Multiple linear regression: p-value=0.25 pre-filter variable selection I have used many times in a multiple logistic regression the criteria of p-value=0.25 like pre-filter variable selection using bivariate logistic regression , then I use a MANUAL stepwise (backward) to finish the variable selection (p-value=0.05) (only main effects models).
I wonder if its possible use this method in multiple linear regression (pre-filter variable selection using bivariate linear regression), then... I have looking in literature this topic but only I got for multiple logistic regression (only main effects models).
I know that stepwise variable selection have a lot of critics in literature (I repeat: I use a manual method, not automated method...) but my focus is in bivariate linear regression like pre-filter using p-value=0.25 in variable selection...
Could use correlation analysis like a pre filter?
I would grateful some reference if exist...
Thanks in advance.
Regards, Rodrigo.
 A: Automated variable selection methods don't respect the causal model used to inform model development. These approaches can omit important confounders, include colliders, and inappropriately (depending on your precise question) include mediating variables. Depending on how interactions are handled, important effect modifiers could also be missed. So this is their first problem: the interpretation of each independent variable in a model is contingent on the other variables included/not included in the model and this cannot be both fully automated and expected to be sensible at the same time.
The second problem here is that all of the problems of stepwise/forward selection/backward elimination apply whether a computer implements the algorithm or you do. While a manual approach might, in theory, give opportunities to consider individual decisions, if the process is objectively performed according to pre-specified criteria, the only difference between the manual and computer-performed version will be that the former will take more time to do. A pre-screening approach is just a single round of forward selection and so will still overestimate the magnitude of effects and underestimate their p-values.
Note that iterative decisions based on information criteria (e.g. AIC and BIC) have the same problems. I strongly recommend reading Frank Harrell's Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis as this contains a lot of practical wisdom and the results of many, many simulations that Frank's already done on our behalf.
The only reasons for using any of these approaches, in my opinion, are if you are not concerned about generalisability (you simply want to fit the simplest adequate model to a set of data) or to identify a set of variables for future investigation. If you want to make generalisable conclusions based on the data in front of you, don't use any form of automated variable selection.
