Presentation: Mechanistic and Agnostic Models for Infectious Disease Epidemiology
Speaker: Forrest Crawford, Ph.D., Associate Professor, Department of Biostatistics, Yale School of Public Health, Yale School of Management (Operations), and Department of Ecology & Evolutionary Biology, Yale University
Abstract: Two competing paradigms dominate statistical approaches to estimating the effects of infectious disease interventions in interconnected groups. “Mechanistic” models capture dynamic features of disease transmission, permitting inferences with real-world interpretations and detailed predictions. “Agnostic” approaches—often based on marginal outcome models—refrain from specifying the full joint distribution of the data, and provide inferences that are robust to unmodeled dependence. Epidemiologists sometimes disagree about which of these paradigms is superior for studies of infectious disease interventions (e.g. vaccine trials), with competing claims about model realism, bias, and credibility of inferences. In this presentation, I define a formal structural model of infectious disease transmission, and ask what causal features of this process marginal estimates recover. I exhibit analytically and by simulation the circumstances under which regression coefficients in a marginal model imply an effect whose direction is opposite that of the true individualistic treatment effect. I illustrate these ideas in a large cluster-cohort study of tuberculosis outcomes within households in Lima, Peru.
Bio: Forrest W. Crawford, Ph.D., is an Associate Professor of Biostatistics, Yale School of Public Health, Yale School of Management (Operations), and Department of Ecology & Evolutionary Biology, Yale University. He is affiliated with the Center for Interdisciplinary Research on AIDS, the Institute for Network Science, the Computational Biology and Bioinformatics program, and the Public Health Modeling concentration. He is the recipient of the NIH Director’s New Innovator Award and a Yale Center for Clinical Investigation Scholar Award. His research interests include causal inference, networks, graphs, stochastic processes, and optimization for applications in epidemiology, public health, and social science.