Comparison of subset selection methods in linear regression in the context of health-related quality of life and substance abuse in Russia

BMC Medical Research Methodology
Olga MorozovaRobert Heimer

Abstract

Automatic stepwise subset selection methods in linear regression often perform poorly, both in terms of variable selection and estimation of coefficients and standard errors, especially when number of independent variables is large and multicollinearity is present. Yet, stepwise algorithms remain the dominant method in medical and epidemiological research. Performance of stepwise (backward elimination and forward selection algorithms using AIC, BIC, and Likelihood Ratio Test, p = 0.05 (LRT)) and alternative subset selection methods in linear regression, including Bayesian model averaging (BMA) and penalized regression (lasso, adaptive lasso, and adaptive elastic net) was investigated in a dataset from a cross-sectional study of drug users in St. Petersburg, Russia in 2012-2013. Dependent variable measured health-related quality of life, and independent correlates included 44 variables measuring demographics, behavioral, and structural factors. In our case study all methods returned models of different size and composition varying from 41 to 11 variables. The percentage of significant variables among those selected in final model varied from 100 % to 27 %. Model selection with stepwise methods was highly unstable, with most (and...Continue Reading

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Software Mentioned

R
MASS
BMS
glmnet
gcdnet
BMA

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