I actually have one more question about the use of proc genmod in a slightly different scenario. I need to model the effect of some variables measured at a baseline assessment on a serial outcome measured in subsequent years (but, notably, not at the baseline assessment). I'm wondering how appropriate the code I have below would be. First, the data set-up is as follows, with id representing unique subjects, year representing the time,outcome representing a dichotomous outcome measure, bl_var1 representing a dichotomous predictor variable, and bl_var2 representing a second dichotomous predictor variable: id year outcome bl_var1 bl_var2 1 2009 1 0 1 2010 0 1 0 1 2011 1 1 0 2 2009 0 1 2 2010 1 0 1 2 2011 0 0 1 The values for bl_var1 and bl_var2 are equal because the year 2009 - or baseline - measurement is the only measurement for these that exists. It has been assigned to the subsequent years as well. Again, I want to model the impact of the baseline characteristics on the outcome measured in 2010 and 2011. I don't want to run separate models for 2010 and 2011 because I assume that the values for the outcome for these years are correlated on subjects. So, I'm using the code below. have specified the autoregressive working correlation matrix because it is a serial measurement of the same outcome. PROC GENMOD descending; WHERE YEAR NE 2009; CLASS id bl_var1(ref="0") bl_var2 (ref="0") year (ref="2010")/PARAM= effect; MODEL outcome = bl_var1 bl_var2 year / type3 dist=binomial link=logit; REPEATED subject=provnum / type=ar(1) corrw; estimate 'bl_var1' bl_var1 1 -1/ exp; estimate 'bl_var2' bl_var2 1 -1/ exp; estimate 'year' year 1 -1/ exp; RUN; Do I need an interaction between my baseline measures and year to see within subjects effects? E.g. would I want to add the following? bl_var1*year bl_var2*year If so, would these be interpreted as a change in the baseline value being associated with a change in the outcome from 2010 to 2011? Thanks again for your help. Devin
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