Journal
STATISTICAL METHODS IN MEDICAL RESEARCH
Volume 27, Issue 12, Pages 3785-3796Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0962280217712088
Keywords
Functional principal component analysis; functional data analysis; missing data; outlier; renal disease
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Funding
- Natural Sciences and Engineering Research Council of Canada (NSERC)
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This article is motivated by some longitudinal clinical data of kidney transplant recipients, where kidney function progression is recorded as the estimated glomerular filtration rates at multiple time points post kidney transplantation. We propose to use the functional principal component analysis method to explore the major source of variations of glomerular filtration rate curves. We find that the estimated functional principal component scores can be used to cluster glomerular filtration rate curves. Ordering functional principal component scores can detect abnormal glomerular filtration rate curves. Finally, functional principal component analysis can effectively estimate missing glomerular filtration rate values and predict future glomerular filtration rate values.
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