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Increasing efficiency for estimating treatment-biomarker interactions with historical data.
Philip S Boonstra1, Jeremy Mg Taylor2, Bhramar Mukherjee2
1Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA philb@umich.edu.
Statistical Methods in Medical Research
|May 24, 2014
Summary
Leveraging historical data can improve treatment-biomarker interaction estimates in small phase II trials. Combining data sources enhances precision, even with differing models, aiding clinical trial design.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Pharmacogenomics
Background:
- Estimating treatment-biomarker interactions is crucial for personalized medicine but challenging in small phase II trials due to limited sample sizes.
- Historical data sources often contain partial information that could potentially improve parameter estimation.
Purpose of the Study:
- To investigate the utility of two historical data sources for estimating treatment-biomarker interaction parameters in randomized phase II studies.
- To quantify the potential gains in precision and efficiency offered by incorporating historical data.
Main Methods:
- Utilized Gaussian outcomes and biomarker data for analysis.
- Calculated asymptotic variance using the expected Fisher information matrix.
- Employed numerical studies and algebraic development to assess efficiency gains.
Main Results:
- Neither historical dataset alone was sufficient for parameter identification.
- Combined historical data sources provided partial information, increasing estimation precision.
- A non-negligible gain in precision was observed, irrespective of identical underlying models between datasets.
Conclusions:
- Historical data can significantly enhance the precision of treatment-biomarker interaction estimates in phase II trials.
- The proposed approach offers a viable strategy for improving efficiency in small clinical studies.
- Findings have implications for optimizing clinical trial design by effectively utilizing available data.
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