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Wed, Oct 9

Conference Registration Wed, Oct 9, 7:30 AM - 6:30 PM
6th Floor Registration Area

 
Assorted Methods Track: Interpreting Change and Responder Analysis for Patient-reported Outcomes (WK 7) Wed, Oct 9, 8:30 AM - 10:15 AM
Spire Parlor
Organizer(s): Joseph Cappelleri, Pfizer

Patient-reported outcome (PRO) measures used for labeling and promotional claims must have: 1) evidence documenting their responsiveness; and 2) interpretation guidelines (e.g., responder definition) to be most useful as effectiveness endpoints in clinical trials. The recommended approach is to estimate the responder definition based on anchor-based methods, which will be discussed during the workshop. However, this workshop will also discuss how distribution-based methods can provide some insights on interpreting the amount of change that signifies an important change in PROs. Confidence in a specific responder change threshold evolves over time and is confirmed by additional research evidence, including clinical trial experience; the responder change threshold may vary by population and context, and no one responder change threshold will be valid for all study applications involving a PRO instrument. During this workshop, the speakers will explain how to demonstrate and identify thresholds for specific study populations in an effort to pursue labeling and promotional claims.

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Interpreting Change and Responder Analysis for Patient-reported Outcomes
Joseph Cappelleri, Pfizer; Lisa A Kammerman, U.S. Food and Drug Administration

 
Observational Studies Track: Sensitivity Analysis for Observational Data: Method and Computation (WK 1) Wed, Oct 9, 8:30 AM - 10:15 AM
Water Tower Parlor
Organizer(s): Bo Lu, Ohio State University

Unlike randomized experiments, treated and control groups may not be comparable at baseline in observational studies. Baseline differences that have been accurately measured in observed covariates can often be removed by matching, stratification or model based adjustments. However, there is usually the concern that some important baseline differences were not measured, so that individuals who appear comparable may not actually be. A sensitivity analysis in an observational study addresses the question what the unmeasured covariate would have to be like to alter the conclusions of the study. This course will start with Cochran’s famous example of the association between smoking and lung cancer, and introduce the methodology formulated by Rosenbaum for the sensitivity analysis with matched or stratified data. The course will also cover the implementation of sensitivity analysis using statistical software package R and illustrate the method with several health related examples.

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Sensitivity Analysis for Observational Data: Method and Computation
Bo Lu, Ohio State University

 
Longitudinal Data Track - Hierarchial Models: Hierarchical Models and Computing for Joint Longitudinal-Survival and other Multiple Component or Endpoint Data (WK 5) Wed, Oct 9, 8:30 AM - 12:15 PM
Adams Room
Organizer(s): Bradley P. Carlin, University of Minnesota; Laura Hatfield, Harvard

Hierarchical modeling is well-known for its ability to properly account for all sources of uncertainty and correlation in complex, high-dimensional data sets. The BUGS language is especially adept at implementing such an approach, since model components may be developed independently and then assembled into arbitrarily complex models. Perhaps the most useful example of this approach is in joint modeling of longitudinal and survival data, where relatively simple survival and longitudinal model components may be connected using latent variables that induce appropriate within-subject correlation. In this workshop, we describe accessible methods and software for handling this problem, as well as a variety of other settings where we also seek to link multiple components in a single larger model, and thus capture complex relationships among variables of different types. For example, we will describe the joint modeling of an exposure and corresponding multiple outcomes, necessary when the exposure is not directly measured and must instead be modeled, is measured with error, or when the outcomes themselves have important relationships (say, when one informatively censors another). We also describe methods for multiple treatment comparisons (meta-analysis) and its application in comparative effectiveness research (CER) when different treatments emerge as better for different outcomes (say, safety versus efficacy). Our presentation will include methods appropriate for carefully designed clinical studies, as well as approaches suited for observational data, including post-marketing surveillance studies of drugs and medical devices.

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Hierarchical Models and Computing for Joint Longitudinal-Survival and other Multiple Component or Endpoint Data
Bradley P. Carlin, University of Minnesota; Laura Hatfield, Harvard

 
Assorted Methods Track: Statistical Analysis of Zero-Inflated Continuous Data (WK 8) Wed, Oct 9, 10:30 AM - 12:15 PM
Spire Parlor
Organizer(s): Lei Liu, Northwestern University

Zero-inflated continuous (or semi-continuous) data arise frequently in medical, economical, and ecological studies. Examples include, though certainly aren't limited to, medical costs, medical care usage, substance abuse, coronary artery calcium score, and daily precipitation levels. Such data are often characterized by the presence of a large portion of zero values, in addition to continuous non zero (i.e., positive) values that are often skewed to the right and heteroscedastic. Both features suggest that no simple parametric distribution is suitable for describing such “zero-inflated continuous” data. In this short course we will review statistical methods to analyze such type of data. We will start from the cross-sectional zero-inflated continuous data. Three approaches are presented to account for the point mass at zero: a two-part model which separately describes the probability of outcome being positive and the amount of positive values; a sample selection approach (e.g., Tobit model) where zero values are considered as “censored” observations; and a zero-inflated Tobit model which accommodates the characteristics of both the sample selection and the two-part approaches. We will then introduce flexible models to characterize right skewness and heteroscedasticity in the positive values, using, e.g., log normal, Gamma, generalized Gamma, log skew normal, Box Cox transformation, and non-parametric methods. The second section involves modeling repeated measures zero-inflated continuous data. Random effects will be used to tackle the correlation on repeated measures of the same subject and that across different parts of the model. We will incorporate such random effects to the models introduced in Section 1. We will also present joint models of longitudinal zero-inflated continuous data and survival, e.g., in the longitudinal medical cost setting, to account for the possible dependent terminal event or informative dropout. Finally, we will present applications to real datasets to illustrate our methods. We will use longitudinal medical costs, clustered medical costs, and alcohol drinking data as examples. SAS codes will be provided to facilitate the applications of these methods. Model comparison will also be conducted. The lecturer has 8 years of hands-on experience in the analysis of zero-inflated continuous data, especially the medical costs and alcohol drinking data. He is PI of three grants funded by NIH and AHRQ on this topic. This application oriented short course is of interest to researchers who would apply up-to-date statistical tools to zero-inflated continuous data.

Statistical Analysis of Zero-Inflated Continuous Data
Lei Liu, Northwestern University

 
Observational Studies Track: Designing an Observational Study with the Propensity Score (WK 2) Wed, Oct 9, 10:30 AM - 12:15 PM
Water Tower Parlor
Organizer(s): Thomas Love, Case Western Reserve University

This workshop describes and demonstrates effective strategies for using propensity score methods to address the potential for selection bias in observational studies comparing exposures. We review the main analytical techniques associated with propensity score methods (multivariable adjustment, matching and stratification/weighting using the propensity score, sensitivity analysis) and describe key strategic concerns related to effective estimation of the propensity score, assessment and display of covariate balance, choice of analytic technique, sensitivity analyses for matched samples, and communicating results effectively. Although we will focus on common, established approaches to dealing with design and analytical challenges, we will conclude the session by reviewing some literature regarding recent methodological advances in propensity scores and application of propensity score methods to problems in health policy research. Attendees will receive detailed handouts, and access to easy-to-use statistical software to be demonstrated in the session.

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Designing an Observational Study with the Propensity Score
Thomas Ezra Love, Case Western Reserve University

 
Assorted Methods Track: Comparative Effectiveness Research (WK 9) Wed, Oct 9, 1:30 PM - 3:15 PM
Spire Parlor
Organizer(s): Bo Lu, Ohio State University

Comparative Effectiveness Research (CER) is now a major initiative in the US, with wide ranging implications for both research and health care policy. The evidence from CER is intended to support clinical and policy decision making at both the individual and the population levels. The mandate of CER places a premium on the study of outcomes that are of primary relevance to patients and on the derivation of conclusions that can inform individual patient choices. The broad scope of CER requires a wide array of methodologic approaches, including randomized studies, observational primary studies, and research synthesis. For example, approaches to identify treatment effect heterogeneity, to handle uncertainty in high-dimensional data, and to pool information across diverse data sources will require further development and experience. In this tutorial, we will survey statistical methodology currently in use and will discuss methodologic challenges in CER for diagnostic and therapeutic interventions.

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Comparative Effectiveness Research
Constantine Gatsonis, Brown University; Sharon Lise Normand, Harvard University

 
Observational Studies Track: Causal Inferences in Health Services Research (WK 3) Wed, Oct 9, 1:30 PM - 3:15 PM
Water Tower Parlor
Organizer(s): Xiao-Hua Andrew Zhou, University of Washington

Workshop Objectives Although randomized clinical trials are a gold standard in medical research, many violations to randomization can occur in health service research, such as non-compliance and missing data. In addition, clinical trials with observational data are often seen in health services research. One main challenge in causal inference with observational data is selection bias where the intervention of interest is often provided for those who have an indication for the treatment, for example, more severely ill. The objective of this workshop is to introduce and discuss different methods in handling protocol violations in randomized clinical trials and selection bias in using observational data where comparative inference is of interest. Target Audience: The workshop is intended for health services researchers and statisticians who are interested in understanding causal inferences in comparative inferences. Assumed Audience Familiarity with Topic: The workshop is designed for the broad health services research audience with moderate levels of statistical understanding.

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Causal Inferences in Health Services Research
Xiao-Hua Andrew Zhou, University of Washington

 
Longitudinal Data Track - Hierarchial Models: Missing Data in Longitudinal Clinical Trials (WK 6) Wed, Oct 9, 1:30 PM - 5:15 PM
Adams Room
Organizer(s): Edward Vonesh, Northwestern University

Missing data due to dropout is a frequently occurring problem in longitudinal clinical trials involving repeated measurements over time. This occurs most often in prospective randomized controlled trials where observations are planned at specified times during the course of follow-up. When there are no missing values present, all of the standard statistical methods used for analyzing repeated measurements data such as maximum likelihood (ML) and generalized estimating equations (GEE) allows one to draw valid inference provided the complete data modeling assumptions are met. However, when we have incomplete data due to dropout, we run into the problem of never being able to verify these assumptions for the unobserved data. This short course will examine different missing data mechanisms that lead to incomplete data as well as various methods one can use to analyze longitudinal data when missing values are assumed to be either ignorable or non-ignorable. Particular emphasis will be placed on those methods which are easily handled using readily available software in SAS. The material will be illustrated using examples from several different clinical trials.

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Missing Data in Longitudinal Clinical Trials
Edward Vonesh, Northwestern University

 
Assorted Methods Track: Mixed Models for Ecological Momentary Assessment (EMA) Data (WK 10) Wed, Oct 9, 3:30 PM - 5:15 PM
Spire Parlor
Organizer(s): Donald Hedeker, University of Illinois at Chicago

Modern data collection procedures, such as ecological momentary assessments (EMA), experience sampling, and diary methods have been developed to record the momentary events and experiences of subjects in daily life. These procedures yield relatively large numbers of subjects and observations per subject, and data from such designs are sometimes referred to as intensive longitudinal data. Data from EMA studies are inherently multilevel with, for example, (level-1) observations nested within (level-2) subjects. Thus, mixed models (aka multilevel or hierarchical linear models) are increasingly used for EMA data analysis. In this workshop, use of mixed models for analysis of EMA data will be described with specific focus on analyses of data from an ongoing adolescent smoking EMA study. An important issue that will be described is the treatment of occasion-varying covariates, and the decomposition of the within-subjects (WS) and between-subjects (BS) effects of such covariates. Furthermore, because there are so many measurements per subject, models for relating covariates to the WS and BS variance will be described, including mixed location-scale models that include random subject scale parameters. Such random scale parameters allow subjects to vary in terms of their variance, or scale, in addition to the more typical random subject location effects. Such extended mixed models can be used to assess the determinants of inter-individual and between-subjects variation. Examples will be presented which focus on the variation of mood that is associated with smoking, and the degree to which subject characteristics influence the mood variation. SAS syntax, available via http://www.uic.edu/~hedeker, will be provided and described to facilitate use of the models presented in this workshop. Though the focus will be on analysis of EMA data, the methods have applicability to other types of “intensive” longitudinal datasets.

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Mixed Models for Ecological Momentary Assessment (EMA) Data
Donald Hedeker, University of Illinois at Chicago

 
Observational Studies Track: The Medical Expenditure Panel Survey (MEPS): A National Data Resource to Inform Health Policy (WK 4) CANCELLED Wed, Oct 9, 3:30 PM - 5:15 PM
Water Tower Parlor
Organizer(s): Jeffrey Rhoades, Agency for Healthcare Research & Quality

The purpose of this Workshop is to facilitate the use of the Medical Expenditure Panel Survey Household Component (MEPS HC) public use data files by the health services research community. To meet this objective, participants are provided with a general overview of the MEPS, a description of available data files, information about on-line data tools, and some examples of the type of research projects the MEPS data can support. Major changes have taken place in the Nation's health care delivery system over the last decade. The most notable is the recent passage of the Affordable Care Act. Also consider the rapid expansion of managed care arrangements such as health maintenance organizations, preferred provider organizations, and other provider networks that seek to minimize increases in health care costs. The MEPS is a vital national data resource designed to continually provide health service researchers, policymakers, health care administrators, businesses, and others with timely, comprehensive information about health care use and costs in the United States. Newly released MEPS public use files provide analysts with opportunities to create unique analytic files for policy relevant analysis in the field of health services research, such as access to care and health disparities. In order to capture the unparalleled scope and detail of the MEPS HC, analysts need to understand the complexities of MEPS data files and data file linkages. This workshop will provide the knowledge necessary to formulate research plans utilizing the various MEPS HC files and linkage capabilities.

Cancelled: The Medical Expenditure Panel Survey (MEPS): A National Data Resource to Inform Health Policy
Jeffrey Rhoades, Agency for Healthcare Research & Quality

 
Interactive Poster Session I & Welcome Reception Wed, Oct 9, 5:30 PM - 6:30 PM
Monroe Room
Organizer(s): Donald Hedeker, University of Illinois at Chicago; Xiao-Hua Andrew Zhou, University of Washington

Variance Estimation of the NPMLE of the Mean with Current Status Data
Zhen Han, University of Illinois at Chicago

Empirical and Smoothed Bayes Factor Type Inferences Based on Empirical Likelihoods for Quantiles
Ge Tao, State University of New York at Buffalo

Assessing the Causal Effect of Treatment in the Presence of Self-Selection of Dosage
Xin Gao, FDA

Constructing dynamic treatment regimes using Greedy-GQ algorithm
Ashkan Ertefaie, Postdoc fellow

Bayesian Nonparametric Rating Scale Model for Health Outcomes Measurement
Ken Akira Fujimoto, University of Illinois-Chicago

Assessing the Impact of Education and Cardiovascular Disease Risk Factors on Cognitive Trajectories in Older Adults
Shelley Han Liu, Harvard School of Public Health

Internal Benchmarks and Healthcare Services Satisfaction Reporting: A Case for the Fair Scorecard
Alan Roshwalb, Ipsos Public Affairs

Gender Differential in Active Life Expectancy in Nepal: Does Education Matter?
Tirth Bhatta, Case Western Reserve University

Cost Effectiveness Methods in Oncology: Bootstrapping a Risk Adjusted, Censored ICER
Gerhardt Michael Pohl, Eli Lilly and Company

A Semiparametric Analysis of Health Care Utilization in Patients with Heart Failure
Zhuokai Li, Indiana University School of Medicine

Prenatal Exposure to Endocrine Disrupting Compounds and ...
Hailemichael Metiku Worku, Leiden University

Latent Supervised Learning for Survival Data
Susan Wei, UNC Chapel Hill

Projecting Benefits and Harms of Novel Cancer Screening Biomarkers: A Study of PCA3 and Prostate Cancer
Jeanette K Birnbaum, University of Washington

Modeling Multiple Wave Ecological Momentary Assessment (EMA) data with a Mixed-Effects Location Scale Model.
Chi C Cho, Aurora Research Institute

The Crossover of p-Values into the Results of Placebos
Ben Locwin, Applied Pharmaceutical Intelligence

Measuring Precision and Recall with Social Medial Data
Yoonsang Kim, UIC Institute for Health Research and Poclity

County-Level Determinants of Prescription Drug Consumption in Selected U.S. States
Demba Fofana, The University of Memphis

An Approach to Handling Multiple Experts in Multiple Imputation
Valerie Pare, University of Connecticut

Finding the smoking gun: Could suspension high school increase smoking 12 years later?
Janet E Rosenbaum, SUNY Downstate

Handling Data with Three Types of Missing Values
Jennifer Boyko, University of Connecticut

Health care professionals’ knowledge and recommendations about SIDS and its risk factors: who are the best at giving advices to parents and what’s the effect of the training campaigns?
Federico de Luca, University of Southampton

A Comparison of Statistical Models for Analyzing Episodes-of-Care Costs for Chronic Obstructive Pulmonary Disease Exacerbations
John Paul Kuwornu, Department of Community Health Sciences, Faculty of Medicine, University of Manitoba

English-Spanish language equivalence on a new health literacy measure: implementation of novel psychometric methods
Elizabeth A Hahn, Northwestern University Feinberg School of Medicine

Using Latent Class Analysis to Explore Social Behaviors Among Children with Developmental Disabilities
Benjamin Zablotsky, CDC, National Center for Health Statistics

Confidence Interval Estimators of the Intraclass Correlation Coefficient in Longitudinal Data with Missingness
Delia Codruta Voronca, Public Health Sciences

An Evaluation of Multilevel Poisson Sample Selection Models
Kyle M. Kepreos, VA Center for Clinical Management Research, Ann Arbor VA Health Services Research and Development

Genome-wide association studies for predicting hypertension: Comparing Support Vector Machines and Permanental Classification
Hsin-Hsiung Huang, Mr.

Model Selection with Incomplete Data Using Adjusted Variance
Ashok Chaurasia, University of Connecticut

The Effects of a Trauma Center Closure on Health Care Outcomes and Costs: An Instrumental Variable Based Approach
Joseph Benitez, University of Illinois at Chicago

Old and New Instrumental Variables Models for Causal Inference: A Biostatistician's Futile Redevelopment?
Paul Rathouz, University of Wisconsin-Madison

 

Thu, Oct 10

Conference Registration Thu, Oct 10, 7:00 AM - 5:00 PM
6th Floor Registration Area

 
Interactive Poster Session II Thu, Oct 10, 7:30 AM - 8:30 AM
Monroe Room
Organizer(s): Donald Hedeker, University of Illinois at Chicago; Xiao-Hua Andrew Zhou, University of Washington

Classification based on a permanental process with application to microarray analysis
Jie Yang, University of Illinois at Chicago

A Location Scale Item Response Theory (IRT) Model for Ordinal Questionnaire Data
Donald Hedeker, University of Illinois at Chicago

Effect of a Diagnostic Imaging Pay-for-Performance Initiative on Imaging Costs: a Longitudinal Analysis
Hui Cao, MS, Bio-Statistician

Differential Equation Modeling Approach for Dynamic Regulatory Network
Tao Lu, University at Albany

Bayesian Network Analysis: HIV Risk in Southern Indian Community
Daniel Philip Heard, Duke University

Exploratory data analysis of racial/ethnic disparities in county hospitalization rates for New York State from 2007 to 2009
Nathan T. Donnelly, University at Albany

An empirical approach in decomposing attributing factors to co-occurring use of marijuana and other forms of illicit drug
Haekyung Jeon-Slaughter, UT Southwestern Medical Center

Trends Over Time in Glucose Control for Type 2 Diabetes Patients with Good or Poor Initial Hba1c Levels: A Multilevel Growth Model
Upali W Jayasinghe, Centre for Primary Health Care and Equity

Self-Centering Time Series Data: Single-Pass And Iterative Methods
Jeffrey D Dawson, University of Iowa College of Public Health

Within cluster resampling method on clustered ROC data
Zhuang Miao, George Mason University

Identifying multiple regulation
Denis Agniel, Harvard University

Applying a Logistic Regression Model to Predict the Accuracy of Administrative Healthcare Claims in Identifying Patients with Chronic Kidney Disease
Zongqiang Liao, Blue Cross Blue Shield of Michigan

Covariate adjusted distributions of random curves
Meng Li, North Carolina State University

Integrating rich survey datasets in computational simulations of hepatitis C virus infection among injecting drug users in Chicago area
Alexander Gutfraind, University of Illinois at Chicago

Real Time Classification of Viruses in 12 Dimensions
Hui Zheng, MS.

The Relationship Between Objectively Measured Sedentary Behavior and Physical Activity: Results from a Randomized Lifestyle Intervention
Peter John D. De Chavez, Northwestern University

Variation in quality by hospital characteristics and the implications for risk-adjustment
Dmitriy Poznyak, Mathematica Policy Research

Overview of Estimators in Survival Analysis for Recurrent Event Data: An Application to Morbidity Outcomes in Veterans with Spinal Cord Injury
Lauren Bailey, Edward J. Hines, Jr. VA Hospital, Hines, IL; University of Illinois at Chicago

Health Care Reform and the Stock Market: Economic Impact, Growth Opportunity and Private Sector Investors
Nathan Dong, Columbia University

The R-Symmetric CoGaussian Distribution, its Extensions and Generalizations: Estimation of Modal Incubation Periods of Acute Viral Infections
Saria Salah Awadalla, University of Illinois at Chicago

Multivariate Analysis of EEG data using Fractal Dimension
Md Rokonuzzaman, Student

Using propensity scores with multiple categories to assess the effects of HIV-serostatus and post-traumatic stress on cognition
Leah H Rubin, Department of Psychiatry, University of Illinois at Chicago (UIC)

A Review and Meta-analysis of 15 Studies Reporting Reactive Arthritis from Non-typhoidal Salmonella Infections
Zhouyang Weng, University of Cincinnati, Department of Environmental Health

Estimation of stationary parameters using dynamic sample weights
Duncan Ermini Leaf, University of Southern California Schaeffer Center for Health Policy and Economics

The relationship between smoking status and monthly medical expenditure in a Japanese population; a gamma regression approach
Yoshitaka Murakami, Shiga University of Medical Science

Multidimensional indices Nonlinear Signed-Rank regression
Brice Merlin Nguelifack, Auburn University

Empowering Asthma Patients by Improving Their Self Efficacy: Identifying Potential Drivers of Self Efficacy
Tasneem Zaihra, McGill University

Involving MS Students in Consulting and Research: Novel Use of Statistical Software in Industrial and Biomedical Statistics
Timothy O’Brien, Loyola University Chicago

 
Welcome and Keynote Address Thu, Oct 10, 8:30 AM - 10:00 AM
Adams Room
Chair(s): Donald Hedeker, University of Illinois at Chicago; Xiao-Hua Andrew Zhou, University of Washington

 
Session 11 Invited Comparative effectiveness research combining diverse data: advances in methods and infrastructure Thu, Oct 10, 10:30 AM - 12:15 PM
Adams Room
Organizer(s): Bradley P. Carlin, University of Minnesota
Chair(s): Bradley P. Carlin, University of Minnesota

10:35 AM

Multivariate techniques for combining information
Christopher Schmid, Brown University

11:00 AM

Combining data to study utilization and effectiveness of medical devices
Laura Hatfield, Harvard

11:25 AM

Combination of survival curves from orthopedic registries
Samprit Banerjee, Weill Medical College, Cornell University

11:50 AM

Comparative effectiveness research combining diverse data: advances in methods and infrastructure
Erin C Holve, AcademyHealth

 
Session 12 Invited Methodological Challenges and Solutions in Health Policy Research in China Thu, Oct 10, 10:30 AM - 12:15 PM
Water Tower Parlor
Organizer(s): Xiao-Hua Andrew Zhou, University of Washington
Chair(s): Wanzhu Tu, Indiana University School of Medicine

10:35 AM

The effectiveness evaluation of methadone maintenance treatment in China: A brief introduction
Li Ling, Sun Yat-sen University

11:00 AM

Gaining from Hospital Competition: Evidence from China
Jie Pan, Sichuan University

11:25 AM

Research in Human Resources for Health in China: An application of Discrete Choice Experiment
Xiaoyun Liu, Peking University

Discussant(s): Xiao-Hua Andrew Zhou, University of Washington

 
Session 13 Topic-Contributed Papers Casual Inference: Methods and Practices Thu, Oct 10, 10:30 AM - 12:15 PM
Spire Parlor
Organizer(s): Lei Liu, Northwestern University
Chair(s): Bo Lu, Ohio State University

10:35 AM

Shock-Based Causal Inference
Bernard Black, Northwestern University

10:55 AM

Dynamic Prediction and Causal Inference
Xuelin Huang, MD Anderson Cancer Center

11:15 AM

Selective and future ignorability in causal inference
Marshall Joffe, University of Pennsylvania

11:35 AM

The assessment of interaction effects via tree-based methods
Joseph Kang, Northwestern University

11:55 AM

Diffusion of Robotic Surgical Systems and Its Impact on Treatment Pattern of Localized Prostate Cancer: An Instrumental Variable Analysis
Chan Shen, University of Texas, MD Anderson Cancer Center

 
Session 14 Contributed Papers Random Effect Models in Longitudinal Data and Survival Data Thu, Oct 10, 10:30 AM - 12:15 PM
Grant Park Parlor
Chair(s): Gerhardt Michael Pohl, Eli Lilly and Company

10:35 AM

Simultaneous Variable Selection in Joint Models with Longitudinal and Survival Outcomes
Zangdong He, Department of Biostatistics, Richard M. Fairbanks School of Public Health, Indiana University

10:50 AM

Modeling the covariance structure of random coefficients to characterize quality variation in health plans
Alan Zaslavsky, Department of Health Care Policy, Harvard Medical School

11:20 AM

Longitudinal Zero-inflated Count Data with Random Effects to Model Instrumental Activities of Daily Living (IADLs)
Ping Yao, Northern Illinois University

11:35 AM

Multivariate Frailty Model for Recurrent-Event Data with Multiple Types
Khaled F Bedair, PhD student, Department of Statistics, Virginia Tech

11:50 AM

A Bivariate Mixed-Effects Location-Scale Model with application to Ecological Momentary Assessment (EMA) data
Oksana Pugach, University of Illinois at Chicago

12:05 PM

Floor Discussion

 
Session 15 Invited Statistical Challenges in Comparative Effectiveness Research Thu, Oct 10, 1:30 PM - 3:15 PM
Adams Room
Organizer(s): Colin Wu, NHLBI
Chair(s): Miguel Hernan, Harvard University

1:35 PM

Person-centered treatment (PeT) effects using instrumental variables: An application to evaluating antipsychotic drugs
Anirban Basu, University of Washington

2:00 PM

Observational studies analyzed like randomized trials, and vice versa
Miguel Hernan, Harvard University

2:25 PM

Learning What Works: Uncertainty and Selective Inference
Sharon Lise Normand, Harvard University

 
Session 16 Invited Innovative designs for evaluating impacts of health system changes on mental health treatment and care Thu, Oct 10, 1:30 PM - 3:15 PM
Water Tower Parlor
Organizer(s): Frank Yoon, Mathematica Policy Research
Chair(s): Mike Baiocchi, Stanford University

1:35 PM

Using orthogonal designs to study care coordination delivery to individuals with severe and persistent mental illness
Jelena Zurovac, Mathematica Policy Research

2:00 PM

Using propensity scores and difference-in-differences methods to estimate the effects of mental health parity
Elizabeth Stuart, Johns Hopkins University

2:25 PM

Designing a multilevel evaluation of health care systems: the Medicaid Emergency Psychiatric Services Demonstration
Frank Yoon, Mathematica Policy Research

Discussant(s): Arlene Ash, University of Massachusetts Med School

 
Session 17 Contributed Papers Casual Analysis / Intent to Treat Analysis Thu, Oct 10, 1:30 PM - 3:15 PM
Spire Parlor
Chair(s): Douglas David Gunzler, Case Western Reserve University

1:35 PM

Principal Surrogacy in a Time-to-Event Setting
Michael Elliott, University of Michigan

1:50 PM

Hypothetical Intervention for Health Related Quality of Life in Children with Juvenile Idiopathic Arthritis during the First Year of Disease
Bin Huang, Cincinnat Children's Hospital Med Ctr

2:05 PM

Mediation Analysis with Time Failure Outcome and Error Prone Mediator
Cheng Zheng, Department of Biostatistics, University of Washington

2:20 PM

Surrogacy Assessment Using Principal Stratification When Surrogate and Outcome Measures are Multivariate Normal
Anna Sadie Chernin Conlon, University of Michigan

2:35 PM

Multi-Year Impacts of Offering a “Consumer-Directed” Health Plan: An Intent-to-Treat Design with Varying Take-Up Levels at the Treatment-Cluster Level
Amelia M. Haviland, Carnegie Mellon University

2:50 PM

Floor Discussion

 
Session 18 Contributed Papers Missing Data Thu, Oct 10, 1:30 PM - 3:15 PM
Grant Park Parlor
Chair(s): Ashok Chaurasia, University of Connecticut

1:35 PM

A Bayesian hierarchical model for network meta-analysis with selection bias
Jing P. Zhang, Division of Biostatistics, University of Minnesota School of Public Health

1:50 PM

Multiple Imputation for Measurement Error Correction in Administrative Health Databases: Effect of the Misclassification Mechanism
Lisa Lix, Department of Community Health Sciences, Faculty of Medicine, University of Manitoba

2:05 PM

A semi-parametric approach to impute mixed continuous and categorical data
Irene B Helenowski, Northwestern University

2:20 PM

Gaussian-based routines for imputing categorical variables in complex designs
Recai M Yucel, State University of New York at Albany

2:35 PM

Sequential Probability Ratio Test Subject to Incomplete Data with Covariate Information
Ofer Harel, University of Connecticut

2:50 PM

Using Interviewer Random Effects to Calculate Unbiased HIV Prevalence Estimates in the Presence of Non-Response: a Bayesian Approach
Mark E. McGovern, Harvard University

3:05 PM

Floor Discussion

 
Session 19 Invited Personalized Medicine and Dynamic Treatment Regimes Thu, Oct 10, 3:30 PM - 5:15 PM
Adams Room
Organizer(s): Xuelin Huang, MD Anderson Cancer Center
Chair(s): Xuelin Huang, MD Anderson Cancer Center

3:35 PM

Identifying Target Subgroup with CART for Pharmacogenetic Approach of Reducing Heavy Alcohol Drinking
Lei Liu, Northwestern University

4:00 PM

Machine Learning Methods for Individualizing Real-Time Treatment Policies
Susan A Murphy, University of Michigan

4:25 PM

Personalized medicine and artificial intelligence
Michael Rene Kosorok, University of North Carolina at Chapel Hill

4:50 PM

Optimal Bayesian Dose-Finding in Two Treatment Cycles based on the Joint Utility of Efficacy and Toxicity
Peter F. Thall, M.D. Anderson Cancer Center

 
Session 20 Invited Statistical Methods for Data Synthesis Thu, Oct 10, 3:30 PM - 5:15 PM
Water Tower Parlor
Organizer(s): Juned Siddique, Northwestern University
Chair(s): Laura Hatfield, Harvard

3:35 PM

Item Response Theory Approaches for Research Synthesis and Harmonization
Robert D Gibbons, The University of Chicago

4:00 PM

A Multiple Imputation approach for combining data across multiple trials that use different outcome measures.
Juned Siddique, Northwestern University

4:25 PM

Empirical Limits in Synthesizing Findings from Individual Data Across Similar and Diverse Randomized Trials
C Hendricks Brown, Northwestern University

Discussant(s): Elizabeth Stuart, Johns Hopkins University

 
Session 21 Topic-Contributed Papers Technology Assessment for Informed Medical Decision-Making: In Memory of Professor Charles E. Metz Thu, Oct 10, 3:30 PM - 5:15 PM
Spire Parlor
Organizer(s): Kelly Zou, Pfizer
Chair(s): Maryellen Giger, University of Chicago

3:35 PM

Estimating Sensitivity and Specificity for Technology Assessment in Observer Agreement Studies
Robert M Nishikawa, University of Chicago

3:55 PM

Adjustment for Verification Bias in Estimation of the area under ROC curve adjusting for Covariates
Xiao-Hua Andrew Zhou, University of Washington

4:15 PM

Estimating an ROC Curve: Models, Assumptions, and Interpretation
Alicia Y Toledano, Biostatistics Consulting, LLC

4:35 PM

A Simplifying Reformulation of the Likelihood-Ratio Binormal Distribution
Stephen L Hillis, University of Iowa

4:55 PM

Bayesian multivariate hierarchical transformation models for ROC analysis
James O'Malley, Dartmouth College

 
Session 22 Contributed Papers Casual Inferences in Comparative Effectiveness with Propensity Score and Instrumental Variables Thu, Oct 10, 3:30 PM - 5:15 PM
Grant Park Parlor
Chair(s): Lihui Zhao, Northwestern University

3:35 PM

Estimating causal effects in an observational study with a survival time endpoint: comparing reformulated versus original antidepressants
Jaeun Choi, Harvard Medical School

3:50 PM

The Invalidity of the Most Common Instrumental Variable Analyses in Comparative Effectiveness Research
Laura Faden Garabedian, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute

4:05 PM

Instrumental Variable Methods for the Comparative Safety of Second-Generation Antipsychotic Medications
Portia Yvonne Cornell, Harvard University

4:20 PM

Uncertainty in Propensity Score Estimation: Bayesian Methods for Variable Selection and Model Averaged Causal Effects
Corwin M Zigler, Harvard School of Public Health

4:35 PM

Floor Discussion

 
Social Event at Phyllis' Musical Inn (on own) Thu, Oct 10, 8:00 PM - 9:30 PM
Offsite - Phyllis' Musical Inn

Located in the nearby Wicker Park neighborhood of Chicago, this event will feature music from The Polkaholics & Special Guest Brad Carlin!

 

Fri, Oct 11

Conference Registration Fri, Oct 11, 8:00 AM - 11:30 AM
6th Floor Registration Area

 
HPSS Awards and Plenary Speaker Fri, Oct 11, 8:30 AM - 10:00 AM
Adams Room
Chair(s): Donald Hedeker, University of Illinois at Chicago; Xiao-Hua Andrew Zhou, University of Washington

 
Session 23 Invited Propensity Score Methods for Estimating the Effects of Latent Treatments, with Applications to Mental Health Research Fri, Oct 11, 10:15 AM - 12:00 PM
Adams Room
Organizer(s): Elizabeth Stuart, Johns Hopkins University
Chair(s): Elizabeth Stuart, Johns Hopkins University

10:20 AM

Estimating Causal Effects of Latent Treatment Classes: Natural clusters of drug treatment services for adolescents
Megan Schuler, Johns Hopkins Bloomberg School of Public Health

10:45 AM

Estimating the Causal Effect of a Latent Class Treatment on Binary and Count Outcomes
Donna L Coffman, The Methodology Center, Penn State

11:10 AM

The Causal Effect of Substance Use Latent Class Membership on a Distal Outcome
Bethany C Bray, The Methodology Center, Penn State

Discussant(s): Juned Siddique, Northwestern University

 
Session 24 Invited Comparative Effectiveness Research: Using Evidence to Engage and Impact Fri, Oct 11, 10:15 AM - 12:00 PM
Water Tower Parlor
Organizer(s): Kelly Zou, Pfizer
Chair(s): Frank Yoon, Mathematica Policy Research

10:20 AM

Evidentiary Challenges in Comparative Effectiveness Research
Sally C. Morton, Biostatistics, University of Pittsburgh

10:45 AM

Statistical methods for benefit risk assessment
Bo Yang, Abbvie

11:10 AM

Bayesian Network Meta-Analysis for Health Technology Assessment and Evaluation for Investigative Treatment
Wei Shen, Eli Lilly and Company

11:35 AM

Health Insurer Use of Comparative Effectiveness Research for Innovation in Health Care Re-Design
Daryl Wansink, Blue Cross Blue Shield of North Carolina

 
Session 25 Contributed Papers Longitudinal Data Fri, Oct 11, 10:15 AM - 12:00 PM
Spire Parlor
Chair(s): Donald Hedeker, University of Illinois at Chicago

10:20 AM

Modeling Conditional Quantile Tumor Growth Curves By Combining Independent Small Sample Study Data
Ella Revzin, University of Illinois at Chicago, Department of Mathematics, Statistics, and Computer Science

10:35 AM

Complex longitudinal model applied in Ecological Momentary Assessment (EMA) Data
Xue Li, Hines Cooperative Studies Program Coordinating Center, VA Hospital

10:50 AM

A Flexible Model for the Mean and Variance Functions with Application to Longitudinal/Clustered Medical Cost Data
Jinsong Chen, University of Illinois at Chicago

11:05 AM

Local Box-Cox Transformation in time varying coefficient models with longitudinal data
Mohammed Rahim Uddin Chowdhury, The George Washington University

11:20 AM

Latent Trait Shared Parameter Mixed-Models For Ecological Momentary Assessment Data
John Cursio, The University of Chicago Medicine

11:35 AM

A Latent Growth Modeling Approach to Longitudinal Mediation Analysis of the Causal Path Between Multiple Sclerosis & Depression
Douglas David Gunzler, Case Western Reserve University

11:50 AM

Floor Discussion

 
Session 26 Contributed Papers Survey / Claims / Patient Reported Outcomes Fri, Oct 11, 10:15 AM - 12:00 PM
Grant Park Parlor
Chair(s): Lisa Lix, Department of Community Health Sciences, Faculty of Medicine, University of Manitoba

10:20 AM

Classification accuracy of provider profiling methods based on Medicare claims
Rebecca A Hubbard, Group Health Research Institute

10:35 AM

Benchmarking Healthcare Provider Performance: Some Statistical Considerations
Susan Paddock, RAND Corporation

10:50 AM

Patient-Reported Outcomes in Clinical Practice and Research
Laura Lee Johnson, NIH

11:05 AM

WITHDRAWN: An Assessment of Medical Expenditure Panel Survey Sampling and Estimation Procedures through Benchmarking with the National Health Interview Survey
Sadeq R Chowdhury, Agency for Healthcare Research and Quality

11:20 AM

A Randomized Experiment Comparing Patient Survey Scores in Telephone and Mail Modes When Vendors Are Paid by the Hospitals Being Evaluated or a Third-Party Survey Vendor
Marc N. Elliott, RAND

11:35 AM

Hypothesis Testing for Personalizing Treatment
Huitian Lei, University of Michigan

11:50 AM

Floor Discussion

 
Special Workshop: Estimation of Re-Identification Risk in De-identified Health Care Data (WK 11) Fri, Oct 11, 12:15 PM - 2:00 PM
Adams Room
Organizer(s): Xiao-Hua Andrew Zhou, University of Washington

Department of Veterans Affairs Health Administration (VHA) and Office of Research and Development (ORD) are committed to promoting transparency by making de-identified health data available. One of the greatest concerns about releasing de-identified health data is the threat of re-identification of individual Veterans. Previously, it has been thought that HIPAA guideline de-identified patient data is not re-identifiable. It is now recognized that the dramatic increase in scope of electronic health data, the ability to merge de-identified health data with identified data obtained from various sources, and the availability of substantial computing power, poses a significant re-identification risk. Numerous statistical methods to assess re-identification risk have been proposed in the literature. The goal of this workshop is for invited speakers to provide guidance regarding the methods that have been used or could apply to protect health data that is already de-identified. The speakers should provide a description and/or references to techniques that have been applied in practice and are interpretable by the general community.

12:20 PM

Estimation of Re-Identification Risk in De-identified Health Care Data
Seth Eisen, Department of Veterans Affairs; Aleksandra Slavkovic, Penn State University; Stephen Fienberg, Carnegie Mellon University; Lawrence Cox, National Institute of Statistical Sciences; Xiao-Hua Andrew Zhou, University of Washington; Yaniv Erlich, Whitehead Institute for Biomedical Research