A study stated that it used forward selection to chose variables for a multivariable regression model (in this case logistic) to evaluate association between predictor and outcome. They started with theoretically plausible variables until they had a decent model, and then tested additional covariates by adding them to the model and checking if the predictor effect size (in this case odds ratio) was changed by 5%. They did not include covariates that did not change the predictor effect size by this (I presume arbitrary?) threshold. I had read the faults of stepwise selection, which uses P-values, but had not come across this method before.
Is this a recognised method and is it any more/less problematic than conventional forward stepwise?
Can someone give an undergrad-level list of common variable selection techniques to read up on? Specifically for epidemiology/sociology, rather than machine learning. I'm coming across penalised regression (lasso/ridge) and PCA (for modelling propensity scores).
I assume variable selection methods depends on the goal of the model (eg. prediction, inference, propensity score, etc)? Can you tell me how/if each technique is suited to each common goals?