Statistical model building: Background "knowledge" based on inappropriate preselection causes misspecification.

ABSTRACT:

Background

Statistical model building requires selection of variables for a model depending on the model's aim. In descriptive and explanatory models, a common recommendation often met in the literature is to include all variables in the model which are assumed or known to be associated with the outcome independent of their identification with data driven selection procedures. An open question is, how reliable this assumed "background knowledge" truly is. In fact, "known" predictors might be findings from preceding studies which may also have employed inappropriate model building strategies.

Methods

We conducted a simulation study assessing the influence of treating variables as "known predictors" in model building when in fact this knowledge resulting from preceding studies

SUBMITTER: Hafermann L 

PROVIDER: S-EPMC8480029 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature