Understanding characteristics of patients with propensity scores in the tails of the propensity score (PS) distribution has relevance for inverse-probability-of-treatment-weighted and PS-based estimation in observational studies. Here we outline a method for identifying variables most responsible for extreme propensity scores. The approach is illustrated in 3 scenarios: 1) a plasmode simulation of adult patients in the National Ambulatory Medical Care Survey (2011-2015) and 2) timing of dexamethasone initiation and 3) timing of remdesivir initiation in patients hospitalized for coronavirus disease 2019 from February 2020 through January 2021. PS models were fitted using relevant baseline covariates, and tails of the PS distribution were defined using asymmetric first and 99th percentiles. After fitting of the PS model in each original data set, values of each key covariate were permuted and model-agnostic variable importance measures were examined. Visualization and variable importance techniques were helpful in identifying variables most responsible for extreme propensity scores and may help identify individual characteristics that might make patients inappropriate for inclusion in a study (e.g., off-label use). Subsetting or restricting the study sample based on variables identified using this approach may help investigators avoid the need for trimming or overlap weights in studies.
Keywords: covariate balance; epidemiologic methods; inverse probability of treatment weighting; propensity score; variable importance measures.
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