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TAD-SIE: sample size estimation for clinical randomized controlled trials using a Trend-Adaptive Design with a
1Department of Electrical and Computer Engineering, Princeton University, Princeton, 08544, NJ, USA. slala@princeton.edu.
Trials
|January 29, 2025
Summary
A new algorithm, Trend-Adaptive Design with a Synthetic-Intervention-Based Estimator (TAD-SIE), improves clinical trial success rates by optimizing sample size estimation. This method enhances power and reduces failure in Phase-3 drug development trials.
Area of Science:
- Clinical Trials
- Biostatistics
- Drug Development
Background:
- Phase-3 clinical trials are crucial for drug approval, providing high-level evidence on safety and effectiveness.
- A significant challenge in these trials is inadequate sample size, leading to a 30-40% failure rate.
- Inaccurate initial estimates of average treatment effects contribute to insufficient sample sizes.
Purpose of the Study:
- To address the obstacle of inadequate sample sizes in Phase-3 clinical trials.
- To introduce a novel algorithm, Trend-Adaptive Design with a Synthetic-Intervention-Based Estimator (TAD-SIE), to enhance trial design and power.
- To improve the accuracy of sample size estimation for drug development.
Main Methods:
- The TAD-SIE algorithm powers parallel-group randomized controlled trials (RCTs) using a novel trend-adaptive design (TAD) and synthetic intervention (SI).
- SI is employed to estimate individual treatment effects, simulating a cross-over design to improve trial power within sample size constraints.
- A new TAD tailored for SI is implemented, allowing for adaptive sample size increases across iterations while controlling significance levels and incorporating futility stopping.
Main Results:
- On a real-world Phase-3 RCT, TAD-SIE achieved operating points with 63% to 84% power and a 3% to 6% significance level.
- Baseline algorithms, in contrast, achieved a maximum of 49% power and a 6% significance level.
- The results demonstrate superior performance of TAD-SIE in achieving target operating points for clinical trials.
Conclusions:
- TAD-SIE is a superior trend-adaptive design method for clinical trials with rapidly measurable outcomes due to its sequential nature.
- The framework offers a valuable tool for researchers aiming to utilize the synthetic intervention algorithm in study design.
- This approach can help practitioners overcome sample size limitations and improve the success rate of drug development trials.
Keywords:
Adaptive designClinical randomized controlled trialsCounterfactual estimationCrossover designSample size estimationSynthetic interventionMore Related Videos
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