Skip to main content


Because of a lapse in government funding, the information on this website may not be up to date, transactions submitted via the website may not be processed, and the agency may not be able to respond to inquiries until appropriations are enacted.

The NIH Clinical Center (the research hospital of NIH) is open. For more details about its operating status, please visit

Updates regarding government operating status and resumption of normal operations can be found at

Grant Details

Grant Number: 5R01CA084438-08 Interpret this number
Primary Investigator: Guo, Wensheng
Organization: University Of Pennsylvania
Project Title: New Functional Models for Biomedical Data
Fiscal Year: 2009
Back to top


DESCRIPTION (provided by applicant): Nonparametric functional models have a wide range of applications in cancer and other biomedical research, such as biomarker data, hormone profiles, circadian patterns and EEG data. Most of the current literature focuses on developing flexible nonparametric models for the mean structure while the stochastic variation around the mean is treated as a nuisance component. In many biomedical applications, such as the data described in this proposal, the inferential focus is on the stochastic variation and on how it is related to the covariates and experimental treatment. Our first specific aim is to develop methods to nonparametrically estimate the unknown covariance structure in the functional data analysis setting. The estimate of the covariance structure can in turn be used to obtain more efficient estimates of the mean parameters. Our second aim is to extend the concept of functional data analysis to time series data, in which the basic unit of the data analysis is a time series and the focus on the analysis is not on the mean, but on how the covariates are related to the stochastic variation over time. A time series is uniquely defined by its spectrum and when we average across a group of spectra, we can obtain a group-average spectrum that uniquely defines a group-average" time series. Functional linear models and functional mixed effects models can also be applied to the spectra to make inference on covariates and treatment effects. We will focus our methods development on nonstationary time series data, which are most common in biomedical research. We will also develop computationally efficient estimation procedures through construction of equivalent state space models. Our third specific aim is to generalize the concept of functional data analysis to density data, in which the basic unit of the data analysis is a density. The proposed density models allow us to investigate covariates or treatment effects without making explicit assumptions on the underlying distributions. The proposed methods are motivated by and will be applied to the clinical studies that the principal investigator is directly or indirectly involved.

Back to top


Factors that influence entry into stages of the menopausal transition.
Authors: Sammel M.D. , Freeman E.W. , Liu Z. , Lin H. , Guo W. .
Source: Menopause (new York, N.y.), 2009 Nov-Dec; 16(6), p. 1218-27.
PMID: 19512950
Related Citations

Joint modeling of longitudinal and survival data via a common frailty.
Authors: Ratcliffe S.J. , Guo W. , Ten Have T.R. .
Source: Biometrics, 2004 Dec; 60(4), p. 892-9.
PMID: 15606409
Related Citations

Functional data analysis in longitudinal settings using smoothing splines.
Authors: Guo W. .
Source: Statistical Methods In Medical Research, 2004 Feb; 13(1), p. 49-62.
PMID: 14746440
Related Citations

Functional mixed effects models.
Authors: Guo W. .
Source: Biometrics, 2002 Mar; 58(1), p. 121-8.
PMID: 11890306
Related Citations

Structural time series models with feedback mechanisms.
Authors: Guo W. , Brown M.B. .
Source: Biometrics, 2000 Sep; 56(3), p. 686-91.
PMID: 10985203
Related Citations

Back to Top