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Schulte PJ, Goldberg JD, Oster RA, Ambrosius WT, Bonner LB, Cabral H, Carter RE, Chen Y, Desai M, Li D, Lindsell CJ, Pomann GM, Slade E, Tosteson TD, Yu F, Spratt H. Peer review of clinical and translational research manuscripts: Perspectives from statistical collaborators. J Clin Transl Sci 2024; 8:e20. [PMID: 38384899 PMCID: PMC10879991 DOI: 10.1017/cts.2023.707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/29/2023] [Accepted: 12/19/2023] [Indexed: 02/23/2024] Open
Abstract
Research articles in the clinical and translational science literature commonly use quantitative data to inform evaluation of interventions, learn about the etiology of disease, or develop methods for diagnostic testing or risk prediction of future events. The peer review process must evaluate the methodology used therein, including use of quantitative statistical methods. In this manuscript, we provide guidance for peer reviewers tasked with assessing quantitative methodology, intended to complement guidelines and recommendations that exist for manuscript authors. We describe components of clinical and translational science research manuscripts that require assessment including study design and hypothesis evaluation, sampling and data acquisition, interventions (for studies that include an intervention), measurement of data, statistical analysis methods, presentation of the study results, and interpretation of the study results. For each component, we describe what reviewers should look for and assess; how reviewers should provide helpful comments for fixable errors or omissions; and how reviewers should communicate uncorrectable and irreparable errors. We then discuss the critical concepts of transparency and acceptance/revision guidelines when communicating with responsible journal editors.
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Affiliation(s)
- Phillip J. Schulte
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Judith D. Goldberg
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Robert A. Oster
- Department of Medicine, Division of Preventive Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Walter T. Ambrosius
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Lauren Balmert Bonner
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Howard Cabral
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Rickey E. Carter
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA
| | - Ye Chen
- Biostatistics, Epidemiology and Research Design (BERD), Tufts Clinical and Translational Science Institute (CTSI), Boston, MA, USA
| | - Manisha Desai
- Quantitative Sciences Unit, Departments of Medicine, Biomedical Data Science, and Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | - Dongmei Li
- Department of Clinical and Translational Research, Obstetrics and Gynecology and Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA
| | | | - Gina-Maria Pomann
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Emily Slade
- Department of Biostatistics, University of Kentucky, Lexington, KY, USA
| | - Tor D. Tosteson
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH, USA
| | - Fang Yu
- Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE, USA
| | - Heidi Spratt
- Department of Biostatistics and Data Science, School of Public and Population Health, University of Texas Medical Branch, Galveston, TX, USA
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Ciolino JD, Kaizer AM, Bonner LB. Guidance on interim analysis methods in clinical trials. J Clin Transl Sci 2023; 7:e124. [PMID: 37313374 PMCID: PMC10260346 DOI: 10.1017/cts.2023.552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 05/08/2023] [Accepted: 05/09/2023] [Indexed: 06/15/2023] Open
Abstract
Interim analyses in clinical trials can take on a multitude of forms. They are often used to guide Data and Safety Monitoring Board (DSMB) recommendations to study teams regarding recruitment targets for large, later-phase clinical trials. As collaborative biostatisticians working and teaching in multiple fields of research and across a broad array of trial phases, we note the large heterogeneity and confusion surrounding interim analyses in clinical trials. Thus, in this paper, we aim to provide a general overview and guidance on interim analyses for a nonstatistical audience. We explain each of the following types of interim analyses: efficacy, futility, safety, and sample size re-estimation, and we provide the reader with reasoning, examples, and implications for each. We emphasize that while the types of interim analyses employed may differ depending on the nature of the study, we would always recommend prespecification of the interim analytic plan to the extent possible with risk mitigation and trial integrity remaining a priority. Finally, we posit that interim analyses should be used as tools to help the DSMB make informed decisions in the context of the overarching study. They should generally not be deemed binding, and they should not be reviewed in isolation.
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Affiliation(s)
- Jody D. Ciolino
- Department of Preventive Medicine (Biostatistics), Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Alexander M. Kaizer
- Department of Biostatistics & Informatics, Colorado School of Public Health, Aurora, Colorado, USA
| | - Lauren Balmert Bonner
- Department of Preventive Medicine (Biostatistics), Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
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