Identifying and mitigating bias in multiple aspects of modern clinical research
- 1Massachusetts General Hospital, Cardiovascular Research Center, Harvard University Medical School, Boston, MA, USA
- 2Broad Institute, Massachusetts Institute of Technology, Cambridge, MA, USA. [email protected]
- 3Massachusetts General Hospital, Cardiovascular Research Center, Harvard University Medical School, Boston, MA, USA. [email protected]
Abstract
In clinical research, bias is a systematic error that creates a difference between observed and true values. The increasing use of large datasets and artificial intelligence (AI) in medicine necessitates a renewed focus on how such errors can be introduced and propagated.This educational primer provides a consolidated framework of the three primary types of bias selection, information, and confounding/analytical. We synthesize these concepts for an interdisciplinary audience of clinicians and data scientists, using illustrative examples from both traditional clinical trials and modern, data-intensive research. Bias can arise at every stage of the research lifecycle: design, conduct, analysis, reporting, and dissemination. We illustrate how classic issues, such as selection bias (systematic differences between those included and those eligible/targeted, thus distorting effect estimates), manifest in data and how modern analytical methods can introduce novel forms of error if not carefully managed. A shared understanding of bias is essential for effective collaboration between clinical and data science teams. This primer offers a practical conceptual map to help these teams proactively identify, mitigate, and transparently report on potential sources of bias, ultimately fostering more robust and equitable clinical evidence.
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BibTeX
@article{thambiraj2026identifying,
title = {{Identifying and mitigating bias in multiple aspects of modern clinical research}},
author = {Geerthy Thambiraj and Antonis A. Armoundas},
journal = {Communications Medicine},
year = {2026},
month = jun,
doi = {10.1038/s43856-026-01721-4},
url = {https://doi.org/10.1038/s43856-026-01721-4},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Identifying and mitigating bias in multiple aspects of modern clinical research
AU - Geerthy Thambiraj
AU - Antonis A. Armoundas
PY - 2026
DA - 2026/06/24
JO - Communications Medicine
DO - 10.1038/s43856-026-01721-4
UR - https://doi.org/10.1038/s43856-026-01721-4
AB - In clinical research, bias is a systematic error that creates a difference between observed and true values. The increasing use of large datasets and artificial intelligence (AI) in medicine necessitates a renewed focus on how such errors can be introduced and propagated.This educational primer provides a consolidated framework of the three primary types of bias selection, information, and confounding/analytical. We synthesize these concepts for an interdisciplinary audience of clinicians and data scientists, using illustrative examples from both traditional clinical trials and modern, data-intensive research. Bias can arise at every stage of the research lifecycle: design, conduct, analysis, reporting, and dissemination. We illustrate how classic issues, such as selection bias (systematic differences between those included and those eligible/targeted, thus distorting effect estimates), manifest in data and how modern analytical methods can introduce novel forms of error if not carefully managed. A shared understanding of bias is essential for effective collaboration between clinical and data science teams. This primer offers a practical conceptual map to help these teams proactively identify, mitigate, and transparently report on potential sources of bias, ultimately fostering more robust and equitable clinical evidence.
SN - 2730-664X
ER -