High‐Dimensional Propensity Scores for Mitigating Confounding: Implementation Using Primary and Secondary Care Data in Hong Kong

Confounding is a key concern in observational studies using healthcare databases. The high‐dimensional propensity score (HDPS) algorithm is an approach for generating and prioritising proxy variables, leveraging all available information in a database to mitigate residual confounding. This study aims to implement HDPS approaches in a novel setting using primary and secondary data available from Hong Kong (HK).