hdps: Implementation of high-dimensional propensity score approaches in Stata

Large healthcare databases are increasingly used for research investigating the effects of medications. However, adequate adjustment for confounding remains a key issue because incorrect conclusions can be drawn amid concerns of residual or unmeasured confounding. The high-dimensional propensity score (hd-PS) has been proposed as a solution to improve confounder adjustment and was developed in the context of US claims data by Schneeweiss et al. (2009). This approach treats information, stored as codes, within healthcare databases as proxies for key underlying confounders. Some proxies are likely to be strongly correlated with the variables typically included in a traditional propensity score or multivariable analysis and others may represent information about patients that is otherwise unmeasured, such as frailty. By including many of these proxies in the analysis, the hd-PS aims to adjust for both measured and unmeasured confounding. I present hdps, a command implementing this approach in Stata. Having defined data dimensions and the level of code truncation, hdps allows the user to set several tuning parameters: the number of codes to retain per dimension (d), the prespecified time frame, and the number of variables to include in the final model (k). The command generates proxy variables and performs a variable selection step to identify important variables for confounder adjustment. I illustrate hdps using a study from the Clinical Practice Research Datalink (CPRD).