Correcting a model is always a somewhat subjective process that can introduce bias, especially when it is done after the study is completed pre-publication. I am not at all opposed to such adjustments and I was actually involved in a research project that improved on pure chance based randomized selection of trial participants into different buckets (substance/placebo) by weighing in the necessary adjustments in the selection phase. That is why I’m intimately familiar with the state of the art in academia. Novel approaches, such as the one I worked on, are unpopular with reviewers which makes them unpopular with researchers even though they would allow, for faster trials, with smaller cohorts or alternatively to find smaller effect sizes. My criticism of the OP Study however is not the adjustments per se, which as you point out seem reasonable (although we don’t know what alternative corrections they could have made with the available data and what impact that would have had on their findings)but the fact that they had what was needed to conduct one of the rare properly executed longitudinal studies on this question and wasted that opportunity.