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Adaptive sampling in two-phase designs: a biomarker study for progression in arthritis
Michael A McIsaac1, Richard J Cook2
1Queen's University, 99 University Avenue, Kingston, Ontario, K7L 3N6, Canada.
Statistics in Medicine
|May 8, 2015
Summary
Adaptive two-phase designs use pilot data to improve statistical efficiency in epidemiological studies. This approach approximates optimal sampling, proving effective in biomarker research.
Area of Science:
- Epidemiology
- Biostatistics
- Biomarker Research
Background:
- Response-dependent two-phase designs are increasingly used in epidemiology for efficient, resource-constrained sampling.
- Optimal designs are challenging due to the need for unknown parameter specification.
Purpose of the Study:
- To propose adaptive two-phase designs that leverage internal pilot study data.
- To approximate optimal sampling schemes for analyses using mean score estimating equations.
Main Methods:
- Developed adaptive two-phase designs utilizing information from an internal pilot study.
- Assessed estimator properties via simulation for frequency analysis.
- Applied the design procedure to a rheumatology biomarker study.
Main Results:
- The proposed adaptive designs demonstrated frequency properties similar to optimal designs.
- Simulations confirmed the effectiveness of the adaptive approach.
Conclusions:
- Adaptive two-phase designs offer a practical method to approximate optimal sampling strategies.
- This approach enhances statistical efficiency in epidemiological studies, particularly in biomarker research.
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