HACASA 2026 ASA Traveling Short Course
Friday 9/11/2026
Survival Analysis Methods Correcting for Treatment Switching Effects in Randomized Clinical Trials: Theory and SAS/R Code
Course flyer
Instructor:
Dr. Jing Xu, Senior Director, Takeda
Dr. Bingxia Wang, Senior Director, Takeda
Dr. Qingxia (Cindy) Chen, Professor, Department of Biostatistics, Vanderbilt University
Date: Friday, September 11, 2026 (9:00am – 4:00pm CT)
Location: Virtual via Zoom
Course Description
In many late phase oncology randomized controlled trials (RCTs), control arm patients are permitted to take active treatment (1-way crossover), or patients in both control and active arms are permitted to take alternative treatments (2-way treatment switching) after disease progression due to ethical considerations. In both situations, the effect of active intervention on overall survival (OS) is no longer directly observable. The intent-to-treat (ITT) analysis of the observed data will reflect the trial outcome per the treatment policy strategy but may not be able to make causal inference for the active intervention effect on OS. The latter is important for the payer agency's evaluation and is helpful for regulatory decisions on drug applications.
During the last decade, several complex statistical methods have been adapted and applied to RCTs to recover the causal OS effect of randomized active intervention under settings that allow for treatment switching. These methods include but are not limited to Marginal Structural Model, Two-Stage Estimation, Inverse Probability of Censoring Weighting, Rank-Preserving Structural Failure Time Model, Iterative Parameter Estimation, Three-State Model. This course will review theory, regulatory guidance and demonstrate SAS/R code for these methods. It will discuss the pros and cons and practical issues when each method is applied under the RCT setting. Case studies will be presented to illustrate the application of each method.
What will you learn from this course
· Available methods, regulatory policy, and appropriate approaches in handling issues associated with treatment switching.
· The principles, strengths, limitations, and practical considerations of treatment-switching adjustment methods in one-way crossover and two-way switching settings.
· How to select appropriate adjustment methods during trial design and pre-specify their use in analysis plans.
· How to construct longitudinal counting-process datasets, implement adjustment methods in SAS/R, and generate weighted log-rank tests and adjusted survival curves when needed.
· Cost: Student $25, Member $40, Non-Member $50. To become a HACASA member, the cost is $1 for students and $8 for non-students.
Schedule (CT)
09:00 – 09:05 am Opening
09:05 – 10:25 am Introduction and MSM (part 1)
10:25 – 10:40 am Break
10:40 – 12:00 pm MSM (part 2) and IPCW
12:00 – 01:00 pm Lunch
01:00 – 02:15 pm TSE and RPSFTM
02:15 – 02:30 pm Break
02:30 – 04:00 pm Three-State Model
Please register via link: Survival Analysis Methods Correcting for Treatment Switching Effects in RCT
If you have any questions, please contact HoustonASA@gmail.com.
HACASA 2026 Spring Chapter Meeting
Friday 4/17/2026
The Spring 2026 Chapter Meeting of the Houston Area Chapter of the American Statistical Association (HACASA) will be held on Friday, April 17, 2026, from 4:00 to 6:00 PM at Rice University (Maxfield Hall). This meeting will feature short talks from two invited speakers. Attendees will have the opportunity to socialize, network with colleagues, and enjoy light refreshments. See attached flyer for more information.
Speaker #1: Zijian Zeng (PROS) Practical Statistical Modeling for Airline Pricing
Speaker #2: Hao Feng (UTHealth Houston) mist: a hierarchical Bayesian framework for detecting differential DNA methylation dynamics in single-cell data
HACASA meetings are free and open to the public! Registration is open for in-person or virtual attendance.
HACASA 2025 Online Workshop
9/19/2025
Course Title: Modern Ethical Issues and Best Practices in Data, Analytics, and AI
https://www.eventbrite.com/e/short-course-modern-ethnical-issues-and-best-practices-tickets-1564071402669/
Course Description: This workshop discusses emerging issues for ethical best practices in data, analytics, and AI. The course includes a half day on data issues such as court cases that are shaping data practice standards, learning from standards in other professions, and the impact of the European General Data Protection Regulation (GDPR) on ethical practices around the world. The second part of the course presents a half-day on issues in analytics and AI such as model bias mitigation, the proposed federal AI Bill of Rights, and the ASA’s new guidelines for ethical practices in AI. The workshop teaches a practical understanding of ethical issues in data and analytics, presenting concrete examples of both good and bad practices, enabling course attendees to better understand the changing ethical landscape and address potential questions and issues.
Course Outline
Part 1: Ethical Issues in Data Use and Standard Practices
- The European General Data Protection Regulation (GDPR) and how its use is shaping ethical practices in the US
- US Data Landscape: court cases that are shaping standard practice
- Learning from Ethical Standards in Other Professions
- Becoming an advocate for ethics in data and analytics
Part 2: Ethical Practices in Analytics and AI
- Model Bias Measurement and Mitigation
- The United States AI Bill of Rights
- The ASA Ethical Guidelines for Statistical Practices to artificial intelligence
- Resources for understanding and implementing ethical best practice
https://drive.google.com/file/d/1q3k8uTcuFyfHglqoazeXhiWNtBf7Nt8k/view?usp=drive_link