TY - JOUR
T1 - Distilling the essence of appraisal
T2 - a mixed methods study of people with multiple sclerosis
AU - Rapkin, Bruce D.
AU - Schwartz, Carolyn E.
N1 - Funding Information:
The authors had full access to the original data. This work was funded in part by a Grant from the Patient-Centered Outcome Research Institute (PCORI #ME-1306-00781) to Dr. Rapkin. Part of the data collected for this work was provided in the context of a Consortium of MS Centers/Global MS Registry Visiting Scientist Fellowship to Dr. Schwartz, which was supported through a Foundation of the Consortium of Multiple Sclerosis Centers Grant from EMD Serono, Inc. CMSC/Global MS Registry is supported by the Consortium of Multiple Sclerosis Centers and its Foundation. We thank Timothy Vollmer, MD, for his help and support over the course of this project; Gary Cutter, Ph.D., Stacey Cofield, Ph.D., and Rita Bode, Ph.D., for data management services early in the project; Jei Li for data analytic support on a later draft of the manuscript; and Ruth Ann Marrie, M.D., Ph.D., and Robert Fox, M.D., for helpful comments on an earlier draft of this manuscript.
Publisher Copyright:
© 2015, Springer International Publishing Switzerland.
PY - 2016/4/1
Y1 - 2016/4/1
N2 - Background: This study aimed to identify the essential parts of the Quality of Life (QOL) Appraisal Profile that capture the most important differences in the ways that people with multiple sclerosis respond to patient-reported outcome (PRO) measures. This process will enable the eventual development of a more practical, less resource-intensive version of the QOL Appraisal Profile to facilitate its use in clinical research and practice. Methods: This is a secondary analysis of longitudinal PRO data (n = 859) of participants in the North American Research Committee on Multiple Sclerosis registry. Following the Rapkin and Schwartz (Health Qual Life Outcomes 2(1):14, 2004) model, we computed a “standard QOL model,” and then multivariate analysis of variance (MANOVA) and discriminant function (DF) analysis to identify patterns of appraisal measures associated with group differences in response to each QOL outcome. Results: The “standard QOL model” explains a moderate amount of variance (i.e., 15–17 %) in physical functioning and disease-specific disability, and very little variance in mental health functioning. The MANOVAs identified the appraisal variables that mattered by PRO, and the DF analysis included 10–16 of the 83 potential appraisal variables in two DFs per outcome that distinguished groups with better, average, and worse expected scores, as well as groups with better-than-expected, as-expected, and worse-than-expected scores. The dominant appraisal parameters were more similar between the generic and disease-specific measure of physical functioning and disability, respectively, than between the mental health measure and the former two measures. Conclusions: The practical implications of this work all revolve around a fundamental recommendation: Whenever one measures QOL, one should measure appraisal.
AB - Background: This study aimed to identify the essential parts of the Quality of Life (QOL) Appraisal Profile that capture the most important differences in the ways that people with multiple sclerosis respond to patient-reported outcome (PRO) measures. This process will enable the eventual development of a more practical, less resource-intensive version of the QOL Appraisal Profile to facilitate its use in clinical research and practice. Methods: This is a secondary analysis of longitudinal PRO data (n = 859) of participants in the North American Research Committee on Multiple Sclerosis registry. Following the Rapkin and Schwartz (Health Qual Life Outcomes 2(1):14, 2004) model, we computed a “standard QOL model,” and then multivariate analysis of variance (MANOVA) and discriminant function (DF) analysis to identify patterns of appraisal measures associated with group differences in response to each QOL outcome. Results: The “standard QOL model” explains a moderate amount of variance (i.e., 15–17 %) in physical functioning and disease-specific disability, and very little variance in mental health functioning. The MANOVAs identified the appraisal variables that mattered by PRO, and the DF analysis included 10–16 of the 83 potential appraisal variables in two DFs per outcome that distinguished groups with better, average, and worse expected scores, as well as groups with better-than-expected, as-expected, and worse-than-expected scores. The dominant appraisal parameters were more similar between the generic and disease-specific measure of physical functioning and disability, respectively, than between the mental health measure and the former two measures. Conclusions: The practical implications of this work all revolve around a fundamental recommendation: Whenever one measures QOL, one should measure appraisal.
KW - Appraisal
KW - Multiple sclerosis
KW - Patient-reported outcomes
KW - Response shift
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U2 - 10.1007/s11136-015-1119-z
DO - 10.1007/s11136-015-1119-z
M3 - Article
C2 - 26342930
AN - SCOPUS:84940862141
SN - 0962-9343
VL - 25
SP - 793
EP - 805
JO - Quality of Life Research
JF - Quality of Life Research
IS - 4
ER -