{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Massachusetts General Hospital, Cardiovascular Research Center, Harvard University Medical School, Boston, MA, USA",
    "Broad Institute, Massachusetts Institute of Technology, Cambridge, MA, USA. Armoundas.Antonis@mgh.harvard.edu",
    "Massachusetts General Hospital, Cardiovascular Research Center, Harvard University Medical School, Boston, MA, USA. Armoundas.Antonis@mgh.harvard.edu"
  ],
  "article_type": "review",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Geerthy Thambiraj",
      "orcid": "0000-0002-3056-8655"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Antonis A. Armoundas",
      "orcid": "0000-0001-5006-1547"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4mg97phq3crj8.bib",
  "cited_by": 8,
  "doi": "10.1038/s43856-026-01721-4",
  "fulltext_url": null,
  "id": "4mg97phq3crj8",
  "issns": [
    "2730-664X"
  ],
  "landing_url": "https://doi.org/10.1038/s43856-026-01721-4",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2613|Statistical Methods in Epidemiology"
    ]
  ],
  "openalex": "W7165798513",
  "pdf_url": "https://www.nature.com/articles/s43856-026-01721-4.pdf",
  "pmcid": "PMC13294366",
  "pmid": "42342882",
  "published": "2026-06-24",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/4mg97phq3crj8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "W7165798513",
      "source": "openalex",
      "url": "https://doi.org/10.1038/s43856-026-01721-4"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "openalex:2613|Statistical Methods in Epidemiology",
      "subfield": "statistics"
    }
  ],
  "title": "Identifying and mitigating bias in multiple aspects of modern clinical research",
  "title_text": "Identifying and mitigating bias in multiple aspects of modern clinical research",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T13:20:21Z",
  "url": "https://research.draconitic.com/paper/4mg97phq3crj8",
  "venue": "Communications Medicine",
  "versions": []
}