Prevention Science

CAPS Methods Core presents: Maya Petersen, MD, PhD, Co-Chair, Graduate Group in Biostatistics, UC Berkeley

Abstract: Targeted Maximum Likelihood Estimation (TMLE) provides an approach for estimating the causal effects of longitudinal interventions with several attractive properties. TMLE uses estimates of both the propensity score (as used in inverse probability weighting) and of a series of outcome regressions (as can be used in parametric G-computation). Machine-learning methods, such as Super Learning (an ensemble approach) can be used to estimate both the propensity score and outcome regressions.

CAPS Community Town Hall presents: Jesse Brooks -- HIV Support with Black Men: "The Brothers Groups"

Jesse Brooks is a longtime activist raised in Oakland, California.  He is currently Co-Chair of the Bay Area State of Emergency Coalition (BASE), Advocacy Coordinator of the AIDS Healthcare Foundation of Northern California, and Co-Chair of UCSF/CAPS Research Community Advisory Board, and a Weekly Columnist for the largest African American paper in the Bay Area, “Post News Group,” reaching over 80,000 readers.

CAPS I&I Town Hall presents: Don Operario, PhD -- Use of financial incentives in interventions

Don Operario is Professor of Public Health in the Department of Behavior and Social Sciences and Associate Dean for Academic Affairs in the School of Public Health.

He was trained as a Social and Health Psychologist (BA, UCLA; MS, PhD, UMass Amherst; Postdoctoral Fellow, UC San Francisco). He was previously on the faculty of the University of Oxford (Department of Social Policy and Social Work) and before that was at the University of California San Francisco (Center for AIDS Prevention Studies - Department of Medicine). 

CAPS Town Hall and CAPS Methods Core present: Colin Walsh, MD, MA, Assistant Professor of Biomedical Informatics, Medicine & Psychiatry at Vanderbilt Univ.

Suicide kills 123 Americans every day and 800,000 people worldwide every year. It is the 10th leading cause of death in the U.S. and the 2nd leading cause of death in those < 34 years old. I will share our experiences incorporating predictive analytics, implementation science, and clinical informatics to catalyze research in mental health.