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

Abstract

Purpose: 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).

Methods: Using data from HK, we implemented HDPS in a cohort study investigating the use of different antihypertensive drug classes and incident dementia risk. The top 250 HDPS covariates were included in inverse probability of treatment weighting in addition to investigator-specified variables. Diagnostics evaluated the performance of the HDPS. Sensitivity analyses included varying the number of HDPS covariates and removing potentially influential or inappropriate covariates.

Results: 434 506 new-users of antihypertensives were included. With a traditional PS approach, no evidence for an association was observed for each antihypertensive comparison. After HDPS implementation, the estimate for beta-blockers shifted from no evidence (Hazard ratio (HR): 0.93, 95% confidence interval (CI): 0.86-1.02) to moderate evidence of a reduced hazard of incident dementia compared to angiotensin-converting enzyme inhibitors (HR: 0.90, 95% CI: 0.82-0.98). A greater overall covariate balance between comparison groups was achieved after the inclusion of HDPS covariates and potential frailty markers were identified as influential.

Conclusions: We successfully implemented the HDPS in HK data, observing improved covariate balance across a wider set of potential confounders. HDPS also identified possible database-specific frailty markers which could be considered more widely when specifying adjustment variables in this setting.