I am trying to find predictors for an outcome. I was taught to perform univariate analyses & put significant variables into a multivariate logistic regression model. Then I remove variables one by one based on p values > 0.05 to obtain the final model.
I saw from some papers that there is another approach. Basically, they do not remove any variable from the multivariate model, adjusting for all.
The first appraoch may not adjust for some potential confounders, but you get a model with less variables, all of which are significant. The second approach adjusts for all, which could be quite a long list. Are there any other important advantages or disadvantages that I should be aware of between the two approaches?