Abstract
ABSTRACT
Background
In observational pharmacoepidemiology, estimating average treatment effects (ATEs) is often challenging due to a lack of practical positivity. In highly selective clinical settings, certain patients almost always or never receive treatment, causing ATE estimators to rely on unstable extrapolation. Incremental propensity score interventions (IPSIs) offer a stochastic alternative by shifting each patient's probability of treatment, providing a more clinically realistic framework that circumvents positivity violations.
Methods
We illustrate the IPSI approach, including key identification results and inferential procedures. Using observational data from a cohort of 996 patients undergoing percutaneous coronary intervention (PCI), we evaluated the effect of shifting each patient's probability of receiving abciximab by a predetermined amount on six‐month mortality. Propensity scores (PSs) and outcome predictions were estimated using a machine learning ensemble (Super Learner) with 10‐fold sample splitting.
Results
The ATE estimate suggested that abciximab administration reduced the 6‐month mortality risk by 5.9 percentage points compared with PCI alone (risk difference = −0.059, 95% CI: −0.104 to −0.015). However, the practical interpretability of the ATE estimate may be limited because it implicitly assumes that patients with a near‐certain probability of treatment could realistically be assigned to withhold abciximab. In contrast, shifting each patient's treatment propensity by odds ratios ranging from 0.1 to 10 showed that 6‐month mortality would be significantly reduced under a strong treatment policy promoting abciximab administration.
Conclusions
IPSIs provide a robust and practical alternative to conventional causal inference methods in pharmacoepidemiology settings where treatment assignment is highly selective and the strict positivity is violated.