Memantine for treatment of moderate or severe Alzheimer’s disease patients in urban China: clinical and economic outcomes from a health economic model

2015 ◽  
Vol 15 (4) ◽  
pp. 565-578 ◽  
Author(s):  
Shanlian Hu ◽  
Xin Yu ◽  
Shengdi Chen ◽  
Emilie Clay ◽  
Mondher Toumi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Health Economic ◽  
Economic Model ◽  
Urban China ◽  
Economic Outcomes ◽  
Health Economic Model

scholarly journals The Potential Added Value of Novel Hearing Therapeutics: An Early Health Economic Model for Hearing Loss

2020 ◽  
Vol 41 (8) ◽  
pp. 1033-1041
Author(s):  
Rishi Mandavia ◽  
Yvette M. Horstink ◽  
Janneke P.C. Grutters ◽  
Evie Landry ◽  
Carl May ◽  
...  
Keyword(s):  
Health Economic ◽  
Hearing Loss ◽  
Economic Model ◽  
Added Value ◽  
Health Economic Model ◽  
Early Health

scholarly journals Estimating the Expected Value of Sample Information across Different Sample Sizes Using Moment Matching and Nonlinear Regression

2019 ◽  
Vol 39 (4) ◽  
pp. 347-359 ◽  
Author(s):  
Anna Heath ◽  
Ioanna Manolopoulou ◽  
Gianluca Baio
Keyword(s):  
Health Economic ◽  
Nonlinear Regression ◽  
Economic Model ◽  
Trial Design ◽  
Expected Value ◽  
Moment Matching ◽  
Sample Sizes ◽  
Sample Information ◽  
Health Economic Model ◽  
The Cost

Background. The expected value of sample information (EVSI) determines the economic value of any future study with a specific design aimed at reducing uncertainty about the parameters underlying a health economic model. This has potential as a tool for trial design; the cost and value of different designs could be compared to find the trial with the greatest net benefit. However, despite recent developments, EVSI analysis can be slow, especially when optimizing over a large number of different designs. Methods. This article develops a method to reduce the computation time required to calculate the EVSI across different sample sizes. Our method extends the moment-matching approach to EVSI estimation to optimize over different sample sizes for the underlying trial while retaining a similar computational cost to a single EVSI estimate. This extension calculates the posterior variance of the net monetary benefit across alternative sample sizes and then uses Bayesian nonlinear regression to estimate the EVSI across these sample sizes. Results. A health economic model developed to assess the cost-effectiveness of interventions for chronic pain demonstrates that this EVSI calculation method is fast and accurate for realistic models. This example also highlights how different trial designs can be compared using the EVSI. Conclusion. The proposed estimation method is fast and accurate when calculating the EVSI across different sample sizes. This will allow researchers to realize the potential of using the EVSI to determine an economically optimal trial design for reducing uncertainty in health economic models. Limitations. Our method involves rerunning the health economic model, which can be more computationally expensive than some recent alternatives, especially in complex models.

scholarly journals PDB82 Development of a Health Economic Model to Compare the Prevention, Treatment and Management Strategies of Type 2 Diabetes

2011 ◽  
Vol 14 (7) ◽  
pp. A486-A487 ◽  
Author(s):  
B. Nagy ◽  
L. Nagyjanosi ◽  
S. Nagyistok ◽  
J. Józwiak-Hagymásy ◽  
Z. Dessewffy ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Health Economic ◽  
Economic Model ◽  
Management Strategies ◽  
Health Economic Model
Sign in / Sign up

Export Citation Format

Share Document