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Díez FJ, Luque M, Arias M, Pérez-Martín J. Cost-effectiveness analysis with unordered decisions. Artif Intell Med 2021; 117:102064. [PMID: 34127243 DOI: 10.1016/j.artmed.2021.102064] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 03/23/2021] [Accepted: 03/25/2021] [Indexed: 11/28/2022]
Abstract
INTRODUCTION Cost-effectiveness analysis (CEA) is used increasingly in medicine to determine whether the health benefit of an intervention is worth the economic cost. Decision trees, the standard decision modeling technique for non-temporal domains, can only perform CEAs for very small problems. Influence diagrams can model much larger problems, but only when the decisions are totally ordered. OBJECTIVE To develop a CEA method for problems with unordered or partially ordered decisions, such as finding the optimal sequence of tests for diagnosing a disease. METHODS We explain how to model those problems using decision analysis networks (DANs), a new type of probabilistic graphical model, somewhat similar to Bayesian networks and influence diagrams. We present an algorithm for evaluating DANs with two criteria, cost and effectiveness, and perform some experiments to study its computational efficiency. We illustrate the representation framework and the algorithm using a hypothetical example involving two therapies and several tests and then present a DAN for a real-world problem, the mediastinal staging of non-small cell lung cancer. RESULTS The evaluation of a DAN with two criteria, cost and effectiveness, returns a set of intervals for the willingness to pay, separated by incremental cost-effectiveness ratios (ICERs). The cost, the effectiveness, and the optimal intervention are specific for each interval, i.e., they depend on the willingness to pay. CONCLUSION Problems involving several unordered decisions can be modeled with DANs and evaluated in a reasonable amount of time. OpenMarkov, an open-source software tool developed by our research group, can be used to build the models and evaluate them using a graphical user interface.
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Affiliation(s)
- Francisco Javier Díez
- Department of Artificial Intelligence, Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain.
| | - Manuel Luque
- Department of Artificial Intelligence, Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain.
| | - Manuel Arias
- Department of Artificial Intelligence, Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain.
| | - Jorge Pérez-Martín
- Department of Artificial Intelligence, Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain.
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Arora P, Boyne D, Slater JJ, Gupta A, Brenner DR, Druzdzel MJ. Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2019; 22:439-445. [PMID: 30975395 DOI: 10.1016/j.jval.2019.01.006] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 11/07/2018] [Accepted: 01/14/2019] [Indexed: 06/09/2023]
Abstract
OBJECTIVE The fields of medicine and public health are undergoing a data revolution. An increasing availability of data has brought about a growing interest in machine-learning algorithms. Our objective is to present the reader with an introduction to a knowledge representation and machine-learning tool for risk estimation in medical science known as Bayesian networks (BNs). STUDY DESIGN In this article we review how BNs are compact and intuitive graphical representations of joint probability distributions (JPDs) that can be used to conduct causal reasoning and risk estimation analysis and offer several advantages over regression-based methods. We discuss how BNs represent a different approach to risk estimation in that they are graphical representations of JPDs that take the form of a network representing model random variables and the influences between them, respectively. METHODS We explore some of the challenges associated with traditional risk prediction methods and then describe BNs, their construction, application, and advantages in risk prediction based on examples in cancer and heart disease. RESULTS Risk modeling with BNs has advantages over regression-based approaches, and in this article we focus on three that are relevant to health outcomes research: (1) the generation of network structures in which relationships between variables can be easily communicated; (2) their ability to apply Bayes's theorem to conduct individual-level risk estimation; and (3) their easy transformation into decision models. CONCLUSIONS Bayesian networks represent a powerful and flexible tool for the analysis of health economics and outcomes research data in the era of precision medicine.
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Affiliation(s)
- Paul Arora
- Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Lighthouse Outcomes, Toronto, ON, Canada.
| | - Devon Boyne
- Lighthouse Outcomes, Toronto, ON, Canada; University of Calgary Cumming School of Medicine, Calgary, AB, Canada
| | | | - Alind Gupta
- Lighthouse Outcomes, Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Darren R Brenner
- Department of Oncology and Community Health Sciences, University of Calgary Cumming School of Medicine, Calgary, AB, Canada
| | - Marek J Druzdzel
- School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, USA
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Song G, Khan F, Yang M. Integrated risk management of hazardous processing facilities. PROCESS SAFETY PROGRESS 2018. [DOI: 10.1002/prs.11978] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Guozheng Song
- Centre for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering and Applied Science; Memorial University of Newfoundland; St. John's Newfoundland A1B 3X5 Canada
| | - Faisal Khan
- Centre for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering and Applied Science; Memorial University of Newfoundland; St. John's Newfoundland A1B 3X5 Canada
| | - Ming Yang
- Department of Chemical Engineering, School of Engineering; Nazarbayev University; Astana 010000 Kazakhstan
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Smith WP, Richard PJ, Zeng J, Apisarnthanarax S, Rengan R, Phillips MH. Decision analytic modeling for the economic analysis of proton radiotherapy for non-small cell lung cancer. Transl Lung Cancer Res 2018; 7:122-133. [PMID: 29876311 DOI: 10.21037/tlcr.2018.03.27] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background Although proton radiation treatments are more costly than photon/X-ray therapy, they may lower overall treatment costs through reducing rates of severe toxicities and the costly management of those toxicities. To study this issue, we created a decision-model comparing proton vs. X-ray radiotherapy for locally advanced non-small cell lung cancer patients. Methods An influence diagram was created to model for radiation delivery, associated 6-month pneumonitis/esophagitis rates, and overall costs (radiation plus toxicity costs). Pneumonitis (age, chemo type, V20, MLD) and esophagitis (V60) predictors were modeled to impact toxicity rates. We performed toxicity-adjusted, rate-adjusted, risk group-adjusted, and radiosensitivity analyses. Results Upfront proton treatment costs exceeded that of photons [$16,730.37 (3DCRT), $23,893.83 (IMRT), $41,061.80 (protons)]. Based upon expected population pneumonitis and esophagitis rates for each modality, protons would be expected to recover $1,065.62 and $1,139.63 of the cost difference compared to 3DCRT or IMRT. For patients treated with IMRT experiencing grade 4 pneumonitis or grade 4 esophagitis, costs exceeded patients treated with protons without this toxicity. 3DCRT patients with grade 4 esophagitis had higher costs than proton patients without this toxicity. For the risk group analysis, high risk patients (age >65, carboplatin/paclitaxel) benefited more from proton therapy. A biomarker may allow patient selection for proton therapy, although the AUC alone is not sufficient to determine if the biomarker is clinically useful. Conclusions The comparison between proton and photon/X-ray radiation therapy for NSCLC needs to consider both the up-front cost of treatment and the possible long term cost of complications. In our analysis, current costs favor X-ray therapy. However, relatively small reductions in the cost of proton therapy may result in a shift to the preference for proton therapy.
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Affiliation(s)
- Wade P Smith
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
| | - Patrick J Richard
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
| | - Jing Zeng
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
| | - Smith Apisarnthanarax
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
| | - Ramesh Rengan
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
| | - Mark H Phillips
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
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Díez FJ, Yebra M, Bermejo I, Palacios-Alonso MA, Calleja MA, Luque M, Pérez-Martín J. Markov Influence Diagrams. Med Decis Making 2017; 37:183-195. [DOI: 10.1177/0272989x16685088] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Markov influence diagrams (MIDs) are a new type of probabilistic graphical model that extends influence diagrams in the same way that Markov decision trees extend decision trees. They have been designed to build state-transition models, mainly in medicine, and perform cost-effectiveness analyses. Using a causal graph that may contain several variables per cycle, MIDs can model various patient characteristics without multiplying the number of states; in particular, they can represent the history of the patient without using tunnel states. OpenMarkov, an open-source tool, allows the decision analyst to build and evaluate MIDs—including cost-effectiveness analysis and several types of deterministic and probabilistic sensitivity analysis—with a graphical user interface, without writing any code. This way, MIDs can be used to easily build and evaluate complex models whose implementation as spreadsheets or decision trees would be cumbersome or unfeasible in practice. Furthermore, many problems that previously required discrete event simulation can be solved with MIDs; i.e., within the paradigm of state-transition models, in which many health economists feel more comfortable.
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Affiliation(s)
- Francisco J. Díez
- Department Artificial Intelligence, UNED, Madrid, Spain (FJD, MA, ML, JP)
- Centre for Biomedical Technology, Technical University of Madrid, Spain (MY)
- School of Health and Related Research, University of Sheffield, UK (IB)
- Computer Science Department, National Institute for Astrophysics, Optics and Electronics, Tonantzintla, Puebla, Mexico (MAP)
| | - Mar Yebra
- Department Artificial Intelligence, UNED, Madrid, Spain (FJD, MA, ML, JP)
- Centre for Biomedical Technology, Technical University of Madrid, Spain (MY)
- School of Health and Related Research, University of Sheffield, UK (IB)
- Computer Science Department, National Institute for Astrophysics, Optics and Electronics, Tonantzintla, Puebla, Mexico (MAP)
| | - Iñigo Bermejo
- Department Artificial Intelligence, UNED, Madrid, Spain (FJD, MA, ML, JP)
- Centre for Biomedical Technology, Technical University of Madrid, Spain (MY)
- School of Health and Related Research, University of Sheffield, UK (IB)
- Computer Science Department, National Institute for Astrophysics, Optics and Electronics, Tonantzintla, Puebla, Mexico (MAP)
| | - Miguel A. Palacios-Alonso
- Department Artificial Intelligence, UNED, Madrid, Spain (FJD, MA, ML, JP)
- Centre for Biomedical Technology, Technical University of Madrid, Spain (MY)
- School of Health and Related Research, University of Sheffield, UK (IB)
- Computer Science Department, National Institute for Astrophysics, Optics and Electronics, Tonantzintla, Puebla, Mexico (MAP)
| | - Manuel Arias Calleja
- Department Artificial Intelligence, UNED, Madrid, Spain (FJD, MA, ML, JP)
- Centre for Biomedical Technology, Technical University of Madrid, Spain (MY)
- School of Health and Related Research, University of Sheffield, UK (IB)
- Computer Science Department, National Institute for Astrophysics, Optics and Electronics, Tonantzintla, Puebla, Mexico (MAP)
| | - Manuel Luque
- Department Artificial Intelligence, UNED, Madrid, Spain (FJD, MA, ML, JP)
- Centre for Biomedical Technology, Technical University of Madrid, Spain (MY)
- School of Health and Related Research, University of Sheffield, UK (IB)
- Computer Science Department, National Institute for Astrophysics, Optics and Electronics, Tonantzintla, Puebla, Mexico (MAP)
| | - Jorge Pérez-Martín
- Department Artificial Intelligence, UNED, Madrid, Spain (FJD, MA, ML, JP)
- Centre for Biomedical Technology, Technical University of Madrid, Spain (MY)
- School of Health and Related Research, University of Sheffield, UK (IB)
- Computer Science Department, National Institute for Astrophysics, Optics and Electronics, Tonantzintla, Puebla, Mexico (MAP)
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