Purposeful covariate selection methods for parsimonious binary logistic regression models

Abstract

Most previous studies have applied the covariate selection method proposed by Hosmer and Lemeshow (2000) (also referred to in the current study as the Hosmer and Lemeshow algorithm (H-L algorithm)) in attempt to fit parsimonious regression models, However, such previous studies did not evaluate or question the efficiency of the H-L algorithm against other common purposeful selection covariate selection methods, but they were merely application studies. As such, little is known about the efficiency of this renowned and novel purposeful covariate selection method. This study sought to bridge this gap. The study conducted a comparative experiment which sought to test the efficiency of the H-L algorithm against the other competing popular approaches namely: the bivariate logistic regressions, stepwise selection and the chi-square test, to identify the most efficient purposeful covariate selection method for fitting parsimonious logistic regression models (LRMs). This was achieved through the application of different model selection criteria which identified the stepwise selection method as the most efficient covariate selection approach compared to the other three methods under study, but this algorithm may tend to select different covariates for the different observations of the same dataset.