Bayesian inference for optimal dynamic treatment regimes in practice Article Swipe
Daniel Rodriguez Duque
,
Erica E. M. Moodie
,
David A. Stephens
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2303.15281
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2303.15281
In this work, we examine recently developed methods for Bayesian inference of optimal dynamic treatment regimes (DTRs). DTRs are a set of treatment decision rules aimed at tailoring patient care to patient-specific characteristics, thereby falling within the realm of precision medicine. In this field, researchers seek to tailor therapy with the intention of improving health outcomes; therefore, they are most interested in identifying optimal DTRs. Recent work has developed Bayesian methods for identifying optimal DTRs in a family indexed by $ψ$ via Bayesian dynamic marginal structural models (MSMs) (Rodriguez Duque et al., 2022a); we review the proposed estimation procedure and illustrate its use via the new BayesDTR R package. Although methods in (Rodriguez Duque et al., 2022a) can estimate optimal DTRs well, they may lead to biased estimators when the model for the expected outcome if everyone in a population were to follow a given treatment strategy, known as a value function, is misspecified or when a grid search for the optimum is employed. We describe recent work that uses a Gaussian process ($GP$) prior on the value function as a means to robustly identify optimal DTRs (Rodriguez Duque et al., 2022b). We demonstrate how a $GP$ approach may be implemented with the BayesDTR package and contrast it with other value-search approaches to identifying optimal DTRs. We use data from an HIV therapeutic trial in order to illustrate a standard analysis with these methods, using both the original observed trial data and an additional simulated component to showcase a longitudinal (two-stage DTR) analysis.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2303.15281
- https://arxiv.org/pdf/2303.15281
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4361194307
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https://openalex.org/W4361194307Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2303.15281Digital Object Identifier
- Title
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Bayesian inference for optimal dynamic treatment regimes in practiceWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-03-27Full publication date if available
- Authors
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Daniel Rodriguez Duque, Erica E. M. Moodie, David A. StephensList of authors in order
- Landing page
-
https://arxiv.org/abs/2303.15281Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2303.15281Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2303.15281Direct OA link when available
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Bayesian probability, Computer science, Inference, Bayesian inference, Machine learning, Estimator, Population, Artificial intelligence, Mathematics, Statistics, Medicine, Environmental healthTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.procedure | 98 |
| abstract_inverted_index.simulated | 244 |
| abstract_inverted_index.strategy, | 146 |
| abstract_inverted_index.tailoring | 27 |
| abstract_inverted_index.treatment | 14, 22, 145 |
| abstract_inverted_index.(Rodriguez | 88, 112, 187 |
| abstract_inverted_index.(two-stage | 250 |
| abstract_inverted_index.additional | 243 |
| abstract_inverted_index.approaches | 211 |
| abstract_inverted_index.estimation | 97 |
| abstract_inverted_index.estimators | 127 |
| abstract_inverted_index.illustrate | 100, 227 |
| abstract_inverted_index.interested | 60 |
| abstract_inverted_index.population | 139 |
| abstract_inverted_index.structural | 85 |
| abstract_inverted_index.therefore, | 56 |
| abstract_inverted_index.demonstrate | 193 |
| abstract_inverted_index.identifying | 62, 72, 213 |
| abstract_inverted_index.implemented | 200 |
| abstract_inverted_index.researchers | 44 |
| abstract_inverted_index.therapeutic | 222 |
| abstract_inverted_index.longitudinal | 249 |
| abstract_inverted_index.misspecified | 153 |
| abstract_inverted_index.value-search | 210 |
| abstract_inverted_index.characteristics, | 32 |
| abstract_inverted_index.patient-specific | 31 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7400000095367432 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile |