Variable entered in logistic regression model is part of another variable entered in the same model I´m trying to find variables predicting a disease by using first logistic regression for each variable on the disease and then entering the significant variables into a multiple logistic regression model. However, one of the variables in the multivariate model is a clinical score, which contains some of the variables adjusted for (among others bmi<18). 
My question is: does it make sense to have both in the multivariate model? And if yes, what is the interpretation of an insignificant clinical score in the multivariate model? Is it insignificant because I controlled for some of its variables?
 A: Two comments:
First, I would explore other methods of variable selection. Looking at a set of unadjusted regressions and choosing the "significant" variables to then include in a final model is not an ideal approach. Your options are plentiful - search around here for topics on model selection to get you started. There are experts on this topic floating around, so perhaps some of them will add more here. 
Second, think about the interpretation of the regression coefficient in a multiple regression. An increase in your clinical score is associated with X change in Y, holding covariates constant. Theoretically, if your clinical score increases, and you hold some component of that score constant, than the estimated association must be due to the other aspects of the clinical scale. 
What to do really depends on what your goals are. Are you more interested in predicting things, or making any causal inferences? Can you discard the clinical scale entirely and focus on its components,or are you stuck using it?
