Effective Semiparametric Modeling for Ultra-Sparse, Unsynchronized and Imprecise Longitudinal Data

RAND Statistics Seminar Series

Effective Semiparametric Modeling for Ultra-Sparse, Unsynchronized and Imprecise Longitudinal Data

Dr. Damla Senturk—UCLA

Wednesday, December 7th, 2011
12:00 noon – 1:30 p.m. PT
Please note: This seminar is scheduled for a different time than usual.
Conference Room 5104
RAND Corporation, Santa Monica, CA

Other Locations/Times:
Washington, D.C., Conf. Rm. 4304 3:00 p.m. ET
Pittsburgh, PA, Conf. Rm. 6207b 3:00 p.m. ET

Please contact Fabiola Lopez if you would like to attend this seminar.

Abstract

The proposed functional varying coefficient model provides a versatile and flexible analysis tool for relating longitudinal responses to longitudinal predictors. Specifically, this approach provides a novel representation of varying coefficient functions through suitable auto- and cross-covariances of the underlying stochastic processes, which is particularly advantageous for sparse and irregular designs, as often encountered in longitudinal studies. Existing methodology for varying coefficient models is not adapted to such data. The proposed approach extends the customary varying coefficient models to a more general setting, in which not only current but also recent past values of the predictor time course may have an impact on the current value of the response time course. The influence of past predictor values is modeled by a smooth history index function, while the effects on the response are described by smooth varying coefficient functions. The resulting estimators for varying coefficient and history index functions are shown to be asymptotically consistent for sparse designs. In addition, prediction of unobserved response trajectories from sparse measurements on a predictor trajectory is obtained, along with asymptotic pointwise confidence bands. The proposed methods perform well in simulations, especially when compared with commonly used local polynomial smoothing methods for varying coefficient models, and are illustrated with longitudinal primary biliary liver cirrhosis data.

Speaker Bio

Damla Senturk, Ph.D. is an Assistant Professor in the Department of Biostatistics at the University of California, Los Angeles. She received her Ph.D. in statistics from University of California Davis in 2004. Dr. Senturk's main methodological research areas are regression model building for repeated measures/ longitudinal data, functional data analysis and semiparametric covariate and error adjustments in regression and correlation models with applications to biomedical data. She works on modeling trends in data that are changing over time and explore dynamical regression relations between variables. Some of her applied work include modeling of molecular inflammation markers, infection status and cardiovascular events in the dialysis population, risk factors for hypertension, genomic markers of disease outcome and association of molecular measures in female carriers of the FMR1 (fragile X mental retardation 1) gene.

Attending a Seminar

RAND visitors are welcome to attend and must RSVP at least one day prior to the seminar. To ensure your attendance please contact Fabiola Lopez at flopez@rand.org with your name, company (or university) affiliation, and national citizenship (for security purposes).

For parking and directions to RAND's Santa Monica office, please see: http://www.rand.org/about/locations/santa-monica.html.

For parking and directions to RAND's Pittsburgh office, please see: http://www.rand.org/about/locations/pittsburgh.html.

For parking and directions to RAND's Washington office, please see: http://www.rand.org/about/locations/washington.html.

For further information and to be added to the mailing list contact Fabiola Lopez at flopez@rand.org.