The following JSM 2019 papers are cosponsored by the Biometrics Section:

Topic-Contributed

• Integrated Principal Components Analysis – Tiffany M. Tang, University of California at Berkeley, and Genevera Allen, Rice University
• Are Clusterings of Multiple Data Views Independent? – Lucy Gao, University of Washington; Daniela Witten, University of Washington; and Jacob Bien, University of Southern California
• 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; and Anru Zhang, University of Wisconsin-Madison
• 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, The Johns Hopkins University
• Tailored Optimal Post-Treatment Surveillance for Cancer Recurrence – Rui Chen, University of Wisconsin-Madison
• Propensity Score Weighting for Causal Inference with Multiple Treatments – Fan Li, Duke University
• 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, The Ohio State University; and Henry Xiang, The Research Institute of Nationwide Children’s Hospital
• Causal Isotonic Regression – Ted Westling, University of Massachusetts-Amherst; Marco Carone, University of Washington; and Peter Gilbert, Fred Hutchinson Cancer Research Center
• Stage-Wise Synthesis of Randomized Trials for Optimizing Dynamic Treatment Regimes – Yuan Chen, Columbia University Mailman School of Public Health; Yuanjia Wang, Columbia University; and Donglin Zeng, UNC Chapel Hill

Invited

• Statistical Inference for High-Dimensional Models via Recursive Online-Score Estimation – Runze Li, Penn State University
• Dimension Reduction for High-Dimensional Censored Data – Shanshan Ding, University of Delaware; Wei Qian, University of Delaware; Lan Wang, University of Minnesota
• 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
• A Decision Theoretic Approach to Preemptive Genotyping – Jonathan Schildcrout, Vanderbilt University Medical Center
• 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-Milwaukee; and Yuxiang Xie, University of Washington
• Integrative Analysis of Multivariate Temporal Biomarkers in Electronic Health Records – Donglin Zeng, UNC Chapel Hill
• Learning Treatment Strategies from Randomized Trials Supplemented by Information in Electronic Health Records – Yuanjia Wang, Columbia University
• Risk Assessment with Imprecise EHR Data – Tianxi Cai, Harvard University
• Penalized Empirical Likelihood for the Sparse Cox Model – Dongliang Wang, SUNY Upstate Medical University; Tong Tong Wu, University of Rochester; and Yichuan Zhao, Georgia State University
• A Copula Model Approach for Regression Analysis of Informatively Interval-Censored Failure Time Data – (Tony) Jianguo Sun, University of Missouri
• Validating Risk Prediction Models with Sub-Samples of Cohorts – Ruth Pfeiffer, National Cancer Institute; Mitchell Henry Gail, National Cancer Institute; and Yei Eun Shin, National Cancer Institute
• 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; and Samiran Sinha, Texas A&M University
• Goodness-of-Fit Tests for the Linear Transformation Models with Interval-Censored Data – Soutrik Mandal, National Cancer Institute; Suojin Wang, Texas A&M University; and Samiran Sinha, Texas A&M University
• Shannon Information Collapse for Phylogenetic Experimental Design – Jeffrey Peter Townsend, Yale University
• Inferring Tumor Phylogenies Using Single-Cell Sequencing Data – Jing Peng, The Ohio State University; Laura Kubatko, The Ohio State University; and Yuan Gao, The Ohio State University
• 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; and Jonathan Terhorst, University of Michigan
• Individualistic Effects in Randomized Trials Under Contagion – Olga Morozova, Yale School of Public Health; Daniel Eck, Yale School of Public Health; and Forrest W. Crawford, Yale School of Public Health
• Matching Methods for Networked Causal Inference – Alexander Volfovsky, Duke University
• Causal Inference with Misspecified Exposure Mappings – Fredrik Sävje, Yale University
• Auto-G-Computation of Causal Effects on a Network – Eric Tchetgen Tchetgen, University of Pennsylvania