Event-time regression discontinuity for biomedical data: LATT estimation and covariate distribution tests.

Estimating causal effects of drugs on clinical biomarkers is often infeasible through randomized trials, while observational data require designs that approximate experimental conditions. Using linked UK Biobank records collected during 2006-2013, we develop an event-time regression discontinuity (RD) framework for settings in which prescription records are longitudinal but biomarkers are measured cross-sectionally. A surrogate running variable aligns individuals by the interval between biomarker measurement and the first recorded prescription, comparing eventual users measured shortly before versus after that date. Under continuity assumptions, this identifies a local average treatment effect among the treated. We apply distributional covariate-balance diagnostics, robust bias-corrected inference, and parsimonious covariate adjustment. Statin analyses show a local downward LDL discontinuity around the first recorded prescription date, with direction and statistical significance robust across bias-corrected inference, bandwidths, and donut specifications. However, the running-variable density test is significant and donut estimates vary from - 1.00 to - 1.72 mmol/L, indicating residual uncertainty about local comparability. The metformin-HbA1c example is less stable: a sizeable parametric local-linear reduction is not corroborated under robust bias-corrected inference. Event-time RD offers potential for pharmacovigilance and real-world causal inference in linked health databases, but conclusions depend on explicit local timing-exogeneity and continuity assumptions.