Optimal designs for dose–response models with linear effects of covariates

Jun Yu, Xiangshun Kong, Mingyao Ai and Kwok Leung Tsui

Computational Statistics & Data Analysis, 2018, vol. 127, issue C, 217-228

Abstract: Personalized medicine is becoming more and more important nowadays since the efficacy of a certain medicine vary among different patients. This requires to combine the effects of the prognostic factors or covariates along with different dosages when planning a dose–response experiment. Statistically, this corresponds to the construction of optimal designs for estimating dose–response curves in the presence of covariates. Some characteristics of the optimal designs are derived in order to search such optimal designs efficiently, and an equivalence theorem of the locally ϕs-optimal designs is established accordingly. Computational issues are also studied and presented with theoretical backups. As applications of the above theories, the locally optimal designs are searched out in several situations. Some simulations reveal that the searched locally optimal designs are robust to the moderate misspecification of the prespecified parameters.

Keywords: Complete class; Equivalence theorem; Locally optimal design; Personalized medicine (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:127:y:2018:i:c:p:217-228

DOI: 10.1016/j.csda.2018.05.017

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