Tuesday, Aug 5: 10:30 AM - 12:20 PM
0598
Topic-Contributed Paper Session
Music City Center
Room: CC-104D
To foster access to high-quality, affordable, and appropriate health technologies and interventions, and to effectively address public health emergencies, there is a growing demand to develop new interventions to treat acute and chronic diseases with unmet medical needs. However, over the years, traditional randomized clinical trials (RCTs) have increasingly become unable to meet the challenges of evaluating new therapies for patients with unmet medical needs in a timely manner and are known to be associated with increasingly high costs. Accordingly, much attention is paid to the so-called complex innovative designs (CIDs), with a view to accelerating drug development.
One component of CIDs is master protocol trial design, which involves multiple sub-studies, evaluating one or more investigational drugs, in one or more disease subtypes, with one or more objectives, all within the same overall trial structure. Master protocol trials that are well designed and executed can accelerate drug development by maximizing the amount of information obtained from the research effort. These trials can be updated to incorporate new scientific information, as medical science advances. Master protocols also reduce administrative costs and time associated with starting up new trial sites for each investigational drug. They can also increase data quality and efficiency through shared and reusable infrastructure.
The session aims to address emerging topics in the design and analysis of master protocol trials and CIDs in general, with emphasis on innovative methods and software, including the use of artificial intelligence, machine learning, and digital technology to streamline the data collection and reporting infrastructure, accelerate patient recruitment, increase diversity and identify the appropriate drugs and biologic targets for trials. Consistent with the 2025 JSM's theme, "Statistics, Data Science, and AI Enriching Society", in this session, world-renowned leaders and scientists from government, academia and the pharmaceutical industry are invited to present and discuss the state-of-the-science approaches to inform decision and drive innovation for master protocol trials.
We believe that statisticians, biostatisticians and applied researchers from government, academia and industry, who are interested in cutting-edge statistical methods and software for complex innovative clinical trials, will be attracted by and benefit from in this unique invited session. Specifically, the first speaker Dr. Lei Nie will present guidelines improving the design and conduct of complex innovative clinical trials from the regulatory perspective. The second speaker Dr. Ting Ye will present an innovative estimand framework and robust inference methods for estimating treatment effects in platform trials. The third speaker Dr. Bo Huang will discuss innovative methods borrowing information from non-concurrent control to enhance the design and inference of platform trials. The fourth speaker Dr. Cindy Lu will discuss current practice for master protocol trials and innovative Bayesian methods borrowing real-world data enhancing the design and analysis of basket trials.
Complex Innovative Trial Design
Platform trial
master protocol
real-world data
basket trial
Applied
Yes
Main Sponsor
Biopharmaceutical Section
Co Sponsors
ENAR
International Chinese Statistical Association
Presentations
Keywords
Master protocols
Speaker
Ting Ye, University of Washington
Platform trials are a type of clinical trial in which multiple treatments can be evaluated simultaneously using a single infrastructure. However, implementing control arms in platform trials can be challenging since new treatments are continually added and tested over time. Non-concurrent control (NCC) in platform trials refers to the use of a common control group for multiple experimental arms that are not evaluated at the same time. Although it is appealing to borrow NCC data to evaluate treatment effect for the benefits of faster accrual, flexibility and improved statistical efficiency, regulatory acceptance of NCC analysis results may be hindered by bias arising from lack of randomization. In this presentation, I will discuss the scenarios when borrowing NCC data in statistical analysis is appropriate, and the statistical methods that can be used to minimize statistical bias. In particular, when the outcome of interest is a time-to-event endpoint, I will introduce a novel approach to borrow the concurrent observation part of the NCC data by left truncation using a simple decision-making flowchart and with the use of restricted mean survival time for robust statistical inference.
Keywords
platform trial
Non-concurrent control
Time-to-event
restricted mean survival time
Basket trials that consists of multiple cancer types have increasingly gained attention in recent years. As one of the master protocols, basket trial provides a flexible framework for evaluating new treatment under various patient subgroups simultaneously. A common practice in phase II basket trials is to include patients with various cancer types treated with investigational drug, which poses challenges for further development decisions without comparable control information. To handle the lack of comparator issue, we propose to use the external control arm (ECA) as a benchmark, which may include external clinical trial data or real world data (RWD). Compared with the conventional design of clinical trial, basket trial allows the potential borrowing of information across sub-trials. In this paper, our focus centered on the fundamental question of the design of basket trial: when to borrow across sub-trials and how to borrow? Three different borrowing approaches are introduced and compared, including the treatment effect borrowing (TEB), treatment response borrowing (TRB), and no borrowing. The two borrowing approaches are implemented using the Bayesian commensurate predictive prior (CPP) method. Based on the results of simulation experiments, we demonstrated the key factors that influence the performance of basket trial borrowing approaches, including 1) the "nugget pattern" (i.e., number of ineffective sub-trials), 2) the effective sample size of the basket trial and RWD, and 3) which scale (the treatment effects or the treatment responses) are more similar across sub-trials. Practical recommendations are provided using a decision-making flowchart for the design of basket trial. We finally illustrate our decision-making flowchart by reexamining the DESTINY-PanTumor02 Trial under different hypothetical scenarios.
Keywords
basket trial
real-world evidence
master protocol
Bayesian commensurate predictive prior
borrowing information
Temporal changes exist in clinical trials. Over time, shifts in patients' characteristics, trial conduct, and other features of a clinical trial may occur. In typical randomized clinical trials, temporal effects, that is, the impact of temporal changes on clinical outcomes and study analysis, are largely mitigated by randomization and usually need not be explicitly addressed. However, temporal effects can be a serious obstacle for conducting clinical trials with complex designs, including the adaptive platform trials that are gaining popularity in recent medical product development. In this paper, we introduce a Bayesian robust prior for mitigating temporal effects based on a hidden Markov model, and propose a particle filtering algorithm for computation. We conduct simulation studies to evaluate the performance of the proposed method and provide illustration examples based on trials of Ebola virus disease therapeutics and hemostat in vascular surgery.