I have a dataset with say about 50 variables related to lifestyle risk factors, and I want to measure the association between these lifestyle predictors (e.g. smoking, alcohol consumption, physical activity etc) and a dependent variable, say high cholesterol (three levels: Normal; Borderline High; High) using multinomial logistic regression.

My questions are:

  1. Should I run the model for each variable separately and interpret the result (significance of association, relative risk ratios etc), or it is better to use all (or at least a group of) the variables?

  2. If likelihood ratio chi-square test shows that the model does not fit significantly better than an empty model, how does it affect the interpretation of the result?


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