Recommendations on how to analyze pre-post data can vary. Typical recommendations include regression analysis or matched pairs analysis for within subject studies and analysis of covariance (ANCOVA) or linear mixed effects model analysis for within and between subject studies.
In this training, we consider the four methods in terms of objectives, how the methods handle missing observations, and interpretations of results. Focus will be on continuous outcomes, with brief mention of approaches for categorical outcomes.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.
Date and Time
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About the Instructor
Julia Sharp is faculty in the Department of Statistics at Colorado State University where she is also the Director of the Graybill Statistics & Data Science Laboratory. She is also the owner of Sharp Analytics LLC, where she is the lead statistical collaborator. She earned her M.S. and Ph.D. in Statistics from Montana State University.
Julia has experience collaborating with researchers in many domains, using her expertise in applied statistics to inform and advance scientific research. Her statistical toolbox is broad and includes study design, analysis of a wide-range of data types (e.g., repeated observations over time, categorical data), and knowledge of popular statistical computing software.