LAUSR

OBJECTIVES To increase transparency in studies reporting propensity scores by using graphical methods that clearly illustrate (1) the number of participant exclusions that occur as a consequence of the analytic strategy and (2) whether treatment effects are constant or heterogeneous across propensity scores. STUDY DESIGN AND SETTING We applied graphical methods to a real-world pharmacoepidemiologic study that evaluated the effect of initiating statin medication on the 1-year all-cause mortality post-myocardial infarction. We propose graphical methods to show the consequences of trimming and matching on the exclusion of participants from the analysis. We also propose the use of meta-analytical forest plots to show the magnitude of effect heterogeneity. RESULTS A density plot with vertical lines demonstrated the proportion of subjects excluded because of trimming. A frequency plot with horizontal lines demonstrated the proportion of subjects excluded because of matching. An augmented forest plot illustrates the amount of effect heterogeneity present in the data. CONCLUSION Our proposed techniques present additional and useful information that helps readers understand the sample that is analyzed with propensity score methods and whether effect heterogeneity is present.