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Optimal designs for a multi-response Emax model and efficient parameter estimation
Stockholm University, Faculty of Social Sciences, Department of Statistics.
(English)Manuscript (preprint) (Other academic)
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 multi-response dose finding studies. We work with diabetes dose response data and compare a system estimation approach that fits a multi-response Emax model to the data to equation-by-equation estimation that fits uni-response Emax models to the data. We then derive some optimal designs for estimating the parameters in the multi- and uni-response Emax model and study the efficiency of these designs.

Keyword [en]
multi-response Emax model, optimal design, system estimation, dose-response studies
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:su:diva-102865OAI: oai:DiVA.org:su-102865DiVA: diva2:713711
Available from: 2014-04-23 Created: 2014-04-23 Last updated: 2014-04-24
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. 38 p.
Keyword
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: 2014-05-05Bibliographically approved

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