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Title: Constructing longitudinal disease progression curves using sparse, short-term individual data with an application to Alzheimer's disease
Austin Authors: Budgeon, Charley A;Murray, Kevin;Turlach, Berwin A;Baker, S ;Villemagne, Victor L ;Burnham, Samantha C
Affiliation: Centre for Applied Statistics, University of Western Australia, Crawley, Western Australia, Australia
eHealth, Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Floreat, Western Australia, Australia
School of Population and Global Health, University of Western Australia, Crawley, Western Australia, Australia
Janssen Research and Development, Titusville, NJ, USA
Department of Nuclear Medicine and Centre for PET, Austin Health, Heidelberg, Victoria, Australia
The Florey Institute for Neuroscience and Mental Health, The University of Melbourne, Victoria, Australia
Issue Date: Jul-2017
Date: 2017-04-25
Publication information: Statistics in Medicine 2017; 36(17): 2720-2734
Abstract: In 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.
DOI: 10.1002/sim.7300
ORCID: 0000-0002-1910-5561
Journal: Statistics in Medicine
PubMed URL:
Type: Journal Article
Subjects: Alzheimer's disease
Longitudinal trajectories
Sigmoidal curves
Appears in Collections:Journal articles

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