IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v11y2015i2p253-271n7.html
   My bibliography  Save this article

Multiple-Objective Optimal Designs for Studying the Dose Response Function and Interesting Dose Levels

Author

Listed:
  • Hyun Seung Won

    (Department of Statistics, North Dakota State University, Fargo, ND 58102, USA)

  • Wong Weng Kee

    (Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA)

Abstract

We construct an optimal design to simultaneously estimate three common interesting features in a dose-finding trial with possibly different emphasis on each feature. These features are (1) the shape of the dose-response curve, (2) the median effective dose and (3) the minimum effective dose level. A main difficulty of this task is that an optimal design for a single objective may not perform well for other objectives. There are optimal designs for dual objectives in the literature but we were unable to find optimal designs for 3 or more objectives to date with a concrete application. A reason for this is that the approach for finding a dual-objective optimal design does not work well for a 3 or more multiple-objective design problem.

Suggested Citation

  • Hyun Seung Won & Wong Weng Kee, 2015. "Multiple-Objective Optimal Designs for Studying the Dose Response Function and Interesting Dose Levels," The International Journal of Biostatistics, De Gruyter, vol. 11(2), pages 253-271, November.
    • Handle: RePEc:bpj:ijbist:v:11:y:2015:i:2:p:253-271:n:7
    DOI: 10.1515/ijb-2015-0044
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/ijb-2015-0044
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/ijb-2015-0044?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ritz, Christian & Streibig, Jens C., 2005. "Bioassay Analysis Using R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i05).
    2. Min Yang & Stefanie Biedermann & Elina Tang, 2013. "On Optimal Designs for Nonlinear Models: A General and Efficient Algorithm," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1411-1420, December.
    3. Miller, Frank & Dette, Holger & Guilbaud, Olivier, 2007. "Optimal designs for estimating the interesting part of a dose-effect curve," Technical Reports 2007,21, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Camilo Guzmán & Manish Bagga & Amanpreet Kaur & Jukka Westermarck & Daniel Abankwa, 2014. "ColonyArea: An ImageJ Plugin to Automatically Quantify Colony Formation in Clonogenic Assays," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-9, March.
    2. Jens Peter Andersen & Björn Hammarfelt, 2011. "Price revisited: on the growth of dissertations in eight research fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 371-383, August.
    3. Belmiro P. M. Duarte, 2023. "Exact Optimal Designs of Experiments for Factorial Models via Mixed-Integer Semidefinite Programming," Mathematics, MDPI, vol. 11(4), pages 1-17, February.
    4. LonÄ arić, Željka & K. Hackenberger, Branimir, 2013. "Stage and age structured Aedes vexans and Culex pipiens (Diptera: Culicidae) climate-dependent matrix population model," Theoretical Population Biology, Elsevier, vol. 83(C), pages 82-94.
    5. Haoyu Wang & Chongqi Zhang, 2022. "The mixture design threshold accepting algorithm for generating $$\varvec{D}$$ D -optimal designs of the mixture models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(3), pages 345-371, April.
    6. Qing Liu & Yihui (Elina) Tang, 2015. "Construction of Heterogeneous Conjoint Choice Designs: A New Approach," Marketing Science, INFORMS, vol. 34(3), pages 346-366, May.
    7. Signe M. Jensen & Christian Ritz, 2015. "Simultaneous Inference for Model Averaging of Derived Parameters," Risk Analysis, John Wiley & Sons, vol. 35(1), pages 68-76, January.
    8. Christoph Kunz & Dominic J. Sturm & Markus Sökefeld & Roland Gerhards, 2017. "Weed suppression and early sugar beet development under different cover crop mulches," Plant Protection Science, Czech Academy of Agricultural Sciences, vol. 53(3), pages 187-193.
    9. Holst, Niels & Lang, Andreas & Lövei, Gabor & Otto, Mathias, 2013. "Increased mortality is predicted of Inachis io larvae caused by Bt-maize pollen in European farmland," Ecological Modelling, Elsevier, vol. 250(C), pages 126-133.
    10. Muhammad Javaid Akhter & Solvejg Kopp Mathiassen & Zelalem Eshetu Bekalu & Henrik Brinch-Pedersen & Per Kudsk, 2021. "Increased Activity of 5-Enolpyruvylshikimate-3-phosphate Synthase (EPSPS) Enzyme Describe the Natural Tolerance of Vulpia myuros to Glyphosate in Comparison with Apera spica-venti," Agriculture, MDPI, vol. 11(8), pages 1-15, July.
    11. Erickson, Richard A. & Cox, Stephen B. & Oates, Jessica L. & Anderson, Todd A. & Salice, Christopher J. & Long, Kevin R., 2014. "A Daphnia population model that considers pesticide exposure and demographic stochasticity," Ecological Modelling, Elsevier, vol. 275(C), pages 37-47.
    12. Lenka Filová & Radoslav Harman, 2020. "Ascent with quadratic assistance for the construction of exact experimental designs," Computational Statistics, Springer, vol. 35(2), pages 775-801, June.
    13. Nedka Dechkova Nikiforova & Rossella Berni & Jesús Fernando López‐Fidalgo, 2022. "Optimal approximate choice designs for a two‐step coffee choice, taste and choice again experiment," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1895-1917, November.
    14. Radoslav Harman & Eva Benková, 2017. "Barycentric algorithm for computing D-optimal size- and cost-constrained designs of experiments," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(2), pages 201-225, February.
    15. Duarte, Belmiro P.M. & Atkinson, Anthony C. & Granjo, Jose F.O & Oliveira, Nuno M.C, 2022. "Optimal design of experiments for implicit models," LSE Research Online Documents on Economics 107584, London School of Economics and Political Science, LSE Library.
    16. Christian Andreasen & Zahra Bitarafan & Johanna Fenselau & Christoph Glasner, 2018. "Exploiting Waste Heat from Combine Harvesters to Damage Harvested Weed Seeds and Reduce Weed Infestation," Agriculture, MDPI, vol. 8(3), pages 1-12, March.
    17. Chen, Ping-Yang & Chen, Ray-Bing & Chen, Yu-Shi & Wong, Weng Kee, 2023. "Numerical Methods for Finding A-optimal Designs Analytically," Econometrics and Statistics, Elsevier, vol. 28(C), pages 155-162.
    18. Bretz, Frank & Dette, Holger & Pinheiro, José, 2008. "Practical considerations for optimal designs in clinical dose finding studies," Technical Reports 2008,22, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    19. Floriane Larras & Agnès Bouchez & Frédéric Rimet & Bernard Montuelle, 2012. "Using Bioassays and Species Sensitivity Distributions to Assess Herbicide Toxicity towards Benthic Diatoms," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-9, August.
    20. Yu, Jun & Meng, Xiran & Wang, Yaping, 2023. "Optimal designs for semi-parametric dose-response models under random contamination," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:ijbist:v:11:y:2015:i:2:p:253-271:n:7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.