Toward a better understanding of when to apply propensity scoring: a comparison with conventional regression in ethnic disparities research.
OBJECTIVE
Despite growing popularity of propensity score (PS) methods used in ethnic disparities studies, many researchers lack clear understanding of when to use PS in place of conventional regression models. One such scenario is presented here: When the relationship between ethnicity and primary care utilization is confounded with and modified by socioeconomic status. Here, standard regression fails to produce an overall disparity estimate, whereas PS methods can through the choice of a reference sample (RS) to which the effect estimate is generalized.


METHODS
Using data from the National Alcohol Surveys, ethnic disparities between White and Hispanics in access to primary care were estimated using PS methods (PS stratification and weighting), standard logistic regression, and the marginal effects from logistic regression models incorporating effect modification.


RESULTS
Whites, Hispanics, and combined White/Hispanic samples were used separately as the RS. Two strategies utilizing PS generated disparities estimates different from those from standard logistic regression, but similar to marginal odd ratios from logistic regression with ethnicity by covariate interactions included in the model.


CONCLUSIONS
When effect modification is present, PS estimates are comparable with marginal estimates from regression models incorporating effect modification. The estimation process requires a priori hypotheses to guide selection of the RS.
Alcohol Research Group, Public Health Institute, Emeryville, CA 94608, USA. yye@arg.org
Yu
Y
Ye
Jason C
JC
Bond
Laura A
LA
Schmidt
Nina
N
Mulia
Tammy W
TW
Tam
D000293:Adolescent; D000328:Adult; D000368:Aged; D000428:Alcohol Drinking / Q000009:adverse effects; D003627:Data Interpretation, Statistical; D006297:Health Services Accessibility / Q000706:statistics & numerical data*; D054624:Health Status Disparities; D054625:Healthcare Disparities / Q000208:ethnology / Q000706:statistics & numerical data*; D006630:Hispanic or Latino / Q000706:statistics & numerical data; D006801:Humans; D008875:Middle Aged; D016017:Odds Ratio; D011320:Primary Health Care / Q000706:statistics & numerical data*; D057216:Propensity Score; D012044:Regression Analysis; D012107:Research Design; D012306:Risk; D012959:Socioeconomic Factors; D014481:United States; D044465:White People / Q000706:statistics & numerical data; D055815:Young Adult