Missing Data in Regression Models for Non-Commensurate Multiple Outcomes

Many studies collect multiple outcomes to characterize treatment effectiveness or evaluate risk factors. These outcomes tend to be correlated because they are measuring related quantities in the same individuals, but the common approach used by researchers is to ignore this correlation and analyze each outcome separately. There may be advantages to consider the simultaneous analysis of the outcomes using multivariate methods. Although the joint analysis of outcomes measured in the same scale (commensurate outcomes) can be undertaken with standard statistical methods, outcomes measured in different scales (noncommensurate outcomes), such as mixed binary and continuous outcomes, present more difficult challenges. In this article, we contrast some statistical approaches to analyze noncommensurate multiple outcomes. We discuss the advantages of a multivariate method for the analysis of noncommensurate outcomes, including situations of missing data. A real data example from a clinical trial, comparing bare-metal with sirolimus-eluting stents, is used to illustrate the differences between the statistical approaches. PMC ID: PMC3595565 (March 2011)