Using Multiple Imputation to Incorporate Cases with Missing Items in a Mental Health Services Study

When data analysis tools require that every variable be observed on each case, then missing items on a subset of variables force investigators either to leave potentially interesting variables out of analysis models or to include these variables but drop incomplete cases from the analysis. For example, in a study considered here, mental health patients were interviewed at two time points about a variety of topics that reflect successful adaptation to outpatient treatment, such as support from family and friends and avoidance of legal problems, although not all patients were successfully interviewed at the second time point. In a previous analysis of these data, logistic regression models were developed to relate baseline patient characteristics and recent treatment cost history to binary outcomes capturing aspects of adaptation. In these models, years of education was omitted as a covariate because it was incompletely observed at baseline. Here, we carry out analyses that include information from partially observed cases. Specifically, we use a multivariate model to produce multiple plausible imputed values for each missing item, and we combine results from separate logistic regression analyses on the completed data sets using the multiple imputation inference technique. Although the majority of inferences about specific regression coefficients paralleled those from the original study, some differences are noted. We discuss the implications of having flexible analysis tools for incomplete data in health services research and comment on issues related to model choice.