There has been increasing emphasis on cost comparison and cost-effectiveness analysis in health care delivery systems. It is important that health care policies are based on reliable inferences about medical care costs and reliable predictions about future costs for medical care. However, reliable inferences and prediction may be impeded by the following four characteristics of medical cost data: (1) non-zero cost observations are highly skewed to the right; (2) a certain proportion of the population is expected to incur no medical care costs during the whole study period; (3) cost observations are hierarchically structured; and (4) cost observations typically exhibit heteroscedasticity. In this application, investigators will develop a general statistical methodology for regression analysis of health care cost data, adjusting for these four characteristics. Furthermore, contributions will be demonstrated in the area of health services research by applying the proposed methods to analyze of total health care costs in four existing datasets.

Agency
National Institute of Health (NIH)
Institute
Agency for Healthcare Research and Quality (AHRQ)
Type
Research Project (R01)
Project #
1R01HS013105-01A1
Application #
6572485
Study Section
Health Care Technology and Decision Science (HTDS)
Program Officer
Taylor, Amy K
Project Start
2002-09-30
Project End
2005-09-29
Budget Start
2002-09-30
Budget End
2003-09-29
Support Year
1
Fiscal Year
2002
Total Cost
Indirect Cost
Name
University of Washington
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
135646524
City
Seattle
State
WA
Country
United States
Zip Code
98195
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Song, Xiao; Zhou, Xiao-Hua (2005) A marginal model approach for analysis of multi-reader multi-test receiver operating characteristic (ROC) data. Biostatistics 6:303-12
Jiang, Hongyu; Zhou, Xiao-Hua (2004) Bootstrap confidence intervals for medical costs with censored observations. Stat Med 23:3365-76