IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v178y2023ics0167947322001955.html
   My bibliography  Save this article

Optimal designs for semi-parametric dose-response models under random contamination

Author

Listed:
  • Yu, Jun
  • Meng, Xiran
  • Wang, Yaping

Abstract

With the increasing popularity of personalized medicine, it is more and more crucial to capture not only the dose-effect but also the effects of the prognostic factors due to individual differences in a dose-response experiment. This paper considers the design issue for predicting semi-parametric dose-response curves in the presence of linear effects of covariates. Inspired by the Neyman-Pearson paradigm, a novel design criterion, namely bias constraint optimality, is introduced to minimize the overall prediction error. The corresponding equivalence theorems are established, the characteristics of the optimal designs are shown, and an equivalent bias compound optimality criterion is proposed for practical implementation. Based on the obtained theoretical results, efficient algorithms for searching for optimal designs are developed. Numerical simulations are given to illustrate the superior performance of the obtained optimal designs.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:csdana:v:178:y:2023:i:c:s0167947322001955
    DOI: 10.1016/j.csda.2022.107615
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947322001955
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2022.107615?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. Fred J. Hickernell, 2002. "Uniform designs limit aliasing," Biometrika, Biometrika Trust, vol. 89(4), pages 893-904, December.
    2. Yu, Jun & Kong, Xiangshun & Ai, Mingyao & Tsui, Kwok Leung, 2018. "Optimal designs for dose–response models with linear effects of covariates," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 217-228.
    3. Norman Draper & Irwin Guttman, 1992. "Treating bias as variance for experimental design purposes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 44(4), pages 659-671, December.
    4. Theodore T. Allen & Liyang Yu & John Schmitz, 2003. "An experimental design criterion for minimizing meta‐model prediction errors applied to die casting process design," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(1), pages 103-117, January.
    5. Huang, Hengzhen & Chen, Xueping, 2021. "Compromise design for combination experiment of two drugs," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    6. Dette, Holger & Bretz, Frank & Pepelyshev, Andrey & Pinheiro, José, 2008. "Optimal Designs for Dose-Finding Studies," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 1225-1237.
    7. 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.
    8. Claeskens,Gerda & Hjort,Nils Lid, 2008. "Model Selection and Model Averaging," Cambridge Books, Cambridge University Press, number 9780521852258.
    9. A. C. Atkinson, 2015. "Optimum designs for two treatments with unequal variances in the presence of covariates," Biometrika, Biometrika Trust, vol. 102(2), pages 494-499.
    10. Chen, Ping-Yang & Chen, Ray-Bing & Lin, C. Devon, 2018. "Optimizing two-level orthogonal arrays for simultaneously estimating main effects and pre-specified two-factor interactions," Computational Statistics & Data Analysis, Elsevier, vol. 118(C), pages 84-97.
    11. H. Dette & C. Kiss & M. Bevanda & F. Bretz, 2010. "Optimal designs for the emax, log-linear and exponential models," Biometrika, Biometrika Trust, vol. 97(2), pages 513-518.
    12. Kaishev, V. K., 1989. "Optimal experimental designs for the B-spline regression," Computational Statistics & Data Analysis, Elsevier, vol. 8(1), pages 39-47, May.
    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. Wiens, Douglas P., 2021. "Robust designs for dose–response studies: Model and labelling robustness," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    2. Kira Alhorn & Holger Dette & Kirsten Schorning, 2021. "Optimal Designs for Model Averaging in non-nested Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 745-778, August.
    3. Yu, Jun & Kong, Xiangshun & Ai, Mingyao & Tsui, Kwok Leung, 2018. "Optimal designs for dose–response models with linear effects of covariates," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 217-228.
    4. Kitagawa, Toru & Muris, Chris, 2016. "Model averaging in semiparametric estimation of treatment effects," Journal of Econometrics, Elsevier, vol. 193(1), pages 271-289.
    5. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    6. Davide Fiaschi & Andrea Mario Lavezzi & Angela Parenti, 2020. "Deep and Proximate Determinants of the World Income Distribution," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 66(3), pages 677-710, September.
    7. Fabio Canova & Christian Matthes, 2021. "Dealing with misspecification in structural macroeconometric models," Quantitative Economics, Econometric Society, vol. 12(2), pages 313-350, May.
    8. Chatterjee, Kashinath & Qin, Hong, 2008. "A new look at discrete discrepancy," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 2988-2991, December.
    9. Hong Qin & Na Zou & Kashinath Chatterjee, 2009. "Connection between uniformity and minimum moment aberration," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 70(1), pages 79-88, June.
    10. Fang Pang & Min-Qian Liu, 2012. "A note on connections among criteria for asymmetrical factorials," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(1), pages 23-32, January.
    11. Zhongqi Liang & Qihua Wang & Yuting Wei, 2022. "Robust model selection with covariables missing at random," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(3), pages 539-557, June.
    12. HAEDO, Christian & MOUCHART , Michel & ,, 2013. "Specialized agglomerations with areal data: model and detection," LIDAM Discussion Papers CORE 2013060, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    13. Bhattacharya, Debopam & Dupas, Pascaline, 2012. "Inferring welfare maximizing treatment assignment under budget constraints," Journal of Econometrics, Elsevier, vol. 167(1), pages 168-196.
    14. Schomaker Michael & Heumann Christian, 2011. "Model Averaging in Factor Analysis: An Analysis of Olympic Decathlon Data," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(1), pages 1-15, January.
    15. Dirick, Lore & Claeskens, Gerda & Vasnev, Andrey & Baesens, Bart, 2022. "A hierarchical mixture cure model with unobserved heterogeneity for credit risk," Econometrics and Statistics, Elsevier, vol. 22(C), pages 39-55.
    16. Tumala, Mohammed M & Olubusoye, Olusanya E & Yaaba, Baba N & Yaya, OlaOluwa S & Akanbi, Olawale B, 2017. "Forecasting Nigerian Inflation using Model Averaging methods: Modelling Frameworks to Central Banks," MPRA Paper 88754, University Library of Munich, Germany, revised Feb 2018.
    17. Mingyao Ai & Shuyuan He, 2006. "Interaction balance for symmetrical factorial designs with generalized minimum aberration," Statistical Papers, Springer, vol. 47(1), pages 125-135, January.
    18. E. Androulakis & C. Koukouvinos, 2013. "A new variable selection method for uniform designs," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(12), pages 2564-2578, December.
    19. José Manuel Cordero Ferrera & Manuel Muñiz Pérez & Rosa Simancas Rodríguez, 2015. "The influence of socioeconomic factors on cognitive and non-cognitive educational outcomes," Investigaciones de Economía de la Educación volume 10, in: Marta Rahona López & Jennifer Graves (ed.), Investigaciones de Economía de la Educación 10, edition 1, volume 10, chapter 21, pages 413-438, Asociación de Economía de la Educación.
    20. Satoshi Hattori & Masayuki Henmi, 2014. "Stratified doubly robust estimators for the average causal effect," Biometrics, The International Biometric Society, vol. 70(2), pages 270-277, June.

    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:eee:csdana:v:178:y:2023:i:c:s0167947322001955. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.