Please use this identifier to cite or link to this item:
Title: Using imputation to provide harmonized longitudinal measures of cognition across AIBL and ADNI.
Austin Authors: Shishegar, Rosita;Cox, Timothy;Rolls, David;Bourgeat, Pierrick;Doré, Vincent ;Lamb, Fiona ;Robertson, Joanne;Laws, Simon M;Porter, Tenielle;Fripp, Jurgen;Tosun, Duygu;Maruff, Paul;Savage, Greg;Rowe, Christopher C ;Masters, Colin L ;Weiner, Michael W;Villemagne, Victor L ;Burnham, Samantha C
Affiliation: Department of Medicine, The University of Melbourne, Parkville, VIC, 3052, Australia
Centre for Precision Health, Edith Cowan University, Joondalup, WA, Australia
Collaborative Genomics and Translation Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Bentley, WA, Australia
The Australian e-Health Research Centre, CSIRO, Melbourne, Australia
School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia.
Molecular Imaging and Therapy
Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA..
The Australian e-Health Research Centre, CSIRO, Melbourne, Australia
Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
Cogstate Ltd., Melbourne, VIC, Australia
Department of Psychology, Macquarie University, Sydney, NSW, Australia
Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, CA, USA..
Issue Date: 2021
Date: 2021
Publication information: Scientific reports 2021; 11(1): 23788
Abstract: To improve understanding of Alzheimer's disease, large observational studies are needed to increase power for more nuanced analyses. Combining data across existing observational studies represents one solution. However, the disparity of such datasets makes this a non-trivial task. Here, a machine learning approach was applied to impute longitudinal neuropsychological test scores across two observational studies, namely the Australian Imaging, Biomarkers and Lifestyle Study (AIBL) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) providing an overall harmonised dataset. MissForest, a machine learning algorithm, capitalises on the underlying structure and relationships of data to impute test scores not measured in one study aligning it to the other study. Results demonstrated that simulated missing values from one dataset could be accurately imputed, and that imputation of actual missing data in one dataset showed comparable discrimination (p < 0.001) for clinical classification to measured data in the other dataset. Further, the increased power of the overall harmonised dataset was demonstrated by observing a significant association between CVLT-II test scores (imputed for ADNI) with PET Amyloid-β in MCI APOE-ε4 homozygotes in the imputed data (N = 65) but not for the original AIBL dataset (N = 11). These results suggest that MissForest can provide a practical solution for data harmonization using imputation across studies to improve power for more nuanced analyses.
DOI: 10.1038/s41598-021-02827-6
Journal: Scientific Reports
PubMed URL: 34893624
Type: Journal Article
Appears in Collections:Journal articles

Show full item record

Page view(s)

checked on Sep 29, 2023

Google ScholarTM


Items in AHRO are protected by copyright, with all rights reserved, unless otherwise indicated.