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Cancer Inform. 2016 Jun 15;15:115-27. doi: 10.4137/CIN.S38122. eCollection 2016.

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

Author information

1
Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, the Netherlands.
2
Department of Medical Oncology, VU University Medical Centre, Amsterdam, the Netherlands.
3
Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, the Netherlands.
4
Professor, Division of Diagnostic Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands.
5
Division of Diagnostic Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands.

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.

KEYWORDS:

cancer progression; melanoma; microsimulation; modeling; tumor growth

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