The effect of variance function estimation on nonlinear calibration inference in immunoassay data.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Belanger, BA; Davidian, M; Giltinan, DM

Published Date

  • March 1996

Published In

Volume / Issue

  • 52 / 1

Start / End Page

  • 158 - 175

PubMed ID

  • 8934590

Pubmed Central ID

  • 8934590

International Standard Serial Number (ISSN)

  • 0006-341X

Language

  • eng

Conference Location

  • United States