Using imputation to provide harmonized longitudinal measures of cognition across AIBL and ADNI
Using imputation to provide harmonized longitudinal measures of cognition across AIBL and ADNI
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Author / Creator
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. and Burnham, Samantha C.
Publisher
London: Nature Publishing Group UK
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English
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Publisher
London: Nature Publishing Group UK
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Contents
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...
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Full title
Using imputation to provide harmonized longitudinal measures of cognition across AIBL and ADNI
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TN_cdi_doaj_primary_oai_doaj_org_article_42fdcdd7f3cb4cf2a359b106d65ccf69
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_42fdcdd7f3cb4cf2a359b106d65ccf69
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ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-021-02827-6
