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Please be aware that this content contains descriptions of violence that may be distressing to some viewers.This research article examines the Reliable Change Index (RCI) as one of the primary tools in the assessment of individual-level patients.
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. The authors 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), The authors 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. (Publisher abstract provided)
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