scholarly journals Methods for expected value of information analysis in complex health economic models: developments on the health economics of interferon-β and glatiramer acetate for multiple sclerosis

2004 ◽  
Vol 8 (27) ◽  
Author(s):  
P Tappenden ◽  
J Chilcott ◽  
S Eggington ◽  
J Oakley ◽  
C McCabe
Keyword(s):  
Multiple Sclerosis ◽  
Health Economic ◽  
Health Economics ◽  
Glatiramer Acetate ◽  
Value Of Information ◽  
Economic Models ◽  
Expected Value ◽  
Information Analysis ◽  
Interferon Β ◽  
Expected Value Of Information

scholarly journals A Review of Methods for Analysis of the Expected Value of Information

2017 ◽  
Vol 37 (7) ◽  
pp. 747-758 ◽  
Author(s):  
Anna Heath ◽  
Ioanna Manolopoulou ◽  
Gianluca Baio
Keyword(s):  
Health Economic ◽  
Value Of Information ◽  
Expected Value ◽  
Estimation Methods ◽  
Computational Time ◽  
Economic Evaluations ◽  
New Methods ◽  
Technical Details ◽  
Web App ◽  
Expected Value Of Information

In recent years, value-of-information analysis has become more widespread in health economic evaluations, specifically as a tool to guide further research and perform probabilistic sensitivity analysis. This is partly due to methodological advancements allowing for the fast computation of a typical summary known as the expected value of partial perfect information (EVPPI). A recent review discussed some approximation methods for calculating the EVPPI, but as the research has been active over the intervening years, that review does not discuss some key estimation methods. Therefore, this paper presents a comprehensive review of these new methods. We begin by providing the technical details of these computation methods. We then present two case studies in order to compare the estimation performance of these new methods. We conclude that a method based on nonparametric regression offers the best method for calculating the EVPPI in terms of accuracy, computational time, and ease of implementation. This means that the EVPPI can now be used practically in health economic evaluations, especially as all the methods are developed in parallel with R functions and a web app to aid practitioners.

scholarly journals Introduction to Metamodeling for Reducing Computational Burden of Advanced Analyses with Health Economic Models: A Structured Overview of Metamodeling Methods in a 6-Step Application Process

2020 ◽  
Vol 40 (3) ◽  
pp. 348-363 ◽  
Author(s):  
Koen Degeling ◽  
Maarten J. IJzerman ◽  
Mariel S. Lavieri ◽  
Mark Strong ◽  
Hendrik Koffijberg
Keyword(s):  
Health Economic ◽  
Health Economics ◽  
Simulation Models ◽  
Economic Models ◽  
Data Sets ◽  
Clinical Prediction ◽  
Information Analysis ◽  
Application Process ◽  
Computational Burden ◽  
Metamodeling Technique

Metamodels can be used to reduce the computational burden associated with computationally demanding analyses of simulation models, although applications within health economics are still scarce. Besides a lack of awareness of their potential within health economics, the absence of guidance on the conceivably complex and time-consuming process of developing and validating metamodels may contribute to their limited uptake. To address these issues, this article introduces metamodeling to the wider health economic audience and presents a process for applying metamodeling in this context, including suitable methods and directions for their selection and use. General (i.e., non–health economic specific) metamodeling literature, clinical prediction modeling literature, and a previously published literature review were exploited to consolidate a process and to identify candidate metamodeling methods. Methods were considered applicable to health economics if they are able to account for mixed (i.e., continuous and discrete) input parameters and continuous outcomes. Six steps were identified as relevant for applying metamodeling methods within health economics: 1) the identification of a suitable metamodeling technique, 2) simulation of data sets according to a design of experiments, 3) fitting of the metamodel, 4) assessment of metamodel performance, 5) conducting the required analysis using the metamodel, and 6) verification of the results. Different methods are discussed to support each step, including their characteristics, directions for use, key references, and relevant R and Python packages. To address challenges regarding metamodeling methods selection, a first guide was developed toward using metamodels to reduce the computational burden of analyses of health economic models. This guidance may increase applications of metamodeling in health economics, enabling increased use of state-of-the-art analyses (e.g., value of information analysis) with computationally burdensome simulation models.

Assessment of CCL27 and IL-11 in Multiple Sclerosis Patients Treated with Interferon-β and Glatiramer Acetate

2019 ◽  
Vol 26 (6) ◽  
pp. 301-306
Author(s):  
Mohsen Ebrahimi Monfared ◽  
Shima Shapoori ◽  
Ghasem Mosayebi ◽  
Behzad Khansarinejad ◽  
Ali Ghazavi ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Glatiramer Acetate ◽  
Interferon Β ◽  
Multiple Sclerosis Patients
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