EconPapers    
Economics at your fingertips  
 

Robust and adaptive anticoagulant control

Peter Avery, Quentin Clairon, Robin Henderson, C. James Taylor and Emma Wilson

Journal of the Royal Statistical Society Series C, 2020, vol. 69, issue 3, 503-524

Abstract: We consider a control theory approach to adaptive dose allocation of anticoagulants, based on an analysis of records of 152 patients on long‐term warfarin treatment. We consider a selection of statistical models for the relationship between the dose of drug and subsequent blood clotting speed, measured through the international normalized ratio. Our main focus is on subsequent use of the model in guiding the choice of the next dose adaptively as patient‐specific information accrues. We compare a naive long‐term approach with a proportional‐integral‐plus method, with parameters estimated by either linear quadratic optimization or by stochastic resource allocation. We demonstrate advantages of the control approaches in comparison with a naive approach in simulations and through calculation of robust stability margins for the observed data.

Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/rssc.12403

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:bla:jorssc:v:69:y:2020:i:3:p:503-524

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().

 
Page updated 2025-03-19
Handle: RePEc:bla:jorssc:v:69:y:2020:i:3:p:503-524