Top Things to Know: Detecting and Mitigating Bias for Inclusive & Trustworthy Clinical Research
Updated: May 04, 2026
Prepared by Kayode Kuku, MBBS Science and Medicine Advisor, American Heart Association
- Bias undermines the rigor and generalizability of clinical research and can worsen disparities in patient care and outcomes. This statement provides strategies to identify and mitigate major forms of bias specifically, selection, attrition, and algorithmic bias, including the use of explainable AI to support equitable predictive modeling.
- Selection bias arises when enrolled participants differ systematically from the target population, often driven by restrictive eligibility criteria, non‑random sampling, inadequate site diversity, or design flaws that distort exposure/outcome distributions.
- Randomization protects randomized clinical trials (RCTs) from confounding but not from selection bias, so RCTs can still be unrepresentative. In contrast observational study designs lack randomization and thus require causal‑inference safeguards to simultaneously manage confounding and selection.
- Attrition bias occurs when dropout is related to exposures/treatment or outcomes. Dropout patterns are commonly driven by health literacy barriers, socioeconomic constraints, or clinical severity—factors that disproportionately affect marginalized populations.
- Algorithmic bias reflects systematic errors in model development and deployment, stemming from unrepresentative training data, opaque “black‑box” models, temporal drift, feedback loops, evaluation bias (e.g., skin‑tone–related device performance), and aggregation of heterogeneous data sources.
- Incorporating sensitive attributes such as race or sex requires a principled causal framework, as their naive inclusion may encode structural inequities. When clinically justified, these variables can improve calibration and subgroup discrimination while emphasizing the importance of interdisciplinary oversight in clinical AI development.
- Bias detection requires harnessing conceptual, statistical, and computational tools, including directed acyclic graph (DAG) based structural assessment, standardized mean differences, attrition profiling, and model level fairness audits using interpretability tools such as Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP).
- Preventive design remains the most effective mitigation strategy, achieved through inclusive eligibility, representative site selection, predefined demographic enrollment targets, culturally responsive recruitment, robust data quality pipelines, transparent programming, and adherence to prespecified analysis plans.
- Bias mitigation should be tailored to the type and mechanism of bias, employing methods such as inverse probability weighting (IPW) or propensity scores, survival‑analysis techniques for length‑biased sampling, multiple imputation or inverse probability of censoring weighting (IPCW) for attrition, and fairness‑aware modeling techniques spanning preprocessing, in‑processing, and post‑processing.
- Bias mitigation requires a rigorous, layered, and interdisciplinary approach. This statement provides a conceptual foundation, and future work should include empirical validation, expansion to additional bias domains, and ongoing evolution to keep pace with rapid technological change.
Citation
Zhong J, Al-Zaiti S, Bennett DA, Do S, Gaudino MFL, Gichoya JW, Musaad SMA, Narayan SM, Sajobi T, Shen Y, Armoundas AA; on behalf of the American Heart Association Data Science and Precision Medicine Committee of the Council on Genomic and Precision Medicine and Council on Clinical Cardiology; Council on Lifestyle and Cardiometabolic Health; Council on Hypertension; and Council on Cardiovascular Radiology and Intervention. Detecting and mitigating bias for inclusive and trustworthy clinical research: a scientific statement from the American Heart Association. Circ Genom Precis Med. Published online May 4, 2026. doi: 10.1161/HCG.0000000000000101