One of the primary tools in the assessment of individual-level patient outcomes is Jacobson and Truax, (1991’s) Reliable Change Index (RCI). Recent efforts to optimize the RCI have revolved around three issues: (a) extending the RCI beyond two timepoints, (b) estimating the RCI using scale scores from item response theory or factor analysis and (c) estimation of person- and time-specific standard errors of measurement.
We present an adaptation of a two-stage procedure, a measurement error-corrected multilevel model, as a tool for RCI estimation (with accompanying Statistical Analysis System syntax). Using DASS-21 data from a community-based mental health center (N = 379), we illustrate the potential for the model as unifying framework for simultaneously addressing all three limitations in modeling individual-level RCI estimates.
Compared to the optimal-fitting RCI model (moderated nonlinear factor analysis scoring with measurement error correction), an RCI model that uses DASS-21 total scores produced errors in RCI inferences in 50.8% of patients; this was largely driven by overestimation of the proportion of patients with statistically significant improvement.
Estimation of the RCI can now be enhanced by the use of latent variables, person- and time-specific measurement errors, and multiple timepoints.