第十届“中国人民大学国际统计论坛”（RUC IFS 2025）将于2025年7月4-6日在中国人民大学召开。大会邀请美国、澳大利亚等国家和地区的知名学者参会，将就“统计学发展史、数理统计、数据科学与人工智能、生物统计学前沿探究、政府统计、金融统计”等问题展开深入交流与讨论。

本次介绍特邀报告人Susan  Halabi，报告主题为“Next Generation Clinical Trials: Transforming Clinical Trials through Innovation, Real-World Evidence, and Artificial Intelligence”。

Susan  Halabi

Title

Next Generation Clinical Trials: Transforming Clinical Trials through Innovation, Real-World Evidence, and Artificial Intelligence

Abstract

Today’s clinical trials are increasingly complex, requiring innovative statistical approaches and flexible design strategies. Rising costs, constrained budgets, and challenges in patient enrollment demand novel methodologies that can accelerate confirmatory studies while accounting for the heterogeneity of patient populations. These innovations are essential for identifying optimal treatment strategies and determining which subgroups of patients are most likely to benefit from specific therapies.

To address these challenges, the integration of real-world evidence (RWE) has emerged as a potential complement to standard randomized trials. Bayesian methods that utilize historical controls can enhance trial designs, particularly in rare diseases and pediatric populations. Additionally, sequential multiple assignment randomized trials (SMART) enable dynamic treatment adjustments based on individual responses. Platform trials and biomarker-driven designs further support flexible and targeted approaches, allowing for treatment optimization tailored to individual patient characteristics.

Artificial intelligence (AI) is playing an increasingly important role in modernizing clinical trials. By leveraging AI to enhance patient selection, identify predictive biomarkers, and analyze complex data in real time, researchers can design more adaptive, precise, and scalable studies. Expanding diversity in clinical trials remains essential to ensuring equitable access to innovative therapies. AI can also help identify and mitigate biases in recruitment and retention, supporting more inclusive research.

Looking ahead, the integration of AI, precision medicine, and RWE is reshaping the clinical trial ecosystem shifting from static, one-size-fits-all models to dynamic, adaptive designs that are more representative and impactful. This transformation holds the promise of accelerating the translation of scientific discoveries into effective, personalized treatments for all patients.

Biography

Susan Halabi, PhD, is the James B. Duke Distinguished Professor of Biostatistics and Bioinformatics and Co-Chief of the Division of Biostatistics in the Department of Biostatistics and Bioinformatics at Duke University Medical Center. For over twenty-five years, she has been at the forefront of designing and analyzing clinical trials in oncology. Dr. Halabi develops novel methods for the design and analysis of clinical studies, including innovative variable selection techniques for biomarkers and high-dimensional data. She is deeply committed to ensuring statistical rigor in clinical research so that findings are both scientifically valid and meaningful to patients and society.

Her most significant contributions include the development and validation of prognostic models for prostate cancer outcomes and the identification of surrogate endpoints for overall survival. Dr. Halabi has authored over 300 peer-reviewed publications and co-edited two foundational books in the field: Oncology Clinical Trials (2nd Edition, Demos, 2018) and the Textbook of Clinical Trials in Oncology (CRC Press, 2019). A past president of the Society for Clinical Trials and the recipient of several prestigious awards, she is also a Fellow of the Society for Clinical Trials, the American Statistical Association, and the American Society for Clinical Oncology.