Carrying out streamlined routine data analyses with reports for observational studies: introduction to a series of generic SAS ® macros [version 2; peer review: 2 approved]

• Published in: F1000 research Vol. 7; p. 1955
• Main Authors: Liu, Yuan, Nickleach, Dana C, Zhang, Chao, Switchenko, Jeffrey M, Kowalski, Jeanne
• Format: Journal Article
• Language: English
• Published: England Faculty of 1000 Ltd 2018; F1000 Research Limited; F1000 Research Ltd
• Subjects: Bias; Bioinformatics; Biomedical Research; Cancer; Data Analysis; Data processing; Databases, Factual; Humans; Hypotheses; Literature reviews; Medical research; Observational studies; Observational Studies as Topic - statistics & numerical data; Population; Researchers; Software; Software Tool; Variables; Atlanta Georgia; United States > US; SAS® macros; observational studies; collaborative; reporting; streamlined data process; Good-Research-Practice
• ISSN: 2046-1402; 2046-1402
• DOI: 10.12688/f1000research.16866.2

For a typical medical research project based on observational data, sequential routine analyses are often essential to comprehend the data on hand and to draw valid conclusions.  However, generating reports in SAS ® for routine analyses can be a time-consuming and tedious process, especially when dealing with large databases with a massive number of variables in an iterative and collaborative research environment. In this work, we present a general workflow of research based on an observational database and a series of SAS ® macros that fits this framework, which covers a streamlined data analyses and produces journal-quality summary tables. The system is generic enough to fit a variety of research projects and enables researchers to build a highly organized and concise coding for quick updates as research evolves. The result reports promote communication in collaborations and will escort the research with ease and efficiency.