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 high-dimensional outcome data measured at a fine time resolution. The overall goals of this proposal are to develop regression methodology, graphical summaries, 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. With a clinical event time, T iota, analysis of biomarker data will focus both on the predictive survived distribution given the current value of the marker, P[T iota> T I Y iota(s)] where Y iota(s) represents the measured biomarker at time s (or a function of its history), and on the time-dependent accuracy of the biomarker as defined by characteristics of the marker distribution conditional on event time, P[Y iota(s) > c ] T iota = t].
This aim will develop semi-parametric statistical methods to estimate covariate specific longitudinal predictive values and longitudinal accuracy as characterized by measures of sensitivity and specificity. 2. Longitudinal categorical data and likelihood inference. Longitudinal studies now routinely collect patient health information at a large number of time points. Examples include observational studies of the health effects of air pollution, and pharmacodynamic studies of allergy symptoms. In each of these examples daily categorical outcome data is recorded for several months during which ambient exposure (pollution, pollen) also varies.
This aim will develop flexible likelihood-based estimation methods for categorical longitudinal data.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
1R01HL072966-01
Application #
6599004
Study Section
Social Sciences, Nursing, Epidemiology and Methods 4 (SNEM)
Program Officer
Werner, Ellen
Project Start
2003-04-25
Project End
2007-03-31
Budget Start
2003-04-25
Budget End
2004-03-31
Support Year
1
Fiscal Year
2003
Total Cost
$189,500
Indirect Cost
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
Schildcrout, Jonathan S; Schisterman, Enrique F; Mercaldo, Nathaniel D et al. (2018) Extending the Case-Control Design to Longitudinal Data: Stratified Sampling Based on Repeated Binary Outcomes. Epidemiology 29:67-75
Skrivankova, Veronika; Heagerty, Patrick J (2018) Single index methods for evaluation of marker-guided treatment rules based on multivariate marker panels. Biometrics 74:663-672
Bansal, Aasthaa; Heagerty, Patrick J (2018) A Tutorial on Evaluating the Time-Varying Discrimination Accuracy of Survival Models Used in Dynamic Decision Making. Med Decis Making 38:904-916
Liang, C Jason; Heagerty, Patrick J (2017) A risk-based measure of time-varying prognostic discrimination for survival models. Biometrics 73:725-734
Maziarz, Marlena; Heagerty, Patrick; Cai, Tianxi et al. (2017) On longitudinal prediction with time-to-event outcome: Comparison of modeling options. Biometrics 73:83-93
Bryan, Matthew; Heagerty, Patrick J (2016) Multivariate analysis of longitudinal rates of change. Stat Med 35:5117-5134
French, Benjamin; Saha-Chaudhuri, Paramita; Ky, Bonnie et al. (2016) Development and evaluation of multi-marker risk scores for clinical prognosis. Stat Methods Med Res 25:255-71
Janes, Holly; Pepe, Margaret S; McShane, Lisa M et al. (2015) The Fundamental Difficulty With Evaluating the Accuracy of Biomarkers for Guiding Treatment. J Natl Cancer Inst 107:
Schildcrout, Jonathan S; Rathouz, Paul J; Zelnick, Leila R et al. (2015) BIASED SAMPLING DESIGNS TO IMPROVE RESEARCH EFFICIENCY: FACTORS INFLUENCING PULMONARY FUNCTION OVER TIME IN CHILDREN WITH ASTHMA. Ann Appl Stat 9:731-753
Juul, Sandra E; Mayock, Dennis E; Comstock, Bryan A et al. (2015) Neuroprotective potential of erythropoietin in neonates; design of a randomized trial. Matern Health Neonatol Perinatol 1:27

Showing the most recent 10 out of 32 publications