Browsing by Author "Barbour, Christopher"

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NeurEx: digitalized neurological examination offers a novel high-resolution disability scale
(2018-10) Kosa, Peter; Barbour, Christopher; Wichman, Alison; Sandford, Mary; Greenwood, Mark C.; Bielekova, Bibiana
Objective To develop a sensitive neurological disability scale for broad utilization in clinical practice. Methods We employed advances of mobile computing to develop an iPad-based App for convenient documentation of the neurological examination into a secure, cloud-linked database. We included features present in four traditional neuroimmunological disability scales and codified their automatic computation. By combining spatial distribution of the neurological deficit with quantitative or semiquantitative rating of its severity we developed a new summary score (called NeurEx; ranging from 0 to 1349 with minimal measurable change of 0.25) and compared its performance with clinician- and App-computed traditional clinical scales. Results In the cross-sectional comparison of 906 neurological examinations, the variance between App-computed and clinician-scored disability scales was comparable to the variance between rating of the identical neurological examination by multiple sclerosis (MS)-trained clinicians. By eliminating rating ambiguity, App-computed scales achieved greater accuracy in measuring disability progression over time (n = 191 patients studied over 880.6 patient-years). The NeurEx score had no apparent ceiling effect and more than 200-fold higher sensitivity for detecting a measurable yearly disability progression (i.e., median progression slope of 8.13 relative to minimum detectable change of 0.25) than Expanded Disability Status Scale (EDSS) with a median yearly progression slope of 0.071 that is lower than the minimal measurable change on EDSS of 0.5. Interpretation NeurEx can be used as a highly sensitive outcome measure in neuroimmunology. The App can be easily modified for use in other areas of neurology and it can bridge private practice practitioners to academic centers in multicenter research studies.
New Multiple Sclerosis Disease Severity Scale Predicts Future Accumulation of Disability
(2017-11) Weideman, Ann Marie; Barbour, Christopher; Tapia-Maltos, Marco Aurelio; Tran, Tan; Jackson, Kayla; Kosa, Peter; Komori, Mika; Wichman, Alison; Johnson, Kory; Greenwood, Mark C.; Bielekova, Bibiana
The search for the genetic foundation of multiple sclerosis (MS) severity remains elusive. It is, in fact, controversial whether MS severity is a stable feature that predicts future disability progression. If MS severity is not stable, it is unlikely that genotype decisively determines disability progression. An alternative explanation tested here is that the apparent instability of MS severity is caused by inaccuracies of its current measurement. We applied statistical learning techniques to a 902 patient-years longitudinal cohort of MS patients, divided into training (n = 133) and validation (n = 68) sub-cohorts, to test four hypotheses: (1) there is intra-individual stability in the rate of accumulation of MS-related disability, which is also influenced by extrinsic factors. (2) Previous results from observational studies are negatively affected by the insensitive nature of the Expanded Disability Status Scale (EDSS). The EDSS-based MS Severity Score (MSSS) is further disadvantaged by the inability to reliably measure MS onset and, consequently, disease duration (DD). (3) Replacing EDSS with a sensitive scale, i.e., Combinatorial Weight-Adjusted Disability Score (CombiWISE), and substituting age for DD will significantly improve predictions of future accumulation of disability. (4) Adjusting measured disability for the efficacy of administered therapies and other relevant external features will further strengthen predictions of future MS course. The result is a MS disease severity scale (MS-DSS) derived by conceptual advancements of MSSS and a statistical learning method called gradient boosting machines (GBM). MS-DSS greatly outperforms MSSS and the recently developed Age Related MS Severity Score in predicting future disability progression. In an independent validation cohort, MS-DSS measured at the first clinic visit correlated significantly with subsequent therapy-adjusted progression slopes (r = 0.5448, p = 1.56e-06) measured by CombiWISE. To facilitate widespread use of MS-DSS, we developed a free, interactive web application that calculates all aspects of MS-DSS and its contributing scales from user-provided raw data. MS-DSS represents a much-needed tool for genotype-phenotype correlations, for identifying biological processes that underlie MS progression, and for aiding therapeutic decisions.
Statistical Consulting and Research Services: Past, Present, and Future
(Montana State Univeristy, 2017-04) Flagg, Kenneth A.; Barbour, Christopher; Mack, Andrea; Schupbach, Jordan; Zhang, Huafeng
Statistical Consulting and Research Services (SCRS) is a group of statisticians at Montana State University (MSU) whose mission is to collaborate with domain experts across campus to improve the scientific research conducted at MSU and within the Montana University System. Since its inception, SCRS has grown at a tremendous rate and our statisticians continue to work with student and faculty researchers from a variety of scientific domains across the Montana University System. We present an overview of the history regarding how SCRS came to be, the services we perform, and the diversity of researchers that we collaborate with. We discuss the technical tools we incorporate in our workflow process and the steps we perform from the initial meeting to the final product. We will also highlight our vision moving into the future including what opportunities we see to continue improving the scientific research across the Montana University System, specifically highlighting the additional services we hope to provide here at MSU.