Bayes and empirical Bayes methods enable the combining of information from similar and independent experiments, yielding improved estimation of both individual and shared model characteristics. Many complex public health problems offer ideal settings for this type of synthesis. For example, data on disease incidence in several adjacent counties may be partially pooled to yield a better estimate for each county, as a result of this borrowing of strength across studies. This proposal focuses on developing necessary sampling based methodological tools for improved analysis of public health and biomedical science data. These methods enable a much broader modeling framework since they allow exact propagation of variation throughout the various stages of the analysis, eliminating the need for approximations to distributional variability and shape. After a brief review of several past areas of application, two areas specific to the health sciences are proposed for in-depth exploration. First, the interim monitoring and final analysis of clinical trials data is shown to be a potentially fruitful area of application. Here, sampling based methods will allow real time evaluation of monitoring plans, and assessment of whether approximate results based on the assumption of normality are adequate. Further, the class of priors leading to a given decision, conditional on the data observed up to the decision point, may be characterized. Such results should help to alleviate concerns that a chosen prior might lead to premature stopping of the trial. A second area for investigation is the use of Bayes and empirical Bayes methods in the context of environmental and occupational health. For example, combining information on the mutagenic potency of several chemicals should produce a more accurate assessment of each. Again, the computer will permit complex, realistic hierarchical modeling of bioassay data without resort to simplifying assumptions or approximations. These two specific proposed problem areas are not intended to be exhaustive, but rather indicative of the broad issues which underlie all biomedical data analyses, and the inevitable future stream of opportunities for combining biostatistical theory with public health application.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
First Independent Research Support & Transition (FIRST) Awards (R29)
Project #
5R29AI033466-03
Application #
2068490
Study Section
Special Emphasis Panel (SSS (R2))
Project Start
1992-12-01
Project End
1997-11-30
Budget Start
1994-12-01
Budget End
1995-11-30
Support Year
3
Fiscal Year
1995
Total Cost
Indirect Cost
Name
University of Minnesota Twin Cities
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
168559177
City
Minneapolis
State
MN
Country
United States
Zip Code
55455
Carlin, B P; Xia, H (1999) Assessing environmental justice using Bayesian hierarchical models: two case studies. J Expo Anal Environ Epidemiol 9:66-78
Xia, H; Carlin, B P (1998) Spatio-temporal models with errors in covariates: mapping Ohio lung cancer mortality. Stat Med 17:2025-43
Carlin, B P; Sargent, D J (1996) Robust Bayesian approaches for clinical trial monitoring. Stat Med 15:1093-106
Goldman, A I; Carlin, B P; Crane, L R et al. (1996) Response of CD4 lymphocytes and clinical consequences of treatment using ddI or ddC in patients with advanced HIV infection. J Acquir Immune Defic Syndr Hum Retrovirol 11:161-9