Methods to Handle Incomplete Data

Abstract

Context: The major question for data analysis is determining the appropriate analytic approach in the presence of incomplete observations. The most common solution to handle missing data in a data set is imputation, where missing values are estimated and filled in. An important problem of imputation is to maintain the statistical significance of the data set. Aim: To compare different imputation techniques − complete case analysis, last observation carried forward (LOCF), mean imputation, hot deck Imputation, regression imputation, and multiple imputation (MI). Settings and Design: The data for the study were collected from a prospective study to find out the predictors of early response to treatment in drug naive schizophrenia patients from a tertiary care centre, India. Methods and Material: The present study tries to compare four imputation methods: complete case analysis, LOCF, mean imputation, hot deck Imputation, regression imputation and MI, in filling up the missing values of the outcome variable. Statistical analysis used: Paired t test was used to compare the imputation methods. Results: At the fourth week, the positive and negative syndrome scale scores were missing for about a minority of the subjects (41%). Mean imputation differed significantly from LOCF (P = 0.001), regression imputation (P = 0.010) and MI (P = 0.002). LOCF differed significantly from all these methods − regression imputation (P = 0.001), hot deck imputation (P = 0.011) and MI (P = 0.001). Conclusions: LOCF and mean imputation methods are different from other imputation methods, and there is no difference between hot deck imputation, MI, and regression imputation.