258085 Towards a Better Understanding of When to Apply Propensity Scoring: A Comparison with Conventional Regression in Ethnic Disparities Research

Wednesday, October 31, 2012 : 12:30 PM - 12:50 PM

Yu Ye, MA , Alcohol Research Group, Public Health Institute, Emeryville, CA
Jason Bond, PhD , Alcohol Research Group, Public Health Institute, Emeryville, CA
Laura A. Schmidt, PhD, MSW, MPH , Philip R. Lee Institute for Health Policy Studies and Department of Anthropology, History and Social Medicine, School of Medicine, University of California, San Francisco, San Francisco, CA
Nina Mulia, DrPH , Alcohol Research Group, Public Health Institute, Emeryville, CA
Tammy W. Tam, PhD , Center for the Vulnerable Child, Children's Hospital & Research Center Oakland, Oakland, CA
In the study of ethnic disparities in health outcomes, methods incorporating Propensity Scores (PS) have been increasingly utilized. However, many researchers lack clear understanding of when PS methods should be deployed in place of conventional regression models. One such scenario is presented here: where the relationship between ethnicity and primary care utilization is confounded with and modified by socioeconomic status (SES). Here, standard regression can only produce estimates of disparities at specific values of SES, a problem that PS methods do not share. PS methods require the choice of a reference sample (RS) to which the effect estimate is generalized. Using data from the National Alcohol Surveys, racial/ethnic disparities between whites and Hispanics in access to primary care intervention were estimated applying PS stratification and weighting, a modified regression-based approach, O-B decomposition, and conventional multivariable regression. Using whites as the RS, two strategies utilizing PS and O-B decomposition achieved similar results, quite different from that from multivariate regression. Using Hispanics as the RS, PS and O-B estimates were similar but quite different from those using whites as the RS. Unlike standard regression, PS or O-B methods can provide estimates that account for effect modification and incorporate a-priori hypotheses that guide selection of reference samples.

Learning Areas:
Biostatistics, economics

Learning Objectives:
To demonstrate the added value of propensity scoring over conventional multivariable regression in existence of effect modification using an example of ethnic disparity evaluation

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I have been interested in methods on casual inference, particularly the propensity score methods, for the last 3-4 years with several application papers published.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.