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Due to inherent differences in the data\u2010generating mechanisms, biases can arise. This paper aims to illustrate how to use quantitative bias analysis (QBA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Advanced non\u2010small cell lung cancer (NSCLC) serves as an example, where many small subsets of patients with molecular tumor subtypes exist. First, some sources of bias that may occur in oncology when comparing RWD to SAT are described. Second, using a hypothetical immunotherapy agent, a dataset is simulated based on expert input for survival analysis of advanced NSCLC. Finally, we illustrate the impact of three biases: missing confounder, misclassification of exposure, and outcome evaluation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>For each simulated scenario, bias was induced by removing or adding data; hazard ratios (HRs) were estimated applying conventional analyses. Estimating the bias\u2010adjusted treatment effect and uncertainty required carefully selecting the bias model and bias factors. Although the magnitude of each biased and bias\u2010adjusted HR appeared moderate in all three hypothetical scenarios, the direction of bias was variable.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These findings suggest that QBA can provide an intuitive framework for bias analysis, providing a key means of challenging assumptions about the evidence. However, the accuracy of bias analysis is itself dependent on correct specification of the bias model and bias factors. Ultimately, study design should reduce bias, but QBA allows us to evaluate the impact of unavoidable bias to assess the quality of the evidence.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/pds.5796","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T03:30:17Z","timestamp":1714361417000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Use of quantitative bias analysis to evaluate single\u2010arm trials with real\u2010world data external controls"],"prefix":"10.1002","volume":"33","author":[{"given":"Christen","family":"Gray","sequence":"first","affiliation":[{"name":"Real World Data Science, Biopharmaceuticals Medical Evidence AstraZeneca  Cambridge UK"},{"name":"Methods and Evidence Generation, Real World Solutions IQVIA  London UK"},{"name":"Health Data Science London School of Hygiene and Tropical Medicine  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7986-8205","authenticated-orcid":false,"given":"Eleanor","family":"Ralphs","sequence":"additional","affiliation":[{"name":"Methods and Evidence Generation, Real World Solutions IQVIA  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew P.","family":"Fox","sequence":"additional","affiliation":[{"name":"Department of Epidemiology Department of Global Health Boston University  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy L.","family":"Lash","sequence":"additional","affiliation":[{"name":"Department of Epidemiology Rollins School of Public Health, Emory University  Atlanta Georgia USA"},{"name":"Cancer Prevention and Control Program Winship Cancer Institute, Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geoffrey","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology and Hematology Princess Margaret Cancer Centre, Universal Health Network  Toronto Ontario Canada"},{"name":"Institute of Medical Science University of Toronto  Toronto Ontario Canada"},{"name":"Applied Molecular Profiling Pharmacogenomic Epidemiologic Laboratory Princess Margaret Cancer Centre, Universal Health Network  Toronto Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tzuyung Doug","family":"Kou","sequence":"additional","affiliation":[{"name":"Global Patient Safety BeiGene  Ridgefield Park New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donna R.","family":"Rivera","sequence":"additional","affiliation":[{"name":"Oncology Center of Excellence United States Food &amp; Drug Administration  Silver Spring Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaclyn","family":"Bosco","sequence":"additional","affiliation":[{"name":"Epidemiology and Database Studies, Real World Solutions IQVIA  Boston Massachusetts USA"},{"name":"Department of Epidemiology Boston University  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kim","family":"Van Naarden Braun","sequence":"additional","affiliation":[{"name":"Translational Epidemiology Informatics and Predictive Sciences, BMS  Summit New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fiona","family":"Grimson","sequence":"additional","affiliation":[{"name":"Health Data Science London School of Hygiene and Tropical Medicine  London UK"},{"name":"Biometrics and Quantitative Sciences UCB Pharma  Slough UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deborah","family":"Layton","sequence":"additional","affiliation":[{"name":"PEPI Consultancy Limited  Southampton UK"},{"name":"School of Life &amp; Medical Sciences University of Hertfordshire  Hatfield UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,4,29]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/pds.4932"},{"key":"e_1_2_10_3_1","unstructured":"HMA\u2010EMA.HMA\u2010EMA Joint Big Data Taskforce: Summary Report 2019; 48. 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