Saving Time and Resources in Clinical Trials with Adaptive Designs ### Sponsor’s Challenge: Slower than expected recruitment, thus anticipating a lengthy clinical trial. The sponsor of a clinical trial faced an increasingly common problem: the recruitment rate for their study was much lower than anticipated. This unexpected delay put the study’s completion timeline at risk, threatening to inflate costs and delay the potential availability of a critical therapy. The sponsor sought to amend the study protocol to potentially wrap up the trial earlier, while still ensuring robust results and regulatory compliance.
The trial was designed with two co-primary endpoints with alpha-splitting. However, from both a scientific and a marketing perspective, one endpoint held significantly more importance than the other.
This scenario presented two important challenges:
- Overcoming the slow recruitment rate without compromising the study’s integrity.
- Streamlining the trial while maintaining statistical rigor across two co-primary endpoints.
Our Solution: A Risk-mitigated, Adaptive Design
To address these challenges, we proposed an amendment to the protocol to include adaptive design elements to gain flexibility and efficiency. The key elements included:
Hierarchical Endpoint Testing: Instead of the traditional approach of alpha-sharing (dividing statistical significance thresholds between endpoints), we recommended hierarchical testing. This prioritized the more important endpoint, ensuring it received the statistical focus it deserved.
Group Sequential Design (GSD): A GSD with alpha-spending function was implemented to allow an interim analysis right away but still allowing for the protocol amendment to be accepted and data management and the blinding process to be updated. A data monitoring committee and the corresponding charter was also put in place before the interim analysis. The alpha-spending methodology lends the possibility to stop the trial early for efficacy. For this particular trial, early stopping was allowed if -
- Statistical significance was achieved on both endpoints or if
- Statistical significance was achieved on the more critical endpoint while the other endpoint was deemed futile at the interim .
- Conditional Power and Sample Size Re-estimation: Sample size re-estimation was also incorporated at the interim look to allow for achieving the target power at the end of the trial if the interim analysis showed that the conditional power for both endpoints were in the “promising zone”. These adaptive strategies ensured that the trial was efficient and aligned with the sponsor’s objectives.
The Results: A Time-Saving Breakthrough
When the Data Monitoring Committee (DMC) reviewed the results at the interim analysis, the first (and more critical) endpoint achieved statistical significance; conditional power on the second endpoint was less than 2%. Based on this outcome, the decision was made to stop the trial early for efficacy.
This decision brought immediate and tangible benefits:
- Time Savings: By incorporating the GSD and stopping early, the trial concluded 6 to 12 months earlier than it would have under the original design.
- Cost Reduction: The sponsor avoided additional costs associated with extended recruitment and follow-up.
- Focus on Key Endpoint: Hierarchical testing ensured the trial prioritized the endpoint with the most scientific and marketing value, maximizing the trial’s impact.
Conclusion
This case study underscores the transformative potential of adaptive trial designs in overcoming challenges like slow recruitment and complex endpoints. By employing hierarchical endpoint testing, group sequential design, and conditional power-based sample size re-estimation, we helped the sponsor achieve their goals efficiently and effectively.
If the sponsor had not sought an innovative solution, the trial might still be ongoing. Instead, they now have critical results in hand, ready to advance their therapy to the next stage—saving valuable time for both their team and the patients awaiting treatment.
Adaptive designs are not just a tool for trial optimization; they are a strategic approach to delivering timely, cost-effective, and scientifically robust results. This success story is a testament to the power of flexibility and innovation in clinical research.