Using SAS for the Longitudinal Analysis of Difference Scores - Brandy Sinco

When longitudinal data has little missing baseline data, analysis of difference scores is one method of normalizing the error terms, even if the original outcome variable is non-normal.  Adjusting for the baseline value as a covariate enables estimation of difference scores, with adjustment for the starting value.

Examples will be presented that show the trajectory of an outcome over time between treatment groups, in table and graphic format. These will include the treatment group improving significantly, in comparison to the control group, and of the treatment group staying the same, while the control group worsened over time.

About the Presenter

Brandy Sinco holds a bachelors degree in engineering from the University of Michigan, a masters degree in mathematics from Eastern Michigan University, and a masters in statistics from Colorado State University.
Since 1997, she has worked as a data analyst and computer programmer at the University of Michigan. She has written the statistical methods sections to numerous articles on health studies and has presented at MWSUG for the past three years. In 2013, she received the "Best Paper Award" from the MWSUG Advanced Analytics section for her presentation, "Adventures in Path Analysis and Preparatory Analysis". Outside of work, Brandy enjoys composing music, playing the piano, singing in choirs, yoga, and tai chi.