Evidence map›Paper›PMID 27346945›Full record

ArticleCancer informatics2016

The Melanoma MAICare Framework: A Microsimulation Model for the Assessment of Individualized Cancer Care.

Elisabeth van der Meijde, Alfons J M van den Eertwegh, Sabine C Linn, Gerrit A Meijer, Remond J A Fijneman, Veerle M H Coupé

Abstract read
In one paragraph

Article in Cancer informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 2 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

3 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. A systematic review of microsimulation models for skin cancer.BMC medical informatics and decision making · 2025
    Pooled it
  2. Pooled it
  3. A Scoping Review on Calibration Methods for Cancer Simulation Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2025
    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

6 authors.

Elisabeth van der MeijdeDepartment of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, the Netherlands.
Alfons J M van den EertweghDepartment of Medical Oncology, VU University Medical Centre, Amsterdam, the Netherlands.
Sabine C LinnDivision of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, the Netherlands.
Gerrit A MeijerProfessor, Division of Diagnostic Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands.
Remond J A FijnemanDivision of Diagnostic Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands.
Veerle M H CoupéDepartment of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recently, new but expensive treatments have become available for metastatic melanoma. These improve survival, but in view of the limited funds available, cost-effectiveness needs to be evaluated. Most cancer cost-effectiveness models are based on the observed clinical events such as recurrence- free and overall survival. Times at which events are recorded depend not only on the effectiveness of treatment but also on the timing of examinations and the types of tests performed. Our objective was to construct a microsimulation model framework that describes the melanoma disease process using a description of underlying tumor growth as well as its interaction with diagnostics, treatments, and surveillance. The framework should allow for exploration of the impact of simultaneously altering curative treatment approaches in different phases of the disease as well as altering diagnostics. The developed framework consists of two components, namely, the disease model and the clinical management module. The disease model consists of a tumor level, describing growth and metastasis of the tumor, and a patient level, describing clinically observed states, such as recurrence and death. The clinical management module consists of the care patients receive. This module interacts with the disease process, influencing the rate of transition between tumor growth states at the tumor level and the rate of detecting a recurrence at the patient level. We describe the framework as the required input and the model output. Furthermore, we illustrate model calibration using registry data and data from the literature.

Indexed as

cancer progressionmelanomamicrosimulationmodelingtumor growth

Identifiers

PMID27346945
PMCPMC4912231

What OpenQuestion holds

Textmetadata
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.