“ Causal Inference In Injury Epidemiology: A Case Study”
Michael Elliott, PhD
Associate Professor, Department of Biostatistics
University of Michigan
"Bayesian Modeling of Epigenetic Variation in Multiple Human Cell Types"
Yu Zhang, PhD Associate Professor of Statistics Department of Statistics Pennsylvania State University
Abstract: With massive amount of sequencing data generated for many chromatin modifications in a variety of cell/tissue types, the chief challenges are to build effective and quantitative models explaining how the dynamics in multiple epigenomes lead to differential gene expression and diverse phenotypes. Current state-of-the-art approaches for characterizing epigenetic landscapes are via genome segmentation, yet existing segmentation tools ignore the critical information of position specificity of epigenetic events and often treat all epigenomes equally without considering cell type-specific regulation in local regions. We developed a unified Bayesian framework for jointly annotating multiple epigenomes and detecting differential regulation among multiple tissues and cell types over regions of varying sizes. The method, called IDEAS (integrative and discriminative epigenome annotation system), achieves superior power and accuracy over existing methods by modeling both position and cell type specific regulatory activities. Using 84 genome-wide epigenetic data sets in 6 cell types from ENCODE, we identified epigenetic variation that are strongly associated with differential gene expression. The detected regions are significantly enriched in genetic variants associated with complex phenotypes that are highly relevant to the corresponding cell types. They yielded much stronger enrichment scores than that achievable by existing approaches. Our analysis of cell type specificity could be of great importance in elucidating the interplay between genetic variants, gene regulation and diseases.
This is a joint work with Feng Yue and Ross Hardison.
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"Representations of Unmeasured Confounding in Causal Models for Observational Data"
Joseph Hogan, ScD
Professor, Department of Biostatistics, Brown University School of Public Health
“A general framework for evaluating bias in two-stage instrumental variable methods”
Fei Wan PhD Candidate Graduate Program in Biostatistics Graduate Group in Epidemiology and Biostatistics
Dissertation Advisors: Nandita Mitra, PhD and Dylan S. Small, PhD Committee Chair: Sharon X. Xie, PhD Committee Member: Justin E. Bekermal, MD
Abstract: Unmeasured confounding is a common concern when clinical and health services researchers attempt to estimate a treatment effect using observational data or randomized studies with non-perfect compliance. To address this concern, instrumental variable (IV) methods, such as two-stage predictor substitution (2SPS) and two-stage residual inclusion (2SRI), have been widely adopted. In many clinical studies of binary and survival outcomes, 2SRI has been accepted as the method of choice over 2SPS but a compelling theoretical rationale has not been postulated.
We propose a novel two-stage structural modeling framework to understanding the bias in estimating the conditional treatment effect for 2SPS and 2SRI when the outcome is binary, count or time to event. Under this framework, we demonstrate that the bias in 2SPS and 2SRI estimators can be reframed to mirror the problem of omitted variables in non-linear models. We demonstrate that only when the influence of the unmeasured covariates on the treatment is proportional to their effect on the outcome that 2SRI estimates are generally unbiased for logit and Cox models. We also propose a novel dissimilarity metric to quantify the difference in these effects and demonstrate that with increasing dissimilarity, the bias of 2SRI increases in magnitude. We investigate these methods using simulation studies and data from an observational study of perinatal care for premature infants.
"Understanding the Impact of Neighborhoods on Health: Evidence and Challenges”
Ana V. Diez Roux, MD, PhD, MPH
Dean of the School of Public Health at Drexel University
“Competing Risks and its Application in Clinical Oncology"