Methods for the analysis of binary outcome results in the presence of missing data

An important, frequent, and unresolved problem in treatment research is deciding how to analyze outcome data when some of the data are missing. After a brief review of alternative procedures and the underlying models on which they are based, an approach is presented for dealing with the most common situation--comparing the outcome results in a 2-group, randomized design in the presence of missing data. The proposed analysis is based on the concept of "modeling our ignorance" by examining all possible outcomes, given a known number of missing results with a binary outcome, and then describing the distribution of those results. This method allows the researcher to define the range of all possible results that could have resulted had the missing data been observed. Extensions to more complex designs are discussed.