Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Optimal designs for a multiresponse Emax model and efficient parameter estimation
Stockholm University, Faculty of Social Sciences, Department of Statistics.
Number of Authors: 12016 (English)In: Biometrical Journal, ISSN 0323-3847, E-ISSN 1521-4036, Vol. 58, no 3, p. 518-534Article in journal (Refereed) Published
Abstract [en]

The aim of dose finding studies is sometimes to estimate parameters in a fitted model. The precision of the parameter estimates should be as high as possible. This can be obtained by increasing the number of subjects in the study, N, choosing a good and efficient estimation approach, and by designing the dose finding study in an optimal way. Increasing the number of subjects is not always feasible because of increasing cost, time limitations, etc. In this paper, we assume fixed N and consider estimation approaches and study designs for multiresponse dose finding studies. We work with diabetes dose-response data and compare a system estimation approach that fits a multiresponse Emax model to the data to equation-by-equation estimation that fits uniresponse Emax models to the data. We then derive some optimal designs for estimating the parameters in the multi- and uniresponse Emax model and study the efficiency of these designs.

Place, publisher, year, edition, pages
2016. Vol. 58, no 3, p. 518-534
Keywords [en]
Dose-response studies, Multiresponse Emax models, Optimal designs, Precise parameter estimates, System estimation
National Category
Mathematics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:su:diva-175556DOI: 10.1002/bimj.201400203ISI: 000375679000005PubMedID: 26467148OAI: oai:DiVA.org:su-175556DiVA, id: diva2:1368291
Available from: 2019-11-06 Created: 2019-11-06 Last updated: 2019-11-06Bibliographically approved
In thesis
1. Estimation and optimal designs for multi-response Emax models
Open this publication in new window or tab >>Estimation and optimal designs for multi-response Emax models
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis concerns optimal designs and estimation approaches for a class of nonlinear dose response models, namely multi-response Emax models. These models describe the relationship between the dose of a drug and two or more efficacy and/or safety variables. In order to obtain precise parameter estimates it is important to choose efficient estimation approaches and to use optimal designs to control the level of the doses administered to the patients in the study.

We provide some optimal designs that are efficient for estimating the parameters, a subset of the parameters, and a function of the parameters in multi-response Emax models. The function of interest is an estimate of the best dose to administer to a group of patients. More specifically the dose that maximizes the Clinical Utility Index (CUI) which assesses the net benefit of a drug taking both effects and side-effects into account. The designs derived in this thesis are locally optimal, that is they depend upon the true parameter values. An important part of this thesis is to study how sensitive the optimal designs are to misspecification of prior parameter values.

For multi-response Emax models it is possible to derive maximum likelihood (ML) estimates separately for the parameters in each dose response relation. However, ML estimation can also be carried out simultaneously for all response profiles by making use of dependencies between the profiles (system estimation). In this thesis we compare the performance of these two approaches by using a simulation study where a bivariate Emax model is fitted and by fitting a four dimensional Emax model to real dose response data. The results are that system estimation can substantially increase the precision of parameter estimates, especially when the correlation between response profiles is strong or when the study has not been designed in an efficient way.

Place, publisher, year, edition, pages
Stockholm: Department of Statistics, Stockholm University, 2014. p. 38
Keywords
multi-response Emax models, Clinical Utility Index (CUI), optimal designs, system estimation, dose-response studies.
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:su:diva-102888 (URN)978-91-7447-909-6 (ISBN)
Public defence
2014-05-30, Nordenskiöldsalen, Geovetenskapens hus, Svante Arrhenius väg 12, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defence the following papers were unpublished and had a status as follows: Paper 1: Manuscript; Paper 2: Manuscript; Paper 3: Manuscript; Paper 4: Manuscript.

Available from: 2014-05-08 Created: 2014-04-24 Last updated: 2019-12-06Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMed

Search in DiVA

By author/editor
Magnúsdóttir, Bergrún Tinna
By organisation
Department of Statistics
In the same journal
Biometrical Journal
Mathematics

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 2 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf