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Keyword Search Criteria: Multiple Imputation returned 45 record(s)
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Sunday, 07/28/2019
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Missingness by Design – Split Questionnaire Designs and Synthetic Data
Joerg Drechsler, Institute for Employment Research; Florian Meinfelder, Universität Bamberg
4:05 PM
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Leveraging Auxiliary Information on Marginal Distributions in Nonignorable Models for Item and Unit Nonresponse in Surveys
Olanrewaju Michael Akande, Duke University; Gabriel Madson, Duke University; D. Sunshine Hillygus, Duke University; Jerry Reiter, Duke University
4:05 PM
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Multiple Imputation of Non-Ignorable Missing Survey Data
Angelina Hammon, University of Bamberg
4:30 PM
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Data Fusion, Multiple Imputation for Clustered Data, and Split Questionnaire Designs: Research Inspired by Our Collaborations with Susie
Trivellore Raghunathan, University of Michigan; Nathaniel Schenker, Retired
4:55 PM
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Monday, 07/29/2019
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Multiple Imputation Versus Machine Learning: Predictive Models to Facilitate Analyzes of Association Between Contemporaneous Medicaid/CHIP Enrollment Status and Health Measures
Jennifer Rammon, National Center for Health Statistics/CDC; Yulei He, CDC; Jennifer Parker, CDC/NCHS/OAE/SPB
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A Comparison of Stacked and Pooled Multiple Imputation
Paul Bernhardt, Villanova University
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How Many Imputations Are Enough When Reporting Clinical Trials?
Anders Gorst-Rasmussen, Novo Nordisk A/S
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Developing and Evaluating Methods to Impute Race/Ethnicity in an Incomplete Dataset
Gabriella Silva, Brown University; Amal N. Trivedi, Brown University; Roee Gutman, Brown University
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Comparison of Missing Data Imputation Methods in Longitudinal Study of ADRD Patients
Yi Cao, Brown University; Roee Gutman, Brown University; Heather Allore, Yale University ; Brent Vander Wyk, Yale University
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Multiple Imputation for Privacy Protection: Where Are We and Where Are We Going?
Jerry Reiter, Duke University
8:35 AM
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Multiple Imputation Procedure for Record Linkage and Causal Inference to Estimate the Effects of Home-Delivered Meals
Mingyang Shan, Brown University; Kali Thomas, Brown University; Roee Gutman, Brown University
9:00 AM
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Multiple Imputation Versus Machine Learning: Predictive Models to Facilitate Analyzes of Association Between Contemporaneous Medicaid/CHIP Enrollment Status and Health Measures
Jennifer Rammon, National Center for Health Statistics/CDC; Yulei He, CDC; Jennifer Parker, CDC/NCHS/OAE/SPB
9:10 AM
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Application of Multiple Imputation Methodology to Address Measurement Error Problems
Trivellore Raghunathan, University of Michigan
9:25 AM
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A Comparison of Stacked and Pooled Multiple Imputation
Paul Bernhardt, Villanova University
9:50 AM
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Developing and Evaluating Methods to Impute Race/Ethnicity in an Incomplete Dataset
Gabriella Silva, Brown University; Amal N. Trivedi, Brown University; Roee Gutman, Brown University
10:35 AM
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Evaluation of Imputation Approaches for Disease Diagnosis When Risk Factors Have Missing Values
Katherine E Irimata, National Center for Health Statistics; Guangyu Zhang, National Center for Health Statistics
10:35 AM
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New Insights into Modeling Exposure Measurements Below the Limit of Detection
Ana Maria Ortega-Villa, National Institutes of Health; Danping Liu, National Cancer Institute; Mary H Ward, National Institutes of Health; Albert S Paul, National Institutes of Health
10:35 AM
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Comparison of Missing Data Imputation Methods in Longitudinal Study of ADRD Patients
Yi Cao, Brown University; Roee Gutman, Brown University; Heather Allore, Yale University ; Brent Vander Wyk, Yale University
10:45 AM
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How Many Imputations Are Enough When Reporting Clinical Trials?
Anders Gorst-Rasmussen, Novo Nordisk A/S
11:35 AM
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Contrasting a Longitudinal Factor Model with a Linear Mixed-Effects Model to Address Incomplete Data on Repeated Measures in an AIDS Prevention Study
Panteha Hayati Rezvan, University of California Los Angeles; Xiang Lu, University of California Los Angeles; Thomas Belin, UCLA
11:50 AM
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Relative Risk Estimation Using Multiple Imputation with Logistic Regression and Discretization
Jay Xu, University of California, Los Angeles; Thomas Belin, UCLA
11:50 AM
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Estimating Additive Interaction Effect in Stratified Two-Phase Case-Control Design
Ai Ni, The Ohio State University; Jaya M Satagopan, Memorial Sloan Kettering Cancer Center
11:55 AM
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Multiple Imputation for Censored Covariate Using Fully Conditional Specification Method
Jingyao Hou; Jing Qian, University of Massachusetts Amherst
12:05 PM
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Tuesday, 07/30/2019
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Estimating Outcome-Exposure Associations When Exposure Biomarker Detection Limits Vary Across Batches
Jonathan Boss, University of Michigan; Bhramar Mukherjee, University of Michigan; Kelly K. Ferguson, National Institute of Environmental Health Sciences; Amira M. Aker, University of Michigan; Akram N. Alshawabkeh, Northeastern University; Jose F. Cordero, University of Georgia; John D. Meeker, University of Michigan; Sehee Kim, University of Michigan
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A Comparison of Several Missing Data Imputation Techniques for Analyzing Different Types of Missingness
Tiantian Yang, Clemson University; William Bridges, Clemson University
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A DECAY MODEL for HANDLING MISSING DATA in CLINICAL TRIALS
Tao Sheng
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A Comparison of Missing Data Imputation Methods for Longitudinal Data
Meghan Sealey; Lan Zhu, Oklahoma State University
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The Comparison of Multiple Imputation and Missing Indicator Methods for Prediction in Regression Analysis
Chi-Hong Tseng, UCLA
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Variance Estimation When Combining Inverse Probability Weighting and Multiple Imputation 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
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An Approach to Multiple Imputation That Avoids the Inclusion of an Outcome in the Imputation Model
Monelle Tamegnon, Janssen R&D
8:35 AM
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Missing Data Imputation with Baseline Information in Longitudinal Clinical Trials
Yilong Zhang, Merck; Zachary Zimmer, Merck; Lei Xu, Merck; Gregory Golm, Merck; Raymond Lam, Merck; Susan Huyck, Merck; Frank G Liu, Merck Sharp & Dohme Inc.
8:50 AM
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Imputation in the American Housing Survey: Comparing Multiple Imputation with Current Hot Deck Methods
Sean Dalby, US Census Bureau
9:05 AM
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Exploring Model Fit Evaluation in Structural Equation Models with Incomplete Ordinal Variables Using the D2 Method
Yu Liu, University of Houston; Suppanut Sriutaisuk, University of Houston
9:20 AM
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Considerations for the Use of Multiple Imputation in a Noninferiority Trial Setting
Kimberly Walters, Statistics Collaborative, Inc.; Jie Zhou, Statistics Collaborative, Inc.; Janet Wittes, Statistics Collaborative, Inc; Lisa Weissfeld, Stats Collaborative
9:35 AM
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A Support Vector Machine Based Semiparametric Mixture Cure Model
Yingwei Peng, Queen's University; Peizhi Li, Dongbei University of Finance and Economics and Queen's University; Qingli Dong, Dongbei University of Finance and Economics and Queen's University
9:50 AM
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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 B Mathur, Harvard University; Kris Kapphahn, Stanford University; Maria Montez-Rath , Stanford University; Manisha Desai, Stanford University Quantitative Sciences Unit
9:50 AM
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An Empirical Study of Correlation Coefficient Aggregation in Multiple Imputation
Jianjun Wang; Xin Ma, University of Kentucky
10:05 AM
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Variance Estimation When Combining Inverse Probability Weighting and Multiple Imputation 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
10:05 AM
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Imputation Strategies When a Continuous Outcome Is to Be Dichotomized for Responder Analysis: a Simulation Study
Lysbeth Floden, University of Arizona; Melanie Bell, University of Arizona
10:05 AM
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Estimation of Average Causal Effect in Clustered Data Using Multiple Imputation
Recai Yucel, SUNY Albany School of Public Health; Meng Wu, Department of Health, NY State
2:05 PM
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Wednesday, 07/31/2019
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Multilevel Multiple Imputation for Electronic Health Record and Survey Data: Your Flexible Friend
James Robert Carpenter, London School of Hygiene & Tropcial Medicine; Matteo Quartagno, London School of Hygiene & Tropcial Medicine
8:35 AM
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New Predictive Mean Matching Imputation Methods for Cluster Randomized Trials
Brittney Bailey, Amherst College; Rebecca Andridge, The Ohio State University College of Public Health
9:00 AM
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Approximate Bayesian Bootstrap Procedures to Estimate Multilevel Treatment in Observational Studies with Application to Type 2 Diabetes Treatment Regimens
Roee Gutman, Brown University; Anthony D. Scotina, Simmons University; Robert J Smith, Brown University; Andrew R Zullo, Brown University
9:00 AM
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Incomplete Data Analysis of Non-Inferiority Clinical Trials: Difference in Binomial Proportions Case
Yulia Sidi, University of Connecticut; Ofer Harel, Dept of Statistics, U of Connecticut
10:50 AM
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