There are studies where the effect size of an exposure on treatment response is small and not statistically significant when modelled as longitudinal change, yet the same variable, when dichotomised, show dramatic effect sizes.
I can only assume there is some underlying methodological issue, but am not smart enough to figure out what! Can you?
Here is one example when studying the effect of smoking on treatment response. The authors used mixed models to compare differences in a continuous outcome variable (called BASDAI: a disease activity index bounded between 0 and 10) according to smoking status. There was no difference between current and never smokers. (They didn’t give the overall effect size, but just to give you an idea: in a subanalysis where they did find a significant difference, the effect size was less than clinically meaningful difference of 1.)
However, when they used logistic models to study a binary version of this outcome (50% reduction, imaginatively called BASDAI50), the odds of achieving this response was reduced by approx 50% in current compared to never smokers!
My immediate thoughts are that outcomes other than non-response are being bundled into this binary variable (can’t meet response criteria if I stopped the drug due to an adverse reaction, or death!). But many such papers, including this one, are in very prestigious journals (I know what you’re going to say…), leading me to think whether there was cleverer explanation?
I don’t think this paper accounted for informative dropout in their longitudinal models but other studies have shown that additionally using inverse-probability censoring weights did not significantly change results.