Regression methods in the empiric analysis of health care data.
OBJECTIVE
The aim of this paper is to provide health care decision makers with a conceptual foundation for regression analysis by describing the principles of correlation, regression, and residual assessment.


CONCLUSIONS
Researchers are often faced with the need to describe quantitatively the relationships between outcomes and predictors , with the objective of explaining trends, testing hypotheses, or developing models for forecasting. Regression models are able to incorporate complex mathematical functions and operands (the variables that are manipulated) to best describe the associations between sets of variables. Unlike many other statistical techniques, regression allows for the inclusion of variables that may control for confounding phenomena or risk factors. For robust analyses to be conducted, however, the assumptions of regression must be understood and researchers must be aware of diagnostic tests and the appropriate procedures that may be used to correct for violations in model assumptions.


CONCLUSIONS
Despite the complexities and intricacies that can exist in regression , this statistical technique may be applied to a wide range of studies in managed care settings. Given the increased availability of data in administrative databases, the application of these procedures to pharmacoeconomics and outcomes assessments may result in more varied and useful scientific investigations and provide a more solid foundation for health care decision making.
Skrepnek
Grant H
GH
University of Arizona College of Pharmacy, 1703 East Mabel St., Tucson, AZ 85721, USA. skrepnek@pharmacy arizona.edu
D035843:Biomedical Research / Q000706:statistics & numerical data; D003362:Cost-Benefit Analysis; D003695:Delivery of Health Care / Q000706:statistics & numerical data*; D008329:Managed Care Programs / Q000191:economics / Q000706:statistics & numerical data; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D012044:Regression Analysis