my friends at Stackexchange, I have been contemplating and searching the literature for the best options to probe the interaction effect between treatment integrity (or fidelity) and treatment effect in RCTs. Let's focus on the simplest "gold standard" RCT = pretest-posttest control group design first. (fidelity = the degree to which a treatment is implemented as intended)
- in my field, many RCTs do not collect fidelity data from the blank/placebo/business-as-usual control arm (understandable because nothing is done in control).
- Even if fidelity data is collected from the control arm, it is NOT the same measure/metric as the one for the treatment arm.
- But it is often reasonable to assume an interaction between fidelity and treatment effect in most cases.
- Let's discuss this issue primarily assuming we use ANCOVA to analyze the data (i.e., DV= posttest, focal predictor= treatment, covariates= demographics + pretest + fidelity).
My two cents:
If we control fidelity as a covariate in ANCOVA, its coefficient (main effect) means the slope of fidelity in the control group only (b/c controlling for everything else as 0). Thus, it means nothing in our situation(constant 0)...
Well, let's ignore the nonsense main effect of fidelity and add an interaction term between fidelity and treatment. Then the coefficient of this term means the difference in slopes between the fidelity in the treatment group vs. control group.
since the control group has nothing about fidelity (constant 0), it basically means the slope of the fidelity in the treatment group.
I was wondering if our mighty SE community has better solutions, or identifies any issues in my humble two cents. Please do extend this discussion to other methods or designs (e.g., longitudinal RCT design)
Thank you so much for your time and efforts in advance!