I do a lot of studies in which we have a disease/outcome and then we collect a lot of information on the patients such as age, gender, BMI, comorbidities, lifestyle factors etc. and then we run a model to see if something sticks. These studies are not to be considered as something you would create new guidelines for, but merely to give future researchers ideas.
So far we've done a lot of stepwise-type of model building in which we do a univariate regression for each independent variable and then include those with alpha <= 0.2 in a multiple regression model and finally report those with alpha <= 0.05 in the multiple model.
I've never quite understood this approach as I would normally use multiple regression to select variables of interest that may influence each other, regardless of their presenting alpha. To me it sounds like something you tell a graduate student to give them something to model their data with, without knowing any statistics to make sure they don't overfit their model.
My question is, I'm considering stopping with the stepwise-type of approach and starting to report studies more in the way of selecting which variables I included in a multiple regression model subjectively based on clinical reasoning.
Would this not strengthen the statistical part of the study?