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: melanoma, microsimulation, modeling, tumor growth, cancer progression
Melanoma incidence is rising worldwide. In the Netherlands, the incidence per 100,000 went up from 12.8 in 2001 to 19.7 in 2011 (world-standardized rate) and mortality increased by 44%.1 Worldwide, over 55,000 people died from melanoma in the year 2012.2 Most melanoma patients are diagnosed with local disease and treated with resection of the primary tumor only. Most of these patients are then cured, but about 15% will develop one or more recurrences.3 Until a few years ago, treatment options for (distant) metastatic melanoma were limited, and three-year overall survival (OS) was only about 15%.4 In the past few years, the number of treatment options for metastatic melanoma has increased with immunotherapeutic drugs such as ipilimumab and nivolumab and targeted drugs such as BRAF and MEK inhibitors. These expensive drugs greatly improve survival for subgroups of patients, but in view of the limited funds available, cost-effectiveness needs to be evaluated.5–7 In addition, expensive forms of diagnostics such as next-generation sequencing and FDG-PET-CT are becoming available. It is important to evaluate whether it would be cost-effective to include these diagnostics in the care for melanoma and whether the timing of their use in the disease process may be optimized.
To evaluate cost-effectiveness in cancer, Markov-type mathematical models are often used.8–11 Health states in these models are usually based on the observed clinical states. In cancer treatment, these are primary tumor, local and regional recurrence, distant metastasis, and death.12–15 The times at which patients remain in these states are equivalent to recurrence-free survival (RFS), distant recurrence-free survival (DRFS), progression-free survival (PFS), disease-specific survival (DSS), and OS. Times at which clinical states are observed, however, largely depend on the timing of examinations and the choice of diagnostic tests.
Most cost-effectiveness models in oncology do not attempt to model the whole disease and care pathway, but only compare interventions within a single-treatment line. When a new treatment is evaluated, the surveillance schedule, imaging techniques, and other tests are kept same. If one of these change, the model would need to be redone. A better solution would be to construct a model of underlying disease including an overlay of diagnostic testing that can be applied at adjustable intervals. Such models already exist for another aspect of cancer care, namely, screening.16–19 These models simulate the underlying tumor growth or their precursors and interact with the screening models, in which frequency and timing of testing, test characteristics, and follow-up procedures are specified. However, such models do not exist for disease progression and care after diagnosis. Although models including the whole cancer treatment pathway exist,20,21 a description of the underlying disease is required to investigate the full impact of new diagnostics, treatments, follow-up, and downstream treatment effects. For recurrence and surveillance of colorectal cancer (CRC), a modeling approach roughly comparable with the approaches used in cancer screening has been applied by Rose et al.22