Negative control-calibrated difference-in-difference analyses: addressing unmeasured confounding in RWD with application to racial/ethnic differences.

ABSTRACT: Real-world data (RWD) from electronic health records and digital health databases present unique opportunities to study causal effects in healthcare. While Difference-in-Differences (DiD) analysis is widely used for such analyses, it can be biased when time-varying unmeasured confounding violates the parallel trends assumption. We propose a negative control-calibrated difference-in-difference (NC-DiD) approach that uses negative control outcomes (NCOs) both before and after the intervention to detect and adjust for such confounding. The method remains robust even with partially unreliable controls. In simulations, NC-DiD reduces bias, controls type-I error, and improves estimation accuracy. We applied NC-DiD to assess racial/ethnic disparities in post COVID-19 health outcomes using RWD emu

SUBMITTER: Zhang D 

PROVIDER: S-EPMC12271539 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature