Evidence‐based statistical analysis and methods in biomedical research (SAMBR) checklists according to design features. Issue 4 (22nd August 2019)

• Record Type: Journal Article
• Authors: Dwivedi, Alok Kumar; Shukla, Rakesh
• Other Names: Bhattacharjee Atanu guestEditor.
• Is Part Of: Cancer reports. Volume 3:Issue 4(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-08-22
• Subjects: checklists -- evidence‐based statistical practice -- statistical analysis -- statistical methods; Cancer -- Periodicals; 616.994005
• DOI: 10.1002/cnr2.1211
• Languages: English
• ISSNs: 2573-8348

Abstract

Abstract: Background: Statistical analysis according to design features and objectives is essential to ensure the validity and reliability of the study findings and conclusions in biomedical research. Heterogeneity in reporting study design elements and conducting statistical analyses is often observed for the same study design and study objective in medical literatures. Sometimes, researchers face a lot of predicaments using appropriate statistical approaches highlighted by methodologists for a specific study design either due to lack of accessibility or understanding of statistical methods or unavailability of checklists related to design and analysis in a concise format. The purpose of this review is to provide the checklist of statistical analysis and methods in biomedical research (SAMBR) to applied researchers. Recent findings: We initially identified the important steps of reporting design features that may influence the choice of statistical analysis in biomedical research and essential steps of data analysis of common studies. We subsequently searched for statistical approaches employed for each study design/study objective available in publications and other resources. Compilation of these steps produced SAMBR guidance document, which includes three parts. Applied researchers can use part (A) and part (B) of SAMBR to describe or evaluate research design features and quality of statistical analysis, respectively, in reviewing studies or designing protocols. Part (C) of SAMBR can be used to perform essential and preferred evidence‐based data analysis specific to study design and objective. Conclusions: We believe that the statistical methods checklists may improve reporting of research design, standardize methodological practices, and promote consistent application of statistical approaches, thus improving the quality of research studies. The checklists do not enforce the use of suggested statistical methods but rather highlight and encourage to conduct the best statistical practices. There is a need to develop an interactive web‐based application of the checklists for users for its wide applications.