Data mining for response shift patterns in multiple sclerosis patients using recursive partitioning tree analysis
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To examine evidence of QOL response shift in patients with multiple sclerosis (MS) using recursive partitioning tree analysis (RPART) technique.
Subjects: MS patients from the NARCOMS registry assessed an average of 6 times at a median interval of 6 months. Outcomes: SF-12v2 Physical & Mental Component Scores (PCS, MCS). Covariates: Patient-determined disease steps, Performance Scales, and symptomatic therapies. RPART trees were fitted separately by 3 disease-trajectory groups: (1) relapsing (n = 1,582); (2) stable (n = 787); and (3) progressive (n = 639). The resulting trees were interpreted by identifying salient terminal nodes that showed the unexpected quantitative patterns of contrasting MCS and PCS scores (e.g., PCS deteriorates but MCS is stable or improves), using a minimally important difference of at least 5 points on the SF-12v2. Qualitative indicators of response shift were different thresholds (recalibration), content (reconceptualization), and order (reprioritization) of disability domains in predicting PCS change by group.
Overall, 20% of patients demonstrated response shift quantitatively, with 10% in the “progressive” cohort, 8% in the “relapsing” cohort, and 2% in the “stable” cohort. RPART trees differed qualitatively across disease-trajectory groups in patterns suggestive of recalibration, reprioritization, and reconceptualization. Disability subscales, but not symptom management, distinguished homogenous groups.
PCS and MCS change scores are obfuscated by response shifts. The contingent true scores for PCS change scores are not comparable across patient groups.
KeywordsResponse shift Statistical analysis Recursive and partitioning tree analysis Methods Multiple sclerosis Appraisal
This work was funded in part by a Visiting Scientist Fellowship from the Consortium of Multiple Sclerosis Centers to Dr. Schwartz. Dr. Li was supported in part by a Weill Cornell Medical College Clinical and Translational Science Award (NIH UL1-RR024996) (PI: Julianne Imperato-McGinley MD). We are grateful to Timothy Vollmer, M.D., for helpful discussions related to classifying patients by disease course, and to Brian Quaranto for assistance in generating figures for manuscript preparation.
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