Applications of logistic regression with missing data

Missing data is a common problem in statistical studies. While ignoring missing data is an option, it is possible to contribute to study by analyzing them with various statistical methods. Missing data analysis includes methods aiming at missing data problem solving. These methods are classified as deletion (Listwise and Pairwise) and imputation (Regression imputation, Expectation Maximization and Multiple Imputation).Logistic regression analysis method, one of the most popular methods applied for modeling two dependent variables, has two possible categories of dependent variable 0 and 1. Logistic Regression Analysis can be expanded according to the dependent variable as nominal and ordinal. There is no limitation for independent variables.The aim of this study is to examine the methods of missing value analysis and logistic regression and to evaluate the performance of different missing value analysis methods on logistic regression.