ArticleThe annals of applied statistics2016
A LAG FUNCTIONAL LINEAR MODEL FOR PREDICTION OF MAGNETIZATION TRANSFER RATIO IN MULTIPLE SCLEROSIS LESIONS.
Article in The annals of applied statistics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Bayesian Wavelet-packet Historical Functional Linear Models.Statistics and computing · 2021Article
- Experimental design and sample size considerations in longitudinal magnetic resonance imaging-based biomarker detection for multiple sclerosis.Statistical methods in medical research · 2020Article
- Longitudinal dynamic functional regression.Journal of the Royal Statistical Society. Series C, Applied statistics · 2020Article
- A Spatio-Temporal Model for Longitudinal Image-on-Image Regression.Statistics in biosciences · 2019Article
- A comparison of risk prediction methods using repeated observations: an application to electronic health records for hemodialysis.Statistics in medicine · 2017Article
- A LAG FUNCTIONAL LINEAR MODEL FOR PREDICTION OF MAGNETIZATION TRANSFER RATIO IN MULTIPLE SCLEROSIS LESIONS.The annals of applied statistics · 2016Article
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Authors and funding
8 authors.
Funding
Abstract
We propose a lag functional linear model to predict a response using multiple functional predictors observed at discrete grids with noise. Two procedures are proposed to estimate the regression parameter functions: (1) an approach that ensures smoothness for each value of time using generalized cross-validation; and (2) a global smoothing approach using a restricted maximum likelihood framework. Numerical studies are presented to analyze predictive accuracy in many realistic scenarios. The methods are employed to estimate a magnetic resonance imaging (MRI)-based measure of tissue damage (the magnetization transfer ratio, or MTR) in multiple sclerosis (MS) lesions, a disease that causes damage to the myelin sheaths around axons in the central nervous system. Our method of estimation of MTR within lesions is useful retrospectively in research applications where MTR was not acquired, as well as in clinical practice settings where acquiring MTR is not currently part of the standard of care. The model facilitates the use of commonly acquired imaging modalities to estimate MTR within lesions, and outperforms cross-sectional models that do not account for temporal patterns of lesion development and repair.
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Registered trials
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