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Keyword Search Criteria: multiple imputation returned 56 record(s)
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Sunday, 07/29/2018
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Statistical Approaches to Decreasing the Discrepancy of Non-Detects in QPCR Data
Love Tanzy, University of Rochester Medical Center; Valeriia Sherina, University of Rochester Medical Center; Matthew N. McCall, University of Rochester Medical Center
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Multiple Imputation of Missing Income Data for the Redesigned National Health Interview Survey
Guangyu Zhang, National Center for Health Statistics; Yulei He, CDC/NCHS; Pavlina Rumcheva, National Center for Health Statistics ; Aaron Maitland, National Center for Health Statistics ; Suresh Srinivasan, National Center for Health Statistics ; Alain Moluh, NCHS; Matthew Bramlett, NCHS; Chris Moriarity, National Center for Health Statistics; Tina Norris, NCHS
4:45 PM
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Monday, 07/30/2018
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Combining Rules for F-Tests from Imputed Data
Ashok Chaurasia
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Combining Inverse Probability Weighting and Multiple Imputation to Adjust for Selection Bias in Electronic Health Records-Based Research
Tanayott Thaweethai, Harvard T.H. Chan School of Public Health; Sebastien Haneuse, Harvard T.H. Chan School of Public Health; David Arterburn, Kaiser Permanente Washington Health Research Institute
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Developing and Evaluating Methods for Estimating Race/Ethnicity in an Incomplete Dataset Using Address, Surname and Family Race
Gabriella Christine Silva, Brown University; Roee Gutman, Brown University
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Multiple Imputation Using Denoising Autoencoders
Lovedeep Gondara
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Estimating Average Causal Treatment Effects Utilizing Fractional Imputation When Confounders Are Subject to Missingness
Nathaniel Corder, North Carolina State University; Shu Yang, North Carolina State University
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Combining Predictive Mean Matching with the Penalized Spline of Propensity Prediction Method When Performing Multiple Imputation
Jay Xu; Roee Gutman, Brown University
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Impact on Statistical Power by Different Imputation Methods for Binary Endpoints with Missing Data
Xiaomei Liao, AbbVie Inc.; Jun Zhao, AbbVie; Bidan Huang, AbbVie Inc.
8:35 AM
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Assessing the Uncertainty Due to Chemicals Below the Detection Limit in Chemical Mixture Estimation
Paul Hargarten, VCU; David C. Wheeler, Virginia Commonwealth University
9:05 AM
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Developing and Evaluating Methods for Estimating Race/Ethnicity in an Incomplete Dataset Using Address, Surname and Family Race
Gabriella Christine Silva, Brown University; Roee Gutman, Brown University
9:20 AM
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Multiple Imputation Using Denoising Autoencoders
Lovedeep Gondara
9:20 AM
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Linking Medicare Current Beneficiary Survey (MCBS) to Augment Post-Market Real World Data from Medicare Claims: a Multiple Imputation Approach
Yun Lu, FDA; Xiyuan Wu, Acumen LLC; Yoganand Chillarige, Acumen LLC; Michael Wernecke, Acumen LLC; Hector Izurieta, FDA; Jeffrey Kelman, CMS; Richard Forshee , FDA
9:20 AM
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Challenges in Implementing a New Imputation Method into Production in the 2017 Economic Census or What to Do When the Research Approach Oversimplifies the Problem
Katherine J Thompson, U.S. Census Bureau; Willam Davie Jr., U.S. Census Bureau; Matthew Thompson, U.S. Census Bureau; Scot Dahl, U.S. Census Bureau
10:35 AM
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Estimating Average Causal Treatment Effects Utilizing Fractional Imputation When Confounders Are Subject to Missingness
Nathaniel Corder, North Carolina State University; Shu Yang, North Carolina State University
10:35 AM
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Combining Inverse Probability Weighting and Multiple Imputation to Adjust for Selection Bias in Electronic Health Records-Based Research
Tanayott Thaweethai, Harvard T.H. Chan School of Public Health; Sebastien Haneuse, Harvard T.H. Chan School of Public Health; David Arterburn, Kaiser Permanente Washington Health Research Institute
10:55 AM
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Variance Estimation for Product Sales in the 2017 Economic Census: Challenges in Implementing Multiple Imputation-Based Variance Estimation
Matthew Thompson, U.S. Census Bureau; Katherine J Thompson, U.S. Census Bureau
10:55 AM
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Constructing a Synthetic Population for Community Profiling Using Publicly Available Data
Joshua Goldstein, Social and Decision Analytics Laboratory, Virginia Tech; David Higdon, Virginia Tech
11:15 AM
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Combining Predictive Mean Matching with the Penalized Spline of Propensity Prediction Method When Performing Multiple Imputation
Jay Xu; Roee Gutman, Brown University
11:50 AM
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Nonparametric Multiple Imputation for Bridging Between Different Industry Coding Systems
Jörg Drechsler, Institute for Employment Research; Birgit Pech, Amt für Statistik Berlin-Brandenburg
2:05 PM
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Multiple Imputation for Adaptive Survey Design
Trivellore Raghunathan, University of Michigan
2:30 PM
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Degrees of Freedom in Multiple Imputation: The Original vs. The Adjusted in 2015 National Hospital Ambulatory Medical Care Survey
Qiyuan Pan, CDC/NCHS/DHCS; Rong Wei, National Center for Health Statistics
2:30 PM
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Strategies for Analyzing Summary Variables in the Presence of Partially Missing Longitudinal Data
Jennifer Thompson, Vanderbilt University; Rameela Chandrasekhar, Vanderbilt University
2:50 PM
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A Robust Multiple Imputation Approach to Causal Inference with Confounding by Indication
Roderick J Little, University of Michigan; Tingting Zhou, University of Michigan; Michael Elliott, University of Michigan
2:55 PM
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Multiple Imputation Strategies for Handling Missing Data When Generalizing Randomized Clinical Trial Findings Through Propensity Score-Based Methodologies
Albee Ling, Stanford University; Maya Mathur, Stanford University; Kris Kapphahn, Stanford University; Maria Montez-Rath, Stanford University; Manisha Desai, Stanford University
3:05 PM
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Tree-Based Doubly-Robust Nonparametric Multiple Imputation
Darryl Creel
3:15 PM
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Multiple Imputation Methods Addressing Planned Missingness in a Multi-Phase Survey
Irina Bondarenko, University of Michigan; Yun Li, University of Michigan; Paul Imbriano, University of Michigan
3:20 PM
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Imputation of Small Number of New Questions in the Large Survey
Di Xiong, UCLA SPH; Yan Wang, Field School of Public Health, UCLA; Honghu Liu, UCLA
3:35 PM
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Tuesday, 07/31/2018
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Methods to Handle Missing Outcome Data in Studies of Acute Illnesses Followed by Recovery
Dashiell Fellini Young-Saver, University of California, Los Angeles; Jeffrey Gornbein, University of California, Los Angeles; Sidney Starkman, University of California, Los Angeles; Jeffrey Lawrence Saver, University of California, Los Angeles
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An Evaluation of Statistical Methods with Missing Data in Small Clinical Trials
Takayuki Abe, Yokohama City University, School of Data Science; Kazuhito Shiosakai, Daiichi Sankyo Co., Ltd.; Manabu Iwasaki, Yokohama City University, School of Data Science
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Analyzing the Evolution of Media Narratives Following Mass Shooting Events Using Modern Bayesian Statistical Methods
Thomas Belin, UCLA; Jay Xu
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Degrees of Freedom in Multiple Imputation: The Original vs. The Adjusted in 2015 National Hospital Ambulatory Medical Care Survey
Qiyuan Pan, CDC/NCHS/DHCS; Rong Wei, National Center for Health Statistics
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Comparison of Missing Data Methods in the Use of LASSO Regression for Model Selection with Applications to the National Trauma Data Bank
Sarah B Peskoe, Duke University; Tracy Truong, Duke University; Lily R Mundy, Duke University School of Medicine; Ronnie L Shammas, Duke University School of Medicine; Scott T Hollenbeck, Duke University School of Medicine
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Imputation of Small Number of New Questions in the Large Survey
Di Xiong, UCLA SPH; Yan Wang, Field School of Public Health, UCLA; Honghu Liu, UCLA
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Tree-Based Doubly-Robust Nonparametric Multiple Imputation
Darryl Creel
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Multiple Imputation Methods Addressing Planned Missingness in a Multi-Phase Survey
Irina Bondarenko, University of Michigan; Yun Li, University of Michigan; Paul Imbriano, University of Michigan
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Correcting for Errors in Variables Derived from Electronic Health Records Using Validation Sampling and Multiple Imputation
Bryan E Shepherd, Vanderbilt University School of Medicine; Mark Giganti, Vanderbilt University School of Medicine
8:35 AM
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Imputation Methods for Individual Participant Data Meta-Analysis
Eloise Kaizar, Ohio State University; Deborah Kunkel, The Ohio State University
9:15 AM
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Functional Regression Models with Highly Irregular Designs
Justin Petrovich, Pennsylvania State University; Matthew Reimherr, Pennsylvania State University; Carrie Daymont, Penn State Hershey Medical Center
9:20 AM
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Comparison of Missing Data Methods in the Use of LASSO Regression for Model Selection with Applications to the National Trauma Data Bank
Sarah B Peskoe, Duke University; Tracy Truong, Duke University; Lily R Mundy, Duke University School of Medicine; Ronnie L Shammas, Duke University School of Medicine; Scott T Hollenbeck, Duke University School of Medicine
11:15 AM
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Imputation Approaches for Animal Movement Modeling
Henry Scharf, Colorado State University; Mevin Hooten, Colorado State University; Devin Johnson, Alaska Fisheries Science Center (NOAA)
2:05 PM
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Bayesian Record Linkage Under Limited Linking Information
Mingyang Shan, Brown University; Roee Gutman, Brown University; Kali Thomas, Brown University
2:25 PM
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Wednesday, 08/01/2018
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A Latent Class Analysis to Identify Subgroups of Heart Failure Under Missingness And/Or Uncertainty in the Indicator Variables
Wendimagegn Alemayehu, University of Alberta; Cynthia M Westerhout, University of Alberta; Jason R Dyck, University of Alberta; Todd Anderson, University of Calgary; Justin A Ezekowitz, University of Alberta
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The Impact of Analysis Method and Model Specification for Handling Missing Covariate Data in Survival Analysis: a Case Study
Evon Okidi, Brown University; Joseph W Hogan, Brown University School of Public Health; Chanelle Howe, Brown University
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Model Compatible Multiple Imputation Method for Minimizing the Impact of Covariate Detection Limit in Logistic Regression
Shahadut Hossain, UAE University
8:35 AM
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Multiply Imputing Missing Values Arising by Design in Transplant Survival Data
Robin Mitra, University of Lancaster
9:05 AM
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Multiple Imputation of Probabilistic Linkage of Employers in Survey and Administrative Data: Creating CenHRS
Dhiren Patki, University of Michigan
9:35 AM
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A Comparison of Multiple Imputation by Fully Conditional Specification and Joint Modeling for Generalized Linear Models with Covariates Subject to Detection Limits
Paul Bernhardt, Villanova University
9:35 AM
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Towards Multiple-Imputation-Proper Predictive Mean Matching
Philipp Gaffert, GfK SE; Florian Meinfelder, Universität Bamberg; Volker Bosch, GfK SE
10:55 AM
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Bootstrap Inference for Multiple Imputation Under Uncongeniality
Jonathan Bartlett, AstraZeneca
11:35 AM
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Approaches to Tipping Point Analyzes for a Binary Endpoint in Longitudinal Clinical Trials
Joseph Wu, Pfizer; Huaming Tan, Pfizer, Inc.; Neal Thomas, Pfizer; Cunshan Wang, Pfizer, Inc.
2:50 PM
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Handling Missing Not at Random Data for Safety Endpoint in the Multiple Dose Titration Clinical Pharmacology Trial
Li Fan, Merck; Tian Zhao, Merck; Patrick Larson, Merck
3:20 PM
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