The effect of variance function estimation on nonlinear calibration inference in immunoassay data.
Often with data from immunoassays, the concentration-response relationship is nonlinear and intra-assay response variance is heterogeneous. Estimation of the standard curve is usually based on a nonlinear heteroscedastic regression model for concentration-response, where variance is modeled as a function of mean response and additional variance parameters. This paper discusses calibration inference for immunoassay data which exhibit this nonlinear heteroscedastic mean-variance relationship. An assessment of the effect of variance function estimation in three types of approximate large-sample confidence intervals for unknown concentrations is given by theoretical and empirical investigation and application to two examples. A major finding is that the accuracy of such calibration intervals depends critically on the nature of response variance and the quality with which variance parameters are estimated.
Duke Scholars
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Related Subject Headings
- Swine
- Statistics & Probability
- Relaxin
- Reference Standards
- Recombinant Proteins
- Radioimmunoassay
- Pharmaceutical Preparations
- Nonlinear Dynamics
- Monte Carlo Method
- Immunoassay
Citation
Published In
ISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Swine
- Statistics & Probability
- Relaxin
- Reference Standards
- Recombinant Proteins
- Radioimmunoassay
- Pharmaceutical Preparations
- Nonlinear Dynamics
- Monte Carlo Method
- Immunoassay