Evidence map›Paper›PMID 40597050›Full record

SynthesisBMC medical informatics and decision making2025

A systematic review of microsimulation models for skin cancer.

Caroline G Watts, Kirstie G McLoughlin, Stephen Wade, Amelia K Smit, H Peter Soyer, Pablo Fernandez-Peñas, David C Whiteman, Pascale Guitera, Gillian Reyes-Marcelino, Karen Canfell and 2 more

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Article
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

12 authors.

Caroline G Watts *The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, 1 King St, Newtown, Sydney, NSW, 2042, Australia. Caroline.watts@sydney.edu.au.
Kirstie G McLoughlin *The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, 1 King St, Newtown, Sydney, NSW, 2042, Australia.
Stephen WadeThe Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, 1 King St, Newtown, Sydney, NSW, 2042, Australia.
Amelia K SmitThe Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, 1 King St, Newtown, Sydney, NSW, 2042, Australia.
H Peter SoyerFrazer Institute, Dermatology Research Centre, The University of Queensland, Brisbane, QLD, Australia.
Pablo Fernandez-PeñasFaculty of Medicine and Health, The University of Sydney, Westmead Clinical School, Westmead, NSW, Australia.
David C WhitemanDepartment of Population Health, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia.
Pascale GuiteraMelanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia.
Gillian Reyes-MarcelinoThe Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, 1 King St, Newtown, Sydney, NSW, 2042, Australia.
Karen CanfellFaculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.
Anne E Cust *The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, 1 King St, Newtown, Sydney, NSW, 2042, Australia.
Michael Caruana *Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.

Funding

National Health and Medical Research Council 1165936
6 · The paper itself

Abstract

backgroundSimulation modelling can assist with health care decision making. To inform the development and improvement of skin cancer focussed microsimulation models, we conducted a systematic review and narrative synthesis of published skin cancer models to assess the structure, parameterisation, and assumptions.

methodsThe electronic databases OVIDMedline including Embase and the Cost-Effectiveness Analysis (CEA) Registry were searched up to 7 May 2025. Studies that included microsimulation of individuals who developed or had skin cancer were eligible for inclusion. No restrictions on publication date or language were applied. The outcomes of interest were the purpose of the models, characteristics of the models and applicability for modelling skin cancer screening.

resultsTwenty-two models were identified from the systematic review. Nineteen papers modelled melanoma, and two papers modelled keratinocyte or non-melanoma skin cancer, and one paper modelled both melanoma and keratinocyte cancer. The models were developed to assess treatment strategies (n = 10), skin cancer screening programs (n = 7), diagnostic techniques (n = 3), post-diagnosis surveillance (n = 3), preventative interventions (n = 1) and time to treatment (n = 1), with three models reporting dual aims. There was substantial variation in the simulation of the natural history of melanoma between models, with more recent models having separate natural history and clinical modules. The quality was assessed using the Quality Assessment Reporting for Microsimulation Models (QARMM) checklist and the majority of models were assessed to be of moderate quality. Limitations from these models included assuming an average tumour behaviour and constant melanoma development and progression over time. Data availability was also noted as a limitation for some models.

conclusionsMost microsimulation models related to skin cancer have focused on late-stage treatment strategies. Tumour characteristics, apart from stage at diagnosis, were not accounted for in most models. PROSPERO REGISTRATION NUMBER: CRD42024504250.

Indexed as

Computer SimulationMelanomaSkin NeoplasmsCost-Benefit AnalysisHumansHealth decision modellingMelanomaMicrosimulationSkin cancer

Identifiers

PMID40597050
PMCPMC12211653

What OpenQuestion holds

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Registered trials

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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.