Online Program
Wed, Oct 9 |
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Conference Registration |
Wed, Oct 9, 7:30 AM - 6:30 PM
6th Floor Registration Area |
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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 |
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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. Download Handouts |
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Interpreting Change and Responder Analysis for Patient-reported Outcomes
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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 |
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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. Download Handouts |
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Sensitivity Analysis for Observational Data: Method and Computation
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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 |
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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. Download Handouts |
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Hierarchical Models and Computing for Joint Longitudinal-Survival and other Multiple Component or Endpoint Data
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Assorted Methods Track: Statistical Analysis of Zero-Inflated Continuous Data (WK 8) |
Wed, Oct 9, 10:30 AM - 12:15 PM
Spire Parlor |
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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. |
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Statistical Analysis of Zero-Inflated Continuous Data
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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 |
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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. Download Handouts |
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Designing an Observational Study with the Propensity Score
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Assorted Methods Track: Comparative Effectiveness Research (WK 9) |
Wed, Oct 9, 1:30 PM - 3:15 PM
Spire Parlor |
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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. Download Handouts |
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Comparative Effectiveness Research
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Observational Studies Track: Causal Inferences in Health Services Research (WK 3) |
Wed, Oct 9, 1:30 PM - 3:15 PM
Water Tower Parlor |
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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. Download Handouts |
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Causal Inferences in Health Services Research
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Longitudinal Data Track - Hierarchial Models: Missing Data in Longitudinal Clinical Trials (WK 6) |
Wed, Oct 9, 1:30 PM - 5:15 PM
Adams Room |
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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. Download Handouts |
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Missing Data in Longitudinal Clinical Trials
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Assorted Methods Track: Mixed Models for Ecological Momentary Assessment (EMA) Data (WK 10) |
Wed, Oct 9, 3:30 PM - 5:15 PM
Spire Parlor |
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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. Download Handouts |
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Mixed Models for Ecological Momentary Assessment (EMA) Data
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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 |
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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. |
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Cancelled: The Medical Expenditure Panel Survey (MEPS): A National Data Resource to Inform Health Policy
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Interactive Poster Session I & Welcome Reception |
Wed, Oct 9, 5:30 PM - 6:30 PM
Monroe Room |
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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
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Empirical and Smoothed Bayes Factor Type Inferences Based on Empirical Likelihoods for Quantiles
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Assessing the Causal Effect of Treatment in the Presence of Self-Selection of Dosage
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Constructing dynamic treatment regimes using Greedy-GQ algorithm
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Bayesian Nonparametric Rating Scale Model for Health Outcomes Measurement
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Assessing the Impact of Education and Cardiovascular Disease Risk Factors on Cognitive Trajectories in Older Adults
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Internal Benchmarks and Healthcare Services Satisfaction Reporting: A Case for the Fair Scorecard
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Gender Differential in Active Life Expectancy in Nepal: Does Education Matter?
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Cost Effectiveness Methods in Oncology: Bootstrapping a Risk Adjusted, Censored ICER
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A Semiparametric Analysis of Health Care Utilization in Patients with Heart Failure
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Prenatal Exposure to Endocrine Disrupting Compounds and ...
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Latent Supervised Learning for Survival Data
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Projecting Benefits and Harms of Novel Cancer Screening Biomarkers: A Study of PCA3 and Prostate Cancer
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Modeling Multiple Wave Ecological Momentary Assessment (EMA) data with a Mixed-Effects Location Scale Model.
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The Crossover of p-Values into the Results of Placebos
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Measuring Precision and Recall with Social Medial Data
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County-Level Determinants of Prescription Drug Consumption in Selected U.S. States
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An Approach to Handling Multiple Experts in Multiple Imputation
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Finding the smoking gun: Could suspension high school increase smoking 12 years later?
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Handling Data with Three Types of Missing Values
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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?
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A Comparison of Statistical Models for Analyzing Episodes-of-Care Costs for Chronic Obstructive Pulmonary Disease Exacerbations
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English-Spanish language equivalence on a new health literacy measure: implementation of novel psychometric methods
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Using Latent Class Analysis to Explore Social Behaviors Among Children with Developmental Disabilities
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Confidence Interval Estimators of the Intraclass Correlation Coefficient in Longitudinal Data with Missingness
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An Evaluation of Multilevel Poisson Sample Selection Models
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Genome-wide association studies for predicting hypertension: Comparing Support Vector Machines and Permanental Classification
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Model Selection with Incomplete Data Using Adjusted Variance
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The Effects of a Trauma Center Closure on Health Care Outcomes and Costs: An Instrumental Variable Based Approach
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Old and New Instrumental Variables Models for Causal Inference: A Biostatistician's Futile Redevelopment?
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Thu, Oct 10 |
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Conference Registration |
Thu, Oct 10, 7:00 AM - 5:00 PM
6th Floor Registration Area |
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Interactive Poster Session II |
Thu, Oct 10, 7:30 AM - 8:30 AM
Monroe Room |
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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
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A Location Scale Item Response Theory (IRT) Model for Ordinal Questionnaire Data
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Effect of a Diagnostic Imaging Pay-for-Performance Initiative on Imaging Costs: a Longitudinal Analysis
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Differential Equation Modeling Approach for Dynamic Regulatory Network
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Bayesian Network Analysis: HIV Risk in Southern Indian Community
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Exploratory data analysis of racial/ethnic disparities in county hospitalization rates for New York State from 2007 to 2009
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An empirical approach in decomposing attributing factors to co-occurring use of marijuana and other forms of illicit drug
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Trends Over Time in Glucose Control for Type 2 Diabetes Patients with Good or Poor Initial Hba1c Levels: A Multilevel Growth Model
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Self-Centering Time Series Data: Single-Pass And Iterative Methods
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Within cluster resampling method on clustered ROC data
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Identifying multiple regulation
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Applying a Logistic Regression Model to Predict the Accuracy of Administrative Healthcare Claims in Identifying Patients with Chronic Kidney Disease
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Covariate adjusted distributions of random curves
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Integrating rich survey datasets in computational simulations of hepatitis C virus infection among injecting drug users in Chicago area
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Real Time Classification of Viruses in 12 Dimensions
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The Relationship Between Objectively Measured Sedentary Behavior and Physical Activity: Results from a Randomized Lifestyle Intervention
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Variation in quality by hospital characteristics and the implications for risk-adjustment
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Overview of Estimators in Survival Analysis for Recurrent Event Data: An Application to Morbidity Outcomes in Veterans with Spinal Cord Injury
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Health Care Reform and the Stock Market: Economic Impact, Growth Opportunity and Private Sector Investors
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The R-Symmetric CoGaussian Distribution, its Extensions and Generalizations: Estimation of Modal Incubation Periods of Acute Viral Infections
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Multivariate Analysis of EEG data using Fractal Dimension
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Using propensity scores with multiple categories to assess the effects of HIV-serostatus and post-traumatic stress on cognition
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A Review and Meta-analysis of 15 Studies Reporting Reactive Arthritis from Non-typhoidal Salmonella Infections
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Estimation of stationary parameters using dynamic sample weights
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The relationship between smoking status and monthly medical expenditure in a Japanese population; a gamma regression approach
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Multidimensional indices Nonlinear Signed-Rank regression
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Empowering Asthma Patients by Improving Their Self Efficacy: Identifying Potential Drivers of Self Efficacy
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Involving MS Students in Consulting and Research: Novel Use of Statistical Software in Industrial and Biomedical Statistics
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Welcome and Keynote Address |
Thu, Oct 10, 8:30 AM - 10:00 AM
Adams Room |
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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 |
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Organizer(s): Bradley P. Carlin, University of Minnesota | ||
Chair(s): Bradley P. Carlin, University of Minnesota | ||
10:35 AM |
Multivariate techniques for combining information
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11:00 AM |
Combining data to study utilization and effectiveness of medical devices
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11:25 AM |
Combination of survival curves from orthopedic registries
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11:50 AM |
Comparative effectiveness research combining diverse data: advances in methods and infrastructure
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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 |
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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
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11:00 AM |
Gaining from Hospital Competition: Evidence from China
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11:25 AM |
Research in Human Resources for Health in China: An application of Discrete Choice Experiment
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Discussant(s): Xiao-Hua Andrew Zhou, University of Washington |
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Session 13 Topic-Contributed Papers Casual Inference: Methods and Practices |
Thu, Oct 10, 10:30 AM - 12:15 PM
Spire Parlor |
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Organizer(s): Lei Liu, Northwestern University | ||
Chair(s): Bo Lu, Ohio State University | ||
10:35 AM |
Shock-Based Causal Inference
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10:55 AM |
Dynamic Prediction and Causal Inference
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11:15 AM |
Selective and future ignorability in causal inference
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11:35 AM |
The assessment of interaction effects via tree-based methods
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11:55 AM |
Diffusion of Robotic Surgical Systems and Its Impact on Treatment Pattern of Localized Prostate Cancer: An Instrumental Variable Analysis
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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 |
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Chair(s): Gerhardt Michael Pohl, Eli Lilly and Company | ||
10:35 AM |
Simultaneous Variable Selection in Joint Models with Longitudinal and Survival Outcomes
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10:50 AM |
Modeling the covariance structure of random coefficients to characterize quality variation in health plans
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11:20 AM |
Longitudinal Zero-inflated Count Data with Random Effects to Model Instrumental Activities of Daily Living (IADLs)
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11:35 AM |
Multivariate Frailty Model for Recurrent-Event Data with Multiple Types
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11:50 AM |
A Bivariate Mixed-Effects Location-Scale Model with application to Ecological Momentary Assessment (EMA) data
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12:05 PM |
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Session 15 Invited Statistical Challenges in Comparative Effectiveness Research |
Thu, Oct 10, 1:30 PM - 3:15 PM
Adams Room |
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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
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2:00 PM |
Observational studies analyzed like randomized trials, and vice versa
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2:25 PM |
Learning What Works: Uncertainty and Selective Inference
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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 |
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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
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2:00 PM |
Using propensity scores and difference-in-differences methods to estimate the effects of mental health parity
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2:25 PM |
Designing a multilevel evaluation of health care systems: the Medicaid Emergency Psychiatric Services Demonstration
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Discussant(s): Arlene Ash, University of Massachusetts Med School |
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Session 17 Contributed Papers Casual Analysis / Intent to Treat Analysis |
Thu, Oct 10, 1:30 PM - 3:15 PM
Spire Parlor |
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Chair(s): Douglas David Gunzler, Case Western Reserve University | ||
1:35 PM |
Principal Surrogacy in a Time-to-Event Setting
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1:50 PM |
Hypothetical Intervention for Health Related Quality of Life in Children with Juvenile Idiopathic Arthritis during the First Year of Disease
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2:05 PM |
Mediation Analysis with Time Failure Outcome and Error Prone Mediator
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2:20 PM |
Surrogacy Assessment Using Principal Stratification When Surrogate and Outcome Measures are Multivariate Normal
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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
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2:50 PM |
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Session 18 Contributed Papers Missing Data |
Thu, Oct 10, 1:30 PM - 3:15 PM
Grant Park Parlor |
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Chair(s): Ashok Chaurasia, University of Connecticut | ||
1:35 PM |
A Bayesian hierarchical model for network meta-analysis with selection bias
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1:50 PM |
Multiple Imputation for Measurement Error Correction in Administrative Health Databases: Effect of the Misclassification Mechanism
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2:05 PM |
A semi-parametric approach to impute mixed continuous and categorical data
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2:20 PM |
Gaussian-based routines for imputing categorical variables in complex designs
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2:35 PM |
Sequential Probability Ratio Test Subject to Incomplete Data with Covariate Information
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2:50 PM |
Using Interviewer Random Effects to Calculate Unbiased HIV Prevalence Estimates in the Presence of Non-Response: a Bayesian Approach
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3:05 PM |
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Session 19 Invited Personalized Medicine and Dynamic Treatment Regimes |
Thu, Oct 10, 3:30 PM - 5:15 PM
Adams Room |
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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
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4:00 PM |
Machine Learning Methods for Individualizing Real-Time Treatment Policies
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4:25 PM |
Personalized medicine and artificial intelligence
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4:50 PM |
Optimal Bayesian Dose-Finding in Two Treatment Cycles based on the Joint Utility of Efficacy and Toxicity
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Session 20 Invited Statistical Methods for Data Synthesis |
Thu, Oct 10, 3:30 PM - 5:15 PM
Water Tower Parlor |
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Organizer(s): Juned Siddique, Northwestern University | ||
Chair(s): Laura Hatfield, Harvard | ||
3:35 PM |
Item Response Theory Approaches for Research Synthesis and Harmonization
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4:00 PM |
A Multiple Imputation approach for combining data across multiple trials that use different outcome measures.
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4:25 PM |
Empirical Limits in Synthesizing Findings from Individual Data Across Similar and Diverse Randomized Trials
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Discussant(s): Elizabeth Stuart, Johns Hopkins University |
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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 |
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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
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3:55 PM |
Adjustment for Verification Bias in Estimation of the area under ROC curve adjusting for Covariates
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4:15 PM |
Estimating an ROC Curve: Models, Assumptions, and Interpretation
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4:35 PM |
A Simplifying Reformulation of the Likelihood-Ratio Binormal Distribution
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4:55 PM |
Bayesian multivariate hierarchical transformation models for ROC analysis
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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 |
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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
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3:50 PM |
The Invalidity of the Most Common Instrumental Variable Analyses in Comparative Effectiveness Research
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4:05 PM |
Instrumental Variable Methods for the Comparative Safety of Second-Generation Antipsychotic Medications
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4:20 PM |
Uncertainty in Propensity Score Estimation: Bayesian Methods for Variable Selection and Model Averaged Causal Effects
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4:35 PM |
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Social Event at Phyllis' Musical Inn (on own) |
Thu, Oct 10, 8:00 PM - 9:30 PM
Offsite - Phyllis' Musical Inn |
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Located in the nearby Wicker Park neighborhood of Chicago, this event will feature music from The Polkaholics & Special Guest Brad Carlin! |
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Fri, Oct 11 |
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Conference Registration |
Fri, Oct 11, 8:00 AM - 11:30 AM
6th Floor Registration Area |
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HPSS Awards and Plenary Speaker |
Fri, Oct 11, 8:30 AM - 10:00 AM
Adams Room |
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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 |
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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
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10:45 AM |
Estimating the Causal Effect of a Latent Class Treatment on Binary and Count Outcomes
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11:10 AM |
The Causal Effect of Substance Use Latent Class Membership on a Distal Outcome
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Discussant(s): Juned Siddique, Northwestern University |
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Session 24 Invited Comparative Effectiveness Research: Using Evidence to Engage and Impact |
Fri, Oct 11, 10:15 AM - 12:00 PM
Water Tower Parlor |
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Organizer(s): Kelly Zou, Pfizer | ||
Chair(s): Frank Yoon, Mathematica Policy Research | ||
10:20 AM |
Evidentiary Challenges in Comparative Effectiveness Research
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10:45 AM |
Statistical methods for benefit risk assessment
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11:10 AM |
Bayesian Network Meta-Analysis for Health Technology Assessment and Evaluation for Investigative Treatment
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11:35 AM |
Health Insurer Use of Comparative Effectiveness Research for Innovation in Health Care Re-Design
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Session 25 Contributed Papers Longitudinal Data |
Fri, Oct 11, 10:15 AM - 12:00 PM
Spire Parlor |
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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
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10:35 AM |
Complex longitudinal model applied in Ecological Momentary Assessment (EMA) Data
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10:50 AM |
A Flexible Model for the Mean and Variance Functions with Application to Longitudinal/Clustered Medical Cost Data
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11:05 AM |
Local Box-Cox Transformation in time varying coefficient models with longitudinal data
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11:20 AM |
Latent Trait Shared Parameter Mixed-Models For Ecological Momentary Assessment Data
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11:35 AM |
A Latent Growth Modeling Approach to Longitudinal Mediation Analysis of the Causal Path Between Multiple Sclerosis & Depression
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11:50 AM |
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Session 26 Contributed Papers Survey / Claims / Patient Reported Outcomes |
Fri, Oct 11, 10:15 AM - 12:00 PM
Grant Park Parlor |
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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
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10:35 AM |
Benchmarking Healthcare Provider Performance: Some Statistical Considerations
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10:50 AM |
Patient-Reported Outcomes in Clinical Practice and Research
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11:05 AM |
WITHDRAWN: An Assessment of Medical Expenditure Panel Survey Sampling and Estimation Procedures through Benchmarking with the National Health Interview Survey
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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
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11:35 AM |
Hypothesis Testing for Personalizing Treatment
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11:50 AM |
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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 |
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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. |
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12:20 PM |
Estimation of Re-Identification Risk in De-identified Health Care Data
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Important Dates & Deadlines
- October 9 - 11, 2013
ICHPS 2013