Overview of New Approaches to Causal Models (Causal Pathways DAGs, Mathematical Models, FPDR Bayesian Approaches)
• Sander Greenland
Professor, Department of Epidemiology
UCLA Public Health – Epidemiology, Los Angeles, CA, United States

Bayesian Bias Analysis
This talk presents extensions of Bayesian methods using data priors to situations in which biases are of concern. First, a basic misclassification problem is illustrated using data from a study of sudden infant death syndrome. A Bayesian analysis is then given in which actual-data records are converted to incomplete records and the prior distribution is converted to complete-data records. This analysis can easily incorporate any complete (“validation” or second-stage) data that might be available, as well as adjustments for confounding and selection bias. The approach illustrates how conventional analyses depend on implicit certainty that bias parameters are null, and how these implausible assumptions can be replaced by realistic bias priors within a regression framework.

Sander Greenland is Professor of Epidemiology and Statistics, University of California, Los Angeles.  Professor Greenland has been a leading contributor to epidemiologic statistics, theory, and methods for over three decades.  His major research contributions include assessment of selection bias, misclassification, and confounding effects in epidemiologic research, and critical evaluation of statistical methods for observational studies.  He has authored or co-authored over 300 articles in the health sciences and statistics, as well as the textbook Modern Epidemiology, and has given invited presentations at universities and conferences throughout the world. He has served as an associate editor for several leading journals, and is a Fellow of the American Statistical Association and of the Royal Statistical Society. Professor Greenland received Bachelor's and Master's degrees in mathematics from the University of California Berkeley, and Master's and Doctoral degrees in Epidemiology from the University of California Los Angeles.