↓ Skip to main content

Verification of Decision-Analytic Models for Health Economic Evaluations: An Overview

Overview of attention for article published in PharmacoEconomics, April 2017
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

blogs
1 blog
twitter
6 X users
facebook
1 Facebook page

Citations

dimensions_citation
14 Dimensions

Readers on

mendeley
58 Mendeley
Title
Verification of Decision-Analytic Models for Health Economic Evaluations: An Overview
Published in
PharmacoEconomics, April 2017
DOI 10.1007/s40273-017-0508-2
Pubmed ID
Authors

Erik J. Dasbach, Elamin H. Elbasha

Abstract

Decision-analytic models for cost-effectiveness analysis are developed in a variety of software packages where the accuracy of the computer code is seldom verified. Although modeling guidelines recommend using state-of-the-art quality assurance and control methods for software engineering to verify models, the fields of pharmacoeconomics and health technology assessment (HTA) have yet to establish and adopt guidance on how to verify health and economic models. The objective of this paper is to introduce to our field the variety of methods the software engineering field uses to verify that software performs as expected. We identify how many of these methods can be incorporated in the development process of decision-analytic models in order to reduce errors and increase transparency. Given the breadth of methods used in software engineering, we recommend a more in-depth initiative to be undertaken (e.g., by an ISPOR-SMDM Task Force) to define the best practices for model verification in our field and to accelerate adoption. Establishing a general guidance for verifying models will benefit the pharmacoeconomics and HTA communities by increasing accuracy of computer programming, transparency, accessibility, sharing, understandability, and trust of models.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
United States 1 2%
Unknown 56 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 28%
Student > Master 11 19%
Researcher 8 14%
Student > Bachelor 4 7%
Student > Doctoral Student 3 5%
Other 4 7%
Unknown 12 21%
Readers by discipline Count As %
Medicine and Dentistry 11 19%
Economics, Econometrics and Finance 6 10%
Nursing and Health Professions 4 7%
Pharmacology, Toxicology and Pharmaceutical Science 4 7%
Agricultural and Biological Sciences 3 5%
Other 18 31%
Unknown 12 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 May 2017.
All research outputs
#2,812,510
of 22,968,808 outputs
Outputs from PharmacoEconomics
#255
of 1,861 outputs
Outputs of similar age
#53,363
of 310,964 outputs
Outputs of similar age from PharmacoEconomics
#6
of 27 outputs
Altmetric has tracked 22,968,808 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,861 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 310,964 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.