BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20260930T083441EDT-4555F5UuWU@132.216.98.100 DTSTAMP:20260930T123441Z DESCRIPTION:Caleb Miles\, PhD\n\nAssistant Professor of Biostatistics\n Colu mbia University Mailman School of Public Health\n\nWHEN: Wednesday\, Janua ry 24\, 2024\, from 3:30 to 4:30 p.m.\n\nWHERE: hybrid | 2001 McGill Colle ge Avenue\, room 1140\; Zoom\n\nNOTE: Dr. Miles will be presenting from Ne w York\n\nAbstract\n\nAs data sources have become more plentiful and readi ly accessible\, the practice of data fusion has become increasingly ubiqui tous. However\, when the focus is on a causal effect on a particular outco me\, a major limitation is that this outcome may not be available in all d ata sources. In fact\, different randomized experiments or observational s tudies of a common exposure will often focus on potentially related\, yet distinct outcomes. One such example is the Database of Cognitive Training and Remediation Studies (DoCTRS)\, which consists of several randomized tr ials of the effect of cognitive remediation therapy on various outcomes am ong patients with schizophrenia. We develop causally principled methodolog y for fusing data sets when multiple outcomes are observed across studies that leverages outcomes of secondary interest as informative proxies for t he missing outcome of primary interest\, thereby maximizing power and effi ciency by making full use of the available data. As this methodology relie s on a key transportability assumption\, we also develop methods to assess the degree of sensitivity to violations of this assumption. We apply this methodology to data from the DoCTRS trials to make improved causal infere nces about the effectiveness of cognitive remediation therapy on cognition among patients with schizophrenia.\n\nSpeaker bio\n\nDr. Miles is an assi stant professor in the Department of Biostatistics at the Columbia Univers ity Mailman School of Public Health. He works on developing semiparametric methods for causal inference and applying them to problems in medicine an d public health. His applied work is largely in HIV/AIDS\, mental health\, and anesthesiology. His current methodological research interests include causal inference\, its intersection with machine learning\, mediation ana lysis\, interference\, and measurement error.\n DTSTART:20240124T203000Z DTEND:20240124T213000Z SUMMARY:Leveraging multi-study\, multi-outcome data to improve external val idity and efficiency of clinical trials for managing schizophrenia URL:https://www.mcgill.ca/epi-biostat-occh/channels/event/leveraging-multi- study-multi-outcome-data-improve-external-validity-and-efficiency-clinical -trials-353739 END:VEVENT END:VCALENDAR