Graphic report of the results from propensity score method analyses

Abstract

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.

Keywords: Density plot; Effect heterogeneity; Frequency matched plot; Meta-analysis; Propensity score; Trimming.