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Greenstreet P, Jaki T, Bedding A, Harbron C, Mozgunov P. A multi-arm multi-stage platform design that allows preplanned addition of arms while still controlling the family-wise error. Stat Med 2024; 43:3613-3632. [PMID: 38880949 DOI: 10.1002/sim.10135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 05/26/2024] [Accepted: 05/29/2024] [Indexed: 06/18/2024]
Abstract
There is growing interest in platform trials that allow for adding of new treatment arms as the trial progresses as well as being able to stop treatments part way through the trial for either lack of benefit/futility or for superiority. In some situations, platform trials need to guarantee that error rates are controlled. This paper presents a multi-stage design, that allows additional arms to be added in a platform trial in a preplanned fashion, while still controlling the family-wise error rate, under the assumption of known number and timing of treatments to be added, and no time trends. A method is given to compute the sample size required to achieve a desired level of power and we show how the distribution of the sample size and the expected sample size can be found. We focus on power under the least favorable configuration which is the power of finding the treatment with a clinically relevant effect out of a set of treatments while the rest have an uninteresting treatment effect. A motivating trial is presented which focuses on two settings, with the first being a set number of stages per active treatment arm and the second being a set total number of stages, with treatments that are added later getting fewer stages. Compared to Bonferroni, the savings in the total maximum sample size are modest in a trial with three arms, <1% of the total sample size. However, the savings are more substantial in trials with more arms.
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Affiliation(s)
- Peter Greenstreet
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
- Exeter Clinical Trials Unit, University of Exeter, Exeter, UK
| | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- University of Regensburg, Regensburg, Germany
| | | | | | - Pavel Mozgunov
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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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; 43:3447-3462. [PMID: 38852991 DOI: 10.1002/sim.10090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 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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3
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Mulier G, Lin R, Aparicio T, Biard L. Bayesian sequential monitoring strategies for trials of digestive cancer therapeutics. BMC Med Res Methodol 2024; 24:154. [PMID: 39030498 DOI: 10.1186/s12874-024-02278-3] [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: 12/19/2023] [Accepted: 07/08/2024] [Indexed: 07/21/2024] Open
Abstract
BACKGROUND New therapeutics in oncology have presented challenges to existing paradigms and trial designs in all phases of drug development. As a motivating example, we considered an ongoing phase II trial planned to evaluate the combination of a MET inhibitor and an anti-PD-L1 immunotherapy to treat advanced oesogastric carcinoma. The objective of the paper was to exemplify the planning of an adaptive phase II trial with novel anti-cancer agents, including prolonged observation windows and joint sequential evaluation of efficacy and toxicity. METHODS We considered various candidate designs and computed decision rules assuming correlations between efficacy and toxicity. Simulations were conducted to evaluate the operating characteristics of all designs. RESULTS Design approaches allowing continuous accrual, such as the time-to-event Bayesian Optimal Phase II design (TOP), showed good operating characteristics while ensuring a reduced trial duration. All designs were sensitive to the specification of the correlation between efficacy and toxicity during planning, but TOP can take that correlation into account more easily. CONCLUSIONS While specifying design working hypotheses requires caution, Bayesian approaches such as the TOP design had desirable operating characteristics and allowed incorporating concomittant information, such as toxicity data from concomitant observations in another relevant patient population (e.g., defined by mutational status).
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Affiliation(s)
- Guillaume Mulier
- ECSTRRA team UMR 1153, INSERM, Saint-Louis hospital, 1 avenue Claude Vellefaux, Paris, 75010, France.
- Service de Biostatistique et Information Médicale, AP-HP Saint-Louis hospital, 1 avenue Claude Vellefaux, Paris, 75010, France.
| | - Ruitao Lin
- Department of Biostatistics, MD Anderson Cancer Center, 7007 Bertner Avenue, Houston, 77030, Texas, USA
| | - Thomas Aparicio
- Service d'hépato-gastro-entérologie, Hôpital Saint-Louis, 1 avenue Claude Vellefaux, Paris, 75010, France
- Université Paris Cité, 12 rue de l'École-de-Médecine, Paris, 75006, France
| | - Lucie Biard
- ECSTRRA team UMR 1153, INSERM, Saint-Louis hospital, 1 avenue Claude Vellefaux, Paris, 75010, France
- Service de Biostatistique et Information Médicale, AP-HP Saint-Louis hospital, 1 avenue Claude Vellefaux, Paris, 75010, France
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Bethe U, Pana ZD, Drosten C, Goossens H, König F, Marchant A, Molenberghs G, Posch M, Van Damme P, Cornely OA. Innovative approaches for vaccine trials as a key component of pandemic preparedness - a white paper. Infection 2024:10.1007/s15010-024-02347-1. [PMID: 39017997 DOI: 10.1007/s15010-024-02347-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Accepted: 07/04/2024] [Indexed: 07/18/2024]
Abstract
BACKGROUND WHO postulates the application of adaptive design features in the global clinical trial ecosystem. However, the adaptive platform trial (APT) methodology has not been widely adopted in clinical research on vaccines. METHODS The VACCELERATE Consortium organized a two-day workshop to discuss the applicability of APT methodology in vaccine trials under non-pandemic as well as pandemic conditions. Core aspects of the discussions are summarized in this article. RESULTS An "ever-warm" APT appears ideally suited to improve efficiency and speed of vaccine research. Continuous learning based on accumulating APT trial data allows for pre-planned adaptations during its course. Given the relative design complexity, alignment of all stakeholders at all stages of an APT is central. Vaccine trial modelling is crucial, both before and in a pandemic emergency. Various inferential paradigms are possible (frequentist, likelihood, or Bayesian). The focus in the interpandemic interval may be on research gaps left by industry trials. For activation in emergency, template Disease X protocols of syndromal design for pathogens yet unknown need to be stockpiled and updated regularly. Governance of a vaccine APT should be fully integrated into supranational pandemic response mechanisms. DISCUSSION A broad range of adaptive features can be applied in platform trials on vaccines. Faster knowledge generation comes with increased complexity of trial design. Design complexity should not preclude simple execution at trial sites. Continuously generated evidence represents a return on investment that will garner societal support for sustainable funding. Adaptive design features will naturally find their way into platform trials on vaccines.
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Affiliation(s)
- Ullrich Bethe
- Institute of Translational Research, Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), Faculty of Medicine and University Hospital Cologne, University of Cologne, Herderstrasse 52, 50931, Cologne, Germany
- Center for Integrated Oncology Aachen Bonn Cologne Düsseldorf (CIO ABCD) and Excellence Center for Medical Mycology (ECMM), Department I of Internal Medicine, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany
- Partner Site Bonn-Cologne, German Centre for Infection Research (DZIF), Cologne, Germany
| | - Zoi D Pana
- Medical School, European University of Cyprus, Nicosia, Cyprus
| | - Christian Drosten
- Institute of Virology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Herman Goossens
- Laboratory of Medical Microbiology, Vaccine & Infectious Disease Institute and Biobank Antwerp, University of Antwerp, Wilrijk, Belgium
| | - Franz König
- Center for Medical Data Science, Institute of Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Arnaud Marchant
- European Plotkin Institute for Vaccinology, Université libre de Bruxelles, Brussels, Belgium
| | - Geert Molenberghs
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Data Science Institute, KU Leuven and Hasselt University, Wilrijk, Belgium
| | - Martin Posch
- Center for Medical Data Science, Institute of Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Pierre Van Damme
- Centre for the Evaluation of Vaccination, VACCINOPOLIS, Vaccine and Infectious Disease Institute, University of Antwerp, Wilrijk, Belgium
| | - Oliver A Cornely
- Institute of Translational Research, Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), Faculty of Medicine and University Hospital Cologne, University of Cologne, Herderstrasse 52, 50931, Cologne, Germany.
- Center for Integrated Oncology Aachen Bonn Cologne Düsseldorf (CIO ABCD) and Excellence Center for Medical Mycology (ECMM), Department I of Internal Medicine, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany.
- Partner Site Bonn-Cologne, German Centre for Infection Research (DZIF), Cologne, Germany.
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Kumar S, Kumar H, Lahon J, Saikia D. A brief review on the lessons learned from COVID-19 on drug discovery and research. Med Pharm Rep 2024; 97:243-248. [PMID: 39234462 PMCID: PMC11370851 DOI: 10.15386/mpr-2641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 11/03/2023] [Accepted: 11/15/2023] [Indexed: 09/06/2024] Open
Abstract
COVID-19 pandemic has taught many lessons regarding drug discovery and development. This review covers these aspects of drug discovery and research for COVID-19 which might be used as a tool for future. It summarizes the positives such as progresses in antiviral drug discovery, drug repurposing, adaptations of clinical trial and its regulations, as well as the negative points such as the need to develop more collaboration among stakeholders and future directions. It also discusses the benefits and limitations of finding new indications for existing drugs, and the lessons learned regarding rigorous and robust clinical trials, pharmacokinetic/pharmacodynamic modelling, as well as combination therapy. The pandemic has also revealed some gaps regarding global collaboration and coordination, data sharing and transparency and equitable distribution. Finally, the review enumerates the future directions and implications of drug discovery and research for COVID-19 and other infectious diseases such as preparedness and resilience, interdisciplinary and integrative approaches, diversity and inclusion, and personalized and precision medicine.
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Affiliation(s)
- Subodh Kumar
- Department of Pharmacology, All India Institute of Medical Sciences, Deoghar, India
| | - Hansraj Kumar
- Department of Pharmacology, All India Institute of Medical Sciences, Deoghar, India
| | - Joonmoni Lahon
- Department of Pharmacology, All India Institute of Medical Sciences, Guwahati, India
| | - Dibyajyoti Saikia
- Department of Pharmacology, All India Institute of Medical Sciences, Guwahati, India
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Focosi D, Franchini M, Maggi F, Shoham S. COVID-19 therapeutics. Clin Microbiol Rev 2024; 37:e0011923. [PMID: 38771027 PMCID: PMC11237566 DOI: 10.1128/cmr.00119-23] [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: 05/22/2024] Open
Abstract
SUMMARYSince the emergence of COVID-19 in 2020, an unprecedented range of therapeutic options has been studied and deployed. Healthcare providers have multiple treatment approaches to choose from, but efficacy of those approaches often remains controversial or compromised by viral evolution. Uncertainties still persist regarding the best therapies for high-risk patients, and the drug pipeline is suffering fatigue and shortage of funding. In this article, we review the antiviral activity, mechanism of action, pharmacokinetics, and safety of COVID-19 antiviral therapies. Additionally, we summarize the evidence from randomized controlled trials on efficacy and safety of the various COVID-19 antivirals and discuss unmet needs which should be addressed.
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Affiliation(s)
- Daniele Focosi
- North-Western Tuscany Blood Bank, Pisa University Hospital, Pisa, Italy
| | - Massimo Franchini
- Division of Hematology and Transfusion Medicine, Carlo Poma Hospital, Mantua, Italy
| | - Fabrizio Maggi
- National Institute for Infectious Diseases "Lazzaro Spallanzani" IRCCS, Rome, Italy
| | - Shmuel Shoham
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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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, Hirakawa A. How Much More Efficient Are Adaptive Platform Trials Than Multiple Stand-Alone Trials? A Comprehensive Simulation Study for Streamlining Drug Development During a Pandemic. Clin Pharmacol Ther 2024; 115:1372-1382. [PMID: 38441177 DOI: 10.1002/cpt.3224] [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: 10/23/2023] [Accepted: 02/12/2024] [Indexed: 05/14/2024]
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.
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Affiliation(s)
- Masanao Sasaki
- Department of Clinical Biostatistics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Hiroyuki Sato
- Department of Clinical Biostatistics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Yukari Uemura
- Biostatistics Section, Department of Data Science, Center of Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan
| | - Ayako Mikami
- Center for Clinical Research, National Center for Child Health and Development, Tokyo, Japan
| | - Nao Ichihara
- Department of Healthcare Quality Assessment, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shigeki Fujitani
- Department of Emergency and Critical Care Medicine, St. Marianna University School of Medicine, Kanagawa, Japan
| | - Masashi Kondo
- Center for Clinical Trial and Research Support, Fujita Health University School of Medicine, Aichi, Japan
- Department of Respiratory Medicine, Fujita Health University School of Medicine, Aichi, Japan
| | - Yohei Doi
- Departments of Microbiology and Infectious Diseases, Fujita Health University School of Medicine, Aichi, Japan
| | - Eriko Morino
- Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan
| | - Daisuke Tokita
- Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan
| | - Norio Ohmagari
- Disease Control and Prevention Center, National Center for Global Health and Medicine, Tokyo, Japan
| | - Wataru Sugiura
- Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan
| | - Akihiro Hirakawa
- Department of Clinical Biostatistics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
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Staibano P, Oulousian E, McKechnie T, Thabane A, Luo S, Gupta MK, Zhang H, Pasternak JD, Au M, Parpia S, Young JEM(T, Bhandari M. Adaptive clinical trials in surgery: A scoping review of methodological and reporting quality. PLoS One 2024; 19:e0299494. [PMID: 38805454 PMCID: PMC11132449 DOI: 10.1371/journal.pone.0299494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 02/11/2024] [Indexed: 05/30/2024] Open
Abstract
IMPORTANCE Adaptive surgical trials are scarce, but adopting these methods may help elevate the quality of surgical research when large-scale RCTs are impractical. OBJECTIVE Randomized-controlled trials (RCTs) are the gold standard for evidence-based healthcare. Despite an increase in the number of RCTs, the number of surgical trials remains unchanged. Adaptive clinical trials can streamline trial design and time to trial reporting. The advantages identified for ACTs may help to improve the quality of future surgical trials. We present a scoping review of the methodological and reporting quality of adaptive surgical trials. EVIDENCE REVIEW We performed a search of Ovid, Web of Science, and Cochrane Collaboration for all adaptive surgical RCTs performed from database inception to October 12, 2023. We included any published trials that had at least one surgical arm. All review and abstraction were performed in duplicate. Risk of bias (RoB) was assessed using the RoB 2.0 instrument and reporting quality was evaluated using CONSORT ACE 2020. All results were analyzed using descriptive methods. FINDINGS Of the 1338 studies identified, six trials met inclusion criteria. Trials were performed in cardiothoracic, oral, orthopedic, and urological surgery. The most common type of adaptive trial was group sequential design with pre-specified interim analyses planned for efficacy, futility, and/or sample size re-estimation. Two trials did use statistical simulations. Our risk of bias evaluation identified a high risk of bias in 50% of included trials. Reporting quality was heterogeneous regarding trial design and outcome assessment and details in relation to randomization and blinding concealment. CONCLUSION AND RELEVANCE Surgical trialists should consider implementing adaptive components to help improve patient recruitment and reduce trial duration. Reporting of future adaptive trials must adhere to existing CONSORT ACE 2020 guidelines. Future research is needed to optimize standardization of adaptive methods across medicine and surgery.
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Affiliation(s)
- Phillip Staibano
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methodology, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Emily Oulousian
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada
- McGill University School of Medicine, McGill University, Montreal, Quebec, Canada
| | - Tyler McKechnie
- Department of Health Research Methodology, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
- Division of General Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada
| | - Alex Thabane
- Department of Health Research Methodology, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Samuel Luo
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada
- Michael G. DeGroote School of Medicine, Hamilton, Ontario, Canada
| | - Michael K. Gupta
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada
| | - Han Zhang
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada
| | - Jesse D. Pasternak
- Endocrine Surgery Section Head, Division of General Surgery, Department of Surgery, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Michael Au
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada
| | - Sameer Parpia
- Department of Health Research Methodology, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - J. E. M. (Ted) Young
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada
| | - Mohit Bhandari
- Department of Health Research Methodology, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
- Division of Orthopedic Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada
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9
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Proschan MA, Nason M, Ortega-Villa AM, Wang J. Changing interim monitoring in response to internal clinical trial data. Biometrics 2024; 80:ujae006. [PMID: 38477484 DOI: 10.1093/biomtc/ujae006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/18/2023] [Accepted: 01/29/2024] [Indexed: 03/14/2024]
Abstract
Designing clinical trials for emerging infectious diseases such as COVID-19 is challenging because information needed for proper planning may be lacking. Pre-specified adaptive designs can be attractive options, but what happens if a trial with no such design needs to be modified? For example, unexpectedly high efficacy (approximately 95%) in two COVID-19 vaccine trials might cause investigators in other COVID-19 vaccine trials to increase the number of interim analyses to allow earlier stopping for efficacy. If such a decision is based solely on external data, there are no issues, but what if internal trial data by arm are also examined? Fortunately, the conditional error principle of Müller and Schäfer (2004) can be used to ensure no inflation of the type 1 error rate, even if no interim analyses were planned. We study the properties, including limitations, of this method. We provide a shiny app to evaluate changes in timing of interim analyses in response to outcome data by arm in clinical trials.
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Affiliation(s)
- Michael A Proschan
- Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA, 20892
| | - Martha Nason
- Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA, 20892
| | - Ana M Ortega-Villa
- Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA, 20892
| | - Jing Wang
- Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA, 20892
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10
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Karbwang J, Torres CE, Navarro AM, Wongwai P, Jimenez EB, Shetty Y, Ramalingam S, Koli P, Amir L, Rachmawati SD, Waworundeng M, Rizki H, Noor ASM, Ghimire P, Gyanwali P, Sharma S, Ghimire N, Wanigatunge C, Yimtae K. The readiness of the Asian research ethics committees in responding to the COVID-19 pandemic: A multi-country survey. F1000Res 2024; 13:19. [PMID: 39165349 PMCID: PMC11333877 DOI: 10.12688/f1000research.143138.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/07/2023] [Indexed: 08/22/2024] Open
Abstract
Background COVID-19 is a highly challenging infectious disease. Research ethics committees (RECs) have challenges reviewing research on this new pandemic disease under a tight timeline and public pressure. This study aimed to assess RECs' responses and review during the outbreak in seven Asian countries where the Strategic Initiative for Developing Capacity in Ethical Review (SIDCER) networks are active. Methods The online survey was conducted in seven Asian countries from April to August 2021. Two sets of online questionnaires were developed, one set for the chairs/secretaries and another set for the REC members.The REC profiles obtained from the REC members are descriptive in nature. Data from the chairs/secretaries were compared between the RECs with external quality assessment (SIDCER-Recognized RECs, SR-RECs) and non-external quality assessment (Non-SIDCER-Recognized RECs, NSR-RECs) and analyzed using a Chi-squared test. Results A total of 688 REC members and 197 REC chairs/secretaries participated in the survey. Most RECs have standard operating procedures (SOPs), and have experience in reviewing all types of protocols, but 18.1% had no experience reviewing COVID-19 protocols. Most REC members need specific training on reviewing COVID-19 protocols (93%). In response to the outbreak, RECs used online reviews, increased meeting frequency and single/central REC. All SR-RECs had a member composition as required by the World Health Organisation ethics guidelines, while some NSR-RECs lacked non-affiliated and/or layperson members. SR-RECs reviewed more COVID-related product development protocols and indicated challenges in reviewing risk/benefit and vulnerability (0.010), informed consent form (0.002), and privacy and confidentiality (P = 0.020) than NSR-RECs. Conclusions Surveyed RECs had a general knowledge of REC operation and played a significant role in reviewing COVID-19-related product development protocols. Having active networks of RECs across regions to share updated information and resources could be one of the strategies to promote readiness for future public health emergencies.
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Affiliation(s)
- Juntra Karbwang
- Drug Discovery and Development Center, Thammasat University, Pathum Thani, Bangkok, 12120, Thailand
- Clinical Coordination and Training Center, Strategic Initiative in Developing Capacity in Ethical Review (SIDCER), Thammasat University, Pathum Thani, 12120, Thailand
| | - Cristina E. Torres
- National Institutes of Health, University of the Philippines Manila, Manila, Metro Manila, 1000, Philippines
- Clinical Coordination and Training Center, Forum for Ethical Review Committees in Asia and the Pacific (FERCAP), Thammasat University, Pathum Thani, 12120, Thailand
| | - Arthur M. Navarro
- Clinical Coordination and Training Center, Forum for Ethical Review Committees in Asia and the Pacific (FERCAP), Thammasat University, Pathum Thani, 12120, Thailand
- Department of Social Sciences, University of the Philippines Manila, Manila, Metro Manila, 1000, Philippines
| | - Phanthipha Wongwai
- Clinical Coordination and Training Center, Forum for Ethical Review Committees in Asia and the Pacific (FERCAP), Thammasat University, Pathum Thani, 12120, Thailand
- Faculty of Medicine, Khon Kaen University, Nai Mueang, Khon Kaen, 40002, Thailand
| | - Edlyn B. Jimenez
- National Institutes of Health, University of the Philippines Manila, Manila, Metro Manila, 1000, Philippines
| | - Yashashri Shetty
- Department of Pharmacology & Therapeutics, Seth Gordhandas Sunderdas Medical College and King Edward Memorial Hospital, Mumbai, 400012, India
| | - Sudha Ramalingam
- Department of Community Medicine, PSG Institute of Medical Sciences and Reseach, Coimbatore, 641004, India
| | - Paresh Koli
- Department of Pharmacology & Therapeutics, Seth Gordhandas Sunderdas Medical College and King Edward Memorial Hospital, Mumbai, 400012, India
| | - Lisa Amir
- Forum of Indonesian Recognized Ethics Committees (FIRREC), Jakarta, 10430, Indonesia
- Dental Research Ethics Committee Faculty of Dentistry, Universitas Indonesia, Jakarta, Jakarta, 10430, Indonesia
| | - Septi Dewi Rachmawati
- Forum of Indonesian Recognized Ethics Committees (FIRREC), Jakarta, 10430, Indonesia
- Health Research Ethics Committee, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, 65145, Indonesia
| | - Monalisa Waworundeng
- Forum of Indonesian Recognized Ethics Committees (FIRREC), Jakarta, 10430, Indonesia
- Ethics Committee, Mochtar Riady Institute for Nanotechnology (MRIN), Tangerang, 15810, Indonesia
| | - Harnawan Rizki
- Forum of Indonesian Recognized Ethics Committees (FIRREC), Jakarta, 10430, Indonesia
| | - Asyraf Syahmi Mohd Noor
- National Institutes of Health, Ministry of Health, Federal Government of Malaysia, Kuala Lumpur, 40170, Malaysia
| | - Prakash Ghimire
- National Ethical Review Board, Nepal Health Research Council, Ministry of Health & Population, Kathmandu, 7626, Nepal
| | - Pradip Gyanwali
- National Ethical Review Board, Nepal Health Research Council, Ministry of Health & Population, Kathmandu, 7626, Nepal
| | - Subhanshi Sharma
- National Ethical Review Board, Nepal Health Research Council, Ministry of Health & Population, Kathmandu, 7626, Nepal
| | - Namita Ghimire
- National Ethical Review Board, Nepal Health Research Council, Ministry of Health & Population, Kathmandu, 7626, Nepal
| | - Chandanie Wanigatunge
- Faculty of Medical Science, University of Sri Jayewardenepura, Gangodawila, Nugegoda, 10250, Sri Lanka
| | - Kwanchanok Yimtae
- Faculty of Medicine, Khon Kaen University, Nai Mueang, Khon Kaen, 40002, Thailand
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11
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Modlin C, Sugarman J, Chongwe G, Kass N, Nazziwa W, Tegli J, Shrestha P, Ali J. Towards achieving transnational research partnership equity: lessons from implementing adaptive platform trials in low- and middle-income countries. Wellcome Open Res 2023; 8:120. [PMID: 38089903 PMCID: PMC10714106 DOI: 10.12688/wellcomeopenres.18915.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/16/2023] [Indexed: 02/01/2024] Open
Abstract
Background Use of adaptive clinical trials, particularly adaptive platform trials, has grown exponentially in response to the coronavirus disease (COVID-19) pandemic. Implementation of these trials in low- and middle-income countries (LMICs) has been fostered through the formation or modification of transnational research partnerships, typically between research groups from LMICs and high-income countries (HICs). While these partnerships are important to promote collaboration and overcome the structural and economic disadvantages faced by LMIC health researchers, it is critical to focus attention on the multiple dimensions of partnership equity. Methods Based on informal literature reviews and a meeting with leaders of one of the multinational COVID-19 adaptive platform trials, we describe some important considerations about research partnership equity in this context. Results We organize these considerations into eight thematic categories: 1) epistemic structures, 2) funding, 3) ethics oversight, 4) regulatory oversight, 5) leadership, 6) post-trial access to interventions, data, and specimens, 7) knowledge translation and dissemination, and 8) research capacity strengthening and maintenance. Within each category we review normative claims that support its relevance to research partnership equity followed by discussion of how adaptive platform trials highlight new dimensions, considerations, or challenges. Conclusion In aggregate, these observations provide insight into procedural and substantive equity-building measures within transnational global health research partnerships more broadly.
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Affiliation(s)
- Chelsea Modlin
- Berman Institute for Bioethics, Johns Hopkins University, Baltimore, MD, 21205, USA
- Division of Infectious Diseases, Johns Hopkins Medicine, Baltimore, Maryland, 21205, USA
| | - Jeremy Sugarman
- Berman Institute for Bioethics, Johns Hopkins University, Baltimore, MD, 21205, USA
- Division of General Internal Medicine, Johns Hopkins Medicine, Baltimore, Maryland, 21205, USA
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, 21205, USA
| | - Gershom Chongwe
- School of Public Health, University of Zambia, Lusaka, Zambia
| | - Nancy Kass
- Berman Institute for Bioethics, Johns Hopkins University, Baltimore, MD, 21205, USA
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, 21205, USA
| | - Winfred Nazziwa
- Uganda National Council for Science and Technology, Kampala, Uganda
| | - Jemee Tegli
- Family Health International Clinical/Partnership for Research on Vaccines and Infectious Diseases in Liberia, Monrovia, Liberia
| | - Prakriti Shrestha
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, 21205, USA
| | - Joseph Ali
- Berman Institute for Bioethics, Johns Hopkins University, Baltimore, MD, 21205, USA
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, 21205, USA
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12
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Lombardo G, Couvert C, Kose M, Begum A, Spiertz C, Worrell C, Hasselbaink D, Didden EM, Sforzini L, Todorovic M, Lewi M, Brown M, Vaterkowski M, Gullet N, Amasi-Hartoonian N, Griffon N, Pais R, Rodriguez Navarro S, Kremer A, Maes C, Tan EH, Moinat M, Ferrer JG, Pariante CM, Kalra D, Ammour N, Kalko S. Electronic health records (EHRs) in clinical research and platform trials: Application of the innovative EHR-based methods developed by EU-PEARL. J Biomed Inform 2023; 148:104553. [PMID: 38000766 DOI: 10.1016/j.jbi.2023.104553] [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: 03/28/2023] [Revised: 11/13/2023] [Accepted: 11/20/2023] [Indexed: 11/26/2023]
Abstract
OBJECTIVE Electronic Health Record (EHR) systems are digital platforms in clinical practice used to collect patients' clinical information related to their health status and represents a useful storage of real-world data. EHRs have a potential role in research studies, in particular, in platform trials. Platform trials are innovative trial designs including multiple trial arms (conducted simultaneously and/or sequentially) on different treatments under a single master protocol. However, the use of EHRs in research comes with important challenges such as incompleteness of records and the need to translate trial eligibility criteria into interoperable queries. In this paper, we aim to review and to describe our proposed innovative methods to tackle some of the most important challenges identified. This work is part of the Innovative Medicines Initiative (IMI) EU Patient-cEntric clinicAl tRial pLatforms (EU-PEARL) project's work package 3 (WP3), whose objective is to deliver tools and guidance for EHR-based protocol feasibility assessment, clinical site selection, and patient pre-screening in platform trials, investing in the building of a data-driven clinical network framework that can execute these complex innovative designs for which feasibility assessments are critically important. METHODS ISO standards and relevant references informed a readiness survey, producing 354 criteria with corresponding questions selected and harmonised through a 7-round scoring process (0-1) in stakeholder meetings, with 85% of consensus being the threshold of acceptance for a criterium/question. ATLAS cohort definition and Cohort Diagnostics were mainly used to create the trial feasibility eligibility (I/E) criteria as executable interoperable queries. RESULTS The WP3/EU-PEARL group developed a readiness survey (eSurvey) for an efficient selection of clinical sites with suitable EHRs, consisting of yes-or-no questions, and a set-up of interoperable proxy queries using physicians' defined trial criteria. Both actions facilitate recruiting trial participants and alignment between study costs/timelines and data-driven recruitment potential. CONCLUSION The eSurvey will help create an archive of clinical sites with mature EHR systems suitable to participate in clinical trials/platform trials, and the interoperable proxy queries of trial eligibility criteria will help identify the number of potential participants. Ultimately, these tools will contribute to the production of EHR-based protocol design.
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Affiliation(s)
- Giulia Lombardo
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, London, UK.
| | - Camille Couvert
- Sanofi R&D, Global Development, Clinical Science & Operations, Chilly-Mazarin, France
| | - Melisa Kose
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, London, UK
| | - Amina Begum
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, London, UK
| | - Cecile Spiertz
- The Janssen Pharmaceutical Companies of Johnson & Johnson, Leiden, The Netherlands
| | - Courtney Worrell
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, London, UK
| | | | - Eva-Maria Didden
- Actelion, a Janssen company of Johnson & Johnson, Allschwil, Basel-Country, Switzerland
| | - Luca Sforzini
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, London, UK
| | - Marija Todorovic
- Johnson & Johnson Clinical Operations (JJCO), Johnson & Johnson company, Belgrade, Serbia
| | - Martine Lewi
- Global Commercial Strategy Organization, the Janssen Pharmaceutical Companies of Johnson & Johnson, Raritan, New Jersey, USA
| | - Mollie Brown
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, London, UK
| | - Morgan Vaterkowski
- Assistance Publique Hôpitaux de Paris, IT Department, Innovation and Data, Paris, France, and EPITA EPITA School of Engineering and Computer Science, Paris, France
| | - Nancy Gullet
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, London, UK
| | - Nare Amasi-Hartoonian
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, London, UK
| | - Nicolas Griffon
- Information Technology Department, AP-HP, Paris, France; LIMICS, Inserm U1142, Sorbonne Université, Paris, France
| | - Raluca Pais
- Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Institute of Cardiometabolism and Nutrition, INSERM UMRS_938, Paris, France
| | | | - Andreas Kremer
- Information Technology for Translational Medicine, ITTM S.A, House of BioHealth, Esch-sur-Alzette, Luxembourg
| | - Christophe Maes
- The European Institute for Innovation through health data, and Department Public Health and Primary Care, Unit of Medical Informatics and Statistics, Faculty of Medicine and Health Sciences, Ghent University, Gent, Belgium
| | - Eng Hooi Tan
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Maxim Moinat
- Erasmus University Medical Center, Rotterdam, the Netherlands
| | | | - Carmine M Pariante
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, London, UK
| | - Dipak Kalra
- The European Institute for Innovation through Health Data and Visiting Professor, University of Ghent, Gent, Belgium
| | - Nadir Ammour
- Sanofi R&D, Global Development, Clinical Science & Operations, Chilly-Mazarin, France
| | - Susana Kalko
- Vall d'Hebron Research Institute (VHIR), Barcelona, Spain.
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Lee H, Hwang S, Jang IJ, Chung JY, Oh J. Adaptive design clinical trials: current status by disease and trial phase in various perspectives. Transl Clin Pharmacol 2023; 31:202-216. [PMID: 38197001 PMCID: PMC10772057 DOI: 10.12793/tcp.2023.31.e21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 10/18/2023] [Accepted: 11/15/2023] [Indexed: 01/11/2024] Open
Abstract
An adaptive design is a clinical trial design that allows for modification of a structured plan in a clinical trial based on data accumulated during pre-planned interim analyses. This flexible approach to clinical trial design improves the success rate of clinical trials while reducing time, cost, and sample size compared to conventional methods. The purpose of this study is to identify the current status of adaptive design and present key considerations for planning an appropriate adaptive design based on specific circumstances. We searched for clinical trials conducted between January 2006 to July 2021 in the Clinical Trials Registry (ClinicalTrials.gov) using keywords specified in the Food and Drug Administration Adaptive Design Clinical Trial Guidelines. In order to analyze the adaptive designs used in selected cases, we classified the results according to the phase of the clinical trial, type of indication, and the specific adaptation method employed. A total of 267 clinical trials were identified on ClinicalTrials.gov. Among them, 236 clinical trials actually applied adaptive designs and were classified according to phase, indication types, and adaptation methods. Adaptive designs were most frequently used in phase 2 clinical trials and oncology research. The most commonly used adaptation method was the adaptive treatment selection design. In the case of coronavirus disease 2019, the most frequently used designs were adaptive platform design and seamless design. Through this study, we expect to provide valuable insights and considerations for the implementation of adaptive design clinical trials in different diseases and stages.
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Affiliation(s)
- Hyunjoon Lee
- Department of Clinical Pharmacology and Therapeutics, Seoul National University Hospital, Seoul National University College of Medicine, Seoul 03080, Korea
- Department of Translational Medicine, Seoul National University College of Medicine, Seoul 03080, Korea
| | - Sejung Hwang
- Department of Clinical Pharmacology and Therapeutics, Seoul National University Hospital, Seoul National University College of Medicine, Seoul 03080, Korea
- Kidney Research Institute, Seoul National University Medical Research Center, Seoul 03080, Korea
| | - In-Jin Jang
- Department of Clinical Pharmacology and Therapeutics, Seoul National University Hospital, Seoul National University College of Medicine, Seoul 03080, Korea
| | - Jae-Yong Chung
- Department of Translational Medicine, Seoul National University College of Medicine, Seoul 03080, Korea
- Department of Clinical Pharmacology and Therapeutics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam 13620, Korea
| | - Jaeseong Oh
- Department of Clinical Pharmacology and Therapeutics, Seoul National University Hospital, Seoul National University College of Medicine, Seoul 03080, Korea
- Department of Pharmacology, Jeju National University College of Medicine, Jeju 63241, Korea
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14
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Chaudhary R, Rohilla M, Chauhan S, Saini M, Aman S, Singla H, bibi A, Ahmed S, Shriwastav S, Kaur N, Dev J, Chalotra R, Singh TG, Mehta S. The pandemic's unseen wounds: COVID-19's profound effects on mental health. Ann Med Surg (Lond) 2023; 85:4954-4963. [PMID: 37811101 PMCID: PMC10552974 DOI: 10.1097/ms9.0000000000001223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/12/2023] [Indexed: 10/10/2023] Open
Abstract
Objective This review aims to explore the impact of the COVID-19 pandemic on mental health, with a focus on the physiological and psychological consequences, including comorbidities. The goal is to understand the direct and indirect populations affected by mental distress and identify potential interventions. Methodology A comprehensive literature search was conducted using various databases, including Google Scholar, ResearchGate, ScienceDirect, PubMed, PLoS One, and Web of Science. The search utilized relevant keywords to investigate the direct and indirect impacts of COVID-19 on mental health. The selected articles were critically evaluated and analyzed to identify key findings and insights. Main findings Mental health, being an intrinsic component of overall well-being, plays a vital role in physiological functioning. The COVID-19 pandemic, caused by the emergence of the novel SARS-CoV-2 virus, has had a devastating global impact. Beyond the respiratory symptoms, individuals recovering from COVID-19 commonly experience additional ailments, such as arrhythmia, depression, anxiety, and fatigue. Healthcare professionals on the frontlines face an elevated risk of mental illness. However, it is crucial to recognize that the general population also grapples with comparable levels of mental distress. Conclusion The COVID-19 pandemic has underscored the significance of addressing mental health concerns. Various strategies can help mitigate the impact, including counselling, fostering open lines of communication, providing mental support, ensuring comprehensive patient care, and administering appropriate medications. In severe cases, treatment may involve the supplementation of essential vitamins and antidepressant therapy. By understanding the direct and indirect impacts of COVID-19 on mental health, healthcare providers and policymakers can develop targeted interventions to support individuals and communities affected by the pandemic. Continued research and collaborative efforts are essential to address this pervasive issue effectively.
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Affiliation(s)
| | - Manni Rohilla
- Chitkara College of Pharmacy, Chitkara University
- Swami Vivekanand College of Pharmacy, Rajpura, Punjab
| | | | - Monika Saini
- M.M. College of Pharmacy
- Swami Vivekanand College of Pharmacy, Rajpura, Punjab
| | - Shahbaz Aman
- Department of Microbiology, M.M. Institute of Medical Science and Research, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana
| | | | | | | | - Shalini Shriwastav
- Department of Microbiology, M.M. Institute of Medical Science and Research, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana
| | - Narinder Kaur
- Department of Microbiology, M.M. Institute of Medical Science and Research, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana
| | - Jai Dev
- Department of Microbiology, M.M. Institute of Medical Science and Research, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana
| | - Rishabh Chalotra
- Department of Pharmacology, Central University of Punjab, Bathinda, India
| | | | - Sachin Mehta
- Birat Medical college Teaching Hospital, Kathmandu University, Nepal
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15
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Ramineni V, Millroth P, Iyadurai L, Jaki T, Kingslake J, Highfield J, Summers C, Bonsall MB, Holmes EA. Treating intrusive memories after trauma in healthcare workers: a Bayesian adaptive randomised trial developing an imagery-competing task intervention. Mol Psychiatry 2023; 28:2985-2994. [PMID: 37100869 PMCID: PMC10131522 DOI: 10.1038/s41380-023-02062-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 03/24/2023] [Accepted: 03/28/2023] [Indexed: 04/28/2023]
Abstract
Intensive care unit (ICU) staff continue to face recurrent work-related traumatic events throughout the COVID-19 pandemic. Intrusive memories (IMs) of such traumatic events comprise sensory image-based memories. Harnessing research on preventing IMs with a novel behavioural intervention on the day of trauma, here we take critical next steps in developing this approach as a treatment for ICU staff who are already experiencing IMs days, weeks, or months post-trauma. To address the urgent need to develop novel mental health interventions, we used Bayesian statistical approaches to optimise a brief imagery-competing task intervention to reduce the number of IMs. We evaluated a digitised version of the intervention for remote, scalable delivery. We conducted a two-arm, parallel-group, randomised, adaptive Bayesian optimisation trial. Eligible participants worked clinically in a UK NHS ICU during the pandemic, experienced at least one work-related traumatic event, and at least three IMs in the week prior to recruitment. Participants were randomised to receive immediate or delayed (after 4 weeks) access to the intervention. Primary outcome was the number of IMs of trauma during week 4, controlling for baseline week. Analyses were conducted on an intention-to-treat basis as a between-group comparison. Prior to final analysis, sequential Bayesian analyses were conducted (n = 20, 23, 29, 37, 41, 45) to inform early stopping of the trial prior to the planned maximum recruitment (n = 150). Final analysis (n = 75) showed strong evidence for a positive treatment effect (Bayes factor, BF = 1.25 × 106): the immediate arm reported fewer IMs (median = 1, IQR = 0-3) than the delayed arm (median = 10, IQR = 6-16.5). With further digital enhancements, the intervention (n = 28) also showed a positive treatment effect (BF = 7.31). Sequential Bayesian analyses provided evidence for reducing IMs of work-related trauma for healthcare workers. This methodology also allowed us to rule out negative effects early, reduced the planned maximum sample size, and allowed evaluation of enhancements. Trial Registration NCT04992390 ( www.clinicaltrials.gov ).
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Affiliation(s)
- Varsha Ramineni
- Department of Psychology, Uppsala University, Uppsala, Uppsala County, Sweden
- P1vital Products Ltd, Wallingford, Oxfordshire, UK
| | - Philip Millroth
- Department of Psychology, Uppsala University, Uppsala, Uppsala County, Sweden
| | | | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, Cambridge, Cambridgeshire, UK
- University of Regensburg, Regensburg, Bavaria, Germany
| | | | | | - Charlotte Summers
- Department of Medicine, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - Michael B Bonsall
- Department of Biology, University of Oxford, Oxford, Oxfordshire, UK
| | - Emily A Holmes
- Department of Psychology, Uppsala University, Uppsala, Uppsala County, Sweden.
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16
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Robertson DS, Choodari-Oskooei B, Dimairo M, Flight L, Pallmann P, Jaki T. Point estimation for adaptive trial designs II: Practical considerations and guidance. Stat Med 2023; 42:2496-2520. [PMID: 37021359 PMCID: PMC7614609 DOI: 10.1002/sim.9734] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 01/20/2023] [Accepted: 03/18/2023] [Indexed: 04/07/2023]
Abstract
In adaptive clinical trials, the conventional end-of-trial point estimate of a treatment effect is prone to bias, that is, a systematic tendency to deviate from its true value. As stated in recent FDA guidance on adaptive designs, it is desirable to report estimates of treatment effects that reduce or remove this bias. However, it may be unclear which of the available estimators are preferable, and their use remains rare in practice. This article is the second in a two-part series that studies the issue of bias in point estimation for adaptive trials. Part I provided a methodological review of approaches to remove or reduce the potential bias in point estimation for adaptive designs. In part II, we discuss how bias can affect standard estimators and assess the negative impact this can have. We review current practice for reporting point estimates and illustrate the computation of different estimators using a real adaptive trial example (including code), which we use as a basis for a simulation study. We show that while on average the values of these estimators can be similar, for a particular trial realization they can give noticeably different values for the estimated treatment effect. Finally, we propose guidelines for researchers around the choice of estimators and the reporting of estimates following an adaptive design. The issue of bias should be considered throughout the whole lifecycle of an adaptive design, with the estimation strategy prespecified in the statistical analysis plan. When available, unbiased or bias-reduced estimates are to be preferred.
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Affiliation(s)
| | - Babak Choodari-Oskooei
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Munya Dimairo
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Laura Flight
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | | | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Faculty of Informatics and Data Science, University of Regensburg, Regensburg, Germany
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17
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Modlin C, Sugarman J, Chongwe G, Kass N, Nazziwa W, Tegli J, Shrestha P, Ali J. Towards achieving transnational research partnership equity: lessons from implementing adaptive platform trials in low- and middle-income countries. Wellcome Open Res 2023. [DOI: 10.12688/wellcomeopenres.18915.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023] Open
Abstract
Background: Use of adaptive clinical trials, particularly adaptive platform trials, has grown exponentially in response to the coronavirus disease (COVID-19) pandemic. Implementation of these trials in low- and middle-income countries (LMICs) has been fostered through the formation or modification of transnational research partnerships, typically between research groups from LMICs and high-income countries (HICs). While these partnerships are important to promote collaboration and overcome the structural and economic disadvantages faced by LMIC health researchers, it is critical to focus attention on the multiple dimensions of partnership equity. Methods: Based on informal literature reviews and meetings with leaders of one of the multinational COVID-19 adaptive platform trials, we describe what can be learned about research partnership equity from these experiences. Results: We organize these considerations into eight thematic categories: 1) epistemic structures, 2) funding, 3) ethics oversight, 4) regulatory oversight, 5) leadership, 6) post-trial access to interventions, data, and specimens, 7) knowledge translation, and 8) research capacity strengthening and maintenance. Within each category we review the normative claims that support its relevance to research partnership equity followed by discussion of how adaptive platform trials highlight new dimensions, considerations, or challenges. Conclusion: These observations provide insight into procedural and substantive equity-building measures within transnational global health research partnerships more broadly.
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18
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Robertson DS, Choodari‐Oskooei B, Dimairo M, Flight L, Pallmann P, Jaki T. Point estimation for adaptive trial designs I: A methodological review. Stat Med 2023; 42:122-145. [PMID: 36451173 PMCID: PMC7613995 DOI: 10.1002/sim.9605] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 10/21/2022] [Accepted: 11/01/2022] [Indexed: 12/02/2022]
Abstract
Recent FDA guidance on adaptive clinical trial designs defines bias as "a systematic tendency for the estimate of treatment effect to deviate from its true value," and states that it is desirable to obtain and report estimates of treatment effects that reduce or remove this bias. The conventional end-of-trial point estimates of the treatment effects are prone to bias in many adaptive designs, because they do not take into account the potential and realized trial adaptations. While much of the methodological developments on adaptive designs have tended to focus on control of type I error rates and power considerations, in contrast the question of biased estimation has received relatively less attention. This article is the first in a two-part series that studies the issue of potential bias in point estimation for adaptive trials. Part I provides a comprehensive review of the methods to remove or reduce the potential bias in point estimation of treatment effects for adaptive designs, while part II illustrates how to implement these in practice and proposes a set of guidelines for trial statisticians. The methods reviewed in this article can be broadly classified into unbiased and bias-reduced estimation, and we also provide a classification of estimators by the type of adaptive design. We compare the proposed methods, highlight available software and code, and discuss potential methodological gaps in the literature.
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Affiliation(s)
| | | | - Munya Dimairo
- School of Health and Related Research (ScHARR)University of SheffieldSheffieldUK
| | - Laura Flight
- School of Health and Related Research (ScHARR)University of SheffieldSheffieldUK
| | | | - Thomas Jaki
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
- Faculty of Informatics and Data ScienceUniversity of RegensburgRegensburgGermany
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19
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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] [MESH Headings] [Grants] [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 SystemsMedical University of ViennaViennaAustria
| | - Peter Mesenbrink
- Analytics DepartmentNovartis Pharmaceuticals CorporationEast HanoverNew JerseyUSA
| | | | - Ekkehard Glimm
- Analytics DepartmentNovartis Pharma AGBaselSwitzerland
- Institute of Biometry and Medical InformaticsUniversity of MagdeburgMagdeburgGermany
| | - Yuhan Li
- Analytics DepartmentNovartis Pharmaceuticals CorporationEast HanoverNew JerseyUSA
| | - Franz König
- Center for Medical Statistics, Informatics, and Intelligent SystemsMedical University of ViennaViennaAustria
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20
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Forni LG. Blood Purification Studies in the ICU: What Endpoints Should We Use? Blood Purif 2022; 51:990-996. [DOI: 10.1159/000523761] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 02/21/2022] [Indexed: 11/19/2022]
Abstract
The potential for treatment of the critically ill using blood purification techniques has been discussed for several decades. However, since the first attempts at applying extracorporeal techniques to patients with sepsis were described, there has been considerable hesitancy towards the widespread adoption of such methods, given the lack of mortality benefit observed and indeed the paucity of randomized controlled studies. However, this is not unique so far as studies on the critically ill are concerned where there is a dearth of studies providing a positive finding to influence clinical practice. Consequently, as well as targeted patient selection, it is perhaps time to consider endpoints other than mortality in studies on the critically ill, particularly in blood purification studies where, to-date, such heterogeneous groups of patients have been studied.
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Jahn B, Friedrich S, Behnke J, Engel J, Garczarek U, Münnich R, Pauly M, Wilhelm A, Wolkenhauer O, Zwick M, Siebert U, Friede T. On the role of data, statistics and decisions in a pandemic. ADVANCES IN STATISTICAL ANALYSIS : ASTA : A JOURNAL OF THE GERMAN STATISTICAL SOCIETY 2022; 106:349-382. [PMID: 35432617 PMCID: PMC8988552 DOI: 10.1007/s10182-022-00439-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 03/09/2022] [Indexed: 12/03/2022]
Abstract
A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.
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Affiliation(s)
- Beate Jahn
- Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT – University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria
| | - Sarah Friedrich
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
| | - Joachim Behnke
- Zeppelin University Friedrichshafen, Friedrichshafen, Germany
| | - Joachim Engel
- Pädagogische Hochschule Ludwigsburg, Ludwigsburg, Germany
| | | | - Ralf Münnich
- Economic and Social Statistics, Trier University, Trier, Germany
| | - Markus Pauly
- Department of Statistics, TU Dortmund University, Dortmund, Germany
| | - Adalbert Wilhelm
- Psychology and Methods, Jacobs University Bremen, Bremen, Germany
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
- Leibniz-Institute for Food Systems Biology, Technical University of Munich, Munich, Germany
| | - Markus Zwick
- Division of Economic Policy and Quantitative Methods, Goethe University Frankfurt, Frankfurt, Germany
| | - Uwe Siebert
- Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT – University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria
- Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
- Center for Health Decision Science and Departments of Epidemiology and Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
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22
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Beyersmann J, Friede T, Schmoor C. Design aspects of COVID-19 treatment trials: Improving probability and time of favorable events. Biom J 2022; 64:440-460. [PMID: 34677829 PMCID: PMC8653377 DOI: 10.1002/bimj.202000359] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 08/13/2021] [Accepted: 09/04/2021] [Indexed: 12/24/2022]
Abstract
As a reaction to the pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a multitude of clinical trials for the treatment of SARS-CoV-2 or the resulting corona disease 2019 (COVID-19) are globally at various stages from planning to completion. Although some attempts were made to standardize study designs, this was hindered by the ferocity of the pandemic and the need to set up clinical trials quickly. We take the view that a successful treatment of COVID-19 patients (i) increases the probability of a recovery or improvement within a certain time interval, say 28 days; (ii) aims to expedite favorable events within this time frame; and (iii) does not increase mortality over this time period. On this background, we discuss the choice of endpoint and its analysis. Furthermore, we consider consequences of this choice for other design aspects including sample size and power and provide some guidance on the application of adaptive designs in this particular context.
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Affiliation(s)
| | - Tim Friede
- Institut für Medizinische StatistikUniversitätsmedizin GöttingenGöttingenGermany
- Deutsches Zentrum für Herz‐Kreislaufforschung (DZHK)Standort GöttingenGöttingenGermany
| | - Claudia Schmoor
- Zentrum Klinische Studien, Universitätsklinikum Freiburg, Medizinische FakultätAlbert‐Ludwigs Universität FreiburgFreiburg im BreisgauGermany
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23
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Ewings S, Saunders G, Jaki T, Mozgunov P. Practical recommendations for implementing a Bayesian adaptive phase I design during a pandemic. BMC Med Res Methodol 2022; 22:25. [PMID: 35057758 PMCID: PMC8771176 DOI: 10.1186/s12874-022-01512-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 01/06/2022] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Modern designs for dose-finding studies (e.g., model-based designs such as continual reassessment method) have been shown to substantially improve the ability to determine a suitable dose for efficacy testing when compared to traditional designs such as the 3 + 3 design. However, implementing such designs requires time and specialist knowledge. METHODS We present a practical approach to developing a model-based design to help support uptake of these methods; in particular, we lay out how to derive the necessary parameters and who should input, and when, to these decisions. Designing a model-based, dose-finding trial is demonstrated using a treatment within the AGILE platform trial, a phase I/II adaptive design for novel COVID-19 treatments. RESULTS We present discussion of the practical delivery of AGILE, covering what information was found to support principled decision making by the Safety Review Committee, and what could be contained within a statistical analysis plan. We also discuss additional challenges we encountered in the study and discuss more generally what (unplanned) adaptations may be acceptable (or not) in studies using model-based designs. CONCLUSIONS This example demonstrates both how to design and deliver an adaptive dose-finding trial in order to support uptake of these methods.
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Affiliation(s)
- Sean Ewings
- Southampton Clinical Trials Unit, University of Southampton, Mailpoint 131, Southampton General Hospital, Tremona Road, Southampton, SO16, UK.
| | - Geoff Saunders
- Southampton Clinical Trials Unit, University of Southampton, Mailpoint 131, Southampton General Hospital, Tremona Road, Southampton, SO16, UK
| | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Mathematics and Statistics, Lancaster University, University of Lancaster, Lancaster, UK
| | - Pavel Mozgunov
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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24
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Johnson R, Jackson C, Presanis A, Villar SS, De Angelis D. Quantifying Efficiency Gains of Innovative Designs of Two-Arm Vaccine Trials for COVID-19 Using an Epidemic Simulation Model. Stat Biopharm Res 2022; 14:33-41. [PMID: 35096276 PMCID: PMC7612285 DOI: 10.1080/19466315.2021.1939774] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 04/18/2021] [Accepted: 05/25/2021] [Indexed: 12/24/2022]
Abstract
Clinical trials of a vaccine during an epidemic face particular challenges, such as the pressure to identify an effective vaccine quickly to control the epidemic, and the effect that time-space-varying infection incidence has on the power of a trial. We illustrate how the operating characteristics of different trial design elements maybe evaluated using a network epidemic and trial simulation model, based on COVID-19 and individually randomized two-arm trials with a binary outcome. We show that "ring" recruitment strategies, prioritizing participants at an imminent risk of infection, can result in substantial improvement in terms of power in the model we present. In addition, we introduce a novel method to make more efficient use of the data from the earliest cases of infection observed in the trial, whose infection may have been too early to be vaccine-preventable. Finally, we compare several methods of response-adaptive randomization (RAR), discussing their advantages and disadvantages in the context of our model and identifying particular adaptation strategies that preserve power and estimation properties, while slightly reducing the number of infections, given an effective vaccine.
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Affiliation(s)
- Rob Johnson
- Imperial College London, Department of Infectious Disease Epidemiology, London, UK
| | - Chris Jackson
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Anne Presanis
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Sofia S. Villar
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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25
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Wilson N, Biggs K, Bowden S, Brown J, Dimairo M, Flight L, Hall J, Hockaday A, Jaki T, Lowe R, Murphy C, Pallmann P, Pilling MA, Snowdon C, Sydes MR, Villar SS, Weir CJ, Welburn J, Yap C, Maier R, Hancock H, Wason JMS. Costs and staffing resource requirements for adaptive clinical trials: quantitative and qualitative results from the Costing Adaptive Trials project. BMC Med 2021; 19:251. [PMID: 34696781 PMCID: PMC8545558 DOI: 10.1186/s12916-021-02124-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 09/13/2021] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Adaptive designs offer great promise in improving the efficiency and patient-benefit of clinical trials. An important barrier to further increased use is a lack of understanding about which additional resources are required to conduct a high-quality adaptive clinical trial, compared to a traditional fixed design. The Costing Adaptive Trials (CAT) project investigated which additional resources may be required to support adaptive trials. METHODS We conducted a mock costing exercise amongst seven Clinical Trials Units (CTUs) in the UK. Five scenarios were developed, derived from funded clinical trials, where a non-adaptive version and an adaptive version were described. Each scenario represented a different type of adaptive design. CTU staff were asked to provide the costs and staff time they estimated would be needed to support the trial, categorised into specified areas (e.g. statistics, data management, trial management). This was calculated separately for the non-adaptive and adaptive version of the trial, allowing paired comparisons. Interviews with 10 CTU staff who had completed the costing exercise were conducted by qualitative researchers to explore reasons for similarities and differences. RESULTS Estimated resources associated with conducting an adaptive trial were always (moderately) higher than for the non-adaptive equivalent. The median increase was between 2 and 4% for all scenarios, except for sample size re-estimation which was 26.5% (as the adaptive design could lead to a lengthened study period). The highest increase was for statistical staff, with lower increases for data management and trial management staff. The percentage increase in resources varied across different CTUs. The interviews identified possible explanations for differences, including (1) experience in adaptive trials, (2) the complexity of the non-adaptive and adaptive design, and (3) the extent of non-trial specific core infrastructure funding the CTU had. CONCLUSIONS This work sheds light on additional resources required to adequately support a high-quality adaptive trial. The percentage increase in costs for supporting an adaptive trial was generally modest and should not be a barrier to adaptive designs being cost-effective to use in practice. Informed by the results of this research, guidance for investigators and funders will be developed on appropriately resourcing adaptive trials.
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Affiliation(s)
- Nina Wilson
- Population Health Sciences Institute, Newcastle University, Baddiley-Clark Building, Richardson Road, Newcastle upon Tyne, NE2 4AX, UK
| | - Katie Biggs
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Sarah Bowden
- Cancer Research UK Clinical Trials Unit (CRCTU), University of Birmingham, Birmingham, UK
| | - Julia Brown
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Munyaradzi Dimairo
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Laura Flight
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Jamie Hall
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Anna Hockaday
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Rachel Lowe
- Centre for Trials Research, Cardiff University, Cardiff, UK
| | - Caroline Murphy
- King's College Trials Unit, King's College London, London, UK
| | | | - Mark A Pilling
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Claire Snowdon
- The Institute of Cancer Research Clinical Trials & Statistics Unit, London, UK
| | | | - Sofía S Villar
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Christopher J Weir
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Jessica Welburn
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Christina Yap
- The Institute of Cancer Research Clinical Trials & Statistics Unit, London, UK
| | - Rebecca Maier
- Population Health Sciences Institute, Newcastle University, Baddiley-Clark Building, Richardson Road, Newcastle upon Tyne, NE2 4AX, UK
- Newcastle Clinical Trials Unit, Newcastle University, Newcastle upon Tyne, UK
| | - Helen Hancock
- Population Health Sciences Institute, Newcastle University, Baddiley-Clark Building, Richardson Road, Newcastle upon Tyne, NE2 4AX, UK
- Newcastle Clinical Trials Unit, Newcastle University, Newcastle upon Tyne, UK
| | - James M S Wason
- Population Health Sciences Institute, Newcastle University, Baddiley-Clark Building, Richardson Road, Newcastle upon Tyne, NE2 4AX, UK.
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26
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Sina-Odunsi AJ. Impact of COVID-19 on oncology practice and research in the United States. PUBLIC HEALTH IN PRACTICE 2021; 2:100191. [PMID: 34608461 PMCID: PMC8482542 DOI: 10.1016/j.puhip.2021.100191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 08/26/2021] [Accepted: 09/05/2021] [Indexed: 11/26/2022] Open
Abstract
The COVID-19 outbreak has affected healthcare services in many ways, such as interruption of regular patient visit to clinical facilities and burdening of the already limited healthcare resources. Prior to the emergence of COVID-19, cancer has been one of the leading causes of deaths in the United States. While the regular care and access disruptions associated with the COVID-19 outbreak have received increasing attention in many areas, the range of gaps in oncology practice and research is not fully understood or well documented. Nonetheless, the global outbreak continues to unevenly affect the wealthiest and best-resourced country in the world and complicates response to other health issues. The aim of this study is to comment on the impact of the COVID-19 pandemic on oncology practice and research in the United States.
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27
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McMillan G, Mayer C, Tang R, Liu Y, LaVange L, Antonijevic Z, Beckman RA. Planning for the Next Pandemic: Ethics and Innovation Today for Improved Clinical Trials Tomorrow. Stat Biopharm Res 2021; 14:22-27. [PMID: 37006380 PMCID: PMC10061983 DOI: 10.1080/19466315.2021.1918236] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 03/22/2021] [Accepted: 04/12/2021] [Indexed: 01/05/2023]
Abstract
The coronavirus pandemic has brought public attention to the steps required to produce valid scientific clinical research in drug development. Traditional ethical principles that guide clinical research remain the guiding compass for physicians, patients, public health officials, investigators, drug developers and the public. Accelerating the process of delivering safe and effective treatments and vaccines against COVID-19 is a moral imperative. The apparent clash between the regulated system of phased randomized clinical trials and urgent public health need requires leveraging innovation with ethical scientific rigor. We reflect on the Belmont principles of autonomy, beneficence and justice as the pandemic unfolds, and illustrate the role of innovative clinical trial designs in alleviating pandemic challenges. Our discussion highlights selected types of innovative trial design and correlates them with ethical parameters and public health benefits. Details are provided for platform trials and other innovative designs such as basket and umbrella trials, designs leveraging external data sources, multi-stage seamless trials, preplanned control arm data sharing between larger trials, and higher order systems of linked trials coordinated more broadly between individual trials and phases of development, recently introduced conceptually as "PIPELINEs."
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Affiliation(s)
- Gianna McMillan
- Bioethics Institute, Loyola Marymount University, Los Angeles, CA
| | | | - Rui Tang
- Servier Pharmaceuticals, Boston, MA
| | - Yi Liu
- Nektar Therapeutics, Data Science and Systems, San Francisco, CA
| | - Lisa LaVange
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC
| | | | - Robert A. Beckman
- Departments of Oncology and of Biostatistics, Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC
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28
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Sverdlov O, Ryeznik Y, Wong WK. Opportunity for efficiency in clinical development: An overview of adaptive clinical trial designs and innovative machine learning tools, with examples from the cardiovascular field. Contemp Clin Trials 2021; 105:106397. [PMID: 33845209 DOI: 10.1016/j.cct.2021.106397] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/28/2021] [Accepted: 04/05/2021] [Indexed: 11/30/2022]
Abstract
Modern data analysis tools and statistical modeling techniques are increasingly used in clinical research to improve diagnosis, estimate disease progression and predict treatment outcomes. What seems less emphasized is the importance of the study design, which can have a serious impact on the study cost, time and statistical efficiency. This paper provides an overview of different types of adaptive designs in clinical trials and their applications to cardiovascular trials. We highlight recent proliferation of work on adaptive designs over the past two decades, including some recent regulatory guidelines on complex trial designs and master protocols. We also describe the increasing role of machine learning and use of metaheuristics to construct increasingly complex adaptive designs or to identify interesting features for improved predictions and classifications.
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Affiliation(s)
- Oleksandr Sverdlov
- Early Development Biostatistics, Novartis Pharmaceuticals Corporation, USA.
| | - Yevgen Ryeznik
- Department of Pharmaceutical Biosciences, Uppsala University, Sweden
| | - Weng Kee Wong
- Department of Biostatistics, University of California Los Angeles, USA
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29
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Meyer EL, Mesenbrink P, Mielke T, Parke T, Evans D, König F. Systematic review of available software for multi-arm multi-stage and platform clinical trial design. Trials 2021; 22:183. [PMID: 33663579 PMCID: PMC7931508 DOI: 10.1186/s13063-021-05130-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 02/13/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND In recent years, the popularity of multi-arm multi-stage, seamless adaptive, and platform trials has increased. However, many design-related questions and questions regarding which operating characteristics should be evaluated to determine the potential performance of a specific trial design remain and are often further complicated by the complexity of such trial designs. METHODS A systematic search was conducted to review existing software for the design of platform trials, whereby multi-arm multi-stage trials were also included. The results of this search are reported both on the literature level and the software level, highlighting the software judged to be particularly useful. RESULTS In recent years, many highly specialized software packages targeting single design elements on platform studies have been released. Only a few of the developed software packages provide extensive design flexibility, at the cost of limited access due to being commercial or not being usable as out-of-the-box solutions. CONCLUSIONS We believe that both an open-source modular software similar to OCTOPUS and a collaborative effort will be necessary to create software that takes advantage of and investigates the impact of all the flexibility that platform trials potentially provide.
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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
- Novartis Pharmaceuticals Corporation, One Health Plaza, East Hanover, NJ, USA
| | | | | | | | - Franz König
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
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30
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Herson J. Clinical trial preparations for the next pandemic. Contemp Clin Trials 2021; 102:106292. [PMID: 33515783 PMCID: PMC7985296 DOI: 10.1016/j.cct.2021.106292] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 01/13/2021] [Accepted: 01/20/2021] [Indexed: 12/29/2022]
Abstract
This paper describes the need to prepare for the development of antiviral therapeutics for the next pandemic. Preparation would consist of a stockpiling of best practices for clinical trial design, analysis and operations during the current SARS-CoV-2 pandemic as well as continuous development of treatments and methodology between pandemics. This development would be facilitated by a global clinical trial pandemic reserve similar to the military reserves consisting of medical and quantitative methods professionals who would remain engaged between pandemics. Continuous identification of potential antiviral drugs and diagnostic methods would also be needed. Specific methodology addressed includes the importance of large simple trials, follow up time, efficacy endpoint, appropriate estimands, non-inferiority trials, more sophisticated patient accrual models and procedures for data sharing between clinical trials.
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Affiliation(s)
- Jay Herson
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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31
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Natanegara F, Zariffa N, Buenconsejo J, Ran Liao, Cooner F, Lakshminarayanan D, Ghosh S, Schindler JS, Gamalo M. Statistical Opportunities to Accelerate Development for COVID-19 Therapeutics. Stat Biopharm Res 2021. [DOI: 10.1080/19466315.2020.1865195] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Fanni Natanegara
- Research and Development – Statistics, Eli Lilly and Co, Indianapolis, IN, USA
| | | | - Joan Buenconsejo
- Biometrics, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USA
| | - Ran Liao
- Research and Development – Statistics, Eli Lilly and Co, Indianapolis, IN, USA
| | - Freda Cooner
- Center for Design and Analysis, Amgen, Thousand Oaks, CA, USA
| | - Divya Lakshminarayanan
- Clinical Statistics, COVID-19, Biostatistics R&D, GlaxoSmithKline, Collegeville, PA, USA
| | - Samiran Ghosh
- Department of Family Medicine & Public Health Sciences and Center for Molecular Medicine and Genetics, School of Medicine, Wayne State University, Detroit, MI, USA
| | | | - Margaret Gamalo
- Research and Development – Statistics, Eli Lilly and Co, Indianapolis, IN, USA
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32
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Posch M, König F. Are p-values Useful to Judge the Evidence Against the Null Hypotheses in Complex Clinical Trials? A Comment on “The Role of p-values in Judging the Strength of Evidence and Realistic Replication Expectations”. Stat Biopharm Res 2020. [DOI: 10.1080/19466315.2020.1847182] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Martin Posch
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Franz König
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
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33
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Friedrich S, Friede T. Causal inference methods for small non-randomized studies: Methods and an application in COVID-19. Contemp Clin Trials 2020; 99:106213. [PMID: 33188930 PMCID: PMC7834813 DOI: 10.1016/j.cct.2020.106213] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 10/09/2020] [Accepted: 11/06/2020] [Indexed: 12/27/2022]
Abstract
The usual development cycles are too slow for the development of vaccines, diagnostics and treatments in pandemics such as the ongoing SARS-CoV-2 pandemic. Given the pressure in such a situation, there is a risk that findings of early clinical trials are overinterpreted despite their limitations in terms of size and design. Motivated by a non-randomized open-label study investigating the efficacy of hydroxychloroquine in patients with COVID-19, we describe in a unified fashion various alternative approaches to the analysis of non-randomized studies. A widely used tool to reduce the impact of treatment-selection bias are so-called propensity score (PS) methods. Conditioning on the propensity score allows one to replicate the design of a randomized controlled trial, conditional on observed covariates. Extensions include the g-computation approach, which is less frequently applied, in particular in clinical studies. Moreover, doubly robust estimators provide additional advantages. Here, we investigate the properties of propensity score based methods including three variations of doubly robust estimators in small sample settings, typical for early trials, in a simulation study. R code for the simulations is provided.
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Affiliation(s)
- Sarah Friedrich
- Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, 37073 Göttingen, Germany.
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, 37073 Göttingen, Germany.
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34
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Mann DL. Will COVID-19 Transform Translational Medicine? JACC Basic Transl Sci 2020; 5:1261-1263. [PMID: 33426380 PMCID: PMC7775954 DOI: 10.1016/j.jacbts.2020.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Douglas L. Mann
- Address for correspondence: Dr. Douglas L. Mann, Editor-in-Chief, JACC: Basic to Translational Science, American College of Cardiology, 2400 N Street NW, Washington, DC 20037, USA.
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35
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Hamasaki T, Bretz F, Cooner F, LaVange LM, Posch M. Statistical Challenges in the Conduct and Management of Ongoing Clinical Trials During the COVID-19 Pandemic. Stat Biopharm Res 2020; 12:397-398. [PMID: 34191970 PMCID: PMC8011482 DOI: 10.1080/19466315.2020.1828692] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Affiliation(s)
| | - Frank Bretz
- Clinical Development & Analytics, Novartis Pharma, Basel, Switzerland
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Freda Cooner
- Statistical Innovation, Amgen, Thousand Oaks, CA
| | - Lisa M. LaVange
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Martin Posch
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
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Kunz CU, Jörgens S, Bretz F, Stallard N, Van Lancker K, Xi D, Zohar S, Gerlinger C, Friede T. Clinical Trials Impacted by the COVID-19 Pandemic: Adaptive Designs to the Rescue? Stat Biopharm Res 2020; 12:461-477. [PMID: 34191979 PMCID: PMC8011492 DOI: 10.1080/19466315.2020.1799857] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 07/17/2020] [Accepted: 07/18/2020] [Indexed: 01/09/2023]
Abstract
Very recently the new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified and the coronavirus disease 2019 (COVID-19) declared a pandemic by the World Health Organization. The pandemic has a number of consequences for ongoing clinical trials in non-COVID-19 conditions. Motivated by four current clinical trials in a variety of disease areas we illustrate the challenges faced by the pandemic and sketch out possible solutions including adaptive designs. Guidance is provided on (i) where blinded adaptations can help; (ii) how to achieve Type I error rate control, if required; (iii) how to deal with potential treatment effect heterogeneity; (iv) how to use early read-outs; and (v) how to use Bayesian techniques. In more detail approaches to resizing a trial affected by the pandemic are developed including considerations to stop a trial early, the use of group-sequential designs or sample size adjustment. All methods considered are implemented in a freely available R shiny app. Furthermore, regulatory and operational issues including the role of data monitoring committees are discussed.
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Affiliation(s)
| | | | - Frank Bretz
- Novartis Pharma AG, Basel, Switzerland
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Nigel Stallard
- Division of Health Sciences, Warwick Medical School, The University of Warwick, Coventry, UK
| | - Kelly Van Lancker
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Dong Xi
- Novartis Pharmaceuticals, East Hanover, NJ
| | - Sarah Zohar
- INSERM, Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris, Paris, France
| | - Christoph Gerlinger
- Statistics and Data Insights, Bayer AG, Berlin, Germany
- Department of Gynecology, Obstetrics and Reproductive Medicine, University Medical School of Saarland, Homburg/Saar, Germany
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Göttingen, Göttingen, Germany
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Zame WR, Bica I, Shen C, Curth A, Lee HS, Bailey S, Weatherall J, Wright D, Bretz F, van der Schaar M. Machine learning for clinical trials in the era of COVID-19. Stat Biopharm Res 2020; 12:506-517. [PMID: 34191983 PMCID: PMC8011491 DOI: 10.1080/19466315.2020.1797867] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 06/18/2020] [Accepted: 07/03/2020] [Indexed: 12/18/2022]
Abstract
The world is in the midst of a pandemic. We still know little about the disease COVID-19 or about the virus (SARS-CoV-2) that causes it. We do not have a vaccine or a treatment (aside from managing symptoms). We do not know if recovery from COVID-19 produces immunity, and if so for how long, hence we do not know if "herd immunity" will eventually reduce the risk or if a successful vaccine can be developed - and this knowledge may be a long time coming. In the meantime, the COVID-19 pandemic is presenting enormous challenges to medical research, and to clinical trials in particular. This paper identifies some of those challenges and suggests ways in which machine learning can help in response to those challenges. We identify three areas of challenge: ongoing clinical trials for non-COVID-19 drugs; clinical trials for repurposing drugs to treat COVID-19, and clinical trials for new drugs to treat COVID-19. Within each of these areas, we identify aspects for which we believe machine learning can provide invaluable assistance.
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Affiliation(s)
- William R. Zame
- Department of Economics and Mathematics, UCLA, Los Angeles, CA, USA
| | - Ioana Bica
- University of Oxford, Oxford, UK
- The Alan Turing Institute, London, UK
| | - Cong Shen
- Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA
| | | | - Hyun-Suk Lee
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK
| | | | | | | | - Frank Bretz
- Novartis Pharma AG, Basel, Switzerland
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Mihaela van der Schaar
- The Alan Turing Institute, London, UK
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK
- Department of Electrical and Computer Engineering, UCLA, Los Angeles, CA, USA
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