Classification and regression tree uncovered hierarchy of psychosocial determinants underlying quality-of-life response shift in HIV/AIDS

Abstract

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.

Figure 1

Full rpart tree with 24 terminal nodes (a) and the pruned tree with 9 terminal nodes (b). To minimized clutter in (a), the most prevalent outcomes in the terminal nodes are represented in symbols, for positive discrepancy (marked with the + sign), negative discrepancy (−) and no discrepancy (*). Many splitting criteria in (a) are omitted.

Figure 2

Graphical output of the plotcp() command in rpart. The horizontal dotted line represents the 1 SE rule---the cutoff cross-validation error statistic at 1 standard deviation above the minimal cross-validation error. The tree with 9 terminal nodes is considered the desired size for pruning because it entails the lowest cross-validation before additional complexity in the tree are accompanied by higher cross-validation errors.