I’m a co-author on @JessGeraldYoung’s new paper out now in Trials & I want to tell you all about it!
Do you use longitudinal data? Are your measurements often enough? Do you know what “often enough” is?
Time for a #tweetorial!
But if exposure can happen over time, so can confounding! & if exposure happens every day, so can confounding😰
But we usually don’t have data everyday. Is that bad? Classic epi answer: it depends!
If we can correctly remove confounding when we analyze this data, then we should get a null effect estimate!
Next, we varied how much data we could “see” in the analysis by creating study visits and only using information from those.
If you’re designing a randomized trial & want to estimate per-protocol effects, or a cohort study & want to assess time-varying exposures, you need to think about strength of confounding & amount of non-adherence when planning study visit frequency!