Archived - Spring 2026

Jan 29, Thursday (10:00 - 11:00 AM [note a different time than our regular meetings], UOP 3-351 Skylab)

Watch webinar, "Design and Analytic Considerations for Time-to-Event Outcomes in Cluster Randomized Clinical Trials" by Denise Esserman, Ph.D. (Want to watch yourself? Register yourself)

Abstract: How to handle censoring and the need to consider competing/semicompeting risks already increases the complexity of designing clinical trials with time-to-event outcomes. Additional complexity is added when using a time-to-event outcome in a cluster randomized trial because the correlation between individuals within a cluster also needs to be considered. Using the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) trial, a cluster randomized trial with the primary outcome of time to first serious fall injury, as a motivating example, this talk will discuss considerations in the design and analysis of cluster randomized trials with time-to-event outcomes in the presence of censoring and competing/semicompeting risks. It will discuss the methods available for clustered time-to-event data and weigh the pros and cons of those methods under varying design considerations. This talk will conclude by outlining gaps in knowledge that still exist.

Feb 13, Friday (10:00 - 11:00 AM, UOP Fishbowl Conference Room [note a different day, time and place than our regular meetings])

Watching webinar, "SPLasso for High-dimensional Additive Hazards Regression with Covariate Measurement Error" by Speaker: Jinfeng Xu, PhD, Associate Professor, City University of Hong Kong; Discussant: Grace Y. Yi, Professor, University of Western Ontario

Mar 6, Friday (11 AM-12 PM, UOP 217 Statdale Conference Room or Zoom [note a different day, time and place than our regular meetings])

Presenter: Dr. Tianmeng Lyu

Topic: Restricted mean survival time (RMST) estimation in a clinical trial with a composite endpoint of an interval-censored event (e.g., progression) and right-censored death, benefits by converting the composite endpoint to interval censoring. 

Reference:  Gao et al. (2025), RMST for Interval-Censored Data in Oncology Clinical Trials

Mar 19, Thursday: (4-5 PM, virtual over Zoom only)

Topic: Roundtable about interesting topics at ENAR 2026, from survival analysis, longitudinal analysis, competing risks, and more related sections. 

Discussion Leader: Our division's Ph.D. candidates, Nitya Shah and Han Lu

Apr 2, Thursday (4-5 PM, UOP 3-351 Skylab Conference Room, or remote over Zoom)

Presenter: Noppawee (Nate) Apichonpongpan

Topic: Our division's master student, Nate, will present on discrete survival and survival prediction models. 

Reference: Suresh, Severn, and Gohsh (2022),  Survival Prediction Models: An Introduction to Discrete-Time Modelling.

Apr 3 Friday (10-11 AM, UOP 2-217 Statdale Conference Room)

    ISBS Webinar recommendation & a pop-up event to watch this webinar together (Zoom link).

    Topic: Win Ratio and Beyond: Evolution, Applications, and Open Questions in Composite Endpoint Analysis.

    Presenter: Dr. Lu Mao, Associate Professor in the Department of Biostatistics and Medical Informatics at the University of Wisconsin–Madison.

Apr 17, Friday (2-5 PM, UOP 3-351 Skylab Conference Room)

    Watch a short course: RMST-Based Survival Analysis by Dr. Lu Tian, Stanford. (Want to register and watch yourself? Here is the registration link)

Apr 30, Thursday (4-5 PM, UOP 3-351 Skylab Conference Room, or remote over Zoom)

    Presenter: Alex Pemberton

    Topic: Our division's master student, Alex, will present on restricted mean survival time (RMST) in randomized controlled trials (RCTs). Alex will discuss a method for RMST used in the interim analysis (see reference below) and his plan B project about RMST-based group sequential design to log-rank test-based sequential design. 

    Reference: Lu and Tian (2020): Statistical Considerations for Sequential Analysis of the Restricted Mean Survival Time for Randomized Clinical Trials

Future topic: Explore a large-language-model-powered, user-friendly, website-based tool to generate individual patient data from a Kaplan-Meier Plot.

    Reference: Zhao et al. (2025), KM-GPT: An Automated Pipeline for Reconstructing Individual Patient Data from Kaplan-Meier Plots (preprint).