Propensity score matching
Match cases to more comparable controls using propensity score matching
Also, just like with regular regression, the independent, predictor, demographic, prognostic, clinical, and confounding variables that are entered into a model must have some sort of theoretical, conceptual, or physiological association or relevance when predicting for group membership. The validity of group membership yielded from propensity score matching is only as accurate as the predictor variables that are used in the regression model. This is the primary weakness of propensity score matching, the predicted group membership is adjusted ONLY for the variables used in the model. Results can easily become biased without important variables being accounted for in propensity score matching.
The four steps for selecting controls using propensity score matching
2. Identify the variables that affect group membership (independent, predictor, demographic, prognostic, clinical, and confounding variables).
3. Enter the variables into either a logistic regression (two groups) or multinomial logistic regression (three or more groups) model predicting for group membership.
4. Match cases to controls with similar propensity scores (estimated likelihood of group membership).
Another use of the propensity score is to statistically adjust for baseline differences in non-probability samples. The propensity score is entered into the regression model or is used as a covariate in a factorial analysis. Non-probability samples cannot assume that differences at baseline are due to chance as with probability samples. Potential selection biases must be accounted for to truly understand treatment effects yielded in observational studies. Propensity scores can serve as this adjustment in the statistical analysis.
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