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Constructing a bootstrap confidence interval for the unknown concentration in radioimmunoassay
1Faculty of Industrial Engineering and Management, Technion - Israel Institute of Technology, Haifa.
Statistics in Medicine
|May 15, 1995
Summary
This study introduces a bootstrap method for creating inverse confidence intervals in radioimmunoassay calibration. This approach enhances the accuracy of statistical estimations in biological assays.
Area of Science:
- Biostatistics
- Radioimmunoassay
- Statistical Modeling
Background:
- Radioimmunoassay (RIA) analysis involves a statistical challenge known as calibration or inverse regression.
- Accurate confidence intervals are crucial for reliable RIA results.
Purpose of the Study:
- To propose a novel bootstrap procedure for constructing inverse confidence intervals.
- To address the univariate calibration problem in RIA.
Main Methods:
- A bootstrap procedure is developed for estimating inverse confidence intervals.
- Calibration curves are estimated using both parametric and non-parametric regression techniques.
Main Results:
- The study demonstrates the application of the proposed bootstrap method.
- The effectiveness of the methods is illustrated through a practical example.
Conclusions:
- The bootstrap procedure offers a viable approach for inverse confidence interval construction in RIA calibration.
- The proposed methods provide valuable tools for statistical analysis in RIA.