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dc.contributor.authorShishegar, Rosita-
dc.contributor.authorCox, Timothy-
dc.contributor.authorRolls, David-
dc.contributor.authorBourgeat, Pierrick-
dc.contributor.authorDoré, Vincent-
dc.contributor.authorLamb, Fiona-
dc.contributor.authorRobertson, Joanne-
dc.contributor.authorLaws, Simon M-
dc.contributor.authorPorter, Tenielle-
dc.contributor.authorFripp, Jurgen-
dc.contributor.authorTosun, Duygu-
dc.contributor.authorMaruff, Paul-
dc.contributor.authorSavage, Greg-
dc.contributor.authorRowe, Christopher C-
dc.contributor.authorMasters, Colin L-
dc.contributor.authorWeiner, Michael W-
dc.contributor.authorVillemagne, Victor L-
dc.contributor.authorBurnham, Samantha C-
dc.identifier.citationScientific reports 2021; 11(1): 23788en
dc.description.abstractTo 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.en
dc.titleUsing imputation to provide harmonized longitudinal measures of cognition across AIBL and ADNI.en
dc.typeJournal Articleen
dc.identifier.journaltitleScientific Reportsen
dc.identifier.affiliationDepartment of Medicine, The University of Melbourne, Parkville, VIC, 3052, Australiaen
dc.identifier.affiliationCentre for Precision Health, Edith Cowan University, Joondalup, WA, Australiaen
dc.identifier.affiliationCollaborative Genomics and Translation Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australiaen
dc.identifier.affiliationSchool of Pharmacy and Biomedical Sciences, Faculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Bentley, WA, Australiaen
dc.identifier.affiliationThe Australian e-Health Research Centre, CSIRO, Melbourne, Australiaen
dc.identifier.affiliationSchool of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia.
dc.identifier.affiliationMolecular Imaging and Therapyen
dc.identifier.affiliationDepartment of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA..en
dc.identifier.affiliationThe Australian e-Health Research Centre, CSIRO, Melbourne, Australiaen
dc.identifier.affiliationFlorey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australiaen
dc.identifier.affiliationCogstate Ltd., Melbourne, VIC, Australiaen
dc.identifier.affiliationDepartment of Psychology, Macquarie University, Sydney, NSW, Australiaen
dc.identifier.affiliationDepartment of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, CA, USA..en
dc.identifier.pubmedid34893624-é, Vincent
item.fulltextNo Fulltext-
item.openairetypeJournal Article-
item.grantfulltextnone- Imaging and Therapy- Imaging and Therapy- Imaging and Therapy- Florey Institute of Neuroscience and Mental Health- Imaging and Therapy-
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