Eric Chamot1, Ilya Kister2, Garry R Cutter3. 1. Department of Epidemiology, University of Alabama at Birmingham School of Public Health, 1665 University Blvd, Suite 217H, Birmingham, AL 35294-0022, USA. Electronic address: echamot@uab.edu. 2. NYU-Multiple Sclerosis Care Center, Department of Neurology, NYU School of Medicine, 240 East 38th Street, NY 10016, USA. Electronic address: ilya.kister@gmail.com. 3. Department of Biostatistics, UAB School of Public Health, 1665 University Blvd, Suite 410B, Birmingham, AL 35294-0022, USA. Electronic address: cutterg@uab.edu.
Abstract
BACKGROUND: Multiple sclerosis (MS) can affect virtually every neurological function which complicates the conceptualization and assessment of disability. Similar challenges are encountered in other medical fields including child cognitive development and psychiatry, for instance. In these disciplines progress in diagnosis and outcome measurement has been recently achieved by capitalizing on the concept of bifactor model. OBJECTIVE: To present in accessible terms an application of bifactor confirmatory factor analysis to study the clinical disability outcomes in MS. METHODS: Data included 480 assessments on 301 patients with relapsing-remitting MS who participated in the North American interferon beta-1a clinical trial (Avonex). Measures consisted of the Expanded Disability Status Scale (EDSS), the three components of the Multiple Sclerosis Functional Composite (MSFC), and five other clinical measures of neurological functions. We determined which of three confirmatory factor analysis models (unidimensional, multidimensional, and bifactor) best described the structure of the data. RESULTS: EDSS scores ranged from 0 to 8 (94% between 0 and 4). The final bifactor model fitted the data well, explained 59.4% of total variance, and provided the most useful representation of the data. In this model, the nine measures defined a scoring dimension of global neurological function (63.1% of total composite score variance) and two auxiliary dimensions of extra variability in leg and cognitive function (17.1% and 9% of total composite score variance). CONCLUSION: Bifactor modeling is a promising approach to further understanding of the structure of disability in MS and for refining composite measures of global disability.
BACKGROUND:Multiple sclerosis (MS) can affect virtually every neurological function which complicates the conceptualization and assessment of disability. Similar challenges are encountered in other medical fields including child cognitive development and psychiatry, for instance. In these disciplines progress in diagnosis and outcome measurement has been recently achieved by capitalizing on the concept of bifactor model. OBJECTIVE: To present in accessible terms an application of bifactor confirmatory factor analysis to study the clinical disability outcomes in MS. METHODS: Data included 480 assessments on 301 patients with relapsing-remitting MS who participated in the North American interferon beta-1a clinical trial (Avonex). Measures consisted of the Expanded Disability Status Scale (EDSS), the three components of the Multiple Sclerosis Functional Composite (MSFC), and five other clinical measures of neurological functions. We determined which of three confirmatory factor analysis models (unidimensional, multidimensional, and bifactor) best described the structure of the data. RESULTS: EDSS scores ranged from 0 to 8 (94% between 0 and 4). The final bifactor model fitted the data well, explained 59.4% of total variance, and provided the most useful representation of the data. In this model, the nine measures defined a scoring dimension of global neurological function (63.1% of total composite score variance) and two auxiliary dimensions of extra variability in leg and cognitive function (17.1% and 9% of total composite score variance). CONCLUSION: Bifactor modeling is a promising approach to further understanding of the structure of disability in MS and for refining composite measures of global disability.