Journal Article

DKFZ-2020-02858

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png An R-shiny application to calculate optimal designs for single substance and interaction trials in dose response experiments.

Holland-Letz, T. (First author)DKFZ* ; Kopp-Schneider, A. (Last author)DKFZ*

2021
Elsevier Science Amsterdam [u.a.]

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Please use a persistent id in citations: doi:10.1016/j.toxlet.2020.11.018

Abstract: Optimal experimental design theory proposes choosing specific settings in experimental trials in order to maximize the precision of the resulting parameter estimates. In dose response experiments, this corresponds to choosing the optimal dose levels for every available observation, and can be applied both to singular dose-response relationships and to interaction experiments where two substances are given simultaneously at several different mixture ratios ('ray designs'). While the theory of experimental design for this situation is well developed, the mathematical complexity prevents widespread use in practical applications. A simple to use application making the theory accessible to practitioners is thus very desirable.Results from established optimal experimental design theory are applied to dose response applications, focusing on log-logistic and Weibull class dose response functions. Suitable optimal design algorithms to solve these problems are implemented into an R-shiny based online application.The application provides an interface to easily calculate D-optimal designs not only for singular dose experiments, but also for interaction trials with several combination rays of substances. Furthermore, the app also allows evaluating the efficiency of existing candidate designs, and finally allows construction of designs which perform robustly under different assumptions in regard to the true parameters.

Keyword(s): Combination index ; D-optimal design ; Dose–response studies ; Optimal experimental design ; R-shiny ; Web application

Note: #EA:C060#LA:C060#

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Contributing Institute(s):

1. C060 Biostatistik (C060)

Research Program(s):

1. 313 - Krebsrisikofaktoren und Prävention (POF4-313) (POF4-313)

Appears in the scientific report 2021

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Database coverage:
; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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