{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T02:59:29Z","timestamp":1792897169734,"version":"4.5.0"},"reference-count":50,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T00:00:00Z","timestamp":1710979200000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000925","name":"National Health and Medical Research Council","doi-asserted-by":"publisher","award":["APP1164852"],"award-info":[{"award-number":["APP1164852"]}],"id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Researchers interested in causal questions must deal with two sources of error: random error (random deviation from the true mean value of a distribution), and bias (systematic deviance from the true mean value due to extraneous factors). For some causal questions, randomization is not feasible, and observational studies are necessary. Bias poses a substantial threat to the validity of observational research and can have important consequences for health policy developed from the findings. The current piece describes bias and its sources, outlines proposed methods to estimate its impacts in an observational study, and demonstrates how these methods may be used to inform debate on the causal relationship between medically assisted reproduction (MAR) and health outcomes, using cancer as an example. In doing so, we aim to enlighten researchers who work with observational data, especially regarding the health effects of MAR and infertility, on the pitfalls of bias, and how to address them. We hope that, in combination with the provided example, we can convince readers that estimating the impact of bias in causal epidemiologic research is not only important but necessary to inform the development of robust health policy and clinical practice recommendations.<\/jats:p>","DOI":"10.1093\/humrep\/deae053","type":"journal-article","created":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T08:50:37Z","timestamp":1711011037000},"page":"869-875","source":"Crossref","is-referenced-by-count":9,"title":["Estimating the impact of bias in causal epidemiological studies: the case of health outcomes following assisted reproduction"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0801-2316","authenticated-orcid":false,"given":"Adrian R","family":"Walker","sequence":"first","affiliation":[{"name":"Centre for Big Data Research in Health, Faculty of Medicine and Health, UNSW Sydney , Sydney, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9591-7222","authenticated-orcid":false,"given":"Christos A","family":"Venetis","sequence":"additional","affiliation":[{"name":"Centre for Big Data Research in Health, Faculty of Medicine and Health, UNSW Sydney , Sydney, NSW, Australia"},{"name":"Unit for Human Reproduction, 1st Department of Obstetrics and Gynaecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki , Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6593-8433","authenticated-orcid":false,"given":"Signe","family":"Opdahl","sequence":"additional","affiliation":[{"name":"Centre for Big Data Research in Health, Faculty of Medicine and Health, UNSW Sydney , Sydney, NSW, Australia"},{"name":"Department of Public Health and Nursing, Norwegian University of Science and Technology , Trondheim, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0215-3249","authenticated-orcid":false,"given":"Georgina M","family":"Chambers","sequence":"additional","affiliation":[{"name":"National Perinatal Epidemiology and Statistics Unit, Centre for Big Data Research in Health and School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney , Sydney, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0390-661X","authenticated-orcid":false,"given":"Louisa R","family":"Jorm","sequence":"additional","affiliation":[{"name":"Centre for Big Data Research in Health, Faculty of Medicine and Health, UNSW Sydney , Sydney, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3612-8298","authenticated-orcid":false,"given":"Claire M","family":"Vajdic","sequence":"additional","affiliation":[{"name":"Kirby Institute, UNSW Sydney 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