Compound optimum designs for clinical trials in personalized medicine

Duarte, Belmiro P. M., Atkinson, Anthony C., Pedrosa, David and van Munster, Marlena (2024) Compound optimum designs for clinical trials in personalized medicine. Mathematics, 12 (19). ISSN 2227-7390

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Abstract

We consider optimal designs for clinical trials when response variance depends on treatment and covariates are included in the response model. These designs are generalizations of Neyman allocation, and commonly employed in personalized medicine where external covariates linearly affect the response. Very often, these designs aim at maximizing the amount of information gathered but fail to assure ethical requirements. We analyze compound optimal designs that maximize a criterion weighting the amount of information and the reward of allocating the patients to the most effective/least risky treatment. We develop a general representation for static (a priori) allocation and propose a semidefinite programming (SDP) formulation to support their numerical computation. This setup is extended assuming the variance and the parameters of the response of all treatments are unknown and an adaptive sequential optimal design scheme is implemented and used for demonstration. Purely information theoretic designs for the same allocation have been addressed elsewhere, and we use them to support the techniques applied to compound designs.

Item Type:	Article
Additional Information:	© 2024 The Author(s)
Divisions:	Statistics
Subjects:	R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine R Medicine
Date Deposited:	10 Oct 2024 13:42
Last Modified:	12 Dec 2024 04:31
URI:	http://eprints.lse.ac.uk/id/eprint/125688

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