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.
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.
|Sitlani, Colleen M; Heagerty, Patrick J (2014) Analyzing longitudinal data to characterize the accuracy of markers used to select treatment. Stat Med 33:2881-96|
|Bryan, Matthew; Heagerty, Patrick J (2014) Direct regression models for longitudinal rates of change. Stat Med 33:2115-36|
|Wakefield, J; Skrivankova, V; Hsu, F-C et al. (2014) Detecting signals in pharmacogenomic genome-wide association studies. Pharmacogenomics J 14:309-15|
|Saha-Chaudhuri, P; Heagerty, P J (2013) Non-parametric estimation of a time-dependent predictive accuracy curve. Biostatistics 14:42-59|
|Heagerty, Patrick J; Comstock, Bryan A (2013) Exploration of lagged associations using longitudinal data. Biometrics 69:197-205|
|French, Benjamin; Farjah, Farhood; Flum, David R et al. (2012) A general framework for estimating volume-outcome associations from longitudinal data. Stat Med 31:366-82|
|Zheng, Yingye; Heagerty, Patrick J; Hsu, Li et al. (2010) On combining family-based and population-based case-control data in association studies. Biometrics 66:1024-33|
|French, Benjamin; Heagerty, Patrick J (2009) Marginal mark regression analysis of recurrent marked point process data. Biometrics 65:415-22|
|French, Benjamin; Heagerty, Patrick J (2008) Analysis of longitudinal data to evaluate a policy change. Stat Med 27:5005-25|
|Schildcrout, Jonathan S; Heagerty, Patrick J (2008) On outcome-dependent sampling designs for longitudinal binary response data with time-varying covariates. Biostatistics 9:735-49|
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