An Efficient Approach for Optimizing the Cost-effective Individualized Treatment Rule Using Conditional Random Forest Article Swipe
Yizhe Xu
,
Tom Greene
,
Adam P. Bress
,
Brandon K. Bellows
,
Yue Zhang
,
Zugui Zhang
,
Paul Kolm
,
William Weintraub
,
Andrew S. Moran
,
Jincheng Shen
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2204.10971
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2204.10971
Evidence from observational studies has become increasingly important for supporting healthcare policy making via cost-effectiveness (CE) analyses. Similar as in comparative effectiveness studies, health economic evaluations that consider subject-level heterogeneity produce individualized treatment rules (ITRs) that are often more cost-effective than one-size-fits-all treatment. Thus, it is of great interest to develop statistical tools for learning such a cost-effective ITR (CE-ITR) under the causal inference framework that allows proper handling of potential confounding and can be applied to both trials and observational studies. In this paper, we use the concept of net-monetary-benefit (NMB) to assess the trade-off between health benefits and related costs. We estimate CE-ITR as a function of patients' characteristics that, when implemented, optimizes the allocation of limited healthcare resources by maximizing health gains while minimizing treatment-related costs. We employ the conditional random forest approach and identify the optimal CE-ITR using NMB-based classification algorithms, where two partitioned estimators are proposed for the subject-specific weights to effectively incorporate information from censored individuals. We conduct simulation studies to evaluate the performance of our proposals. We apply our top-performing algorithm to the NIH-funded Systolic Blood Pressure Intervention Trial (SPRINT) to illustrate the CE gains of assigning customized intensive blood pressure therapy.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2204.10971
- https://arxiv.org/pdf/2204.10971
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4303684598Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2204.10971Digital Object Identifier
- Title
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An Efficient Approach for Optimizing the Cost-effective Individualized Treatment Rule Using Conditional Random ForestWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
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2022-04-23Full publication date if available
- Authors
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Yizhe Xu, Tom Greene, Adam P. Bress, Brandon K. Bellows, Yue Zhang, Zugui Zhang, Paul Kolm, William Weintraub, Andrew S. Moran, Jincheng ShenList of authors in order
- Landing page
-
https://arxiv.org/abs/2204.10971Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2204.10971Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2204.10971Direct OA link when available
- Concepts
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Observational study, Causal inference, Computer science, Estimator, Random forest, Inference, Health care, Mathematical optimization, Machine learning, Econometrics, Artificial intelligence, Mathematics, Statistics, Economics, Economic growthTop 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.allows | 66 |
| abstract_inverted_index.assess | 93 |
| abstract_inverted_index.become | 5 |
| abstract_inverted_index.causal | 62 |
| abstract_inverted_index.costs. | 101, 128 |
| abstract_inverted_index.employ | 130 |
| abstract_inverted_index.forest | 134 |
| abstract_inverted_index.health | 23, 97, 123 |
| abstract_inverted_index.making | 12 |
| abstract_inverted_index.paper, | 84 |
| abstract_inverted_index.policy | 11 |
| abstract_inverted_index.proper | 67 |
| abstract_inverted_index.random | 133 |
| abstract_inverted_index.trials | 78 |
| abstract_inverted_index.Similar | 17 |
| abstract_inverted_index.applied | 75 |
| abstract_inverted_index.between | 96 |
| abstract_inverted_index.concept | 88 |
| abstract_inverted_index.conduct | 163 |
| abstract_inverted_index.develop | 50 |
| abstract_inverted_index.limited | 118 |
| abstract_inverted_index.optimal | 139 |
| abstract_inverted_index.produce | 30 |
| abstract_inverted_index.related | 100 |
| abstract_inverted_index.studies | 3, 165 |
| abstract_inverted_index.weights | 154 |
| abstract_inverted_index.(CE-ITR) | 59 |
| abstract_inverted_index.(SPRINT) | 186 |
| abstract_inverted_index.Evidence | 0 |
| abstract_inverted_index.Pressure | 183 |
| abstract_inverted_index.Systolic | 181 |
| abstract_inverted_index.approach | 135 |
| abstract_inverted_index.benefits | 98 |
| abstract_inverted_index.censored | 160 |
| abstract_inverted_index.consider | 27 |
| abstract_inverted_index.economic | 24 |
| abstract_inverted_index.estimate | 103 |
| abstract_inverted_index.evaluate | 167 |
| abstract_inverted_index.function | 107 |
| abstract_inverted_index.handling | 68 |
| abstract_inverted_index.identify | 137 |
| abstract_inverted_index.interest | 48 |
| abstract_inverted_index.learning | 54 |
| abstract_inverted_index.pressure | 197 |
| abstract_inverted_index.proposed | 150 |
| abstract_inverted_index.studies, | 22 |
| abstract_inverted_index.studies. | 81 |
| abstract_inverted_index.therapy. | 198 |
| abstract_inverted_index.NMB-based | 142 |
| abstract_inverted_index.algorithm | 177 |
| abstract_inverted_index.analyses. | 16 |
| abstract_inverted_index.assigning | 193 |
| abstract_inverted_index.framework | 64 |
| abstract_inverted_index.important | 7 |
| abstract_inverted_index.inference | 63 |
| abstract_inverted_index.intensive | 195 |
| abstract_inverted_index.optimizes | 114 |
| abstract_inverted_index.patients' | 109 |
| abstract_inverted_index.potential | 70 |
| abstract_inverted_index.resources | 120 |
| abstract_inverted_index.trade-off | 95 |
| abstract_inverted_index.treatment | 32 |
| abstract_inverted_index.NIH-funded | 180 |
| abstract_inverted_index.allocation | 116 |
| abstract_inverted_index.customized | 194 |
| abstract_inverted_index.estimators | 148 |
| abstract_inverted_index.healthcare | 10, 119 |
| abstract_inverted_index.illustrate | 188 |
| abstract_inverted_index.maximizing | 122 |
| abstract_inverted_index.minimizing | 126 |
| abstract_inverted_index.proposals. | 172 |
| abstract_inverted_index.simulation | 164 |
| abstract_inverted_index.supporting | 9 |
| abstract_inverted_index.treatment. | 42 |
| abstract_inverted_index.algorithms, | 144 |
| abstract_inverted_index.comparative | 20 |
| abstract_inverted_index.conditional | 132 |
| abstract_inverted_index.confounding | 71 |
| abstract_inverted_index.effectively | 156 |
| abstract_inverted_index.evaluations | 25 |
| abstract_inverted_index.incorporate | 157 |
| abstract_inverted_index.information | 158 |
| abstract_inverted_index.partitioned | 147 |
| abstract_inverted_index.performance | 169 |
| abstract_inverted_index.statistical | 51 |
| abstract_inverted_index.Intervention | 184 |
| abstract_inverted_index.implemented, | 113 |
| abstract_inverted_index.increasingly | 6 |
| abstract_inverted_index.individuals. | 161 |
| abstract_inverted_index.effectiveness | 21 |
| abstract_inverted_index.heterogeneity | 29 |
| abstract_inverted_index.observational | 2, 80 |
| abstract_inverted_index.subject-level | 28 |
| abstract_inverted_index.classification | 143 |
| abstract_inverted_index.cost-effective | 39, 57 |
| abstract_inverted_index.individualized | 31 |
| abstract_inverted_index.top-performing | 176 |
| abstract_inverted_index.characteristics | 110 |
| abstract_inverted_index.subject-specific | 153 |
| abstract_inverted_index.one-size-fits-all | 41 |
| abstract_inverted_index.treatment-related | 127 |
| abstract_inverted_index.cost-effectiveness | 14 |
| abstract_inverted_index.net-monetary-benefit | 90 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile |