For example, I want to know how baseline characteristics of patients (age, BMI...) and the confounding factors (smoking, diabetes or other chronic diseases) affect the serum vitamin D value. Which regression model should I use?
I use SPSS and R for analyzing the data
It depends on how the DV, serum Vit. D is measured. See Rosner Fundamentals of Biostatistics available in the z-library. Best wishes David Booth
if the vitamin D is measured as continuous variable ie concentration , then multiple linear regression would be appropriate. Note that multivariate regression really means multiple dependent variables, although terminology is not consistently applied in the literature..
If the dependent variables are continuous and the independent variables are coded properly, this approach should work to estimate the relationships. However, this method or any other correlational method will NOT determine if the IVs affect the DVs. In order to do that, you must use a designed experiment where the IVs are purposely varied and the DVs are randomly assigned. A significant correlation is a necessary, but not sufficient condition for causation.
Multi-variate regression analysis, you should run a group of independent variables analysis with dependent variable to consider confounding factors for associations.
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