Below please find the following information:
JSM 2019 Topic contributed paper sessions sponsored by the Biometrics Section
JSM 2019 Invited sessions sponsored by the Biometrics Section
JSM 2019 Courses cosponsored by the Biometrics Section
ENAR 2020 invited session proposal
Topic contributed paper sessions sponsored by the Biometrics Section:
284 Tue, 7/30/2019, 8:30 AM - 10:20 AM CC-101 ASA Biometrics Section JSM Travel Awards (II) — Topic Contributed Papers Biometrics Section Organizer(s): Rebecca Hubbard, University of Pennsylvania Chair(s): Elizabeth Ogburn, Johns Hopkins Bloomberg School of Public Health 8:35 AM Integrated Principal Components Analysis Tiffany M Tang, University of California at Berkeley; Genevera Allen, Rice University 8:55 AM Are Clusterings of Multiple Data Views Independent? Lucy Gao, University of Washington; Daniela Witten, University of Washington; Jacob Bien, University of Southern California 9:15 AM High-dimensional Log-Error-in-Variable Regression with Applications to Microbial Compositional Data Analysis Pixu Shi, University of Wisconsin-Madison; Yuchen Zhou, University of Wisconsin-Madison; Anru Zhang, University of Wisconsin-Madison 9:35 AM A Spatial Bayesian Modeling Approach for Cortical Surface fMRI Data Analysis Amanda Mejia, IU; Yu Yue, The City University of New York; David Bolin, University of Gothenburg; Finn Lindgren, University of Edinburgh; Martin Lindquist, Johns Hopkins University 9:55 AM Tailored Optimal Post-Treatment Surveillance for Cancer Recurrence Rui Chen, UW-Madison Dept. of Statistics 10:15 AM Floor Discussion
393 Tue, 7/30/2019, 2:00 PM - 3:50 PM CC-111 ASA Biometrics Section JSM Travel Awards (I) — Topic Contributed Papers Biometrics Section Organizer(s): Rebecca Hubbard, University of Pennsylvania Chair(s): Sheng Luo, Duke University Medical Center 2:05 PM Propensity Score Weighting for Causal Inference with Multiple Treatments Fan Li, Duke University; Fan Li, Department of Statistical Science, Duke University 2:25 PM Triplet Matching for Estimating Causal Effects with Three Treatment Arms and Extensions Giovanni Nattino, The Ohio State University; Bo Lu, The Ohio State University; Junxin Shi, The Research Institute of Nationwide Children's Hospital; Stanley Lemeshow, Ohio State University; Henry Xiang, The Research Institute of Nationwide Children's Hospital 2:45 PM Causal Isotonic Regression Ted Westling, University of Massachusetts Amherst; Marco Carone, University of Washington; Peter Gilbert, Fred Hutchinson Cancer Research Center 3:05 PM Stage-Wise Synthesis of Randomized Trials for Optimizing Dynamic Treatment Regimes Yuan Chen, Columbia University Mailman School of Public Health, Department of Biostatistics; Yuanjia Wang, Columbia University; Donglin Zeng, UNC Chapel Hill 3:25 PM Discussant: Rebecca Hubbard, University of Pennsylvania 3:45 PM Floor Discussion.
Invited sessions sponsored by the Biometrics Section:
48 * ! Sun, 7/28/2019, 4:00 PM - 5:50 PM CC-104 New Frontiers in High Dimensional and Complex Data Analyses — Invited Papers Biometrics Section, International Chinese Statistical Association, Section on Nonparametric Statistics Organizer(s): Yichuan Zhao, Georgia State University Chair(s): Lexin Li, University of California at Berkeley 4:05 PM Statistical Inference for High-Dimensional Models via Recursive Online-Score Estimation Runze Li, Penn State University 4:30 PM Dimension Reduction for High Dimensional Censored Data Shanshan Ding, University of Delaware; Wei Qian, University of Delaware; Lan Wang, University of Minnesota 4:55 PM Network Response Regression for Modeling Population of Networks with Covariates Emma Jingfei Zhang, University of Miami; Will Wei Sun, University of Miami; Lexin Li, University of California at Berkeley 5:20 PM Penalized Empirical Likelihood for the Sparse Cox Model Dongliang Wang, SUNY Upstate Medical University; Tong Tong Wu, University of Rochester; Yichuan Zhao, Georgia State University 5:45 PM Floor Discussion
216 * ! Mon, 7/29/2019, 2:00 PM - 3:50 PM CC-704 Promises and Pitfalls of Making Decisions with Real World Data — Invited Papers Biometrics Section, ENAR, Health Policy Statistics Section Organizer(s): Yuanjia Wang, Columbia University Chair(s): Ying Liu, Medical College of Wisconsin 2:05 PM A Decision Theoretic Approach to Pre-emptive Genotyping Jonathan Schildcrout, Vanderbilt University Medical Center 2:25 PM Data Enriched Regression via Generalized Linear Models Ying Qing Chen, Fred Hutchinson Cancer Research Center; Sayan Dasgupta, Fred Hutchinson Cancer Research Center; Cheng Zheng, University of Wisconsin at Milwakee; Yuxiang Xie, University of Washington 2:45 PM Integrative Analysis of Multivariate Temporal Biomarkers in Electronic Health Records Donglin Zeng, UNC Chapel Hill 3:05 PM Learning Treatment Strategies from Randomized Trials Supplemented by Information in Electronic Health Records Yuanjia Wang, Columbia University 3:25 PM Risk Assessment with Imprecise EHR Data Tianxi Cai, Harvard University 3:45 PM Floor Discussion
322 ! Tue, 7/30/2019, 10:30 AM - 12:20 PM CC-106 Time-to-event Models in Complex Observational Studies — Invited Papers Biometrics Section, ENAR, Biopharmaceutical Section Organizer(s): Soutrik Mandal, National Cancer Institute Chair(s): Ana Maria Ortega-Villa, National Institutes of Health 10:35 AM A Copula Model Approach for Regression Analysis of Informatively Interval-censored Failure Time Data (Tony) Jianguo Sun, University of Missouri 11:00 AM Validating risk prediction models with sub-samples of cohorts Ruth Pfeiffer, National Cancer Institute; Mitchell Henry Gail, National Cancer Institute, Division of Cancer Epidemiology and Genetics; Yei Eun Shin, National Cancer Institute 11:25 AM Cure Rate Frailty Models for Clustered Current Status Data with Informative Cluster Size Kejun He, Renmin University; Wei Ma, Renmin University; Tong Wang, Texas A&M University; Dipankar Bandyopadhyay, Virginia Commonwealth University; Samiran Sinha, Texas A&M University 11:50 AM Goodness-of-fit Tests for the Linear Transformation Models with Interval-censored Data Soutrik Mandal, National Cancer Institute; Suojin Wang, Texas A&M University; Samiran Sinha, Texas A&M University 12:15 PM Floor Discussion
446 * ! Wed, 7/31/2019, 8:30 AM - 10:20 AM CC-101 New Statistical Methods in Evolutionary Biology — Invited Papers Biometrics Section, International Indian Statistical Association, WNAR Organizer(s): Arindam RoyChoudhury, Cornell University Chair(s): Arindam RoyChoudhury, Cornell University 8:35 AM Shannon information collapse for phylogenetic experimental design Jeffrey Peter Townsend, Yale University 9:00 AM Inferring tumor phylogenies using single-cell sequencing data Jing Peng, The Ohio State University; Laura Kubatko, The Ohio State University; Yuan Gao, The Ohio State University 9:25 AM Neutrality test on evolutionary tree topologies: Where statistics, physics, and geometric analysis meet Dan D. Erdmann-Pham, University of California, Berkeley; Yun S. Song, University of California, Berkeley; Jonathan Terhorst, University of Michigan 9:50 AM Discussant: Marc Suchard, UCLA 10:15 AM Floor Discussion
592 * ! Thu, 8/1/2019, 8:30 AM - 10:20 AM CC-702 Evaluating Impact in Networks: Causal Inference with Interference — Invited Papers Biometrics Section, Section on Statistics in Epidemiology, ENAR Organizer(s): Michael Hudgens, University of North Carolina at Chapel Hill Chair(s): Michael Hudgens, University of North Carolina at Chapel Hill 8:35 AM Individualistic Effects in Randomized Trials Under Contagion Olga Morozova, Yale School of Public Health; Daniel Eck, Yale School of Public Health; Forrest W Crawford, Yale School of Public Health 8:55 AM Matching methods for networked causal inference Alexander Volfovsky, Duke University 9:15 AM Causal inference with misspecified exposure mappings Fredrik Sävje, Yale University 9:35 AM Auto-G-Computation of Causal Effects on a Network Eric Tchetgen Tchetgen, University of Pennsylvania 9:55 AM Discussant: Dean Eckles, MIT 10:15 AM Floor Discussion
Courses cosponsored by the Biometrics Section: Saturday, July 27 CE_02C8:30 a.m. - 5:00 p.m.Reproducible ComputingInstructor(s): Colin RundelSponsor: Biometrics SectionSuccess in statistics and data science is dependent on the development of both analytical and computational skills. This workshop will cover:- Recognizing the problems that reproducible research helps address.- Identifying pain points in getting your analysis to be reproducible.- The role of documentation, sharing, version control, automation, and organization in making your research more reproducible.- Introducing tools to solve these problems, specifically R, RStudio, RMarkdown, git, GitHub, and make.- Strategies for scaling these tools and methods for larger more complex projects.Workshop attendees will work through several exercises and get first-hand experience with using relevant tool-chains and techniques, including R/RStudio, literate programming with R Markdown, automation with make, and collaboration and version control with git/GitHub. CE_07C8:00 a.m. - 12:00 p.m.Statistical and Computational Methods for Microbiome and Metagenomics Data AnalysisInstructor(s): Curtis Huttenhower and Hongzhe LeeSponsor: Biometrics SectionHigh throughput sequencing technologies enable individualized characterization of the microbiome composition and functions. The human microbiome, defined as community of microbes in and on the human body, impacts human health and risk of disease by dynamically interacting with host diet, genetics, metabolism and environment. The resulting data can potentially be used for personalized diagnostic assessment, risk stratification, disease prevention and treatment. Microbiome has become one of the most active areas of research in biomedical sciences. New computational and statistical methods are being developed to understand the function of microbial communities. In this short course, we will give detailed presentations on the statistical and computational methods for measuring various important features of the microbiome based on 16S rRNA and shotgun metagenomic sequencing data, and how these features are used as an outcome of an intervention, as a mediator of a treatment and as a covariate to be controlled for when studying disease/exposure associations. The statistics underlying some of the most popular tools in microbiome data analysis will be presented, including bioBakery tools for meta'omic profiling and tools for microbial community profiling (MetaPhlAn, HUMAnN, Data2, DEMIC, etc), together with advanced methods for compositional data analysis and kernel-based association analysis. Sunday, July 28 CE_09C8:30 a.m. - 5:00 p.m.Regression Modeling StrategiesInstructor(s): Frank HarrellSponsor: Biometrics SectionAll standard regression models have assumptions that must be verified for the model to have power to test hypotheses and for it to be able to predict accurately. Of the principal assumptions (linearity, additivity, distributional), this course will emphasize methods for assessing and satisfying the first two. Practical but powerful tools are presented for validating model assumptions and presenting model results. This course provides methods for estimating the shape of the relationship between predictors and response using the widely applicable method of augmenting the design matrix using restricted cubic splines. Even when assumptions are satisfied, overfitting can ruin a model’s predictive ability for future observations. Methods for data reduction will be introduced to deal with the common case where the number of potential predictors is large in comparison with the number of observations. Methods of model validation (bootstrap and cross–validation) will be covered, as will auxiliary topics such as modeling interaction surfaces, variable selection, overly influential observations, collinearity, and shrinkage, and a brief introduction to the R rms package for handling these problems. The methods covered will apply to almost any regression model, including ordinary least squares, logistic regression models, ordinal regression, quantile regression, longitudinal data analysis, and survival models. CE_12C1:00 p.m. - 5:00 p.m.Functional Data Analysis for Wearables: Methods and ApplicationsInstructor(s): Vadim Zipunnikov and Jeff GoldsmithSponsor: Biometrics SectionTechnological advances have made many wearable devices available for use in large epidemiological cohorts, national biobanks, and clinical studies. This opens up a tremendous opportunity for clinical and public health researchers to unveil previously hidden but pivotal physiological and behavioral signatures and relate them to disability and disease. Therefore, understanding, interpreta- tion and analysis of complex multimodal and multilevel data produced by such devices becomes crucial.The main goal of this workshop is to present an overview of the functional data analysis methods for modeling physical activity data, review their strengths and limitations, and demonstrate their implementation in R packages refund and mgcv. We will also examine several non-functional approaches for extracting informative and interpretable features from wearable data. We will discuss applications in epidemiological studies such as Head Start Program and National Health and Nutrition Examination Survey and a clinical study of Congestive Heart Failure. Tuesday, July 30 CE_21C8:00 a.m. - 12:00 p.m.Measuring the Impact of Nonignorable Missing DataInstructor(s): Daniel Heitjan and Hui XieSponsor: Biometrics SectionThe popular but typically unverifiable assumption of ignorability greatly simplifies analyses with incomplete data, both conceptually and computationally. We say that missingness is ignorable when the probability that an observation is missing depends only on fully observed information, and nonignorable when the probability that an observation is missing depends on the value of the observation, even after conditioning on available design variables and covariates. For example, in a clinical trial the data are plausibly nonignorably missing when the subjects who drop out are those for whom the drug is either ineffective or excessively toxic. The possibility that the missing observations in a study are the result of a nonignorable mechanism casts doubt on the validity of conclusions based on the assumption of ignorability. Unfortunately, it is generally impossible to robustly assess the validity of this assumption with just the data at hand. One way to address this problem is to conduct a local sensitivity analysis: Essentially, re-compute estimated parameters of interest under models that slightly violate the assumption of ignorability. If the parameters change only modestly under violation of the assumption, then it is safe to proceed with an ignorable model. If they change drastically, then a simple ignorable analysis is of questionable validity. To conduct such a sensitivity analysis in a systematic and efficient way, we have developed a measure that we call the index of local sensitivity to nonignorability (ISNI), which evaluates the rate of change of parameter estimates in the neighborhood of an ignorable model. Computation of ISNI is straightforward and avoids the need to estimate a nonignorable model or to posit a specific magnitude of nonignorability. We have developed a suite of statistical methods for ISNI analysis, now implemented in an R package named isni. In this half-day short course we will describe these methods and train users to apply them to inform evaluations of the reliability of empirical findings when data are incomplete. CE_24C8:30 a.m. - 5:00 p.m.An Introduction to the Joint Modeling of Longitudinal and Survival Data, with Applications in RInstructor(s): Dimitris RizopoulosSponsor: Biometrics SectionIn follow-up studies, different types of outcomes are typically collected for each subject. These include longitudinally measured responses (e.g., biomarkers), and the time until an event of interest occurs (e.g., death, dropout). Often these outcomes are separately analyzed, but on many occasions, it is of scientific interest to study their association. This type of research question has given rise in the class of joint models for longitudinal and time-to-event data. These models constitute an attractive paradigm for the analysis of follow-up data that is mainly applicable in two settings: First, when focus is on a survival outcome, and we wish to account for the effect of endogenous time-dependent covariates measured with error, and second, when focus is on the longitudinal outcome and we wish to correct for non-random dropout. This full-day course is aimed at applied researchers and graduate students and will provide a comprehensive introduction to this modeling framework. We will explain when these models should be used in practice, which are the key assumptions behind them, and how they can be utilized to extract relevant information from the data. Emphasis is given on applications, and after the end of the course, participants will be able to define appropriate joint models to answer their questions of interest.*Necessary background for the course*: This course assumes knowledge of basic statistical concepts, such as standard statistical inference using maximum likelihood, and regression models. Also, basic knowledge of R would be beneficial but is not required. Participants are required to bring their laptop with the battery fully charged. Before the course instructions will be sent for installing the required software. CE_26C1:00 p.m. - 5:00 p.m.Adaptive treatment strategies: An introduction to statistical approaches for estimationInstructor(s): Erica MoodieSponsor: Biometrics SectionEvidence-based medicine relies on using data to provide recommendations for effective treatment decisions. However, in many settings, response is heterogeneous across patients. Patient response may also vary over time, and physicians are faced with the daunting task of making sequential therapeutic decisions having seen few patients with a given clinical history.Adaptive treatment strategies (ATS) operationalize the sequential decision-making process in the precision medicine paradigm, offering statisticians principled estimation tools that can be used to incorporate patient’s characteristics into a clinical decision-making framework so as to adapt the type, dosage or timing of treatment according to patients’ evolving needs.This half-day course will provide an overview of precision medicine from the statistical perspective. We will begin with a discussion of relevant data sources. We will then turn our attention to estimation, and consider multiple approaches – and their relative strengths and weaknesses – to estimating tailored treatment rules in a one-stage setting. Next, we will consider the multi-stage setting and inferential challenges in this area. Relevant clinical examples will be discussed, as well available software tools.
ENAR 2020 invited session proposalThe Program Committee for the 2020 ENAR Spring Meeting (March 22-25, Nashville, TN) is soliciting formal proposals for invited paper sessions. Proposals on topics that have broad potential scientific impact are particularly encouraged. The submission deadline is June 15, 2019 at 11:59 pm EDT. The invited session proposals will be selected by the program committee.Please formally submit your invited session proposal by clicking the following link:ENAR 2020 Invited Session Proposals https://forms.gle/fdZEmF36J9W2HFbF7 ENAR 2020 Invited Session Proposal Formforms.glePlease provide details of your session in the form below. Use navigation buttons provided on this form and not the browser back button in order to avoid loss of data. Please contact the Program Chair, Juned Siddique, at siddique@northwestern.edu or Associate Chair, Chenguang Wang at cwang68@jhmi.edu, for any queries.
Concise, self-contained proposals with confirmed speakers and talk abstracts have a much better chance of being accepted!All invited sessions are scheduled for 105 minutes. We will consider different formats including a session with 4 speakers, a session with 3 speakers plus a discussant, or a panel discussion. Each participant may be a speaker/panelist in at most one invited or contributed session.Please contact the Program Chair, Juned Siddique (siddique@northwestern.edu) or the Biometrics section representative, Zheyu Wang (wangzy@jhu.edu) if you have any questions.