|
Sunday, 08/01/2010
|
Model-Based Approaches for Characterizing Environmental Effects on Spatial Genetic Flow
Ephraim M. Hanks, Utah State University; Mevin B. Hooten, Utah State University
2:35 PM
|
Multiple Imputation Framework for Combining Data from Multiple Sources
Trivellore Raghunathan, University of Michigan
3:20 PM
|
Regression-Based Data Fusion: Robust Residual Imputation
Chris Moriarity, National Center for Health Statistics; Fritz Scheuren, NORC
4:20 PM
|
More for Less? Comparing Small-Area Estimation, Spatial Microsimulation, and Mass Imputation
Stephen John Haslett, Massey University; Geoffrey Jones, Massey University; Alasdair Dewar Noble, Massey University; Dimitris Ballas, University of Sheffield
4:50 PM
|
Statistical Disclosure Risk of Synthetic Datasets
Anne-Sophie Charest, Carnegie Mellon University
5:05 PM
|
|
Monday, 08/02/2010
|
A Multiple Imputation Approach for Estimating Rank Correlation with Left-Censored Data
John M. Williamson, CDC; Sara B. Crawford, Valparaiso University; Hung-Mo Lin, Mount Sinai School of Medicine
|
Utilizing Multiple Imputation for Missing Race in the Active Bacterial Core Surveillance (ABCs) System
Melissa Lewis, CDC; Tracy Pondo, CDC; Elizabeth R. Zell, CDC
|
Modeling Menstrual Cycle Length at the Approach of Menopause Using Bayesian Changepoint Models
Xiaobi Huang, University of Michigan; Michael R. Elliott, University of Michigan
|
Analyzing Methods of Imputation
Scott Daniel Crawford, Texas A&M University
|
Handling Missing Data in a Longitudinal Community Epidemiological Study of Urban Children's Exposure to Violence
Lingqi Tang, University of California, Los Angeles; Thomas R. Belin, University of California, Los Angeles; Michele Cooley-Strickland, University of California, Los Angeles Center for Culture and Health
|
Causal Inference Using Semi-parametric Imputation
Andrea Piesse, Westat; David Judkins, Westat; Laura Alvarez-Rojas, Westat; William R. Shadish, University of California, Merced
8:35 AM
|
Evaluating Imputation Methods and Software for Missing Vaccination Data in the National Immunization Survey
Taylor Lewis, National Center for Health Statistics; Meena Khare, National Center for Health Statistics
8:50 AM
|
Multiple Imputation for causal Inference
Irina Bondarenko, University of Michigan; Trivellore Raghunathan, University of Michigan
8:50 AM
|
Multivariate Longitudinal Bayesian Imputation for Global Statistical Test, with Applications to Parkinson's Disease
Sheng Luo, The University of Texas School of Public Health; Andrew Lawson, Medical University of South Carolina; Jordan J. Elm, Medical University of South Carolina; Barbara C. Tilley, The University of Texas Health Science Center at Houston
9:20 AM
|
Association Analysis of Untyped SNPs: Maximum Likelihood and Imputation Methods
Danyu Lin, The University of North Carolina at Chapel Hill
9:25 AM
|
Multiple Imputation for Missing Values Through Conditional Semiparametric Odds Ratio Models
Hua Yun Chen, University of Illinois at Chicago; Hui Xie, University of Illinois at Chicago
9:35 AM
|
Analysis of Untyped SNPs: Maximum Likelihood and Single Imputation Methods
Yijuan Hu, The University of North Carolina at Chapel Hill; Danyu Lin, The University of North Carolina at Chapel Hill
9:50 AM
|
What Can We Do When EM Is Not Applicable? Self Consistency: A General Recipe for Semiparametric and Nonparametric Estimation with Incomplete and Irregularly Spaced Data
Xiao-Li Meng, Harvard University
10:35 AM
|
A New Approach to Protect Confidentiality for Census Microdata with Missing Values
Yajuan Si, Duke University; Jerome P. Reiter, Duke University
10:35 AM
|
Performing Hypothesis Tests with Multiply Imputed Data
Xianchao Xie, Harvard University; Xiao-Li Meng, Harvard University
10:50 AM
|
The Influence of Prior Experiences in Managing Current and Future Risks During Survey Transition Points on the National Survey on Drug Use and Health (NSDUH)
Joe Gfroerer, Substance Abuse and Mental Health Services Administration; Jonaki Bose, Substance Abuse and Mental Health Services Administration
11:25 AM
|
Examining the Challenges of Missing Data Analysis in Phase Three of the Agricultural Resource Management Survey
Darcy Miller, National Agricultural Statistics Service
2:05 PM
|
Nested Multiple Imputation in the Active Bacterial Core Surveillance System
Tracy Pondo, CDC; Elizabeth R. Zell, CDC; Chris A. Van Beneden, CDC; Benjamin J. Silk, CDC
2:05 PM
|
Bayesian Meta-Analysis of Genetic Association Studies: Effective Combining Evidence and Efficent Handling Missing Data
Xiaoquan Wen, The University of Chicago; Matthew Stephens, The University of Chicago
2:05 PM
|
Estimating Technology Adoption and Aggregate Volumes from U.S. Payments Surveys in the Presence of Complex Item Nonresponse
May X. Liu, Federal Reserve Board; Geoffrey R. Gerdes, Federal Reserve Board
2:20 PM
|
Innovative Imputation Techniques Designed for the Agricultural
Michael Robbins, National Institute of Statistical Sciences
2:25 PM
|
An Assessment of Imputation Methods for the USDA's Agricultural Resource Management Survey
Sujit Kumar Ghosh, North Carolina State University; Barry Goodwin, North Carolina State University; Joshua D. Habiger, National Agricultural Statistics Service/National Institute of Statistical Sciences; Timothy Paul Keller, National Agricultural Statistics Service; Darcy Miller, National Agricultural Statistics Service; Michael Robbins, National Institute of Statistical Sciences; Kirk White, National Agricultural Statistics Service
2:45 PM
|
The Variance Calculation Following Multiple Imputation: Illustration with Cholera Mortality Data Collected by John Snow
James A. Hanley, McGill University; Juli Atherton, McGill University
2:50 PM
|
Evaluation of Methods for Dealing with Outcome Missing Data in Random Controlled Trials
Breda Munoz, RTI International; Virginia M. Lesser, Oregon State University; James Hersey, RTI International
3:05 PM
|
Hot-Deck Multiple Imputation via Predictive Moment Matching
Chia-Ning Wang, University of Michigan; Roderick Joseph Little, University of Michigan
3:20 PM
|
Incorporating Nonresponse Follow-Up Into a Main Survey Data Set
Shin-Jung Lee, University of Michigan; James Lepkowski, University of Michigan; Kristen Olson, University of Nebraska-Lincoln; Dandan Zhang, Westat
3:20 PM
|
|
Tuesday, 08/03/2010
|
Estimation in Hierarchical Models with Incomplete Ordinal Response and Ordinal Covariates
Yong Zhang, University of Michigan; Trivellore Raghunathan, University of Michigan
|
Multiple Imputation for Microarray Missing Data with Slice Sampler
Hui Xie, Pennington Biomedical Research Center; Leann Myers, Tulane University; Steven Smith, Pennington Biomedical Research Center; Janet Rice, Tulane University; Michelle Lacey, Tulane University
|
Profiles of Responses Over the 2006 Canadian Census Collection Period: What Are the Differences for Early and Late Responses?
Laurent Roy, Statistics Canada; Catherine Corriveau, Statistics Canada; Laura McFarlane, Statistics Canada
8:35 AM
|
Normalization and Missing Value Imputation for Proteomics Analysis
Yuliya V. Karpievitch, Pacific Northwest National Laboratory
8:55 AM
|
Doubly robust inference with missing data in survey sampling
Jae-Kwang Kim, Iowa State University; David Haziza, Université de Montréal
9:00 AM
|
Incorporating Nonlinear Relationships in Microarray Missing Value Imputation
Hesen Peng, Emory University; Tianwei Yu , Emory University; Wei Sun, The University of North Carolina at Chapel Hill
9:05 AM
|
Sharing Precise Geographies in Public Use Data
Hao Wang, Duke University; Jerome P. Reiter, Duke University
9:05 AM
|
Imputation-Based Efficient Estimation Procedures for Length-Biased Data Under Semiparametric Regression Models
Hao Liu, Baylor College of Medicine; Jing Qin, National Institute of Allergy and Infectious Diseases; Yu Shen, MD Anderson Cancer Center
9:15 AM
|
Multiple Imputation for Regression Models with Interaction
Soeun Kim, University of California, Los Angeles; Thomas R. Belin, University of California, Los Angeles; Catherine A. Sugar, University of California, Los Angeles School of Public Health
9:20 AM
|
Weights, Double Protection, and Multiple Imputation
Phillip S. Kott, RTI International ; Ralph E. Folsom, RTI International
9:25 AM
|
Analysis of Missing Mechanism in IVUS Imaging Clinical Trials with Missing Covariates
Tianyue Zhou, sanofi-aventis; Ming-Xiu Hu, Millennium Pharmaceuticals, Inc.
10:35 AM
|
Longitudinal Data Analysis: Tackling Dropouts and Those Pesky Outliers
Devan V. Mehrotra, Merck Research Laboratories; Xiaoming Li, Merck Research Laboratories; Jiajun Liu, Merck Research Laboratories
10:50 AM
|
A Multiple Imputation Model for Ascertaining Chronic Disease Cases in Administrative Data: Application to Osteoporosis
Lisa M. Lix, University of Saskatchewan; Heather J. Prior, Manitoba Centre for Health Policy; Chel Hee Lee, University of Saskatchewan; Marina Yogendran, Manitoba Centre for Health Policy
10:50 AM
|
Variable Selection for Multiply-Imputed Data
Qixuan Chen, Columbia University; Sijian Wang, University of Wisconsin-Madison
10:55 AM
|
Handling Missing Data in Rheumatoid Arthritis Trials
Guoguang Ma, Amgen Inc.; Liyun Ni, Amgen Inc.; Ling Chen, Washington University School of Medicine
11:05 AM
|
Multiple Imputation of Disease Status Based on a Two-Phase Sample Design
Steven G. Heeringa, University of Michigan
11:15 AM
|
Use Multiple Imputation to Handle Missing Data in Longitudinal Clinical Trials with Multiple Correlated Endpoints
Yahong Peng, Pfizer Inc.; Lian Liu, Roche Product Development in Asia Pacific; Ruifeng Xu, Merck Research Laboratories
11:20 AM
|
Multiple Imputation Method for Disclosure Limitation in Longitudinal Data
Di An, Merck & Co., Inc.; Roderick Joseph Little, University of Michigan; James W. McNally, University of Michigan
11:35 AM
|
Remarks on Robust Nonparametric Imputation
Philip E. Cheng, Academia Sinica
11:35 AM
|
Estimating Progression-Free Survival When the Progression Status of Some Patients Is Unknown
Ying Yuan, MD Anderson Cancer Center; Peter F. Thall, MD Anderson Cancer Center; Johannes Wolff, MD Anderson Cancer Center
11:55 AM
|
Analysis of a 2-by-2 Crossover Trial of Binary Data with Missing Values Using Multiple Imputation
Junxiang Luo, Eli Lilly and Company; Timothy Costigan, Eli Lilly and Company; Eileen Brown, Eli Lilly and Company
2:20 PM
|
Quality and Quantity: Using Administrative Data for Scientific Purposes in Labor Market Research
Patrycja Scioch, IAB
2:20 PM
|
Missing Data in a Large Long-Term Clinical Trial: A Case Study
Dacheng Liu, Boehringer Ingelheim Pharmaceuticals, Inc.
2:25 PM
|
Imputation Strategies for Missing Binary Outcomes in Cluster Randomized Trials
Jinhui Ma, McMaster University; Noori Akhtar-Danesh, McMaster University; Lisa Dolovich, McMaster University; Lehana Thabane, McMaster University
2:35 PM
|
Full Bayesian Procedure for File Linking to Analyze End-of-Life Medical Costs
Roee Gutman, Harvard University; Alan Zaslavsky, Harvard Medical School
2:45 PM
|
Missing Data Methodology: Past, Present, and Future Trends
Cristina Labrador Sotto, I-Biostat
2:45 PM
|
Robust Multiple Imputation Based on Bayesian Bootstrap Predictive Mean Matching
Florian Koller-Meinfelder, Universität Bamberg
2:50 PM
|
Addressing the Missing Data Problem in Clinical Trials
Linda Yau, Genentech
3:25 PM
|
|
Wednesday, 08/04/2010
|
Balanced Random Imputation for Estimating Coefficients of Correlations in Surveys
David Haziza, Université de Montréal; Guillaume Chauvet, ENSAI
8:35 AM
|
Imputation Methods for Wave Nonresponse in Panel Surveys
Kyu-Seong Kim, University of Seoul
8:50 AM
|
Evaluation of the Quality of Imputation of Goods and Services Tax (GST) Revenue for Late Transactions
Joanne Leung, Statistics Canada
9:05 AM
|
Combining Information on Adjuvant Cancer Therapies from Multiple Sources to Correct Misreporting
Yulei He, Harvard Medical School; Alan Zaslavsky, Harvard Medical School
9:05 AM
|
Using SRMI for Nonresponse Adjustments in IRS' Taxpayer Compliance Studies
Karen Masken, IRS; Wei Liu, IRS; Getaneh Yismaw, IRS
9:20 AM
|
Bootstrap Variance Estimation in the Presence of Imputed Data
Christian Léger, Université de Montréal; Zeinab Mashreghi, Université de Montréal; David Haziza, Université de Montréal
9:35 AM
|
Multivariate Imputation Methods for Agricultural Resource Management Survey (ARMS) Data
Sujit Kumar Ghosh, North Carolina State University; Michael Robbins, National Institute of Statistical Sciences; Joshua D. Habiger, National Agricultural Statistics Service/National Institute of Statistical Sciences; Darcy Miller, National Agricultural Statistics Service
10:35 AM
|
Empirical Imputation: A New Imputation Method Model
Yijie Xue, The University of Georgia; Nicole Lazar, The University of Georgia
11:20 AM
|
Simulation of Incorporating the Variance from Missing Data in Census Coverage Measurement (CCM) Variance Estimates
Richard Griffin, U.S. Census Bureau
11:50 AM
|
Imputation Variance Estimation Protocols for the NAS Poverty Measure: The New York City Poverty Measure Experience
Frank Potter, Mathematica Policy Research, Inc.; Eric Grau, Mathematica Policy Research, Inc.; John Czajka, Mathematica Policy Research, Inc.; Mark Levitan, NYC Center for Economic Opportunity
2:45 PM
|
|