Liz

]]>For the rest, things get complex. One good source is Hedeker & Gibbons, *Longitudinal Data Analysis * but any good book on longitudinal data will explain things.

This is a great article! I am working with someone who has performed a randomized controlled trial where a variable was measured over three times. There are some missing data. She has created a composite (like with the weight example you give) of the difference in the variable (a1c levels). Basically she subtracts the amount at time one from time 3. To assess treatment effects she runs a regression and controls for time 1 level of a1c. The simple model is a1cdiff = a1c1 treatgroup. Because of the missing data and the fact that she measured many variables over three time points, I wanted to use proc mixed instead. Her data were in the “wide” format. We played around and created a “long” format data set and I ran a basic proc mixed model using a1c as the DV. While in the wide format, the effects of treatgroup on that “diff” variable were significant. While in the long format, the effects were not significant. She thinks it is the proc mixed, but based on gut and some things I’ve learned, and your article, I believe it is wrong to use that composite variable. She actually has higher statistical power with it, though. Can you explain why there would be a diff in wide to long models and the effects? It’s mathematical, I know. I apologize for the length of this message! Thanks for your help in advance! Also – there was a “survey” that popped up on your page. It asks for my contact information. I wasn’t sure if it was you or coming from some other untrustworthy source.

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