Missing data are a recurring problem that can cause bias or lead to inefficient analyses. Statistical methods to address missingness have been actively pursued in recent years, including imputation, likelihood, EM algorithm and Bayesian approaches. Each approach is more complicated when there are many patterns of missing values, or when both categorical and continuous random variables are involved. Implementations of routines to incorporate observations with incomplete variables in regression models are now widely available.