Browsing by Author "Ambrosius, Walter T."

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    Guidance for biostatisticians on their essential contributions to clinical and translational research protocol review
    (Cambridge University Press, 2021-07-12) Ciolino, Jody D.; Spino, Cathie; Ambrosius, Walter T.; Khalatbari, Shokoufeh; Messinger Cayetano, Shari; Lapidus, Jodi A.; Nietert, Paul J.; Oster, Robert A.; Perkins, Susan M.; Pollock, Brad H.; Pomann, Gina-Maria; Price, Lori Lyn; Rice, Todd W.; Tosteson, Tor D.; Lindsell, Christopher J.; Spratt, Heidi; Biostatistics and Health Data Science, School of Medicine
    Rigorous scientific review of research protocols is critical to making funding decisions, and to the protection of both human and non-human research participants. Given the increasing complexity of research designs and data analysis methods, quantitative experts, such as biostatisticians, play an essential role in evaluating the rigor and reproducibility of proposed methods. However, there is a common misconception that a statistician’s input is relevant only to sample size/power and statistical analysis sections of a protocol. The comprehensive nature of a biostatistical review coupled with limited guidance on key components of protocol review motived this work. Members of the Biostatistics, Epidemiology, and Research Design Special Interest Group of the Association for Clinical and Translational Science used a consensus approach to identify the elements of research protocols that a biostatistician should consider in a review, and provide specific guidance on how each element should be reviewed. We present the resulting review framework as an educational tool and guideline for biostatisticians navigating review boards and panels. We briefly describe the approach to developing the framework, and we provide a comprehensive checklist and guidance on review of each protocol element. We posit that the biostatistical reviewer, through their breadth of engagement across multiple disciplines and experience with a range of research designs, can and should contribute significantly beyond review of the statistical analysis plan and sample size justification. Through careful scientific review, we hope to prevent excess resource expenditure and risk to humans and animals on poorly planned studies.