Variable selection - A review and recommendations for the practicing statistician.

ABSTRACT: Statistical models support medical research by facilitating individualized outcome prognostication conditional on independent variables or by estimating effects of risk factors adjusted for covariates. Theory of statistical models is well-established if the set of independent variables to consider is fixed and small. Hence, we can assume that effect estimates are unbiased and the usual methods for confidence interval estimation are valid. 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. This number is often too large to be considered in a statistical model. We provide an overview of various available variable selection methods that are based on significan

SUBMITTER: Heinze G 

PROVIDER: S-EPMC5969114 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature