Ian Shrier1, Menglan Pang2, Robert W Platt2. 1. Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, 3755 Cote Ste-Catherine Road, Montreal H3T 1E2, Canada. Electronic address: ian.shrier@mcgill.ca. 2. Department of Epidemiology, Biostatistics and Occupational Health, Purvis Hall McGill University, 1020 Pine Ave. West, Montreal, Quebec H3A 1A2, Canada.
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.
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.
Authors: Gabrielle Simoneau; Fabio Pellegrini; Thomas Pa Debray; Julie Rouette; Johanna Muñoz; Robert W Platt; John Petkau; Justin Bohn; Changyu Shen; Carl de Moor; Mohammad Ehsanul Karim Journal: Mult Scler Date: 2022-04-06 Impact factor: 5.855
Authors: Ingrid S Sketris; Nancy Carter; Robyn L Traynor; Dorian Watts; Kim Kelly Journal: Pharmacoepidemiol Drug Saf Date: 2019-02-20 Impact factor: 2.890
Authors: Mohammad Ehsanul Karim; Fabio Pellegrini; Robert W Platt; Gabrielle Simoneau; Julie Rouette; Carl de Moor Journal: Mult Scler Date: 2020-11-12 Impact factor: 5.855
Authors: Michelle Samuel; Brice Batomen; Julie Rouette; Joanne Kim; Robert W Platt; James M Brophy; Jay S Kaufman Journal: BMJ Open Date: 2020-08-26 Impact factor: 2.692