Primary Submission Category: Causal Inference and Bias/Discrimination
A health equity perspective on data-driven treatment decisions in cardiovascular care: risk assessments versus individualized treatment rules
Authors: Safiya Sirota, Daniel Malinsky,
Presenting Author: Safiya Sirota*
It is standard in clinical care to inform medical decisions based on estimated risk scores, e.g., to inform assignment of antihypertensive medications based on risk of adverse cardiac events, as is currently recommended by national ACC/AHA cardiovascular guidelines. We will investigate the consequences of this practice in cardiovascular care from the perspective of health equity and health disparities. Complex associations between racial/ethnic categories, social determinants of health, and other disease risk factors may lead to disparities in treatment allocation that are exacerbated, not mitigated, by risk-based decision-making. An alternative is to base decisions on individualized treatment rules (ITRs), which are rules sensitive to causal effect heterogeneity that optimally direct therapies to patients based on their individual characteristics. We investigate how allocations based on ITRs may mitigate disparities in treatment assignment, using both simulated data and real data from a large observational cohort study. We find that recommending treatment according to the ITR paradigm may have substantial consequences for treatment recommendations and possibly health disparities.