1
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Manyara AM, Davies P, Stewart D, Weir CJ, Young AE, Blazeby J, Butcher NJ, Bujkiewicz S, Chan AW, Dawoud D, Offringa M, Ouwens M, Hróbjartssson A, Amstutz A, Bertolaccini L, Bruno VD, Devane D, Faria CDCM, Gilbert PB, Harris R, Lassere M, Marinelli L, Markham S, Powers JH, Rezaei Y, Richert L, Schwendicke F, Tereshchenko LG, Thoma A, Turan A, Worrall A, Christensen R, Collins GS, Ross JS, Taylor RS, Ciani O. Reporting of surrogate endpoints in randomised controlled trial reports (CONSORT-Surrogate): extension checklist with explanation and elaboration. BMJ 2024; 386:e078524. [PMID: 38981645 PMCID: PMC11231881 DOI: 10.1136/bmj-2023-078524] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/30/2024] [Indexed: 07/11/2024]
Affiliation(s)
- Anthony Muchai Manyara
- MRC/CSO Social and Public Health Sciences Unit, School of Health and Wellbeing, University of Glasgow, Glasgow, UK
- Global Health and Ageing Research Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Philippa Davies
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Christopher J Weir
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Amber E Young
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jane Blazeby
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
- University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
| | - Nancy J Butcher
- Child Health Evaluative Sciences, Hospital for Sick Children Research Institute, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Sylwia Bujkiewicz
- Biostatistics Research Group, Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - An-Wen Chan
- Women's College Research Institute, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Dalia Dawoud
- Science, Evidence, and Analytics Directorate, Science Policy and Research Programme, National Institute for Health and Care Excellence, London, UK
- Faculty of Pharmacy, Cairo University, Cairo, Egypt
| | - Martin Offringa
- Child Health Evaluative Sciences, Hospital for Sick Children Research Institute, Toronto, ON, Canada
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada
| | | | - Asbjørn Hróbjartssson
- Centre for Evidence-Based Medicine Odense and Cochrane Denmark, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Open Patient data Explorative Network, Odense University hospital, Odense, Denmark
| | - Alain Amstutz
- CLEAR Methods Centre, Division of Clinical Epidemiology, Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
- Bristol Medical School, University of Bristol, Bristol, UK
| | - Luca Bertolaccini
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Vito Domenico Bruno
- IRCCS Galeazzi-Sant'Ambrogio Hospital, Department of Minimally Invasive Cardiac Surgery, Milan, Italy
| | - Declan Devane
- University of Galway, Galway, Ireland
- Health Research Board-Trials Methodology Research Network, University of Galway, Galway, Ireland
| | - Christina D C M Faria
- Department of Physical Therapy, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | | | - Marissa Lassere
- St George Hospital and School of Population Health, University of New South Wales, Sydney, NSW, Australia
| | - Lucio Marinelli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Sarah Markham
- Patient author, UK
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - John H Powers
- George Washington University School of Medicine, Washington, DC, USA
| | - Yousef Rezaei
- Heart Valve Disease Research Centre, Rajaie Cardiovascular Medical and Research Centre, Iran University of Medical Sciences, Tehran, Iran
- Ardabil University of Medical Sciences, Ardabil, Iran
- Behyan Clinic, Pardis New Town, Tehran, Iran
| | - Laura Richert
- University of Bordeaux, Centre d'Investigation Clinique-Epidémiologie Clinique 1401, Research in Clinical Epidemiology and in Public Health and European Clinical Trials Platform & Development/French Clinical Research Infrastructure Network, Institut National de la Santé et de la Recherche Médicale/Institut Bergonié/Centre Hospitalier Universitaire Bordeaux, Bordeaux, France
| | | | - Larisa G Tereshchenko
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | | | - Alparslan Turan
- Department of Outcomes Research, Anaesthesiology Institute, Cleveland Clinic, OH, USA
| | | | - Robin Christensen
- Section for Biostatistics and Evidence-Based Research, the Parker Institute, Bispebjerg and Frederiksberg Hospital, Copenhagen and Research Unit of Rheumatology, Department of Clinical Research, University of Southern Denmark, Odense University Hospital, Odense, Denmark
| | - Gary S Collins
- UK EQUATOR Centre, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Joseph S Ross
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
- Section of General Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Rod S Taylor
- MRC/CSO Social and Public Health Sciences Unit, School of Health and Wellbeing, University of Glasgow, Glasgow, UK
- Robertson Centre for Biostatistics, School of Health and Well Being, University of Glasgow, Glasgow, UK
| | - Oriana Ciani
- Centre for Research on Health and Social Care Management, Bocconi University, Milan 20136, Italy
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2
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Manyara AM, Davies P, Stewart D, Weir CJ, Young AE, Blazeby J, Butcher NJ, Bujkiewicz S, Chan AW, Dawoud D, Offringa M, Ouwens M, Hróbjartssson A, Amstutz A, Bertolaccini L, Bruno VD, Devane D, Faria CDCM, Gilbert PB, Harris R, Lassere M, Marinelli L, Markham S, Powers JH, Rezaei Y, Richert L, Schwendicke F, Tereshchenko LG, Thoma A, Turan A, Worrall A, Christensen R, Collins GS, Ross JS, Taylor RS, Ciani O. Reporting of surrogate endpoints in randomised controlled trial protocols (SPIRIT-Surrogate): extension checklist with explanation and elaboration. BMJ 2024; 386:e078525. [PMID: 38981624 PMCID: PMC11231880 DOI: 10.1136/bmj-2023-078525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/30/2024] [Indexed: 07/11/2024]
Affiliation(s)
- Anthony Muchai Manyara
- MRC/CSO Social and Public Health Sciences Unit, School of Health and Wellbeing, University of Glasgow, Glasgow, UK
- Global Health and Ageing Research Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Philippa Davies
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Christopher J Weir
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Amber E Young
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jane Blazeby
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
- University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
| | - Nancy J Butcher
- Child Health Evaluative Sciences, Hospital for Sick Children Research Institute, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Sylwia Bujkiewicz
- Biostatistics Research Group, Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - An-Wen Chan
- Women's College Research Institute, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Dalia Dawoud
- Science, Evidence, and Analytics Directorate, Science Policy and Research Programme, National Institute for Health and Care Excellence, London, UK
- Faculty of Pharmacy, Cairo University, Cairo, Egypt
| | - Martin Offringa
- Child Health Evaluative Sciences, Hospital for Sick Children Research Institute, Toronto, ON, Canada
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada
| | | | - Asbjørn Hróbjartssson
- Centre for Evidence-Based Medicine Odense and Cochrane Denmark, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Open Patient data Explorative Network, Odense University hospital, Odense, Denmark
| | - Alain Amstutz
- CLEAR Methods Centre, Division of Clinical Epidemiology, Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
- Bristol Medical School, University of Bristol, Bristol, UK
| | - Luca Bertolaccini
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Vito Domenico Bruno
- IRCCS Galeazzi-Sant'Ambrogio Hospital, Department of Minimally Invasive Cardiac Surgery, Milan, Italy
| | - Declan Devane
- University of Galway, Galway, Ireland
- Health Research Board-Trials Methodology Research Network, University of Galway, Galway, Ireland
| | - Christina D C M Faria
- Department of Physical Therapy, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | | | - Marissa Lassere
- St George Hospital and School of Population Health, University of New South Wales, Sydney, NSW, Australia
| | - Lucio Marinelli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Sarah Markham
- Patient author, UK
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - John H Powers
- George Washington University School of Medicine, Washington, DC, USA
| | - Yousef Rezaei
- Heart Valve Disease Research Centre, Rajaie Cardiovascular Medical and Research Centre, Iran University of Medical Sciences, Tehran, Iran
- Ardabil University of Medical Sciences, Ardabil, Iran
- Behyan Clinic, Pardis New Town, Tehran, Iran
| | - Laura Richert
- University of Bordeaux, Centre d'Investigation Clinique-Epidémiologie Clinique 1401, Research in Clinical Epidemiology and in Public Health and European Clinical Trials Platform & Development/French Clinical Research Infrastructure Network, Institut National de la Santé et de la Recherche Médicale/Institut Bergonié/Centre Hospitalier Universitaire Bordeaux, Bordeaux, France
| | | | - Larisa G Tereshchenko
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | | | - Alparslan Turan
- Department of Outcomes Research, Anaesthesiology Institute, Cleveland Clinic, OH, USA
| | | | - Robin Christensen
- Section for Biostatistics and Evidence-Based Research, the Parker Institute, Bispebjerg and Frederiksberg Hospital, Copenhagen and Research Unit of Rheumatology, Department of Clinical Research, University of Southern Denmark, Odense University Hospital, Odense, Denmark
| | - Gary S Collins
- UK EQUATOR Centre, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Joseph S Ross
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
- Section of General Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Rod S Taylor
- MRC/CSO Social and Public Health Sciences Unit, School of Health and Wellbeing, University of Glasgow, Glasgow, UK
- Robertson Centre for Biostatistics, School of Health and Well Being, University of Glasgow, Glasgow, UK
| | - Oriana Ciani
- Centre for Research on Health and Social Care Management, Bocconi University, Milan 20136, Italy
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Yan J, Wen Y, Liu X, Deng M, Ye B, Li T, Wang H, Jia C, Liao J, Zhang L. The effectiveness of problem-based learning and case-based learning teaching methods in clinical practical teaching in TACE treatment for hepatocellular carcinoma in China: a bayesian network meta-analysis. BMC MEDICAL EDUCATION 2024; 24:665. [PMID: 38886707 PMCID: PMC11184776 DOI: 10.1186/s12909-024-05615-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 05/29/2024] [Indexed: 06/20/2024]
Abstract
PURPOSE To investigate the effectiveness of problem-based learning (PBL) and case-based learning (CBL) teaching methods in clinical practical teaching in transarterial chemoembolization (TACE) treatment in China. MATERIALS AND METHODS A comprehensive search of PubMed, the Chinese National Knowledge Infrastructure (CNKI) database, the Weipu database and the Wanfang database up to June 2023 was performed to collect studies that evaluate the effectiveness of problem-based learning and case-based learning teaching methods in clinical practical teaching in TACE treatment in China. Statistical analysis was performed by R software (4.2.1) calling JAGS software (4.3.1) in a Bayesian framework using the Markov chain-Monte Carlo method for direct and indirect comparisons. The R packages "gemtc", "rjags", "openxlsx", and "ggplot2" were used for statistical analysis and data output. RESULTS Finally, 7 studies (five RCTs and two observational studies) were included in the meta-analysis. The combination of PBL and CBL showed more effectiveness in clinical thinking capacity, clinical practice capacity, knowledge understanding degree, literature reading ability, method satisfaction degree, learning efficiency, learning interest, practical skills examination scores and theoretical knowledge examination scores. CONCLUSIONS Network meta-analysis revealed that the application of PBL combined with the CBL teaching mode in the teaching of liver cancer intervention therapy significantly improves the teaching effect and significantly improves the theoretical and surgical operations, meeting the requirements of clinical education.
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Affiliation(s)
- Jingxin Yan
- West China Hospital, Sichuan University, Chengdu, China
| | - Yonghao Wen
- Department of Hepatopancreatobiliary Surgery, Affiliated Hospital of Qinghai University, Xining, China
- Department of Postgraduate, Qinghai University, Xining, China
| | - Xinlian Liu
- Department of Pathology and Pathophysiology, Chengdu Medical College, Chengdu, China
| | - Manjun Deng
- Department of Hepatopancreatobiliary Surgery, Affiliated Hospital of Qinghai University, Xining, China
- Department of Postgraduate, Qinghai University, Xining, China
| | - Bin Ye
- Department of General Surgery, Rongxian People's Hospital, Zigong, China
| | - Ting Li
- Department of Orthopedics, Sichuan Provincial People's Hospital, Chengdu, China
| | - Huanwei Wang
- Department of Ultrasonography, Hainan General Hospital/Hainan Affiliated Hospital of Hainan Medical University, Haikou, 570100, China
| | - Cui Jia
- Department of Pathology and Pathophysiology, Chengdu Medical College, Chengdu, China
| | - Jinsong Liao
- Department of Anesthesiology, Affiliated Hospital of Chengdu University, Chengdu University, Chengdu, China.
| | - Lushun Zhang
- Department of Pathology and Pathophysiology, Chengdu Medical College, Chengdu, China.
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4
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Thall PF, Zang Y, Chapple AG, Yuan Y, Lin R, Marin D, Msaouel P. Novel Clinical Trial Designs with Dose Optimization to Improve Long-term Outcomes. Clin Cancer Res 2023; 29:4549-4554. [PMID: 37725573 PMCID: PMC10841062 DOI: 10.1158/1078-0432.ccr-23-2222] [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: 07/26/2023] [Revised: 08/25/2023] [Accepted: 09/14/2023] [Indexed: 09/21/2023]
Abstract
Conventional designs for choosing a dose for a new therapy may select doses that are unsafe or ineffective and fail to optimize progression-free survival time, overall survival time, or response/remission duration. We explain and illustrate limitations of conventional dose-finding designs and make four recommendations to address these problems. When feasible, a dose-finding design should account for long-term outcomes, include screening rules that drop unsafe or ineffective doses, enroll an adequate sample size, and randomize patients among doses. As illustrations, we review three designs that include one or more of these features. The first illustration is a trial that randomized patients among two cell therapy doses and standard of care in a setting where it was assumed on biological grounds that dose toxicity and dose-response curves did not necessarily increase with cell dose. The second design generalizes phase I-II by first identifying a set of candidate doses, rather than one dose, randomizing additional patients among the candidates, and selecting an optimal dose to maximize progression-free survival over a longer follow-up period. The third design combines a phase I-II trial and a group sequential randomized phase III trial by using survival time data available after the first stage of phase III to reoptimize the dose selected in phase I-II. By incorporating one or more of the recommended features, these designs improve the likelihood that a selected dose or schedule will be optimal, and thus will benefit future patients and obtain regulatory approval.
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Affiliation(s)
- Peter F. Thall
- Department of Biostatistics, M.D. Anderson Cancer Center, Houston, Texas, USA
| | - Yong Zang
- Department of Biostatistics and Health Data Science, Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Andrew G. Chapple
- Department of Interdisciplinary Oncology, School of Medicine, LSU Health Sciences Center, New Orleans, USA
| | - Ying Yuan
- Department of Biostatistics, M.D. Anderson Cancer Center, Houston, Texas, USA
| | - Ruitao Lin
- Department of Biostatistics, M.D. Anderson Cancer Center, Houston, Texas, USA
| | - David Marin
- Department of Stem Cell Transplantation and Cellular Therapy, M.D. Anderson Cancer Center, Houston, Texas, USA
| | - Pavlos Msaouel
- Department of Genitourinary Medical Oncology, M.D. Anderson Cancer Center, Houston, Texas, USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- David H. Koch Center for Applied Research of Genitourinary Cancers, The University of Texas, MD Anderson Cancer Center, Houston, USA
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5
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Ciani O, Manyara AM, Davies P, Stewart D, Weir CJ, Young AE, Blazeby J, Butcher NJ, Bujkiewicz S, Chan AW, Dawoud D, Offringa M, Ouwens M, Hróbjartssson A, Amstutz A, Bertolaccini L, Bruno VD, Devane D, Faria CD, Gilbert PB, Harris R, Lassere M, Marinelli L, Markham S, Powers JH, Rezaei Y, Richert L, Schwendicke F, Tereshchenko LG, Thoma A, Turan A, Worrall A, Christensen R, Collins GS, Ross JS, Taylor RS. A framework for the definition and interpretation of the use of surrogate endpoints in interventional trials. EClinicalMedicine 2023; 65:102283. [PMID: 37877001 PMCID: PMC10590868 DOI: 10.1016/j.eclinm.2023.102283] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/03/2023] [Accepted: 10/03/2023] [Indexed: 10/26/2023] Open
Abstract
Background Interventional trials that evaluate treatment effects using surrogate endpoints have become increasingly common. This paper describes four linked empirical studies and the development of a framework for defining, interpreting and reporting surrogate endpoints in trials. Methods As part of developing the CONSORT (Consolidated Standards of Reporting Trials) and SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) extensions for randomised trials reporting surrogate endpoints, we undertook a scoping review, e-Delphi study, consensus meeting, and a web survey to examine current definitions and stakeholder (including clinicians, trial investigators, patients and public partners, journal editors, and health technology experts) interpretations of surrogate endpoints as primary outcome measures in trials. Findings Current surrogate endpoint definitional frameworks are inconsistent and unclear. Surrogate endpoints are used in trials as a substitute of the treatment effects of an intervention on the target outcome(s) of ultimate interest, events measuring how patients feel, function, or survive. Traditionally the consideration of surrogate endpoints in trials has focused on biomarkers (e.g., HDL cholesterol, blood pressure, tumour response), especially in the medical product regulatory setting. Nevertheless, the concept of surrogacy in trials is potentially broader. Intermediate outcomes that include a measure of function or symptoms (e.g., angina frequency, exercise tolerance) can also be used as substitute for target outcomes (e.g., all-cause mortality)-thereby acting as surrogate endpoints. However, we found a lack of consensus among stakeholders on accepting and interpreting intermediate outcomes in trials as surrogate endpoints or target outcomes. In our assessment, patients and health technology assessment experts appeared more likely to consider intermediate outcomes to be surrogate endpoints than clinicians and regulators. Interpretation There is an urgent need for better understanding and reporting on the use of surrogate endpoints, especially in the setting of interventional trials. We provide a framework for the definition of surrogate endpoints (biomarkers and intermediate outcomes) and target outcomes in trials to improve future reporting and aid stakeholders' interpretation and use of trial surrogate endpoint evidence. Funding SPIRIT-SURROGATE/CONSORT-SURROGATE project is Medical Research Council Better Research Better Health (MR/V038400/1) funded.
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Affiliation(s)
- Oriana Ciani
- Centre for Research on Health and Social Care Management, SDA Bocconi School of Management, Milan, Italy
| | - Anthony M. Manyara
- MRC/CSO Social and Public Health Sciences Unit, School of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Philippa Davies
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Christopher J. Weir
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, Edinburgh, UK
| | | | - Jane Blazeby
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
- University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
| | - Nancy J. Butcher
- Child Health Evaluative Sciences, The Hospital for Sick Children Research Institute, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Sylwia Bujkiewicz
- Biostatistics Research Group, Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - An-Wen Chan
- Women's College Research Institute, Toronto, Canada
- Department of Medicine, University of Toronto, Toronto, Canada
| | - Dalia Dawoud
- Science, Evidence and Analytics Directorate, Science Policy and Research Programme, National Institute for Health and Care Excellence, London, UK
| | - Martin Offringa
- Child Health Evaluative Sciences, The Hospital for Sick Children Research Institute, Toronto, Canada
- Department of Paediatrics, University of Toronto, Toronto, Canada
| | | | - Asbjørn Hróbjartssson
- Centre for Evidence-Based Medicine Odense (CEBMO) and Cochrane Denmark, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Open Patient Data Explorative Network (OPEN), Odense University Hospital, Odense, Denmark
| | - Alain Amstutz
- CLEAR Methods Center, Division of Clinical Epidemiology, Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Luca Bertolaccini
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Vito Domenico Bruno
- Department of Minimally Invasive Cardiac Surgery, IRCCS Galeazzi – Sant’Ambrogio Hospital, Milan, Italy
| | - Declan Devane
- University of Galway, Galway, Ireland
- Health Research Board-Trials Methodology Research Network, University of Galway, Galway, Ireland
| | - Christina D.C.M. Faria
- Department of Physical Therapy, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Ray Harris
- Patient and Public Involvement Partner, UK
| | - Marissa Lassere
- St George Hospital and School of Population Health, The University of New South Wales, Sydney, Australia
| | - Lucio Marinelli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Sarah Markham
- Department of Biostatistics, King's College London, London, UK
| | - John H. Powers
- George Washington University School of Medicine, Washington, USA
| | - Yousef Rezaei
- Heart Valve Disease Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
- Ardabil University of Medical Sciences, Ardabil, Iran
- Behyan Clinic, Pardis New Town, Tehran, Iran
| | - Laura Richert
- University Bordeaux, INSERM, Institut Bergonié, CHU Bordeaux, BPH U1219, CIC-EC 1401, RECaP and Euclid/F-CRIN, Bordeaux, France
| | | | - Larisa G. Tereshchenko
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | | | - Alparslan Turan
- Department of Outcomes Research, Anesthesiology Institute, Cleveland Clinic, OH, USA
| | | | - Robin Christensen
- Section for Biostatistics and Evidence-Based Research, The Parker Institute, Bispebjerg and Frederiksberg Hospital, Copenhagen & Research Unit of Rheumatology, Department of Clinical Research, University of Southern Denmark, Odense University Hospital, Odense, Denmark
| | - Gary S. Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Joseph S. Ross
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
- Section of General Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Rod S. Taylor
- MRC/CSO Social and Public Health Sciences Unit, School of Health and Wellbeing, University of Glasgow, Glasgow, UK
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6
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Msaouel P, Lee J, Thall PF. Interpreting Randomized Controlled Trials. Cancers (Basel) 2023; 15:4674. [PMID: 37835368 PMCID: PMC10571666 DOI: 10.3390/cancers15194674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 09/19/2023] [Accepted: 09/19/2023] [Indexed: 10/15/2023] Open
Abstract
This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative random sample from a patient population is impossible for a clinical trial because patients are accrued sequentially over time and thus comprise a convenience sample, subject only to protocol entry criteria. Consequently, the trial's sample is unlikely to represent a definable patient population. We use causal diagrams to illustrate the difference between random allocation of interventions within a clinical trial sample and true simple or stratified random sampling, as executed in surveys. We argue that group-specific statistics, such as a median survival time estimate for a treatment arm in an RCT, have limited meaning as estimates of larger patient population parameters. In contrast, random allocation between interventions facilitates comparative causal inferences about between-treatment effects, such as hazard ratios or differences between probabilities of response. Comparative inferences also require the assumption of transportability from a clinical trial's convenience sample to a targeted patient population. We focus on the consequences and limitations of randomization procedures in order to clarify the distinctions between pairs of complementary concepts of fundamental importance to data science and RCT interpretation. These include internal and external validity, generalizability and transportability, uncertainty and variability, representativeness and inclusiveness, blocking and stratification, relevance and robustness, forward and reverse causal inference, intention to treat and per protocol analyses, and potential outcomes and counterfactuals.
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Affiliation(s)
- Pavlos Msaouel
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- David H. Koch Center for Applied Research of Genitourinary Cancers, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Juhee Lee
- Department of Statistics, University of California Santa Cruz, Santa Cruz, CA 95064, USA;
| | - Peter F. Thall
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
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7
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Msaouel P. Less is More? First Impressions From COSMIC-313. Cancer Invest 2023; 41:101-106. [PMID: 36239611 DOI: 10.1080/07357907.2022.2136681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The COSMIC-313 phase 3 randomized controlled trial tested the triplet combination of cabozantinib with nivolumab and ipilimumab in comparison with nivolumab plus ipilimumab control as fist-line systemic therapy in metastatic clear cell renal cell carcinoma. The first results presented at the 2022 European Society of Medical Oncology Congress are a milestone for the renal cell carcinoma field because they signal the advent of triplet combinations as potential treatment options for our patients. The present commentary highlights some considerations and potential next steps based on these first impressions.
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Affiliation(s)
- Pavlos Msaouel
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,David H. Koch Center for Applied Research of Genitourinary Cancers, The University of Texas, MD Anderson Cancer Center, Houston, Texas, USA
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[Progress and Application of Bayesian Approach in the Early Research and Development of New Anticancer Drugs]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2022; 25:730-734. [PMID: 36285392 PMCID: PMC9619348 DOI: 10.3779/j.issn.1009-3419.2022.102.43] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Bayesian statistics is an approach for learning from evidences as it accumulates, combining prior distribution with current information on a quantity of interest, in which posterior distribution and inferences are being updated each time new data become available using Bayes' Theorem. Though frequentist approach has dominated medical studies, Bayesian approach has been more and more widely recognized by its flexibility and efficiency. Research and development (R&D) on anti-cancer new drugs have been so hot globally in recent years in spite of relatively high failure rate. It is the common demand of pharmaceutical enterprises and researchers to identify the optimal dose, regime and right population in the early-phase R&D stage more accurately and efficiently, especially when the following three major changes have been observed. The R&D on anticancer drugs have transformed from chemical drugs to biological products, from monotherapy to combination therapy, and the study design has also gradually changed from traditional way to innovative and adaptive mode. This also raises a number of subsequent challenges on decision-making of early R&D, such as inability to determine MTD, flexibility to deal with delayed toxicity, delayed response and dose-response changing relationships. It is because of the above emerging changes and challenges that the Bayesian approach is getting more and more attention from the industry. At least, Bayesian approach has more information for decision-making, which could potentially help enterprises achieve higher efficiency, shorter period and lower investment. This study also expounds the application of Bayesian statistics in the early R&D on anticancer new drugs, and compares and analyzes its idea and application scenarios with frequentist statistics, aiming to provide macroscopic and systematic reference for all related stakeholders.
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