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Sample Size Estimation Using a Partially Clustered Frailty Model for Biomarker-Strategy Designs With Multiple
Derek Dinart1,2, Virginie Rondeau1,3, Carine Bellera1,2
1Bordeaux Population Health Research Center, Epicene Team, U1219, University of Bordeaux, Inserm, Bordeaux, France.
Pharmaceutical Statistics
|July 17, 2024
Summary
Biomarker-strategy design (BSD) optimizes personalized medicine by comparing treatment strategies. A new simulation method using a partially clustered frailty model (PCFM) helps estimate sample sizes for these trials.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Personalized Medicine
Background:
- Biomarker-guided therapy is advancing medical research.
- Optimizing biomarker use requires innovative study designs.
- Biomarker-strategy design (BSD) focuses on treatment strategies, not just molecules.
Purpose of the Study:
- To propose a simulation method for sample size estimation in biomarker-strategy designs (BSD) with multiple targeted treatments.
- To evaluate factors influencing sample size in BSD.
- To offer an alternative to traditional sample size calculation methodologies.
Main Methods:
- A simulation method based on a partially clustered frailty model (PCFM) was developed.
- An extension of the Freidlin formula was used for sample size estimation.
- The method was applied to BSD with multiple targeted treatments.
Main Results:
- The proposed PCFM simulation method provides sample size estimates for BSD.
- Key factors influencing sample size include treatment effect heterogeneity, proportion of biomarker-negative patients, and randomization ratio.
- The PCFM is suitable for the data structure in BSD.
Conclusions:
- The PCFM-based simulation method is a viable approach for sample size calculation in biomarker-strategy designs.
- This method offers an alternative to traditional statistical methodologies for complex trial designs.
- Accurate sample size estimation is crucial for the success of biomarker-guided therapeutic strategies.
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