Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy
An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model‐based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a posteriori (MAP) estimate). T...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-03-01
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| Series: | CPT: Pharmacometrics & Systems Pharmacology |
| Online Access: | https://doi.org/10.1002/psp4.12492 |
| _version_ | 1819262853492768768 |
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| author | Corinna Maier Niklas Hartung Jana deWiljes Charlotte Kloft Wilhelm Huisinga |
| author_facet | Corinna Maier Niklas Hartung Jana deWiljes Charlotte Kloft Wilhelm Huisinga |
| author_sort | Corinna Maier |
| collection | DOAJ |
| description | An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model‐based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a posteriori (MAP) estimate). This MAP‐based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity. Bayesian data assimilation (DA) methods overcome these limitations by providing a comprehensive uncertainty quantification. We compare DA methods with MAP‐based approaches and show how probabilistic statements about key markers related to chemotherapy‐induced neutropenia can be leveraged for more informative decision support in individualized chemotherapy. Sequential Bayesian DA proved to be most computationally efficient for handling interoccasion variability and integrating TDM data. For new digital monitoring devices enabling more frequent data collection, these features will be of critical importance to improve patient care decisions in various therapeutic areas. |
| first_indexed | 2024-12-23T20:04:18Z |
| format | Article |
| id | doaj.art-64e9c1f0e75542fc8623bc493bf19d47 |
| institution | Directory Open Access Journal |
| issn | 2163-8306 |
| language | English |
| last_indexed | 2024-12-23T20:04:18Z |
| publishDate | 2020-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | CPT: Pharmacometrics & Systems Pharmacology |
| spelling | doaj.art-64e9c1f0e75542fc8623bc493bf19d472022-12-21T17:32:59ZengWileyCPT: Pharmacometrics & Systems Pharmacology2163-83062020-03-019315316410.1002/psp4.12492Bayesian Data Assimilation to Support Informed Decision Making in Individualized ChemotherapyCorinna Maier0Niklas Hartung1Jana deWiljes2Charlotte Kloft3Wilhelm Huisinga4Institute of Mathematics University of Potsdam Potsdam GermanyInstitute of Mathematics University of Potsdam Potsdam GermanyInstitute of Mathematics University of Potsdam Potsdam GermanyDepartment of Clinical Pharmacy and Biochemistry Institute of Pharmacy Freie Universität Berlin Berlin GermanyInstitute of Mathematics University of Potsdam Potsdam GermanyAn essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model‐based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a posteriori (MAP) estimate). This MAP‐based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity. Bayesian data assimilation (DA) methods overcome these limitations by providing a comprehensive uncertainty quantification. We compare DA methods with MAP‐based approaches and show how probabilistic statements about key markers related to chemotherapy‐induced neutropenia can be leveraged for more informative decision support in individualized chemotherapy. Sequential Bayesian DA proved to be most computationally efficient for handling interoccasion variability and integrating TDM data. For new digital monitoring devices enabling more frequent data collection, these features will be of critical importance to improve patient care decisions in various therapeutic areas.https://doi.org/10.1002/psp4.12492 |
| spellingShingle | Corinna Maier Niklas Hartung Jana deWiljes Charlotte Kloft Wilhelm Huisinga Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy CPT: Pharmacometrics & Systems Pharmacology |
| title | Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy |
| title_full | Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy |
| title_fullStr | Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy |
| title_full_unstemmed | Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy |
| title_short | Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy |
| title_sort | bayesian data assimilation to support informed decision making in individualized chemotherapy |
| url | https://doi.org/10.1002/psp4.12492 |
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