FEBIO POSTVIEW AVERAGE OF SELECTION SOFTWARE

They connect an outcome variable to one or several so‐called independent variables (IVs a list of abbreviations can be found in the Supporting Information Table S1) and quantify the strength of association between IVs and outcome variable. Statistical models are useful tools applied in many research fields dealing with empirical data.

FEBIO POSTVIEW AVERAGE OF SELECTION SOFTWARE

We also propose some quantities based on resampling the entire variable selection process to be routinely reported by software packages offering automated variable selection algorithms. Therefore, we give pragmatic recommendations for the practicing statistician on application of variable selection methods in general (low‐dimensional) modeling problems and on performing stability investigations and inference. Variable selection, in particular if used in explanatory modeling where effect estimates are of central interest, can compromise stability of a final model, unbiasedness of regression coefficients, and validity of p‐values or confidence intervals. These methods were usually developed in the context of a linear regression model and then transferred to more generalized linear models or models for censored survival data. We provide an overview of various available variable selection methods that are based on significance or information criteria, penalized likelihood, the change‐in‐estimate criterion, background knowledge, or combinations thereof. This number is often too large to be considered in a statistical model. In routine work, however, it is not known a priori which covariates should be included in a model, and often we are confronted with the number of candidate variables in the range 10–30. Hence, we can assume that effect estimates are unbiased and the usual methods for confidence interval estimation are valid.

Theory of statistical models is well‐established if the set of independent variables to consider is fixed and small. Statistical models support medical research by facilitating individualized outcome prognostication conditional on independent variables or by estimating effects of risk factors adjusted for covariates.

Febio postview average of selection