Levin Lecture Series: Spring 2018 Colloquium Seminars

January 25, 2018

Topic: "Causal Inference for a Single Group of Causally-Connected Units Under Stratified Interference"

Speaker: Caleb Miles, Postdoctoral Fellow, Department of Biostatistics, Epidemiology and Informatics, University of California- Berkeley

11:30am-12:30pm

 

AR Building, 8th Floor Auditorium

 

Hosted by: Dr. Jeff Goldsmith

 

Abstract: The assumption that no subject's exposure affects another subject's outcome, known as the assumption of no interference, has long held a foundational position in the study of causal inference. However, this assumption may be violated in many settings, and in recent years has been relaxed considerably. Often this has been achieved with either the aid of knowledge of an underlying network, or the assumption that the population can be partitioned into separate groups, between which there is no interference, and within which each subject's outcome may be affected by all the other subjects in the group, but only as a function of the total number of subjects exposed (the stratified interference assumption). In this paper, we consider a setting in which we can rely on neither of these aids, as each subject affects every other subject's outcome. In particular, we consider settings in which the stratified interference assumption is reasonable for a single group consisting of the entire sample, i.e., a subject's outcome is affected by all other subjects' exposures, but only via the total number of subjects exposed. This can occur when the exposure is a shared resource whose efficacy is modified by the number of subjects among whom it is shared. We present a doubly-robust estimator that allows for incorporation of machine learning, and tools for inference for a class of causal parameters that includes direct effects and overall effects under certain interventions. We conduct a simulation study, and present results from a data application where we study the effect of a nurse-based triage system on the outcomes of patients receiving HIV care in Kenyan health clinics.

 

february 1, 2018

Topic: "Association tests for neuroimaging studies of development and disorders"

Speaker: Simon Vandekar, PhD Candidate, Department of Statistics and Data, University of Pennsylvania

11:30AM-12:30PM

 

AR BUILDING, 8TH FLOOR AUDITORIUM

 

HOSTED BY: DR. JEFF GOLDSMITH

 

Abstract: In development and adulthood, cognitive symptoms of disorders are preceded by neuroanatomical abnormalities that are indicative of disease vulnerability. There are growing bodies of literature identifying neuroanatomical markers of psychosis in adolescence and dementia in adulthood. Prevention is an increasing target of intervention for reducing the burden of these disorders. Establishing early biomarkers for disease is critical for identifying individuals who are at risk and may benefit from early therapy. In this talk, we will briefly discuss tools developed in collaborative work to characterize healthy developmental patterns of neuroanatomy and cerebral blood flow through adolescence. We will then focus in depth on identifying neuroanatomical features associated with Alzheimer’s disease risk.  We propose and develop a framework for testing the association of a high-dimensional imaging measurement with a diagnostic outcome, and for localizing signal to identify regions of the brain that are associated with disease. Our procedure is based on a modification of the score test that projects the imaging data to a lower dimensional subspace. Local regional inference can then be performed using the score statistics that are projected into the lower dimensional space, which have smaller variance and degrees of freedom.

 

FEBRUARY 5, 2018 (monday seminar)

Topic: "Integrative Directed Cyclic Graphical Models with Heterogeneous Samples”​

Speaker: Yang Ni, Postdoctoral Fellow, Department of Statistics and Data Sciences, University of Texas-Austin

11:30AM-12:30PM

 

AR BUILDING, hess commons

 

HOSTED BY: DR. JEFF GOLDSMITH

 

Abstract: In this talk, I will introduce hierarchical directed cyclic graphical models to infer gene networks by integrating genomic data across platforms and across diseases.  The proposed model takes into account tumor heterogeneity. In the case of data that can be naturally divided into known groups, we propose to connect graphs by introducing a hierarchical prior across group-specific graphs, including a correlation on edge strengths across graphs. A novel thresholding prior is applied to induce sparsity of the estimated networks and its connection to spike-and-slab prior and non-local prior will also be discussed. In the case of unknown groups, we cluster subjects into subpopulations and jointly estimate cluster-specific gene networks, again using similar hierarchical priors across clusters. Two applications with multiplatform genomic data for multiple cancers will be presented to illustrate the utility of our model.

 

FEBRUARY 8, 2018

Topic: "Causal Inference with Unmeasured Confounding: an Instrumental Variable Approach"

Speaker: Linbo Wang, Postdoctoral Fellow, Department of Biostatistics, Harvard University

11:30AM-12:30PM

 

AR BUILDING, 8TH FLOOR AUDITORIUM

 

HOSTED BY: DR. JEFF GOLDSMITH

 

Abstract: Causal inference is a challenging problem because causation cannot be established from observational data alone. Researchers typically rely on additional sources of information to infer causation from association. Such information may come from powerful designs such as randomization, or background knowledge such as information on all confounders. However, perfect designs or background knowledge required for establishing causality may not always be available in practice. In this talk, I use novel causal identification results to show that the instrumental variable approach can be used to combine the power of design and background knowledge to draw causal conclusions. I also introduce novel estimation tools to construct estimators that are robust, efficient and enjoy good finite sample properties. These methods will be discussed in the context of a randomized encouragement design for a flu vaccine.

 

FEBRUARY 12, 2018 (Monday)

Topic: "Inference for statistical interactions under misspecified or high-dimensional main effects"

Speaker: Zihuai He, Postdoctoral Fellow, Department of Biostatistics, Columbia University

11:30AM-12:30PM

 

AR BUILDING, 8TH FLOOR AUDITORIUM

 

HOSTED BY: DR. JEFF GOLDSMITH

 

Abstract: An increasing number multi-omic studies (e.g. genetics, genomics, epigenetics) have generated complex high-dimensional data. A primary focus of these studies is to determine whether exposures interact in the effect that they produce on an outcome of interest. Interaction is commonly assessed by fitting regression models in which the linear predictor includes the product between those exposures. When the main interest lies in interactions, the standard approach is not satisfactory because it is prone to (possibly severe) type I error inflation when the main exposure effects are misspecified or high-dimensional. I will propose generalized score type tests for high-dimensional interaction effects on correlated outcomes. I will also discuss the theoretical justification of some empirical observations regarding Type I error control, and introduce solutions to achieve robust inference for statistical interactions. The proposed methods will be illustrated using an example from the Multi-Ethnic Study of Atherosclerosis (MESA), investigating interaction between measures of neighborhood environment and genetic regions on longitudinal measures of blood pressure over a study period of about seven years with four exams.

 

FEBRUARY 15, 2018 

Topic: "Is most published research really false? "

Speaker: Jeff Leek, Associate Professor, Department of Biostatistics, Johns Hopkins University

11:30AM-12:30PM

 

AR BUILDING, 8TH FLOOR AUDITORIUM

 

HOSTED BY: DR. gen li

 

Abstract: There has been increasing concern in both the scientific and popular press that most published research is false. In this talk I will discuss a framework for defining false discoveries in the medical literature and present estimates of the science-wise false discovery rate across science using a new regression modeling approach and data from 2.5 million published p-values. I will discuss how data analyst choices are a major contributor to the science-wise false discovery rate and how we are performing human-data interaction experiments to address these problems.

 

FEBRUARY 22, 2018 

Topic: "Fine Mapping and Alleleic Heterogeneity"

Speaker: Elezar Eskin, Professor, Departments of Computer Science and Human Genetics, UCLA

11:30AM-12:30PM

 

AR BUILDING, 8TH FLOOR AUDITORIUM

 

HOSTED BY: DR. iuliana Ionita-laza

 

Abstract: Genome Wide Association Studies (GWAS) have identified many genomic regions or loci which harbor genetic variants that affect traits.   However, within each of these regions, there are many genetic variants which as associated with the trait, yet most of these variants do not have a direct effect on the trait.  The process of identifying the actual variant in the region which has an effect on the disease is referred to as “fine mapping.”  In addition to finding the actual variants affecting a disease, fine mapping also seeks to address questions that are related to the genetic basis of disease. First, how many causal variants does a locus contain? A disease could be caused by one, single variant or multiple variants that independently affect disease status. We refer to the latter phenomenon as allelic heterogeneity (AH).  Second, when analyzing results from multiple GWASes, are the same causal variants affect both traits or are different variants effecting each trait?  Differentiating between shared and distinct causal variants is referred to as Colocalization.  In this talk, I present recent work from our group on fine mapping methods which provides a framework for identifying causal variants and can be applied to discover and quantify allelic heterogeneity and colocolization.

 

march 1, 2018 

Topic: "TBA"

Speaker: Ying Qing Chen, Full Member Board of Governors , Program in Biostatistics and Biomathematics, Fred Hutchinson Cancer Research Center

11:30AM-12:30PM

 

AR BUILDING, 8TH FLOOR AUDITORIUM

 

HOSTED BY: DR. zhezhen jin

 

Abstract: "TBA"

 

MARCH 8, 2018 

Topic: "TBA"

Speaker: Glen ShaferProfessor, Rutgers Business School – Newark and New Brunswick

11:30AM-12:30PM

 

AR BUILDING, 8TH FLOOR AUDITORIUM

 

HOSTED BY: DR. Prakash GORROCCHURN

 

 

Abstract: "TBA"

 

MARCH 22, 2018 

Topic: "TBA"

Speaker: Yuan Ji, Director, Program of Computational Genomics & Medicine, NorthShore University HealthSystem

11:30AM-12:30PM

 

AR BUILDING, 8TH FLOOR AUDITORIUM

 

HOSTED BY: DR. codruta "cody" chiuzan

 

Abstract: "TBA"

 

MARCH 29, 2018 

Topic: "TBA"

Speaker: Michael Kosorok, W.R. Kenan, Jr. Distinguished Professor and Chair, Department of Biostatistics, UNC-Chapel Hill

11:30AM-12:30PM

 

AR BUILDING, 8TH FLOOR AUDITORIUM

 

HOSTED BY: DR. min qian

 

Abstract: "TBA"

 

 

april 5, 2018 

Topic: "TBA"

Speaker: Jeff Morris, Professor, Deputy Chair Ad Interim, Department of Biostatistics, The University of Texas MD Anderson Cancer Center

11:30AM-12:30PM

 

AR BUILDING, 8TH FLOOR AUDITORIUM

 

HOSTED BY: DR. gen li

 

Abstract: "TBA"