Objectives: Rapkin and Schwartz define response shift as otherwise unexplained, discrepant change in health-related quality of life (HRQOL) that is associated with change in cognitive appraisal. In this article, we demonstrate how a recursive partitioning (rpart) regression tree analytic approach may be used to explore cognitive changes to gain additional insight into response-shift phenomena. Study Design and Setting: Data are from the "Choices in Care Study," an evaluation of HIV+ Medicaid recipients' experiences and outcomes in care (N = 394). Cognitive assessment was based on the QOL appraisal battery. HRQOL was measured by the SF-36 Health Survey, version 2 (SF-36v2). Results: We used rpart to examine 6-month change in SF-36v2 mental composite score as a function of changes in appraisal, after controlling for patient characteristics, health changes, and intervening events. Rpart identified nine distinct patterns of cognitive change, including three associated with negative discrepancies, four with positive discrepancies, and two with no discrepancies. Conclusion: Rpart classification provides a nuanced treatment of response shift. This methodology has implications for evaluating programs, guiding decisions, and targeting care.
- Classification and regression trees
- Health-related quality of life
- Idiographic quality of life assessment
- Response shift
- Segmentation strategies
ASJC Scopus subject areas