Using sign score regression models to select variables in case-control studies

This paper evaluates the performance of four variable selection methods suitable for case-control studies. Two of the methods are logistic regression and the rank transformed version of it which uses the ranks of the explanatory variables in place of the original observations. The third method is based on Kendall's tau b correlations. I propose a fourth method, a sign score regression model to select variables. To evaluate these four methods, I generate many data sets for a case group and a control group with the use of several different distributions and covariance matrices. I evaluate the methods on their ability to select correctly the variables related to case-control status while not selecting the unrelated variables. Using this criterion, the sign score regression method and the tau b method are more effective than the other two methods with uncorrelated or weakly correlated variables. The sign score regression method is more effective than the tau b method for all simulations that use normal variables and for some that use log-normal variables. Overall, the sign score regression method is the most effective variable selection method for data sets that have low or moderate correlations between variables.