- Thread starter MelPat
- Start date

Run a regression instead

Thank you. Do you mean that I should take the difference in pre/post weight and make that the dependent variable and then run it with sex/ethnicity/deprivation as the independent variable? Would I need to run separate regression for all the independent variable or can I add them all into the same analysis?

But the other option is to use the post weights as the dependent variable and include the pre weights in the list of independent variables. Mathematically if the estimated coefficient for the pre-weight is 1 then you'd essentially be doing the same thing (for inference) as taking the differences directly.

If the coefficient is 0 it would suggest the pre weight is unrelated to the post weights so it would be more akin to how one might analyze a completely randomized design where we don't know the pre weights.

So by going this route you'd kind of let the data choose what is appropriate.

So everyone got the intervention (natural experiment)?

You would want to add them all into the same model I would think. You *could* manually compute the differences and use that as the dependent variable. That would be more analogous to what a paired t-test does.

But the other option is to use the post weights as the dependent variable and include the pre weights in the list of independent variables. Mathematically if the estimated coefficient for the pre-weight is 1 then you'd essentially be doing the same thing (for inference) as taking the differences directly.

If the coefficient is 0 it would suggest the pre weight is unrelated to the post weights so it would be more akin to how one might analyze a completely randomized design where we don't know the pre weights.

So by going this route you'd kind of let the data choose what is appropriate.

But the other option is to use the post weights as the dependent variable and include the pre weights in the list of independent variables. Mathematically if the estimated coefficient for the pre-weight is 1 then you'd essentially be doing the same thing (for inference) as taking the differences directly.

If the coefficient is 0 it would suggest the pre weight is unrelated to the post weights so it would be more akin to how one might analyze a completely randomized design where we don't know the pre weights.

So by going this route you'd kind of let the data choose what is appropriate.

Side note, without a control group you can't conclusive attribute all changes in weight to the intervention.