Simultaneous confidence bands for nonlinear regression models with application to population pharmacokinetic analyses

Gsteiger, S., Bretz, F. and Liu, W. (2011) Simultaneous confidence bands for nonlinear regression models with application to population pharmacokinetic analyses Journal of Biopharmaceutical Statistics, 21, (4), pp. 708-725. (doi:10.1080/10543406.2011.551332).


Full text not available from this repository.


Many applications in biostatistics rely on nonlinear regression models, such as, for example, population pharmacokinetic and pharmacodynamic modeling, or modeling approaches for dose-response characterization and dose selection. Such models are often expressed as nonlinear mixed-effects models, which are implemented in all major statistical software packages. Inference on the model curve can be based on the estimated parameters, from which pointwise confidence intervals for the mean profile at any single point in the covariate region (time, dose, etc.) can be derived. These pointwise confidence intervals, however, should not be used for simultaneous inferences beyond that single covariate value. If assessment over the entire covariate region is required, the joint coverage probability by using the combined pointwise confidence intervals is likely to be less than the nominal coverage probability. In this paper we consider simultaneous confidence bands for the mean profile over the covariate region of interest and propose two large-sample methods for their construction. The first method is based on the Schwarz inequality and an asymptotic ? 2 distribution. The second method relies on simulating from a multivariate normal distribution. We illustrate the methods with the pharmacokinetics of theophylline. In addition, we report the results of an extensive simulation study to investigate the operating characteristics of the two construction methods. Finally, we present extensions to construct simultaneous confidence bands for the difference of two models and to assess equivalence between two models in biosimilarity applications

Item Type: Article
Digital Object Identifier (DOI): doi:10.1080/10543406.2011.551332
ISSNs: 1054-3406 (print)
Subjects: H Social Sciences > HA Statistics
Organisations: Statistics, Statistical Sciences Research Institute
ePrint ID: 337147
Date :
Date Event
22 April 2011e-pub ahead of print
Date Deposited: 19 Apr 2012 13:54
Last Modified: 17 Apr 2017 17:18
Further Information:Google Scholar

Actions (login required)

View Item View Item