In the article titled “High-dimensional Propensity Score Adjustment in Studies of Treatment Effects Using Health Care Claims Data,” the authors introduce a semiautomated variable selection algorithm for high-dimensional proxy adjustment within insurance health care claims databases.1 The high-dimensional propensity score (HDPS) algorithm evaluates thousands of diagnostic, procedural, and medication claims codes and, for each code, generates binary variables based on the frequency of occurrence for each code during a defined pre-exposure covariate assessment period. The HDPS then prioritizes or ranks each variable based on its potential for bias by assessing the variable’s prevalence and univariate association with the treatment and outcome according to the Bross formula.1,2 From this ordered list, investigators then specify the number of variables to include in the HDPS model along with prespecified variables such as age and sex.1 A full description of the HDPS algorithm is provided elsewhere.1
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