I want to assess the impact of my intervention in a repeated-measures design. I have subject as a random intercept in order to account for the dependence of measurements within subjects:
Outcome ~ Condition*Time + (1|Subject), where
Condition is treatment or placebo. I expected the effect of my intervention to increase over time, and I indeed see a significant
Condition*Time interaction, which supports my hypothesis.
However, in reality, I guess that subjects will have variable effects of
Outcome. Indeed a model comparison confirms that the model
Outcome ~ Condition*Time + (1+Time|Subject) fits the data significantly better, but my
Condition*Time interaction is no longer significant in the presence of this random slope.
Now, I am wondering if the random slope may have masked the effect of my intervention, by falsely attributing the variation in the effect of
Subject, rather than to
Condition. Is this possible?