Related Experiment Video
Updated: Sep 10, 2025
A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
Published on: September 20, 2019
Incorporating data from multiple ongoing trials for Bayesian two-stage phase II single-arm studies
Susan Halabi1,2, Taehwa Choi1,3, Elizabeth Garrett-Mayer4
1Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
Background/Aim:
Basket designs have been utilized in recent oncology clinical trials due to an increased interest in precision medicine. One current successful basket trial is the American Society for Clinical Oncology Targeted Agent and Profiling Utilization Registry (TAPUR) study, a pragmatic phase II trial where patients are matched based on their tumor genomic profile to treatments that target specific genomic alterations. Despite its success, recruiting patients with rare genomic alterations remains challenging. This study aims to introduce and evaluate a Bayesian approach for integrating data from ongoing independent basket trials that share similar primary aims to improve interim decisions and final analyses and reduce necessary to evaluate treatments.
Methods:
We introduce a Bayesian two-stage phase II single-arm trial specifically for rare cancers utilizing a hierarchical Bayesian random effects model that incorporate data from ongoing trials. We compare this approach with the standard Simon two-stage design through extensive numerical simulations and apply it to real-world scenarios.
Results:
Simulation results demonstrate that in rare populations our Bayesian approach has attractive operating characteristics. The simulations show that our approach performs well across a broad set of scenarios with fixed and variable numbers of trials.
Conclusion:
Our proposed Bayesian two-stage approach effectively integrates data from multiple ongoing basket trials, enhancing the ability to recruit and analyze patients with rare genomic alterations. This approach improves the timing of interim decision-making and final analysis, making it a valuable tool for trials with slow accrual rates.
Insights
This study introduces a Bayesian approach to combine data from multiple oncology basket trials, improving recruitment and analysis for rare genomic alterations. This method enhances decision-making in clinical trials with slow patient enrollment.
Area of Science:
- Oncology
- Clinical Trials
- Biostatistics
Background:
- Basket designs are increasingly used in precision medicine oncology clinical trials.
- The American Society for Clinical Oncology Targeted Agent and Profiling Utilization Registry (TAPUR) study exemplifies a successful basket trial matching patients to targeted therapies based on genomic profiles.
- Recruiting patients with rare genomic alterations in these trials remains a significant challenge.
Purpose of the Study:
- To introduce and evaluate a Bayesian approach for integrating data from independent, ongoing basket trials with similar aims.
- To enhance interim decisions and final analyses in clinical trials.
- To reduce the number of patients required for treatment evaluation.
Main Methods:
- A Bayesian two-stage phase II single-arm trial design was developed for rare cancers.
- The approach utilizes a hierarchical Bayesian random effects model to incorporate data from concurrent trials.
- The proposed method was compared against the standard Simon two-stage design using extensive numerical simulations and applied to real-world data.
Main Results:
- Simulation results indicate that the Bayesian approach demonstrates favorable operating characteristics in rare cancer populations.
- The proposed method performs effectively across various scenarios, including those with fixed and variable numbers of contributing trials.
- The approach shows promise for improving trial efficiency and decision-making.
Conclusions:
- The Bayesian two-stage approach successfully integrates data from multiple basket trials, improving the recruitment and analysis of patients with rare genomic alterations.
- This method optimizes the timing of interim decision-making and final analyses.
- It offers a valuable strategy for clinical trials facing challenges with slow accrual rates, particularly in rare oncology indications.
More Related Videos
08:28Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms
Published on: March 3, 2023
06:55Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Related Concept Videos
01:11Clinical Trials: Overview
01:16Clinical Trials
There are four phases in a clinical trial. A phase one...
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
01:16Crossover Experiments
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
01:15Study Design in Statistics
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
00:59Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...