Abstract
The NIH Office of Research on Women's Health (ORWH) established the 4Cs framework-Consider, Collect, Characterize, Communicate-to promote the integration of Sex as a Biological Variable (SABV) in clinical research. Building on this foundation, we provide applied statistical guidance for implementing SABV across study design, analysis, and reporting. Using a simulated myocardial infarction example, we illustrate how sex-related bias can arise from omitted variables, underrepresentation, and unmodeled interactions. These methodological oversights can mask important sex-specific patterns in health outcomes and limit generalizability. While grounded in U.S. policy efforts, the statistical principles and approaches described are broadly applicable across epidemiologic research to improve scientific rigor and equity.