BIAS REDUCTION USING PROPENSITY SCORE MATCHING IN OBSERVATIONAL DATA (STATISTICS PROJECT TOPICS AND MATERIALS)
ABSTRACT
In observational studies, ―case-control groups‖ often exhibit imbalance on covariates. This covariate imbalance is confounded with treatments. It is difficult to attribute differences in responses to the ―treatment‖ because the covariates are also believed to influence the response. Propensity score matching attempts to reduce the confounding effects of covariates, and so allows differences of responses to be attributed to differences of treatments. In addition, the values of the propensity scores can serve as a diagnostic tool to evaluate the comparability of the groups in a quantitative way. When two groups are being compared, the propensity score can be calculated as the predicted probability of group membership from a logistic regression. It represents the ‗tendency‘ for an observation to be in one group or the other. By adjusting for the value of the propensity score in a linear model, one effectively adjusts for any group differences attributed to the variables used to create the propensity score.
Here we present an experiment where propensity scores were used to adjust for differences between a case and a control group (treatment group and a non-randomized control group). Propensity scores were created using SPSS Version 16 Binary Logistic Regression Procedure on a Windows Vista platform. A linear model was also estimated using the same. Groups were compared using independent samples t-tests and chi-square tests as appropriate. Standardized differences were calculated and matching was done with Microsoft Excel Version 2007 on a Windows Vista platform. The results showed that the Propensity Score Matching was successful in reducing the bias on the covariates.
