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Sessions Were Renumbered as of May 19.

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CC-W = McCormick Place Convention Center, West Building,   CC-N = McCormick Place Convention Center, North Building
H = Hilton Chicago,   UC= Conference Chicago at University Center
* = applied session       ! = JSM meeting theme

Keyword Search Criteria: Multiple imputation returned 51 record(s)
Sunday, 07/31/2016
Accounting for Potential Measurement Errors in Environmental Preterm Studies
Yinjun Zhao, Yale University; Shuangge Ma, Yale University
2:50 PM

Imputing Drone Strikes Casualty Counts Given Estimated Interval Ranges
Earvin Balderama, Loyola University Chicago
3:20 PM

Multiple Imputation Framework to Estimate Causal Effect of Testing on Treatment Decision
Irina Bondarenko, University of Michigan; Yun Li, University of Michigan
4:05 PM

Bayesian Multiple Imputation Procedures to Equate Health Assessment Questionnaires
Chenyang Gu, Brown University; Roee Gutman, Brown University
4:25 PM

Survey Integration and Estimation of Joint Distributions with Conditionally Representative Data Sources
Maria DeYoreo, Duke University; Bailey Fosdick, Colorado State University
4:45 PM

An Examination of Statistical Disclosure Issues Related to Publication of Aggregate Statistics in the Presence of a Known Subset of the Data Set Using Baseball Hall of Fame Ballots
Gregory Matthews; Petala Gardenia da Silva Estrela Tuy, Loyola University Chicago
4:50 PM

An Entropy-Based Model Selection Criterion for Latent Class Analysis of Incomplete Data
Chantal Larose, SUNY New Paltz; Ofer Harel, University of Connecticut; Katarzyna Kordas, University of Bristol; Dipak Dey, University of Connecticut
5:05 PM

Monday, 08/01/2016
Evaluation of Sensitivity of Statistical Methods That Assume Missing at Random
Takayuki Abe, Keio University School of Medicine; Kazuhito Shiosakai, Daiichi Sankyo Co.; Rachel Roberts, Keio University School of Medicine; Fumiya Sano, Keio University School of Medicine; Manabu Iwasaki, Seikei University


Effects of Missing Data on Student Growth Estimates
Katherine Wright, Loyola University Chicago; John Gatta, Northwestern University, ECRA Group; Therese D. Pigott, Loyola University Chicago


Modeling Blood Organic Mercury as a Function of Usual Fish Consumption and Demographics Using NHANES Data
John Rogers, Westat; Rebecca Birch, Westat


Modeling Blood Organic Mercury as a Function of Usual Fish Consumption and Demographics Using NHANES Data
John Rogers, Westat; Rebecca Birch, Westat
8:45 AM

Data Fusion for Predicting Long-Term Program Impacts
Michael Robbins, RAND Corporation
8:55 AM

A Note on Multiple Imputation Under Informative Sampling
Jae-kwang Kim, Iowa State University; Shu Yang, Harvard
10:35 AM

Missing Data in the Context of Student Growth Models
Katherine Wright, Loyola University Chicago; John Gatta, Northwestern University, ECRA Group; Therese D. Pigott, Loyola University Chicago
10:50 AM

Sequential BART for Imputation of Missing Covariates
Dandan Xu, University of Florida; Michael Daniels, The University of Texas at Austin; Almut G. Winterstein, University of Florida
11:15 AM

The Treatment of Missing Data in a Large Cardiovascular Clinical Outcomes Study
Roderick Joseph Little, University of Michigan
11:25 AM

Benchmarking and Assessment for Multiple Imputation
Gerko Vink, Utrecht University
2:05 PM

Interactions and Squares: Don't Transform, Just Impute!
Philipp Gaffert, GfK SE; Volker Bosch, GfK SE; Florian Meinfelder, Otto-Friedrich-Universität Bamberg
2:25 PM

Optimal Split Questionnaire Survey Design in the Longitudinal Setting
Paul Michael Imbriano, University of Michigan; Trivellore Raghunathan, University of Michigan
2:45 PM

Optimal Missing-by-Design Patterns with Genetic Algorithms
Florian Meinfelder, Otto-Friedrich-Universität Bamberg; Sara Bahrami, Leibniz Institute for Educational Trajectories
3:05 PM

Tuesday, 08/02/2016
Multiple Imputation for Meta-Analysis: A Comparison of Existing Methods
Deborah Kunkel, The Ohio State University; Eloise Kaizar, The Ohio State University


Two-Level Joint Model for Imputing Subject-Level Variables of Mixed Type
David Kline, The Ohio State University; Rebecca Andridge, The Ohio State University; Eloise Kaizar, The Ohio State University


Imputing Data That Are Missing at High Rates Using a Boosting Algorithm
Katherine Cauthen, Sandia National Laboratories; Gregory Lambert, Sandia National Laboratories; Jaideep Ray, Sandia National Laboratories; Sophia Lefantzi, Sandia National Laboratories
8:35 AM

Multiple Imputation for Meta-Analysis: A Comparison of Existing Methods
Deborah Kunkel, The Ohio State University; Eloise Kaizar, The Ohio State University
8:35 AM

Regression Analysis of Incomplete Data from Event History Studies with the Proportional Rates Model
Guanglei Yu, University of Missouri - Columbia; Liang Zhu, St. Jude Children's Research Hospital; Jianguo Sun, University of Missouri; Leslie L. Robison, St. Jude Children's Research Hospital
8:50 AM

Two-Level Joint Model for Imputing Subject-Level Variables of Mixed Type
David Kline, The Ohio State University; Rebecca Andridge, The Ohio State University; Eloise Kaizar, The Ohio State University
9:00 AM

Multilevel Multiple Imputation: Tipping Point Sensitivity Analysis Using the JOMO Package in R with Longitudinal Olympic Regeneration in East London (ORiEL) Data
Melanie Smuk, Queen Mary University of London; Matteo Quartagno, London School of Hygiene and Tropical Medicine; Charlotte Clark, Queen Mary University of London; Stephen Stansfeld, Queen Mary University of London; Steven Cummins, London School of Hygiene and Tropical Medicine
9:05 AM

Comparing Methods of Multiple Imputation for a Score-Variable Measured Repeatedly Over Time
Elizabeth L. McCabe, Boston University; Joseph M. Massaro, Boston University; Kathryn L. Lunetta, Boston University; Susan Cheng, Framingham Heart Study; Joanne M. Murabito, Framingham Heart Study; Martin G. Larson, Boston University
9:35 AM

Two Approaches for Conducting Control-Based Imputation in Handling Missing Data
Guanghan Liu, Merck Research Laboratories
10:35 AM

Novel Imputation Methods for Binary, Time-to-Event, and Recurrent-Event Outcomes
Michael O'Kelly, Quintiles
11:35 AM

Multiple Imputation Methods to Enhance the NHANES-CMS Medicaid Linked Data - Demonstrated by Examining Cotinine as a Biomarker for Second-Hand Smoke Among Children Ages 3--17
Jennifer Rammon, CDC
2:50 PM

Effects of number of imputations on fraction of missing information in multiple imputation
Qiyuan Pan, CDC/NCHS
3:05 PM

A Novel Bayesian Multiple Imputation Framework for Massive Multivariate Data with Mixed Types of Marginals
Hakan Demirtas, University of Illinois at Chicago
3:25 PM

Wednesday, 08/03/2016
Missing Data and Complex Sample Surveys: The Impact of Listwise Deletion vs. Multiple Imputation on Point and Interval Estimates When Data Are MCAR and MAR
DeAnn Trevathan, University of South Florida; Anh Kellermann, University of South Florida; Jeffrey Kromrey, University of South Florida


Multiple Imputation for Non-Detects in QPCR
Valeriia Sherina, University of Rochester; Matthew Nicholson McCall, University of Rochester


Multiple Imputation for Non-Detects in QPCR
Valeriia Sherina, University of Rochester; Matthew Nicholson McCall, University of Rochester
9:15 AM

Multiple Imputation Method Based on Weighted Quantile Regression Models for Longitudinal Censored Biomarker Data with Missing Early Visits
MinJae Lee, The University of Texas Health Science Center at Houston ; Mohammad H. Rahbar, The University of Texas Health Science Center at Houston; John D. Reveille, The University of Texas Health Science Center at Houston ; Michael Weisman, Cedars-Sinai Medical Center; Michael M. Ward, National Institute of Arthritis and Musculoskeletal and Skin Diseases; Lianne Gensler, University of California at San Francisco; Matthew Brown, University of Queensland Diamantina Institute
10:05 AM

The Role of Multiple Imputation in Noninferiority Trials
Brian Wiens, Portola Pharmaceuticals; Ilya Lipkovich, Quintiles
11:35 AM

Multiple Imputation for Survey Integration Under Informative Sampling
Seho Park, Iowa State University; Jae-kwang Kim, Iowa State University
11:35 AM

Using Machine Learning Algorithms for Handling Missingness: Application to Predicting Drug-Disease and Drug-Drug Interactions
Ruoshui Zhai, Brown University; Roee Gutman, Brown University
11:35 AM

A General Semiparametric Accelerated Failure Time Model Imputation Approach for Censored Covariate
Shengchun Kong, Gilead Sciences; Ying Ding, University of Pittsburgh; Shan Kang, Robert Bosch LLC
12:05 PM

Missing Data and Prediction Models
Sarah Fletcher, Vanderbilt University School of Medicine; Jeffrey David Blume, Vanderbilt University School of Medicine
12:05 PM

On the Use of the Treatment Effect in the Imputation Model for Multiple Imputation Analyses of Missing Data
Robert Small, Sanofi Pasteur
2:20 PM

Testing Treatment Effect in Clinical Trials with Patient Dropout Using Latent Mixture Models
Fanhui Kong, FDA; Yeh-Fong Chen, FDA
2:20 PM

Do Secondary Data and Multiple Attempts of Survey Data Collection Reduce Nonresponse Bias?
Xiaowei Yan; Walter F. Stewart, Sutter Health; Vatche Minassian, Brigham and Women's Hospital
2:35 PM

Handling Missing Data in Multiple-Attack Migraine Studies
Kaifeng Lu, Allergan
2:50 PM

A Simulation Study to Compare Multiple Imputation Methods Under Missing Not-at-Random Assumption
David Li, Pfizer; Lingfeng Yang, BMS
3:20 PM

Thursday, 08/04/2016
Combining Information from Two Data Sources with Misreporting and Incompleteness to Assess Hospice-Use Among Cancer Patients: A Multiple Imputation Approach
Yulei He, CDC; Mary-Beth Landrum, Harvard Medical School; Alan M. Zaslavsky, Harvard Medical School
8:35 AM

Combining Item Response Theory with Multiple Imputation to Equate Health Assessment Questionnaires
Chenyang Gu, Brown University; Roee Gutman, Brown University; Vincent Mor, Brown University
9:25 AM

Comparison of Multiple Imputation Methods for Categorical Survey Items with High Missing Rates: Application to the Family Life, Activity, Sun, Health, and Eating Study
Benmei Liu, National Cancer Institute; Erin Hennessy, National Cancer Institute; April Oh, National Cancer Institute; Linda Nebeling, National Cancer Institute
10:35 AM

Nonparametric Imputation for Nonignorable Missing Data
Domonique Watson, Emory University; Qi Long, Emory University
10:50 AM

 
 
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