期刊
STATISTICAL MODELLING
卷 15, 期 3, 页码 256-278出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/1471082X14565526
关键词
functional data analysis; Survival analysis; Cox proportional hazards model; nonparametric statistics; intensive care unit
资金
- NIH from the National Institute of Environmental Health Sciences [2T32ES012871]
- National Heart, Lung, and Blood Institute of the National Institutes of Health [R01HL123407]
We extend the Cox proportional hazards model to cases when the exposure is a densely sampled functional process, measured at baseline. The fundamental idea is to combine penalized signal regression with methods developed for mixed effects proportional hazards models. The model is fit by maximizing the penalized partial likelihood, with smoothing parameters estimated by a likelihood-based criterion such as AIC or EPIC. The model may be extended to allow for multiple functional predictors, time varying coefficients, and missing or unequally spaced data. Methods were inspired by and applied to a study of the association between time to death after hospital discharge and daily measures of disease severity collected in the intensive care unit, among survivors of acute respiratory distress syndrome.
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