2023 LiDS Conference

Quick Links:

• Keynote Speakers

1. Cross-Sectional Data, Population Dynamics and Beyond (Mei-Cheng Wang)
2. The Joy of Pseudovalues (Per Kragh Andersen)

• Short Courses

1. Analysis of Recurrent Event Data (Jianwen Cai)
2. Prediction Modeling with Censored Data (Michael Pencina and Chuan Hong)
3. Statistical Methods for Time-to-Event Data from Multiple Sources: A Causal Inference Perspective (Xiaofei Wang and Shu Yang)

• Program Schedule
• Program Book
• Scientific Program Committee
• Local Organizing Committee
• Online Platform Committee
• Registration
  
• Accommodations
  
• Logistics
• Student Paper Competition
  
• Poster Session (Student Poster Competition)
  

Keynote Speakers

Mei-Cheng Wang (Johns Hopkins University)
Cross-Sectional Data, Epidemic Dynamics and Beyond
Thursday, June 1, 2023

Per Kragh Andersen (University of Copenhagen)
The Joy of Pseudovalues
Friday, June 2, 2023

Keynote Presentation I

Cross-Sectional Data, Epidemic Dynamics and Beyond Thursday, June 1, 2023 Mei-Cheng Wang (Johns Hopkins University) Mei-Cheng Wang is a Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. Professor Wang has done foundational work on methods for truncation, length-bias and prevalent sampling, with an emphasis on data collected through cohort studies or healthcare systems. She served as PI on multiple NIH-sponsored grants to develop statistical methods for longitudinal and survival data, and has led the Survival, Longitudinal and Multivariate (SLAM) Data Working Group at JHU-SPH since 1997.

Keynote Presentation II

The Joy of Pseudovalues Friday, June 2, 2023 Per Kragh Andersen (University of Copenhagen) Per Kragh Andersen is a Professor in the Department of Public Health, Biostatistics Section, at the University of Copenhagen. Professor Andersen has published extensively in many domains of survival and event history analysis, including groundbreaking work establishing large-sample properties of the Cox model via martingale theory. He is also the lead author of the well-known textbook, “Statistical Models Based on Counting Processes.”

Short Courses

All workshops will be held on Wednesday, May 31, 2023, from 8:00 am to 5:00 pm.

Short Course I: Analysis of Recurrent Event Data
Instructor: Jianwen Cai (University of North Carolina at Chapel Hill)
Wednesday, May 31, 2023, 8:00 am - 12:00 pm

Short Course II: Prediction Modeling with Censored Data
Instructors: Michael Pencina (Duke University) and Chuan Hong (Duke University)
Wednesday, May 31, 2023, 1:00 pm - 5:00 pm

Short Course III: Statistical Methods for Time-to-Event Data from Multiple Sources: A Causal Inference Perspective
Instructors: Xiaofei Wang (Duke University) and Shu Yang (North Carolina State University)
Wednesday, May 31, 2023, 8:00 am - 12:00 pm and 1:00 pm - 5:00 pm

Short Course I: Analysis of Recurrent Event Data

Instructor

Jianwen Cai is the Cary C. Boshamer Distinguished Professor in the Department of Biostatistics at the University of North Carolina at Chapel Hill.  Dr. Cai has served as Interim Chair of the Department of Biostatistics on multiple occasions. She has developed dozens of highly-cited methods for the analysis of recurrent event data, largely through four methodological grants funded by the NIH for which she has served as PI.

Course Description

This course will provide a comprehensive examination of methods and models for the analysis of recurrent event data. The methods will be illustrated using a variety of examples from the biomedical literature. Topics will include data structure; recurrent events as a special case of multivariate survival data; rate function versus intensity function (versus hazard function); marginal models; conditional models; recurrent events of multiple types; models with both marginal and conditional elements; recurrent/terminal event data; available software; examples.

Short Course II: Prediction Modeling with Censored Data

Instructors

Michael Pencina is a Professor of Biostatistics and Bioinformatics at Duke University and Director of Duke AI Health.  He previously served as Director of Biostatistics at the Duke Clinical Research Institute.  Dr. Pencina is an internationally recognized authority in risk prediction model development and evaluation. Expert panels and guideline groups frequently recommend methods proposed in his research and have adopted them as the most promising new statistical tools in assessing and quantifying model performance.

Chuan Hong is an Assistant Professor of Biostatistics at Duke University, where her research focuses on developing advanced statistical and machine learning methods with a particular emphasis on predictive modeling, high throughput phenotyping and precision medicine using large scale biomedical data. She also has extensive expertise in data harmonization and privacy-preserving federated learning, enabling co-training models across multiple cohorts without sharing individual patient information.

Course Description

This course will provide a comprehensive examination of best practices for constructing and evaluating risk prediction algorithms and resulting clinical decision support tools. The methods will be illustrated using a variety of examples from the current practice and literature. Topics will include principles of algorithm development (data, outcomes, mathematical models, clinical applications); algorithm versus clinical decision support (CDS) tool; evaluation metrics; sources of bias in algorithms and CDS tools; recent regulatory frameworks; examples.

Short Course III: Statistical Methods for Time-to-Event Data from Multiple Sources: A Causal Inference Perspective

Instructors

Xiaofei Wang is a Professor of Biostatistics and Bioinformatics at Duke University School of Medicine, and the Director of Statistics for Alliance Statistics and Data Management Center. Dr. Wang has been involved in clinical trials, observational studies, and translational studies for Alliance/CALGB and Duke Cancer Institute. His methodology research has been funded by NIH with a focus on biased sampling, causal inference, survival analysis, methods for predictive and diagnostic medicine, and clinical trial design. He is an Associate Editor for Statistics in Biopharmaceutical Statistics, and an elected fellow for American Statistical Association (ASA).

Shu Yang is an Associate Professor of Statistics, Goodnight Early Career Innovator, and University Faculty Scholar at North Carolina State University. Her primary research interest is causal inference and data integration, particularly with applications to comparative effectiveness research in health studies. She also works extensively on methods for missing data and spatial statistics. Dr. Yang has been a Principal Investigator for the U.S. National Science Foundation and National Institute of Health research projects and a Co-Investigator for a recent grant from the Patient-Centered Outcomes Research Institute.

Course Description

The short course will review important statistical methods for survival data arising from multiple data sources, including randomized clinical trials and observational studies. The entire short course consists of four parts and all parts will be discussed in a unified causal inference framework. In each part, we will review the theoretical background. Supplemented with data examples, the application of these methods in practice and implementation of these methods in freely available statistical software will be emphasized. Each part takes approximately 2 hours to cover.

• Morning sessions (Instructor: Xiaofei Wang)

In Part 1, we will review key issues and methods in designing randomized clinical trials (RCTs), including randomization, determination of the number of events, determination of numbers of patients and follow-up schedule, and group sequential design. Statistical tests, such as logrank test and its weighted variants, and inference for hazard ratio with Cox promotional hazards (PH) model, and other esitmand-based survival functions (e.g. restricted mean survival difference), will be discussed. Examples and data from cancer clinical trials will be used to illustrate these methods.

In Part 2, standard survival analysis methods, such as the Kaplan-Meier estimator, log-rank test, and Cox PH models, have been commonly used to analyze survival data arising from observational studies, in which treatment groups are not randomly assigned as in RCTs). We will start with an introduction of the statistical framework causal inference, then shift the focus to the causal inference methods for survival data. We will first review various methods that allow valid visualization and testing for confounder-adjusted survival curves, including G-Formula, Inverse Probability of Treatment Weighting, Propensity Score Matching, calibration weighting, and Augmented Inverse Probability of Treatment Weighting. Examples and data from cancer observational studies will be used to illustrate these methods.

• Afternoon sessions (Instructor: Shu Yang)

The afternoon sections will cover the objectives and methods that allow integrative analyses of data from RCTs and observational studies. These methods exploit the complementing features of RCTs and observational studies to estimate the average treatment effect (ATE), heterogeneity of treatment effect (HTE), and individualized treatment rules (ITRs) over a target population.

In Part 3, we will review existing statistical methods for generalizing RCT findings to a target population leveraging the representativeness of the observational studies. Due to population heterogeneity, the ATE and ITRs estimated from the RCTs lack external validity/generalizability to a target population. We will review the statistical methods for conducting generalizable RCT analysis for the targeted ATE and ITRs, including inverse probability sampling weighting, calibration weighting, outcome regression, and doubly robust estimators. R software and applications will also be covered.

In Part 4, we will review existing statistical methods for integrating RCTs and observational studies for robust and efficient estimation of the HTE. RCTs have been regarded as the gold standard for treatment effect evaluation due to the randomization of treatment, which may be Underpowered to detect HTEs due to practical limitations. On the other hand, large observational studies contain rich information on how patients respond to treatment, which, however, may be confounded. We will review statistical methods for robust and efficient estimation of the HTE leveraging the treatment randomization in RCTs and rich information in observational studies, including calibration, test-based integrative analysis, and confounding function modeling. R software and applications will also be covered.

Scientific Program Committee

• Douglas Schaubel (Co-Chair, University of Pennsylvania Perelman School of Medicine)
• Mimi Kim (Co-Chair, Albert Einstein College of Medicine)
• Grace Y. Yi (University of Western Ontario)

Local Organizing Committee

• Shanshan Zhao (Co-Chair, National Institute of Environmental Health Sciences, National Institutes of Heath)
• Wenbin Lu (Co-Chair, North Carolina State University)
• Amy Shi (AstraZeneca)
• Xiaofei Wang (Duke University)
• Feng-Chang Lin (University of North Carolina at Chapel Hill)
• Qingning Zhou (University of North Carolina at Charlotte)
  
  

Online Platform Committee

• Wenbo Wu (Chair, New York University Grossman School of Medicine)
• Douglas Schaubel (University of Pennsylvania Perelman School of Medicine)

Registration

• Non-ASA member
• ASA non-LiDS member
• ASA LiDS member
• Student

Accommodations

• The conference rate at Raleigh Marriott City Center is US$195 per night, valid through April 30, 2023. Use the link below to make reservations. For government attendees, please contact Dr. Shanshan Zhao (shanshan.zhao AT nih.gov) for a special rate.