Online Program Home
  My Program

All Times EDT

* = applied session       ! = JSM meeting theme

Activity Details

286 Wed, 8/11/2021, 1:30 PM - 3:20 PM Virtual
Missing Data Methods — Contributed Speed
Biometrics Section
Chair(s): Mingzhao Hu, University of California Santa Barbara
Direct Estimation of the Area Under the Receiver Operating Characteristic Curve with Verification Biased Data
Gengsheng Qin, Georgia State University; Yan Hai, Georgia State University
Impact of Inconsistent Imputation Models in Mediation Analysis with Clustered Data
Bo Ye, State University of New York at Albany; Recai Yucel, Temple University
Flexible Variable Selection in the Presence of Missing Data
Brian Williamson, Fred Hutchinson Cancer Research Center; Ying Huang, Fred Hutchinson Cancer Research Center
Variability in Causal Effects of E-Assist on a Binary Outcome and One-Sided Noncompliance in a Multi-Site Randomized Trial
Xinxin Sun, Virginia Commonwealth University; Yongyun Shin, Virginia Commonwealth University
The Competing Risks Cox Model When Failure Type Is Missing Not at Random
Benjamin W Langworthy, Harvard University; Tomotaka W Ugai, Harvard University; Shuji Ogino, Harvard University; Molin Wang, Harvard T.H. Chan School of Public Health
Data Fusion for Time-to-Event Outcomes
Fatema Shafie Khorassani, University of Michigan; Xu Shi, Department of Biostatistics, University of Michigan; Jeremy M.G. Taylor, University of Michigan
Positive Unlabeled Learning with Missing Data in Electronic Health Records
Tanayott Thaweethai, Massachusetts General Hospital Biostatistics Center; Caitlin Ann Selvaggi, Massachusetts General Hospital Biostatistics Center; Andrea Sarah Foulkes, Massachusetts General Hospital Biostatistics Center
Score Tests with Incomplete Covariates and High-Dimensional Auxiliary Variables
Kin Yau Wong, The Hong Kong Polytechnic University
Understanding Algorithmic Bias in Clinical Prediction Models
Mengying Yan, Department of Biostatistics & Bioinformatics, Duke University; Michael Pencina, Department of Biostatistics & Bioinformatics, Duke University; Benjamin A Goldstein, Duke University
Mining for Equitable, Intelligent Health: Assessing the Impact of Missing Data in Raw Electronic Health Records
Emily Getzen, Department of Biostatistics at UPenn; Qi Long, Department of Biostatistics at UPenn
Penalized Regression and Multiple Imputation: A Simple Aggregation Rule That Works Surprisingly Well
Ryan A Peterson, University of Colorado
Impact of Degree of Missingness and Sample Size on the Performance of Imputation Methods for Mass Spectrometry Data
Sandra L Taylor, University of California, Davis; Matthew Dominic Ponzini , University of California, Davis; Machelle D Wilson, University of California, Davis; Kyoungmi Kim, University of California, Davis
Statistical Collaboration for Competing Risk Analysis: Smoking Cessation and the Risk of Second Primary Lung Cancer Among Lung Cancer Survivors
Sophia Luo, Stanford University School of Medicine; Eunji Choi, Stanford University School of Medicine; Summer S. Han, Stanford University School of Medicine
Comparison of Variable Selection Methods Based on LASSO After Multiple Imputation of Missing Covariates in Survival Data
Qian Yang, Duke University; Susan Halabi, Duke University; Bin Luo, Duke University
Analyzing Left-Truncated Samples with the Cox Model in the Presence of Missing Covariates
Hayley Richardson, University of Pennsylvania; Sharon Xie, University of Pennsylvania
The Efficiency of Multistage Pooling in Estimating the Prevalence of Multiple Infections
Carlos Wilensky, Radford University; Md S. Warasi, Radford University
Construction and Assessment of Prognostic Rules in the Presence of Missing Predictor Data Using Multiple Imputation: Methodology and Evaluation on Two Data Sets and Simulations
Bart Mertens, Leiden University Medical Centre
Statistical Analysis of Doubly Censored Recurrent Emergency Departments (ED) Visits Data
Yi Xiong, Simon Fraser University; Joan X. Hu, Simon Fraser University; Rhonda J. Rosychuk, University of Alberta
A joint latent class model of longitudinal and survival data with time-varying membership probability and covariance modelling
Ruoyu Miao, University of Manchester; Christiana Charalambous, University of Manchester