Investigators often go to great lengths to obtain careful, detailed measures of exposure, which may have multiple dimensions and may change over time, e.g. diet, stress, or blood pressure. In assessing the association between an individual's exposure history and the time or rate of occurrence of a health event, it is important to reduce the dimensionality of this multivariate exposure history data in order to increase statistical power. Although replacing the multivariate exposure information with a simple summary, as is typically done in practice, can sometimes improve interpretability and statistical power, it is typically not clear how best to summarize the information at hand. In addition, reducing detailed data into naive summaries often runs counter to the study goals of obtaining the most accurate assessment of exposure possible. We are interested in developing and applying statistical methods, allowing evidence-based summaries to be constructed objectively in a manner that maximizes information about the outcomes of interest. Motivated by data from the Pregnancy, Infection, and Nutrition (PIN) study, a prospective cohort study of preterm birth, we propose a Bayesian hierarchical model for a multiple episode exposure process and a reproductive outcome. Data on timing, duration, and intensity of exposure are summarized using a shared frailty term within a framework that accounts for changes in the process over time, correlated exposure measures, and missing data. Inferences on exposure effects can be based on the posterior density obtained using an efficient MCMC algorithm. The methods will be applied to vaginal bleeding and duration of gestation using the Pregnancy, Infection, and Nutrition study data but have broad applications in reproductive health, epidemiology, and other areas.

Agency
National Institute of Health (NIH)
Institute
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Type
Small Research Grants (R03)
Project #
1R03HD045780-01A1
Application #
6819423
Study Section
Special Emphasis Panel (ZHD1-MCHG-B (HA))
Program Officer
Reddy, Uma M
Project Start
2004-07-01
Project End
2006-06-30
Budget Start
2004-07-01
Budget End
2005-06-30
Support Year
1
Fiscal Year
2004
Total Cost
$71,160
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Slaughter, James C; Herring, Amy H; Thorp, John M (2009) A Bayesian latent variable mixture model for longitudinal fetal growth. Biometrics 65:1233-42
Slaughter, James C; Herring, Amy H; Hartmann, Katherine E (2008) Bayesian modeling of embryonic growth using latent variables. Biostatistics 9:373-89
Johnson, Brent A; Herring, Amy H; Ibrahim, Joseph G et al. (2007) Structured measurement error in nutritional epidemiology: applications in the Pregnancy, Infection, and Nutrition (PIN) Study. J Am Stat Assoc 102:856-866
MacLehose, Richard F; Dunson, David B; Herring, Amy H et al. (2007) Bayesian methods for highly correlated exposure data. Epidemiology 18:199-207
Herring, Amy H; Yang, Juan (2007) Bayesian modeling of multiple episode occurrence and severity with a terminating event. Biometrics 63:381-8