Which covariates to use in order to answer a specific research question in longitudinal model? I have a longitudinal dataset of 2 time points where Body mass index of patients has been recorded in 2018 and 2020.
the research question is to investigate the evolution over time ( from 2018 to 2020) of BMI in different covariate groups.( age gender region education level …)
to answer the research question, I assume I should include in my model the interaction of every covariate with time(categorical with two levels 2018 and 2020).
the issue is that for example when I fit the model with only main effect ( no interactions), I get a significant of some covariates and non significant of others. and when I include the interactions, some of the main effects that were significant they become non-significant and vice versa.
Same issue also with interaction effect themselves, their significance is affected by the presence of other covariates in the model.
How should my final model be? I don't know what to include and what to exclude.
Thank you in advance.
 A: To add to the context here, finding significant predictors is easy. If you want a p < .05, then all you need is 100 or so predictors and you'll get a handful that come out as significant. Significance of predictors is hardly interesting unless there's good theoretical reason to believe that statistical significance corresponds to clinical interest.
You want to know "which factors impact the evolution over time of BMI," so say that education is a significant predictor. Do I gain/lose weight because I went to school longer than someone else? Education does not cause weight or height changes that would impact BMI over time, so finding that education is a significant predictor does not mean that education is causing BMI changes over a two year period. If education is significant, then you might have some beliefs about why that is the case.
What is being recommended is a model-based approach to statistical thinking. The goal of the model is to simplify the world. In this case, you're simplifying BMI changes into a set of linear predictors that are all only indirectly related to BMI (i.e., it doesn't seem to me that you're collecting data on caloric intake, activity level, genetic predispositions, medications and side effects, etc). At the point at which we are simplifying the world for the sake of building a model, it behooves us to think about what factors would help us recreate the data generation process. In this way of thinking, something like education or region start to make sense as informative about BMI. For example, I might think that people with higher educations have more health literacy and may thus eat healthier, or they may have higher average wages that let them afford better foods. Similarly, people in certain regions may all have similar kinds of diets that would make that a useful way of predicting BMI compared to people from other regions with different diets. Significance of individual predictors is irrelevant if the model is capturing meaningful aspects of the underlying data generation (i.e., the true processes affecting BMI change over a two year interval). You'll just want to guard against violating assumptions (e.g., including a bunch of interactions can sometimes inflate collinearity) and overfitting the model. To avoid overfitting, making very careful a priori statements about what is giving rise to your data (e.g., through DAGs) is important. Just like anyone can get significant predictors, it's not hard to get a "good fitting" model. It's much harder to develop a meaningful model
