Nonmemory Composite Embedded Performance Validity Formulas in Patients with Multiple Sclerosis

Research regarding performance validity tests (PVTs) in patients with multiple sclerosis (MS) is scant, with recommended batteries for neuropsychological evaluations in this population lacking suggestions to include PVTs. Moreover, limited work has examined embedded PVTs in this population. As previous investigations indicated that nonmemory-based embedded PVTs provide clinical utility in other populations, this study sought to determine if a logistic regression-derived PVT formula can be identified from selected nonmemory variables in a sample of patients with MS.

A total of 184 patients (M age = 48.45; 76.6% female) with MS were referred for neuropsychological assessment at a large, Midwestern academic medical center. Patients were placed into "credible" (n = 146) or "noncredible" (n = 38) groups according to performance on standalone PVT. Missing data were imputed with HOTDECK.

Classification statistics for a variety of embedded PVTs were examined, with none appearing psychometrically appropriate in isolation (areas under the curve [AUCs] = .48-.64). Four exponentiated equations were created via logistic regression. Six, five, and three predictor equations yielded acceptable discriminability (AUC = .71-.74) with modest sensitivity (.34-.39) while maintaining good specificity (≥.90). The two predictor equation appeared unacceptable (AUC = .67).

Results suggest that multivariate combinations of embedded PVTs may provide some clinical utility while minimizing test burden in determining performance validity in patients with MS. Nonetheless, the authors recommend routine inclusion of several PVTs and utilization of comprehensive clinical judgment to maximize signal detection of noncredible performance and avoid incorrect conclusions. Clinical implications, limitations, and avenues for future research are discussed.