Member Training: A Data Analyst’s Guide to Methods and Tools for Reproducible Research

Have you ever experienced befuddlement when you dust off a data analysis that you ran six months ago? 

Ever gritted your teeth when your collaborator invalidates all your hard work by telling you that the data set you were working on had “a few minor changes”?

Or panicked when someone running a big meta-analysis asks you to share your data?

If any of these experiences rings true to you, then you need to adopt the philosophy of reproducible research.

Reproducible research refers to methods and tools developed by large software development teams but which can help you keep a sense of order in your data, analysis programs, and results.

In this webinar, we’ll review reproducible research methodology, using recent recommendations in the literature. Although these recommendations may ask you to break long entrenched habits, the approach is easy to implement, even for data analysts who are new to computation.

Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

About the Instructor

Steve Simon works as an independent statistical consultant and as a part-time faculty member in the Department of Biomedical and Health Informatics at the University of Missouri-Kansas City. He has previously worked at Children’s Mercy Hospital, the National Institute for Occupational Safety and Health, and Bowling Green State University.

Steve has over 90 peer-reviewed publications, four of which have won major awards. He has written one book, Statistical Evidence in Medical Trials, and is the author of a major website about Statistics, Research Design, and Evidence Based Medicine, www.pmean.com. One of his current areas of interest is using Bayesian models to forecast patient accrual in clinical trials. Steve received a Ph.D. in Statistics from the University of Iowa in 1982.

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