The Potential of Composite Cognitive Scores for Tracking Progression in Huntington's Disease
Article type: Research Article
Authors: Jones, Rebecca | Stout, Julie C. | Labuschagne, Izelle | Say, Miranda | Justo, Damian | Coleman, Allison | Dumas, Eve M. | Hart, Ellen | Owen, Gail | Durr, Alexandra; | Leavitt, Blair R. | Roos, Raymund | O'Regan, Alison | Langbehn, Doug | Tabrizi, Sarah J. | Frost, Chris
Affiliations: Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK | School of Psychology and Psychiatry, Monash University, Melbourne, Australia | UCL Institute of Neurology, University College London, London, UK | Inserm, UMR_S975, CRICM and UPMC Univ Paris 06, UMR_S975 and CNRS UMR 7225, Paris, France | Department of Medical Genetics, University of British Columbia, Vancouver, Canada | Department of Neurology, Leiden University Medical Centre, Leiden, The Netherlands | AP-HP, Hôpital de la Salpêtriére, Département de Génétique et Cytogénétique, Paris, France | Departments of Psychiatry and Biostatistics, University of Iowa, IA, USA
Note: [] Correspondence to: Rebecca Jones, Medical Statistics Department, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK. Tel.: +44 0207 927 2592; Fax: +44 0207 637 2853; E-mail: rebecca.jones@lshtm.ac.uk
Abstract: Background: Composite scores derived from joint statistical modelling of individual risk factors are widely used to identify individuals who are at increased risk of developing disease or of faster disease progression. Objective: We investigated the ability of composite measures developed using statistical models to differentiate progressive cognitive deterioration in Huntington's disease (HD) from natural decline in healthy controls. Methods: Using longitudinal data from TRACK-HD, the optimal combinations of quantitative cognitive measures to differentiate premanifest and early stage HD individuals respectively from controls was determined using logistic regression. Composite scores were calculated from the parameters of each statistical model. Linear regression models were used to calculate effect sizes (ES) quantifying the difference in longitudinal change over 24 months between premanifest and early stage HD groups respectively and controls. ES for the composites were compared with ES for individual cognitive outcomes and other measures used in HD research. The 0.632 bootstrap was used to eliminate biases which result from developing and testing models in the same sample. Results: In early HD, the composite score from the HD change prediction model produced an ES for difference in rate of 24-month change relative to controls of 1.14 (95% CI: 0.90 to 1.39), larger than the ES for any individual cognitive outcome and UHDRS Total Motor Score and Total Functional Capacity. In addition, this composite gave a statistically significant difference in rate of change in premanifest HD compared to controls over 24-months (ES: 0.24; 95% CI: 0.04 to 0.44), even though none of the individual cognitive outcomes produced statistically significant ES over this period. Conclusions: Composite scores developed using appropriate statistical modelling techniques have the potential to materially reduce required sample sizes for randomised controlled trials.
Keywords: Huntington disease, cognition, composite score, effect size, 0.632 bootstrap
DOI: 10.3233/JHD-140101
Journal: Journal of Huntington's Disease, vol. 3, no. 2, pp. 197-207, 2014
