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

Abstract:

: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.

journal_name

Biometrics

journal_title

Biometrics

authors

Belanger BA,Davidian M,Giltinan DM

subject

Has Abstract

pub_date

1996-03-01 00:00:00

pages

158-75

issue

1

eissn

0006-341X

issn

1541-0420

journal_volume

52

pub_type

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