multivariate analysis of intervention effect with unpaired data

[PedroAntonio delaRosa]

Dear all,

I want to test the effectiveness of a educational videogame to increase protective factors of alcohol consumption in schools. For this purpose, we have recruited 8 schools which had to fill the same questionnaire on two different times (Q1 and Q2). Those schools were randomized to be assigned to 2 groups:

• implement the videogame between Q1 and Q2 (intervention group)
• Implement the videogame after Q2 (control group: there is no intervention between Q1 and Q2)

Due to research issues because of recent european laws, we decided to make the surveys 100% anonymous (No match codes were used).

I have a dataset of 20 variables and aproximately 800 observations (400 from questionnaire 1 and 400 from questionnaire 2).

Some of the variables are dependent variables in several models, but I want only to consider one hypothetical analysis, so I will only use one dependent variable.

Let's call my variables the following way:

• dependent variable in Q1: y₁
• dependent variable in Q2: y₂
• independent variables in Q1: x_a1 x_b1 x_c1
• independent variables in Q2: x_a2 x_b2 x_c2
• intervention variable: intervention₁ intervention₂
• cluster code: cluster

To test the effectiveness of the intervention, I can run unpaired ttest, or cltest command, but we wish to adjust the analysis for potential confounders. We have searched for multivariable models of unpaired data, but we have found nothing for this issue.

One easy solution to our problem would be to generate the "different from Q1 mean", and then run a linear regression to test if there is "difference between within group differences":

egen ymean1 = mean(y1)
gen difference= y2 - ymean1

regress difference intervention2 xa1 xb1 xc1 || cluster:

My question is: can STATA run adjusted analysis of unpaired data by other ways than this?


Thanks in advance