Literature DB >> 27340305

Optimal designs for comparing curves.

Holger Dette1, Kirsten Schorning1.   

Abstract

We consider the optimal design problem for a comparison of two regression curves, which is used to establish the similarity between the dose response relationships of two groups. An optimal pair of designs minimizes the width of the confidence band for the difference between the two regression functions. Optimal design theory (equivalence theorems, efficiency bounds) is developed for this non standard design problem and for some commonly used dose response models optimal designs are found explicitly. The results are illustrated in several examples modeling dose response relationships. It is demonstrated that the optimal pair of designs for the comparison of the regression curves is not the pair of the optimal designs for the individual models. In particular it is shown that the use of the optimal designs proposed in this paper instead of commonly used "non-optimal" designs yields a reduction of the width of the confidence band by more than 50%.

Entities:  

Keywords:  confidence band; optimal design; similarity of regression curves

Year:  2016        PMID: 27340305      PMCID: PMC4914141          DOI: 10.1214/15-AOS1399

Source DB:  PubMed          Journal:  Ann Stat        ISSN: 0090-5364            Impact factor:   4.028


  6 in total

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Authors:  W Zhu; W K Wong
Journal:  J Biopharm Stat       Date:  2000-02       Impact factor: 1.051

2.  Combining multiple comparisons and modeling techniques in dose-response studies.

Authors:  F Bretz; J C Pinheiro; M Branson
Journal:  Biometrics       Date:  2005-09       Impact factor: 2.571

3.  Assessing nonsuperiority, noninferiority, or equivalence when comparing two regression models over a restricted covariate region.

Authors:  W Liu; F Bretz; A J Hayter; H P Wynn
Journal:  Biometrics       Date:  2009-12       Impact factor: 2.571

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

Authors:  S Gsteiger; F Bretz; W Liu
Journal:  J Biopharm Stat       Date:  2011-07       Impact factor: 1.051

5.  On the efficiency of two-stage response-adaptive designs.

Authors:  Holger Dette; Björn Bornkamp; Frank Bretz
Journal:  Stat Med       Date:  2012-08-03       Impact factor: 2.373

6.  Discussion of Dette and Trampisch's paper "A general approach to D-optimal designs for weighted univariate polynomial regression models"

Authors:  Weng Kee Wong
Journal:  J Korean Stat Soc       Date:  2010-03-01       Impact factor: 0.805

  6 in total
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  2 in total

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