The overall number of members is somewhere around 90,000 (I am using PROC SORT first to help the PROC MIXED run faster). The number of members per practice range from 100 to 1,000+ and there are about 200 unique practices in total.

I have 8 variables which would be considered RANDOM EFFECTS (i.e. practice level effects)

1. the practice's survey points score (which is ultimately my variable of interest)

2. the number of physicians at the practice

3. the number of nurse practitioners at the practice

4. the number of physician assistants at the practice

5. the total size of the practice (in terms of how many patients belong to that practice)

6. the number of pharmacists at the practice

7. the number of care managers at the practice

8. the number of behavioral health specialists at the practice

When I add these 8 variables to the RANDOM statement, it results in the following (I'm just showing the intercept and survey score to keep things concise)

Effect Level Estimate Standard Error DF t*Value Pr > |t|

Intercept -408.01 278.28 9.10E+04 -1.47 0.1426

SURV_PTS -9.0612 10.727 9.10E+04 -0.84 0.3983

.... and so on which would suggest that, all things being equal, each additional point to the practice's survey would yield a decrease in cost of $9.06 per member per year (though not statistically significant, p-value=.3983).

I also do get output for this COVARIANCE PARAMETER ESTIMATES (the variables in my random statement) that I don't quite understand.

**Covariance Parameter Estimates **

Cov Parm Subject Estimate Standard Error Z Value Pr > Z

Intercept FAC_SITE_NAME 0 . . .

SURV_PTS FAC_SITE_NAME 0 . . .

physician_count FAC_SITE_NAME 0 . . .

NP_count FAC_SITE_NAME 20924 15496 1.35 0.0885

PA_count FAC_SITE_NAME 1203.92 3224.92 0.37 0.3545

MBR_count FAC_SITE_NAME 0 . . .

RX_count FAC_SITE_NAME 0 . . .

CM_count FAC_SITE_NAME 0 . . .

BH_count FAC_SITE_NAME 0 . . .

Residual 1.75E+08 822510 213.28 <.0001

so my total code would look something like this (it's still a bit condensed since, again, there are a lot of variables):

I mean if this looks all fine that's great.....I'm guessing interpretation of the parameters and such would be the same as any type of regression (like how I described the $9.06 savings per member per year for each additional survey point above).

__My questions:__

1. Is what I did actually correct, at least from anyone can tell?

2. The RANDOM line is literally just for the effects considered random? I ask because in an article using students/classrooms, the authors used a pre-test score in the RANDOM line (which would be a fixed student effect) but did not put any random classroom effects in it).

3. What exactly is happening with all of the RANDOM effects in that line? I mean I guess I understand it's calculating different slopes for each value?

4. When I have a random effect that is categorical and add it to the RANDOM line, none of the parameter estimates seems to be output. Does this suggest that only continuous variables can be random effects (at least in this line of code)?