A SASr Algorithm for Imputing Discrete Missing Outcomes Based on Minimum Distance

Missing outcome data are encountered in many clinical trials and public health studies and present challenges in imputation. We present a simple and easy to use SAS-based imputation method for missing discrete outcome data. The method is based on minimum distance between baseline covariates of those with missing data and those without missing data. The imputation algorithm, a method that may be viewed as a variant of the hot dec imputation method, imputes missing values that are "close to" the observed values, implying that had there been data on those missing, it would have been similar to those non-missing. An illustrative example is presented.