Keyword Search
Sessions Were Renumbered as of May 19.
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H = Hilton Chicago, UC= Conference Chicago at University Center
* = applied session ! = JSM meeting theme
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Keyword Search Criteria: Missing data returned 78 record(s)
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Sunday, 07/31/2016
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Choosing Estimands in Clinical Trials with Missing Data
Craig Mallinckrodt, Eli Lilly and Company
2:05 PM
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Bayesian Estimation and Variable Selection for Reliability in Multicomponent Systems
Yiqing Tian, North Carolina State University; Howard Bondell, North Carolina State University; Alyson Wilson, North Carolina State University
2:20 PM
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Reference-Based Imputation Versus Dropout = Failure Imputation for Tackling Missing Data
Devan V. Mehrotra, Merck; Fang Liu, Merck
2:25 PM
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Adjusting for Enrichment Effects When Estimating Oncology Biomarker Clinical Utility
Jared Lunceford, Merck Research Laboratories
2:45 PM
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Role of Simulations in the Selection of the Primary Estimand and Statistical Methods for Handling Missing Data
Elena Polverejan, Janssen R&D
2:45 PM
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Imputing Drone Strikes Casualty Counts Given Estimated Interval Ranges
Earvin Balderama, Loyola University Chicago
3:20 PM
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Bayesian Multiple Imputation Procedures to Equate Health Assessment Questionnaires
Chenyang Gu, Brown University; Roee Gutman, Brown University
4:25 PM
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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
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Monday, 08/01/2016
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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
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Determining a Plus/Minus Metric for NCAA Women's Volleyball from Incomplete Court Presence Information
Zachary Hass; Bruce A. Craig, Purdue University
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Multivariate Two-Part Statistics for Analysis of Correlated Mass Spectrometry Data from Multiple Biological Matrices
Kyoungmi Kim, University of California at Davis; Sandra L. Taylor, University of California at Davis
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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
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The Orthogonally Partitioned EM Algorithm: Extending the EM Algorithm for Algorithmic Stability and Bias Correction Due to Imperfect Data
Michael Regier, West Virginia University; Erica E. M. Moodie, McGill University
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Sparse Regression for Block Missing Data Without Imputation
Yufeng Liu, The University of North Carolina at Chapel Hill
8:35 AM
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Using Auxiliary Marginal Information to Deal with Nonignorable Missing Data
Mauricio Sadinle, Duke University/National Institute of Statistical Sciences; Jerome Reiter, Duke University
9:15 AM
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Semiparametric Fractional Imputation Using Empirical Likelihood in Survey Sampling
Sixia Chen, University of Oklahoma; Jae-kwang Kim, Iowa State University
9:35 AM
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Joint Modeling of Longitudinal and Survival Data with Missing and Left-Censored Time-Varying Covariates
Qingxia Chen, Vanderbilt University; Ryan May, The EMMES Corporation; Joseph G. Ibrahim, The University of North Carolina at Chapel Hill; Haitao Chu, University of Minnesota ; Stephen R. Cole, The University of North Carolina at Chapel Hill
9:50 AM
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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
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Some Observations on De Jure Estimation
Thomas Permutt, FDA
11:00 AM
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The Treatment of Missing Data in a Large Cardiovascular Clinical Outcomes Study
Roderick Joseph Little, University of Michigan
11:25 AM
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Binary Exposure and Longitudinal Cognition Outcomes in the Presence of Noningorable Dropout and Death
Maria Josefsson, CEDAR
11:35 AM
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The Orthogonally Partitioned EM Algorithm: Extending the EM Algorithm for Algorithmic Stability and Bias Correction Due to Imperfect Data
Michael Regier, West Virginia University; Erica E. M. Moodie, McGill University
12:15 PM
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A Bayesian HSROC Model for Meta-Analysis of Multiple Diagnostic Tests
Qinshu Lian, University of Minnesota; Haitao Chu, University of Minnesota
2:25 PM
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Optimal Split Questionnaire Survey Design in the Longitudinal Setting
Paul Michael Imbriano, University of Michigan; Trivellore Raghunathan, University of Michigan
2:45 PM
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A Note on Posterior Predictive Assessment to Assess Model Fit
Arkendu S. Chatterjee, Novartis; Dandan Xu, University of Florida; Michael Daniels, The University of Texas at Austin
3:25 PM
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Tuesday, 08/02/2016
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Variable Selection for Multistate Models in the Presence of Missing Data
Lauren J. Beesley, University of Michigan; Jeremy M. G. Taylor, University of Michigan
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Quantifying Power and Bias in Cluster-Randomized Trials Using Mixed Models Versus Cluster-Level Analysis in the Presence of Missing Data: A Simulation Study
Brenda Vincent, University of Arizona; Melanie L. Bell, University of Arizona
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Implication of Missing Data in the Study of Association Between Immigrant Status and MRSA/MSSA Recurrence
Paola Martins, UFF; Nancy Piper Jenks, Clinical Directors Network; Caroline Jiang, Rockefeller University; Brianna D'Orazio, Clinical Directors Network; Jonathan N. Tobin, Clinical Directors Network; Joel Correa da Rosa, Rockefeller University
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Multiple Imputation for Meta-Analysis: A Comparison of Existing Methods
Deborah Kunkel, The Ohio State University; Eloise Kaizar, The Ohio State University
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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
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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
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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
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On Double Robustness in Estimating a Causal Effect When a Confounder Is Missing at Random
Katherine Evans, Harvard; Eric Tchetgen Tchetgen, Harvard
9:50 AM
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Network Meta-Analysis of Multiple Factors
Lifeng Lin, University of Minnesota; Haitao Chu, University of Minnesota
10:05 AM
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Model Calibration Utilizing Summary-Level Information from External Big Data
Nilanjan Chatterjee, The Johns Hopkins University; Yi-Hau Chen, Academia Sinica; Paige Maas, National Cancer Institute; Raymond Carroll, Texas A&M University
10:35 AM
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Two Approaches for Conducting Control-Based Imputation in Handling Missing Data
Guanghan Liu, Merck Research Laboratories
10:35 AM
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Making Use of the Predictive Distribution for Missing Data
Gerry Gray, FDA/CDRH
10:55 AM
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Who Watches the Watchers? What We Can't Know About Police Violence
Laurel Eckhouse, University of California at Berkeley
11:15 AM
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Novel Missing Data Imputation Methods
Peter Mesenbrink, Novartis Pharma
11:15 AM
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Robust Approaches for the Analysis of High-Throughput Proteomic Data
Naim Rashid
11:50 AM
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Developing PRO Instruments in Clinical Trials: Issues, Considerations, and Solutions
Cheryl Coon, Outcometrix; Dennis Revicki, Evidera; Scott Komo, FDA/CDER; Kendra DeBusk, Genentech; Lisa Kammerman, AstraZeneca; Stacie Hudgens, Clinical Outcome Solutions
2:05 PM
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Sufficient Dimension Reduction with Missing Data
Qi Xia, Temple University; Yuexiao Dong, Temple University; Chengyong Tang, Temple University
2:20 PM
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An Evaluation of Backwards Imputation for the Annual Survey of Public Employment & Payroll
Junilsa Toribio, U.S. Census Bureau
2:20 PM
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A Prediction Approach to Missing Data from the Exponential Family
Valbona Bejleri, USDA/NASS; Darcy Miller, USDA/NASS; Kay Turner, USDA/NASS
2:35 PM
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Recent Developments in Fractional Imputation
Wayne Fuller, Iowa State University
2:55 PM
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Effects of number of imputations on fraction of missing information in multiple imputation
Qiyuan Pan, CDC/NCHS
3:05 PM
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Wednesday, 08/03/2016
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A Likelihood-Based Approach for Multivariate One-Sided Tests with Missing Data
Guohai Zhou, University of British Columbia; Lang Wu, University of British Columbia; Rollin Brant, University of British Columbia; J Mark Ansermino
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Methods for Identifying Outliers for Carry-Forward Imputation in the Survey of Graduate Students and Postdoctorates in Science and Engineering
Jiantong Wang, Research Triangle Institute International; Kimberly Ault, Research Triangle Institute International; Rachel Harter, RTI International
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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
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A Scalable Algorithm and R Package to Measure the Impact of Nonignorable Missing Data
Weihua Gao, University of Illinois at Chicago; Baodong Xing, University of Illinois at Chicago; Donald Hedeker, The University of Chicago; Robin J. Mermelstein, University of Illinois at Chicago; Hui Xie, University of Illinois at Chicago
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Missing Patterns May Determine Results and Limit Generalizability
Yumei Cao, Medical College of Wisconsin; Joshua Field, Blood Center of Wisconsin; Joel Linden, La Jolla Institute for Allergy and Immunology; Pippa Simpson, Medical College of Wisconsin
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Finding the 'Best' Measurement of Body Weight Status Was Difficult with Missing Data
Liyun Zhang, Medical College of Wisconsin; Michele Polfuss, University of Wisconsin - Milwaukee; Kathleen J. Sawin, University of Wisconsin - Milwaukee; Pippa Simpson, Medical College of Wisconsin
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Multiple Imputation for Non-Detects in QPCR
Valeriia Sherina, University of Rochester; Matthew Nicholson McCall, University of Rochester
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New Progresses in Statistical Analysis of Network Tomography
Ke Deng, Tsinghua University; Yang Li, Harvard; Weiping Zhu, University of New South Wales; Jun S. Liu, Harvard
8:35 AM
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Multiple Imputation for Non-Detects in QPCR
Valeriia Sherina, University of Rochester; Matthew Nicholson McCall, University of Rochester
9:15 AM
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Using Auxiliary Information to Enhance Prediction Models with Many Covariates
Jeremy M. G. Taylor, University of Michigan; Philip Simon Boonstra, University of Michigan; Bhramar Mukherjee, University of Michigan
11:00 AM
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Extension to the Bayesian Improved Surname Geocoding (BISG) Method
Marc N. Elliott, RAND Corporation; Amelia M. Haviland, Carnegie Mellon University; Ann Haas, RAND Corporation; John Adams, Kaiser Permanente; Joshua Mallet, RAND Corporation; Jake Dembosky, RAND Corporation; Sarah Gaillot, Centers for Medicare and Medicaid Services; Samuel "Chris" Haffer, Centers for Medicare and Medicaid Services
11:15 AM
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The Role of Multiple Imputation in Noninferiority Trials
Brian Wiens, Portola Pharmaceuticals; Ilya Lipkovich, Quintiles
11:35 AM
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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
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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
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Missing Data and Prediction Models
Sarah Fletcher, Vanderbilt University School of Medicine; Jeffrey David Blume, Vanderbilt University School of Medicine
12:05 PM
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Statistical Computation with Mixture Data
Shiju Zhang, St. Cloud State University
12:05 PM
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Missing Data Imputation in Phase III Study with Time-to-Event Outcome
Gang Jia, Merck; Paul DeLucca, Merck; Steven Bird, Merck; Bruce Binkowitz, Merck; Weichung J. Shih, Rutgers University
2:05 PM
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Testing for Missing Always at Random and Row Exchangeability in Multivariate Data with Missing Values
Iavor Bojinov, Harvard; Natesh Pillai, Harvard; Donald B. Rubin, Harvard
2:05 PM
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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
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Design of Primary and Sensitivity Analyses for Handling Nonfuture Dependence Missing Data in Clinical Trials with an Emphasis on the Type I Error Rate Using Pattern Mixture Model
Lixian Peng, Celgene; Weichung J. Shih, Rutgers University
2:35 PM
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Handling Missing Data in Multiple-Attack Migraine Studies
Kaifeng Lu, Allergan
2:50 PM
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A Multivariate Selection Model for Cluster-Level Outcome-Dependent Missing Data
Jiebiao Wang, The University of Chicago; Pei Wang, Icahn School of Medicine at Mount Sinai; Lin Chen, The University of Chicago
2:50 PM
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Missing Data Approaches in Categorical Latent Growth and Multilevel Proportional Odds Models
Karen Traxler, University of Northern Colorado; Niloofar Ramezani, University of Northern Colorado
3:20 PM
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Inverse Probability Weighting (IPW) Estimator for Comparing Two Proportions Under Nonignorable Missingness Mechanism
Madan Gopal Kundu, Novartis Oncology
3:35 PM
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Thursday, 08/04/2016
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Correcting for Measurement Error in Self-Reported Dietary Data from a Longitudinal Lifestyle Intervention Trial Using an External Validation Study
Juned Siddique, Northwestern University; Laurence Freedman, Gertner Institute for Epidemiology and Health Policy Research; Raymond Carroll, Texas A&M University; Trivellore Raghunathan, University of Michigan; Elizabeth Stuart, Johns Hopkins Bloomberg School of Public Health
9:00 AM
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Methods for Joint Clustering of Longitudinal HIV Biomarker Trajectories in the Presence of Missing and Censored Observations
Miranda Lynch, University of Connecticut Health Center; Marianna Baum, Florida International University; Vladimir Novitsky, Harvard T.H. Chan School of Public Health; Victor De Gruttola, Harvard
9:20 AM
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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
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Imputing Missing Values for Neuroimaging Data Based on Principal Component Analysis
Lan Kong, Penn State University College of Medicine; Menghan Li, Penn State University College of Medicine
9:35 AM
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Analysis of Bivariate Zero-Inflated Count Data with Missing Responses
Miao Yang, Oregon State University; Kalyan Das, Calcutta University; Anandamayee Majumdar, Soochow University
10:50 AM
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Nonparametric Imputation for Nonignorable Missing Data
Domonique Watson, Emory University; Qi Long, Emory University
10:50 AM
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Fixed Choice Design and Augmented Fixed Choice Design for Missing Data in Social Networks
Miles Ott, Augsburg College; Joseph Hogan, Brown University; Nancy Barnett, Brown University; Krista Gile, University of Massachusetts - Amherst; Matthew Harrison, Brown University
11:05 AM
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On Analysis of Longitudinal Clinical Trials with Missing Data Using Reference-Based Imputation
Lei Pang, Merck; Guanghan Liu, Merck Research Laboratories
11:50 AM
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