rules and simulation results. For a set of assumptions, we present the results of a simulation study recently discussed with two health authorities and the learnings from these meetings from a trial design perspective. Since the proposed design uses co-primary binary endpoints, we furthermore discuss the different options and practical considerations for simulating correlated binary endpoints.
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Affiliation(s)
- Elias Laurin Meyer
- Center for Medical Data Science, Medical University of Vienna, Vienna, Austria
| | - Peter Mesenbrink
- Novartis Pharmaceuticals Corporation, One Health Plaza, East Hanover, NJ, United States of America
| | | | - Juan M. Pericàs
- Liver Unit, Internal Medicine Department, Vall d’Hebron University Hospital, Vall d’Hebron Institute for Research (VHIR), Barcelona, Spain
- Centros de Investigación Biomédica en Red Enfermedades Hepáticas y Digestivas (CIBERehd), ISCIII, Madrid, Spain
| | - Ekkehard Glimm
- Novartis Pharma AG, Basel, Switzerland
- Institute of Biometry and Medical Informatics, University of Magdeburg, Magdeburg, Germany
| | - Vlad Ratziu
- Assistance Publique-Hôpitaux de Paris, Hôpital Pitie-Salpetriere, University of Paris, Paris, France
| | - Elena Sena
- Liver Unit, Internal Medicine Department, Vall d’Hebron University Hospital, Vall d’Hebron Institute for Research (VHIR), Barcelona, Spain
| | - Franz König
- Center for Medical Data Science, Medical University of Vienna, Vienna, Austria
- * E-mail:
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Granholm A, Kaas-Hansen BS, Lange T, Schjørring OL, Andersen LW, Perner A, Jensen AKG, Møller MH. An overview of methodological considerations regarding adaptive stopping, arm dropping, and randomization in clinical trials. J Clin Epidemiol 2023; 153:45-54. [PMID: 36400262 DOI: 10.1016/j.jclinepi.2022.11.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 10/17/2022] [Accepted: 11/02/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND AND OBJECTIVES Adaptive features may increase flexibility and efficiency of clinical trials, and improve participants' chances of being allocated to better interventions. Our objective is to provide thorough guidance on key methodological considerations for adaptive clinical trials. METHODS We provide an overview of key methodological considerations for clinical trials employing adaptive stopping, adaptive arm dropping, and response-adaptive randomization. We cover pros and cons of different decisions and provide guidance on using simulation to compare different adaptive trial designs. We focus on Bayesian multi-arm adaptive trials, although the same general considerations apply to frequentist adaptive trials. RESULTS We provide guidance on 1) interventions and possible common control, 2) outcome selection, follow-up duration and model choice, 3) timing of adaptive analyses, 4) decision rules for adaptive stopping and arm dropping, 5) randomization strategies, 6) performance metrics, their prioritization, and arm selection strategies, and 7) simulations, assessment of performance under different scenarios, and reporting. Finally, we provide an example using a newly developed R simulation engine that may be used to evaluate and compare different adaptive trial designs. CONCLUSION This overview may help trialists design better and more transparent adaptive clinical trials and to adequately compare them before initiation.
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Affiliation(s)
- Anders Granholm
- Department of Intensive Care, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark.
| | - Benjamin Skov Kaas-Hansen
- Department of Intensive Care, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark; Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Theis Lange
- Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Olav Lilleholt Schjørring
- Department of Anaesthesia and Intensive Care, Aalborg University Hospital, Aalborg, Denmark; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Lars W Andersen
- Research Center for Emergency Medicine, Department of Clinical Medicine, Aarhus University and Aarhus University Hospital, Aarhus, Denmark; Department of Anesthesiology and Intensive Care, Aarhus University Hospital, Aarhus, Denmark; Prehospital Emergency Medical Services, Central Denmark Region, Aarhus, Denmark
| | - Anders Perner
- Department of Intensive Care, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Aksel Karl Georg Jensen
- Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Morten Hylander Møller
- Department of Intensive Care, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
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Park JJH, Sharif B, Harari O, Dron L, Heath A, Meade M, Zarychanski R, Lee R, Tremblay G, Mills EJ, Jemiai Y, Mehta C, Wathen JK. Economic Evaluation of Cost and Time Required for a Platform Trial vs Conventional Trials. JAMA Netw Open 2022; 5:e2221140. [PMID: 35819785 PMCID: PMC9277502 DOI: 10.1001/jamanetworkopen.2022.21140] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
IMPORTANCE Platform trial design allows the introduction of new interventions after the trial is initiated and offers efficiencies to clinical research. However, limited guidance exists on the economic resources required to establish and maintain platform trials. OBJECTIVE To compare cost (US dollars) and time requirements of conducting a platform trial vs a series of conventional (nonplatform) trials using a real-life example. DESIGN, SETTING, AND PARTICIPANTS For this economic evaluation, an online survey was administered to a group of international experts (146 participants) with publication records of platform trials to elicit their opinions on cost and time to set up and conduct platform, multigroup, and 2-group trials. Using the reported entry dates of 10 interventions into Systemic Therapy in Advancing Metastatic Prostate Cancer: Evaluation of Drug Efficacy, the longest ongoing platform trial, 3 scenarios were designed involving a single platform trial (scenario 1), 1 multigroup followed by 5 2-group trials (scenario 2), and a series of 10 2-group trials (scenario 3). All scenarios started with 5 interventions, then 5 more interventions were either added to the platform or evaluated independently. Simulations with the survey results as inputs were used to compare the platform vs conventional trial designs. Data were analyzed from July to September 2021. EXPOSURE Platform trial design. MAIN OUTCOMES AND MEASURES Total trial setup and conduct cost and cumulative duration. RESULTS Although setup time and cost requirements of a single trial were highest for the platform trial, cumulative requirements of setting up a series of multiple trials in scenarios 2 and 3 were larger. Compared with the platform trial, there was a median (IQR) increase of 216.7% (202.2%-242.5%) in cumulative setup costs for scenario 2 and 391.1% (365.3%-437.9%) for scenario 3. In terms of total cost, there was a median (IQR) increase of 17.4% (12.1%-22.5%) for scenario 2 and 57.5% (43.1%-69.9%) for scenario 3. There was a median (IQR) increase in cumulative trial duration of 171.1% (158.3%-184.3%) for scenario 2 and 311.9% (282.0%-349.1%) for scenario 3. Cost and time reductions in the platform trial were observed in both the initial and subsequently evaluated interventions. CONCLUSIONS AND RELEVANCE Although setting up platform trials can take longer and be costly, the findings of this study suggest that having a single infrastructure can improve efficiencies with respect to costs and efforts.
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Affiliation(s)
- Jay J. H. Park
- Experimental Medicine, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University Health Sciences Centre, Hamilton, Ontario, Canada
| | | | | | | | - Anna Heath
- Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Department of Statistical Science, University College London, London, United Kingdom
| | - Maureen Meade
- Department of Health Research Methods, Evidence, and Impact, McMaster University Health Sciences Centre, Hamilton, Ontario, Canada
- Interdepartmental Division of Critical Care, Hamilton Health Sciences, Critical Care, Hamilton, Ontario, Canada
| | - Ryan Zarychanski
- Department of Internal Medicine, Section of Critical Care, University of Manitoba, Winnipeg, Manitoba, Canada
- Department of Internal Medicine, Section of Hematology/Medical Oncology, University of Manitoba, Winnipeg, Manitoba, Canada
| | | | | | - Edward J. Mills
- Department of Health Research Methods, Evidence, and Impact, McMaster University Health Sciences Centre, Hamilton, Ontario, Canada
| | | | - Cyrus Mehta
- Cytel, Inc, Waltham, Massachusetts
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Cambridge, Massachusetts
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Meyer EL, Mesenbrink P, Dunger-Baldauf C, Glimm E, Li Y, König F. Decision rules for identifying combination therapies in open-entry, randomized controlled platform trials. Pharm Stat 2022; 21:671-690. [PMID: 35102685 PMCID: PMC9304586 DOI: 10.1002/pst.2194] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 10/29/2021] [Accepted: 01/09/2022] [Indexed: 12/28/2022]
Abstract
Platform trials have become increasingly popular for drug development programs, attracting interest from statisticians, clinicians and regulatory agencies. Many statistical questions related to designing platform trials—such as the impact of decision rules, sharing of information across cohorts, and allocation ratios on operating characteristics and error rates—remain unanswered. In many platform trials, the definition of error rates is not straightforward as classical error rate concepts are not applicable. For an open‐entry, exploratory platform trial design comparing combination therapies to the respective monotherapies and standard‐of‐care, we define a set of error rates and operating characteristics and then use these to compare a set of design parameters under a range of simulation assumptions. When setting up the simulations, we aimed for realistic trial trajectories, such that for example, a priori we do not know the exact number of treatments that will be included over time in a specific simulation run as this follows a stochastic mechanism. Our results indicate that the method of data sharing, exact specification of decision rules and a priori assumptions regarding the treatment efficacy all strongly contribute to the operating characteristics of the platform trial. Furthermore, different operating characteristics might be of importance to different stakeholders. Together with the potential flexibility and complexity of a platform trial, which also impact the achieved operating characteristics via, for example, the degree of efficiency of data sharing this implies that utmost care needs to be given to evaluation of different assumptions and design parameters at the design stage.
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Affiliation(s)
- Elias Laurin Meyer
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Peter Mesenbrink
- Analytics Department, Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA
| | | | - Ekkehard Glimm
- Analytics Department, Novartis Pharma AG, Basel, Switzerland.,Institute of Biometry and Medical Informatics, University of Magdeburg, Magdeburg, Germany
| | - Yuhan Li
- Analytics Department, Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA
| | - Franz König
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
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