High-dimensional propensity score analysis automatically selects independent variables for calculating propensity scores, using a vast amount of information from real-world health care databases. This technique can reduce confounding by indication or unmeasured confounders more precisely compared with conventional propensity score analysis. High-dimensional propensity score analysis assumes that proxy information for important unmeasured confounders can be obtained from the underlying data. The number of published studies using high-dimensional propensity score analysis has increased, with pharmacoepidemiology as the main area in which these studies have been published. This report explains the main assumption and the limitations of this analytical method and provides step-by-step procedures to implement the method.