Longitudinal studies in medicine are faced with new analysis challenges due to continually advancing measurement and database technologies. Specifically, innovations in molecular assays, medical imaging, and psychological assessment have generated numerous new putative markers of disease progression. Also, advances in electronic data recording now allow longitudinal investigations to collect observational data measuring changes in outcomes and changes in treatments where dynamic treatment covariates are driven by current clinical guidelines, by unfolding patient health characteristics, or other factors. The overall goals of this proposal are to develop statistical methodology and software tools for analyzing modern longitudinal biomedical data. The specific areas of emphasis are: 1. Repeated measures and time-dependent accuracy. Biomarkers are measurements that characterize specific aspects of patient health status.
This aim will develop semi-parametric and non-parametric statistical methods to estimate the ability of prognostic scores or markers to accurately predict event times as characterized by time-dependent measures of sensitivity and specificity. 2. Observational longitudinal data and time-dependent exposure. Longitudinal studies now routinely collect both patient health information and changing covariate (treatment, exposure) data.
This aim will develop statistical methods that can be used to estimate causal effects of exposure, and to evaluate the ability of marker values to guide the choice or timing of treatment.

Public Health Relevance

In this proposal, we will develop new statistical methods for the analysis of longitudinal data. In particular, we will develop methods that can evaluate the time-dependent sensitivity and specificity of a biomarker for the prediction of future event times such as disease onset or death. In addition, we will evaluate and develop methods for the analysis of observational longitudinal data commonly recorded in electronic medical records where both measures of health status and measures of treatment change over time. We will focus research on estimation of the causal effect of treatments that are modified over time, and on the estimation of the ability of biomarkers to be used to guide the choice of which subjects are likely to obtain the largest benefit from specific treatment options.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL072966-06
Application #
8318008
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Werner, Ellen
Project Start
2003-04-25
Project End
2015-06-30
Budget Start
2012-07-01
Budget End
2013-06-30
Support Year
6
Fiscal Year
2012
Total Cost
$291,131
Indirect Cost
$91,131
Name
University of Washington
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195
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Wakefield, J; Skrivankova, V; Hsu, F-C et al. (2014) Detecting signals in pharmacogenomic genome-wide association studies. Pharmacogenomics J 14:309-15

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