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- SAS/STAT Procedures A-Z

## PSMATCH Procedure

The PSMATCH procedure provides a variety of tools for performing propensity score analysis. The PSMATCH procedure reduces the effects of confounding in nonrandomized trials or observational studies where the subjects are not randomly assigned to the treatment and control groups.

The PSMATCH procedure computes propensity scores, which estimate the probability that a subject is assigned to treatment given a set of pretreatment (baseline) covariates. The following methods for using the propensity scores to adjust the data for valid estimation of treatment effect are available:

- Inverse probability of treatment weighting and weighting by the odds.
- Stratification of observations that have similar propensity scores. In a subsequent outcome analysis, the treatment effect can be estimated within each stratum, and the estimates can be combined across strata to compute an average treatment effect.
- Matching treated unit with one or more control units that have a similar value of the propensity score. Methods of matching include:
- fixed ratio matching
- variable ratio matching
- full matching

Provides various plots for assessing balance. Included plots are:

- cloud plots, which are scatter plots in which the points are jittered to prevent overplotting
- box plots for continuous variables
- bar charts for classification variables
- a standardized differences plot that summarizes differences between the treated and control groups

The PSMATCH procedures saves propensity scores and weights in an output data set that contains a sample that has been adjusted either by weighting, stratification, or matching. If the sample is stratified, you can save the strata identification in the output data set. If the sample is matched, you can save the matching identification in the output data set.

For further details see the SAS/STAT User's Guide:
The PSMATCH Procedure

( PDF | HTML )

- Example 96.1: Propensity Score Weighting
- Example 96.2: Propensity Score Stratification
- Example 96.3: Optimal Variable Ratio Matching
- Example 96.4: Greedy Nearest Neighbor Matching
- Example 96.5: Outcome Analysis after Matching
- Example 96.6: Matching with Replacement
- Example 96.7: Mahalanobis Distance Matching
- Example 96.8: Matching with Precomputed Propensity Scores
- Example 96.9: Sensitivity Analysis after One-to-One Matching