{
  "abstract": "Objective Summarize the framework for incorporating bias appraisal for observational epidemiology studies into evidence synthesis for cancer hazard identification, as recommended in the recently published IARC volume. Describe how bias assessment has been incorporated into recent Monographs evaluations.Methods The recommended framework for incorporating bias assessments into evidence synthesis focuses on identification of key biases among informative studies, including direction and (where possible) magnitude, assessing their impact on study estimates, and triangulating findings among studies with different sources of bias. Scenarios are described for situations with few vs. many informative studies. We examined use of the bias assessment approaches laid out in the book for confounding, information bias, and selection bias in Monographs Volumes 131–138 (evaluations conducted in 2022–2025).Results Each of the eight volumes considered has incorporated bias assessment tools into the evidence synthesis. For example, in an evaluation of antimony (Vol. 131), an assessment of bias resulting from co-exposure to arsenic among smelter workers led to a conclusion that arsenic could not entirely explain the observed lung cancer risk. In evaluations of occupational exposure as a firefighter (Vol. 132) and several pharmaceuticals (Vol. 137), meta-analyses stratified by major sources of bias were used in evidence triangulation. In the evaluation of PFOA (Vol. 135), information bias from single-timepoint exposure measurement was quantified using repeated-measurement data. For acrylonitrile (Vol. 136), externally conducted bias adjustment for healthy worker survivor bias was important for the determination of sufficient evidence for lung cancer.Conclusions Incorporation of bias appraisal tools into evidence synthesis has strengthened recent IARC Monographs cancer hazard identification, giving tools to inform and complement Working Groups’ expert judgment.",
  "authors": [
    {
      "affiliations": [
        "Evidence Synthesis and Classification Branch, International Agency for Research on Cancer, France"
      ],
      "name": "Mary K Schubauer-Berigan"
    },
    {
      "affiliations": [
        "Evidence Synthesis and Classification Branch, International Agency for Research on Cancer, France"
      ],
      "name": "Andrew Kunzmann"
    },
    {
      "affiliations": [
        "Evidence Synthesis and Classification Branch, International Agency for Research on Cancer, France"
      ],
      "name": "Elisa Pasqual"
    }
  ],
  "title": "8000015 Incorporating bias appraisal into evidence synthesis: examples from recent IARC Monographs evaluations",
  "uid": "c17a2bbe-38c3-574f-8598-2c3c79e58180"
}