Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/16642
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dc.contributor.authorBudgeon, Charley A-
dc.contributor.authorMurray, Kevin-
dc.contributor.authorTurlach, Berwin A-
dc.contributor.authorBaker, S-
dc.contributor.authorVillemagne, Victor L-
dc.contributor.authorBurnham, Samantha C-
dc.date2017-04-25-
dc.date.accessioned2017-05-04T01:51:18Z-
dc.date.available2017-05-04T01:51:18Z-
dc.date.issued2017-07-
dc.identifier.citationStatistics in Medicine 2017; 36(17): 2720-2734en_US
dc.identifier.urihttps://ahro.austin.org.au/austinjspui/handle/1/16642-
dc.description.abstractIn epidemiology, cohort studies utilised to monitor and assess disease status and progression often result in short-term and sparse follow-up data. Thus, gaining an understanding of the full-term disease pathogenesis can be difficult, requiring shorter-term data from many individuals to be collated. We investigate and evaluate methods to construct and quantify the underlying long-term longitudinal trajectories for disease markers using short-term follow-up data, specifically applied to Alzheimer's disease. We generate individuals' follow-up data to investigate approaches to this problem adopting a four-step modelling approach that (i) determines individual slopes and anchor points for their short-term trajectory, (ii) fits polynomials to these slopes and anchor points, (iii) integrates the reciprocated polynomials and (iv) inverts the resulting curve providing an estimate of the underlying longitudinal trajectory. To alleviate the potential problem of roots of polynomials falling into the region over which we integrate, we propose the use of non-negative polynomials in Step 2. We demonstrate that our approach can construct underlying sigmoidal trajectories from individuals' sparse, short-term follow-up data. Furthermore, to determine an optimal methodology, we consider variations to our modelling approach including contrasting linear mixed effects regression to linear regression in Step 1 and investigating different orders of polynomials in Step 2. Cubic order polynomials provided more accurate results, and there were negligible differences between regression methodologies. We use bootstrap confidence intervals to quantify the variability in our estimates of the underlying longitudinal trajectory and apply these methods to data from the Alzheimer's Disease Neuroimaging Initiative to demonstrate their practical use.en_US
dc.subjectAlzheimer's diseaseen_US
dc.subjectLongitudinal trajectoriesen_US
dc.subjectSigmoidal curvesen_US
dc.titleConstructing longitudinal disease progression curves using sparse, short-term individual data with an application to Alzheimer's diseaseen_US
dc.typeJournal Articleen_US
dc.identifier.journaltitleStatistics in Medicineen_US
dc.identifier.affiliationCentre for Applied Statistics, University of Western Australia, Crawley, Western Australia, Australiaen_US
dc.identifier.affiliationeHealth, Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Floreat, Western Australia, Australiaen_US
dc.identifier.affiliationSchool of Population and Global Health, University of Western Australia, Crawley, Western Australia, Australiaen_US
dc.identifier.affiliationJanssen Research and Development, Titusville, NJ, USAen_US
dc.identifier.affiliationDepartment of Nuclear Medicine and Centre for PET, Austin Health, Heidelberg, Victoria, Australiaen_US
dc.identifier.affiliationThe Florey Institute for Neuroscience and Mental Health, The University of Melbourne, Victoria, Australiaen_US
dc.identifier.pubmedurihttps://pubmed.ncbi.nlm.nih.gov/28444781en_US
dc.identifier.doi10.1002/sim.7300en_US
dc.type.contentTexten_US
dc.identifier.orcid0000-0002-1910-5561en_US
dc.identifier.orcid0000-0002-8856-6046en_US
dc.identifier.orcid0000-0001-8795-471Xen_US
dc.identifier.orcid0000-0003-4805-5193en_US
dc.type.austinJournal Articleen_US
local.name.researcherBaker, Scott T
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairetypeJournal Article-
crisitem.author.deptEndocrinology-
crisitem.author.deptMolecular Imaging and Therapy-
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