TY - JOUR
T1 - Variable-Domain Functional Regression for Modeling ICU Data
AU - Gellar, Jonathan E.
AU - Colantuoni, Elizabeth
AU - Needham, Dale M.
AU - Crainiceanu, Ciprian M.
N1 - Funding Information:
Jonathan E. Gellar, MPH, is a PhD student, Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205; Pulmonary & Critical Care Medicine, and Physical Medicine & Rehabilitation, School of Medicine, Johns Hopkins University, Baltimore, MD 21205 (E-mail: [email protected]). Elizabeth Colantuoni, PhD (E-mail: [email protected]), is Assistant Scientist, and Ciprian M. Crainiceanu, PhD (E-mail: [email protected]), is Associate Professor in the Department of Biostatistics at Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205. Dale M. Needham, FCA, MD, PhD, is Associate Professor in Pulmonary & Critical Care Medicine, and Physical Medicine & Rehabilitation at the Johns Hopkins School of Medicine, Baltimore, MD 21205 (E-mail: [email protected]). This project was supported by NIH grants 2T32ES012871 from the National Institute of Environmental Health Sciences, RO1 EB012547 from the National Institute of Biomedical Imaging And Bioengineering, RO1 NS060910 and RO1 NS085211 from the National Institute of Neurological Disorders and Stroke, RO1 MH095836 from the National Institute of Mental Health, and Acute Lung Injury SCCOR grant P050 HL73994.
Publisher Copyright:
© 2014, © 2014 American Statistical Association.
PY - 2014/10/2
Y1 - 2014/10/2
N2 - We introduce a class of scalar-on-function regression models with subject-specific functional predictor domains. The fundamental idea is to consider a bivariate functional parameter that depends both on the functional argument and on the width of the functional predictor domain. Both parametric and nonparametric models are introduced to fit the functional coefficient. The nonparametric model is theoretically and practically invariant to functional support transformation, or support registration. Methods were motivated by and applied to a study of association between daily measures of the Intensive Care Unit (ICU) sequential organ failure assessment (SOFA) score and two outcomes: in-hospital mortality, and physical impairment at hospital discharge among survivors. Methods are generally applicable to a large number of new studies that record a continuous variables over unequal domains. Supplementary materials for this article are available online.
AB - We introduce a class of scalar-on-function regression models with subject-specific functional predictor domains. The fundamental idea is to consider a bivariate functional parameter that depends both on the functional argument and on the width of the functional predictor domain. Both parametric and nonparametric models are introduced to fit the functional coefficient. The nonparametric model is theoretically and practically invariant to functional support transformation, or support registration. Methods were motivated by and applied to a study of association between daily measures of the Intensive Care Unit (ICU) sequential organ failure assessment (SOFA) score and two outcomes: in-hospital mortality, and physical impairment at hospital discharge among survivors. Methods are generally applicable to a large number of new studies that record a continuous variables over unequal domains. Supplementary materials for this article are available online.
KW - Functional data analysis
KW - Longitudinal data
KW - Nonparametric statistics
KW - Scalar-on-function regression
KW - Variable-domain functional regression
KW - Varying-coefficient model
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U2 - 10.1080/01621459.2014.940044
DO - 10.1080/01621459.2014.940044
M3 - Article
C2 - 25663725
AN - SCOPUS:84919819684
SN - 0162-1459
VL - 109
SP - 1425
EP - 1439
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 508
ER -