Project

Maximum Individualized Change Analysis (MICA)

Project Description

This study expands the research base on analytic methods to assess intervention effects of interventions that target multiple outcomes in heterogeneous populations. Specifically, the project continues the study of the Maximum Individualized Change Analysis (MICA) procedure developed by Boothroyd, Banks, Evans, Greenbaum, and Brown (2004). The method was initially developed as an analytic alternative to the more traditional multivariate and latent model approaches often used in studies examining individually tailored interventions. Our initial developmental work on this approach suggests that MICA offers a number of significant advantages over traditional statistical approaches in studies where a number of measures are used to assess potential treatment outcomes. These advantages include increased statistical power to detect smaller program effects and few problems associated with missing data. The study will consist of two phases. First, an expanded simulation study of the MICA procedure is being conducted to systematically examine 1) levels of correlation among outcome measures, 2) numbers of outcome measures being assessed, 3) distribution of response effects, 4) numbers of outcome measures on which a person improves, 5) sample size, 6) weighting outcome measures based on qualitative assessment regarding the likelihood of change, and 7) amount of missing data. In phase two, the MICA procedure will be used in the re-analysis of data collected as part of a SAMHSA-funded multi-site national study examining the impact of managed care (Leff, et al., 2005).