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Article: A utility approach to individualized optimal dose selection using biomarkers

TitleA utility approach to individualized optimal dose selection using biomarkers
Authors
Keywordsconstrained LASSO
efficacy toxicity trade-off
optimal treatment regime
personalized medicine
utility
Issue Date6-Nov-2019
PublisherWiley-VCH Verlag
Citation
Biometrical Journal, 2019, v. 62, n. 2, p. 386-397 How to Cite?
Abstract

In many settings, including oncology, increasing the dose of treatment results in both increased efficacy and toxicity. With the increasing availability of validated biomarkers and prediction models, there is the potential for individualized dosing based on patient specific factors. We consider the setting where there is an existing dataset of patients treated with heterogenous doses and including binary efficacy and toxicity outcomes and patient factors such as clinical features and biomarkers. The goal is to analyze the data to estimate an optimal dose for each (future) patient based on their clinical features and biomarkers. We propose an optimal individualized dose finding rule by maximizing utility functions for individual patients while limiting the rate of toxicity. The utility is defined as a weighted combination of efficacy and toxicity probabilities. This approach maximizes overall efficacy at a prespecified constraint on overall toxicity. We model the binary efficacy and toxicity outcomes using logistic regression with dose, biomarkers and dose–biomarker interactions. To incorporate the large number of potential parameters, we use the LASSO method. We additionally constrain the dose effect to be non-negative for both efficacy and toxicity for all patients. Simulation studies show that the utility approach combined with any of the modeling methods can improve efficacy without increasing toxicity relative to fixed dosing. The proposed methods are illustrated using a dataset of patients with lung cancer treated with radiation therapy.


Persistent Identifierhttp://hdl.handle.net/10722/344094
ISSN
2023 Impact Factor: 1.3
2023 SCImago Journal Rankings: 0.996

 

DC FieldValueLanguage
dc.contributor.authorLi, Pin-
dc.contributor.authorTaylor, Jeremy MG-
dc.contributor.authorKong, FS-
dc.contributor.authorJolly, Shruti-
dc.contributor.authorSchipper, Matthew J-
dc.date.accessioned2024-07-03T08:40:36Z-
dc.date.available2024-07-03T08:40:36Z-
dc.date.issued2019-11-06-
dc.identifier.citationBiometrical Journal, 2019, v. 62, n. 2, p. 386-397-
dc.identifier.issn0323-3847-
dc.identifier.urihttp://hdl.handle.net/10722/344094-
dc.description.abstract<p>In many settings, including oncology, increasing the dose of treatment results in both increased efficacy and toxicity. With the increasing availability of validated biomarkers and prediction models, there is the potential for individualized dosing based on patient specific factors. We consider the setting where there is an existing dataset of patients treated with heterogenous doses and including binary efficacy and toxicity outcomes and patient factors such as clinical features and biomarkers. The goal is to analyze the data to estimate an optimal dose for each (future) patient based on their clinical features and biomarkers. We propose an optimal individualized dose finding rule by maximizing utility functions for individual patients while limiting the rate of toxicity. The utility is defined as a weighted combination of efficacy and toxicity probabilities. This approach maximizes overall efficacy at a prespecified constraint on overall toxicity. We model the binary efficacy and toxicity outcomes using logistic regression with dose, biomarkers and dose–biomarker interactions. To incorporate the large number of potential parameters, we use the LASSO method. We additionally constrain the dose effect to be non-negative for both efficacy and toxicity for all patients. Simulation studies show that the utility approach combined with any of the modeling methods can improve efficacy without increasing toxicity relative to fixed dosing. The proposed methods are illustrated using a dataset of patients with lung cancer treated with radiation therapy.</p>-
dc.languageeng-
dc.publisherWiley-VCH Verlag-
dc.relation.ispartofBiometrical Journal-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectconstrained LASSO-
dc.subjectefficacy toxicity trade-off-
dc.subjectoptimal treatment regime-
dc.subjectpersonalized medicine-
dc.subjectutility-
dc.titleA utility approach to individualized optimal dose selection using biomarkers-
dc.typeArticle-
dc.identifier.doi10.1002/bimj.201900030-
dc.identifier.scopuseid_2-s2.0-85074914975-
dc.identifier.volume62-
dc.identifier.issue2-
dc.identifier.spage386-
dc.identifier.epage397-
dc.identifier.eissn1521-4036-
dc.identifier.issnl0323-3847-

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