Evidence map›Paper›PMID 31665967›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2018

Discrete Event Simulation for Decision Modeling in Health Care: Lessons from Abdominal Aortic Aneurysm Screening.

Matthew J Glover, Edmund Jones, Katya L Masconi, Michael J Sweeting, Simon G Thompson, SWAN Collaborators, SWAN collaborative group

Abstract read
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Application of Discrete Event Simulation Models for COPD Management: A Systematic Review.International journal of chronic obstructive pulmonary disease · 2025
    Pooled it
  2. A Tutorial on Discrete Event Simulation Models Using a Cost-Effectiveness Analysis Example in R.Medical decision making : an international journal of the Society for Medical Decision Making · 2026
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  12. Agile Six Sigma in Healthcare: Case Study at Santobono Pediatric Hospital.International journal of environmental research and public health · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Matthew J GloverHealth Economics Research Group, Brunel University London, Uxbridge, Middlesex, UK.ORCID 0000-0001-9454-2668
Edmund JonesCardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, Cambridgeshire, UK.
Katya L MasconiCardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, Cambridgeshire, UK.
Michael J SweetingCardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, Cambridgeshire, UK.
Simon G ThompsonCardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, Cambridgeshire, UK.
SWAN Collaborators
SWAN collaborative group

Funding

Department of Health 14/179/01Medical Research Council MR/L003120/1
6 · The paper itself

Abstract

Markov models are often used to evaluate the cost-effectiveness of new healthcare interventions but they are sometimes not flexible enough to allow accurate modeling or investigation of alternative scenarios and policies. A Markov model previously demonstrated that a one-off invitation to screening for abdominal aortic aneurysm (AAA) for men aged 65 y in the UK and subsequent follow-up of identified AAAs was likely to be highly cost-effective at thresholds commonly adopted in the UK (£20,000 to £30,000 per quality adjusted life-year). However, new evidence has emerged and the decision problem has evolved to include exploration of the circumstances under which AAA screening may be cost-effective, which the Markov model is not easily able to address. A new model to handle this more complex decision problem was needed, and the case of AAA screening thus provides an illustration of the relative merits of Markov models and discrete event simulation (DES) models. An individual-level DES model was built using the R programming language to reflect possible events and pathways of individuals invited to screening v. those not invited. The model was validated against key events and cost-effectiveness, as observed in a large, randomized trial. Different screening protocol scenarios were investigated to demonstrate the flexibility of the DES. The case of AAA screening highlights the benefits of DES, particularly in the context of screening studies.

Indexed as

Decision Support TechniquesAgedAortic Aneurysm, AbdominalHumansMaleMarkov ChainsModels, TheoreticalQuality-Adjusted Life Yearsabdominal aortic aneurysmdecision analytic modeldiscrete event simulationMarkov modelscreening

Identifiers

PMID31665967
PMCPMC5950023

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.