BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20251110T105130EST-9065BOReko@132.216.98.100 DTSTAMP:20251110T155130Z DESCRIPTION:Title: Screens per death averted: a less biased estimator of th e performance of lung cancer screening programs.\n\nAbstract: Wilber Deck has a BSc in Mathematics at Queen's University and graduated from º£½Ç¾«Æ·ºÚÁÏ M edical School in 1990\, and continued at º£½Ç¾«Æ·ºÚÁÏ to complete an MSc in Epid emiology and Biostatistics and specialty training as a public health docto r in 1995. Since then he has lived and practiced in Gaspé as a public heal th physician and in clinical oncology and has collaborated on numerous res earch projects\, largely involving cancer screening\, at the INESSS and IN SPQ.\n \n James Hanley’s website: http://www.medicine.mcgill.ca/epidemiology /hanley/\n\n\nIntroduction: Screening trials and meta-analyses have typica lly used the risk ratio (RR) or hazard ratio (HR)\, essentially the ratio of cancer death rates in the screening and control groups\, but this analy sis is biased toward the null hypothesis by the fact that early and late f ollow-up will tend to include deaths that could not be affected by screeni ng. The use of risk difference (RD) avoids this bias\, but will not be inv ariant with regard to risk\, making it necessary to interpret RD in the co ntext of a group’s or an individual’s risk level.\n \n Methods: We review tr aditional analysis of cancer screening effectiveness and show how risk rat ios are biased to the null. We use the example of lung cancer (LC) screeni ng trials to illustrate an unbiased estimator\, risk difference between co ntrol and screened groups\, and use linear regression to validate the assu mption that this varies with risk level. Results: Regression of RD on risk of LC death (derived from LC death experience in the control groups) tend ed to confirm our hypothesis of a linear relation. LC mortality results fo r 9 computed tomography (CT) screening trials for LC are presented\, along with calculations of RD and screens per LC death averted. Overall\, trial s needed 950 invitations to screening to avert one LC death\, or 850 scree ns conducted to avert one death. Adjusted for LC mortality risk\, these nu mbers were x and y respectively. Discussion: Calculation of RD is feasible and has a natural interpretation\, as long as it is adjusted for LC morta lity risk level. The number of invitations to screening required to avert one death is the screening equivalent of per protocol number needed to tre at (NNT)\, and actual screens required to avert one death is the ‘as condu cted’ equivalent. These statistics avoid the bias to the null inherent in ratio statistics (RR\, HR) and provide for better evaluation of a program’ s effect or an individual’s projected benefit\, in the context of informed consent.\n\nVia Zoom-Please visit our website for the Zoom Link: https:// www.mcgill.ca/epi-biostat-occh/seminars-events/seminars/biostati...\n\n \n DTSTART:20220928T193000Z DTEND:20220928T203000Z SUMMARY:Wilber Deck\, MD\, Gaspésie-ÃŽles-de-la-Madeleine Public Health Depa rtment\, James Hanley\, PhD\, º£½Ç¾«Æ·ºÚÁÏ URL:/mathstat/channels/event/wilber-deck-md-gaspesie-i les-de-la-madeleine-public-health-department-james-hanley-phd-mcgill-34205 4 END:VEVENT END:VCALENDAR