Say I want to analyze how a relation changes though time. Like usual, I would throw in the potential confounders into the regression model. But what if some of the confounders are not determined at the beginning of the study but at some point within time?(adsbygoogle = window.adsbygoogle || []).push({});

For example, say I want to analyze the relation between creatine doses and sports performance over time. So a basic linear mixed model would be: ##Performance_{ij} \sim Dose_{ij}+Time_{ij}+Dose_{ij}*Time_{ij}+PotentialConfounders_{i}+RandomEffects_{i}+error_{ij}##

Where i is the ith subject and j is within that subject. Say Dose and Performance are measure longitudinally. and the rest of covariates are just recorded once and is the same for all levels within the ith subject.

Say I suspect that gender might confound the relation between creatine and performance. Well, a persons gender is fixed throughout the study.

But what about say injury? Say I have an injury indicator variable thats says whether the athlete experiences injury during the study. Where I don't know when it happened in the study. This is different from say gender, where even if the data is collected after, we know it couldn't have changed.Anyways,If I consider Injury Status as potential confounder, should I include it? I mean, it might not be present at all time points. It seems that whatever inference obtained by including it would be invalid. But then again, if injury is a confounder, then not adjusting for it would also result in invalid inferences.

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# A Leaving out a confounder in longitudinal regression?

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