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

Jun Yu, Xiran Meng and Yaping Wang

Computational Statistics & Data Analysis, 2023, vol. 178, issue C

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.

Keywords: Constraint and compound optimal designs; Neyman-Pearson paradigm; Partial linear model; Personalized medicine; Spline regression (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947322001955
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:178:y:2023:i:c:s0167947322001955

DOI: 10.1016/j.csda.2022.107615

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().