Development and validation of a decision model for the evaluation of novel lung cancer treatments in the Netherlands
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Published on: April 6, 2016
Zakile A Mfumbilwa1, Janneke A Wilschut1, Martijn J H G Simons2,3
1Department of Epidemiology and Data Science, Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit Amsterdam, de Boelelaan 1117, PO Box 7057, 1007 MB, Amsterdam, The Netherlands.
Abstract:
Recent discoveries in molecular diagnostics and drug treatments have improved the treatment of patients with advanced (inoperable) non-squamous non-small cell lung cancer (NSCLC) from solely platinum-based chemotherapy to more personalized treatment, including targeted therapies and immunotherapies. However, these improvements come at considerable costs, highlighting the need to assess their cost-effectiveness in order to optimize lung cancer care. Traditionally, cost-effectiveness models for the evaluation of new lung cancer treatments were based on the findings of the randomized control trials (RCTs). However, the strict RCT inclusion criteria make RCT patients not representative of patients in the real-world. Patients in RCTs have a better prognosis than patients in a real-world setting. Therefore, in this study, we developed and validated a diagnosis-treatment decision model for patients with advanced (inoperable) non-squamous NSCLC based on real-world data in the Netherlands. The model is a patient-level microsimulation model implemented as discrete event simulation with five health events. Patients are simulated from diagnosis to death, including at most three treatment lines. The base-model (non-personalized strategy) was populated using real-world data of patients treated with platinum-based chemotherapy between 2008 and 2014 in one of six Dutch teaching hospitals. To simulate personalized care, molecular tumor characteristics were incorporated in the model based on the literature. The impact of novel targeted treatments and immunotherapies was included based on published RCTs. To validate the model, we compared survival under a personalized treatment strategy with observed real-world survival. This model can be used for health-care evaluation of personalized treatment for patients with advanced (inoperable) NSCLC in the Netherlands.
Insights
This study developed a real-world data model for advanced non-squamous non-small cell lung cancer (NSCLC) treatment decisions. The model aids in evaluating personalized therapies, including targeted treatments and immunotherapies, for cost-effectiveness in lung cancer care.
Area of Science:
- Oncology
- Health Economics
- Biostatistics
Background:
- Advanced non-squamous non-small cell lung cancer (NSCLC) treatment has evolved beyond chemotherapy to personalized medicine, including targeted therapies and immunotherapies.
- The high cost of these novel treatments necessitates cost-effectiveness evaluations for optimizing lung cancer care.
- Traditional cost-effectiveness models rely on randomized controlled trials (RCTs), whose patient populations may not represent real-world scenarios.
Purpose of the Study:
- To develop and validate a patient-level microsimulation model for advanced, inoperable non-squamous NSCLC.
- To incorporate real-world data and molecular characteristics for personalized treatment simulation.
- To enable health-care evaluation of personalized treatment strategies in the Netherlands.
Main Methods:
- A discrete event simulation model with five health events was developed, simulating patients from diagnosis to death.
- The base model used real-world data from Dutch teaching hospitals (2008-2014) for platinum-based chemotherapy.
- Personalized care was simulated by integrating molecular tumor characteristics and published RCT data for targeted treatments and immunotherapies; model validation involved comparing simulated survival with observed real-world survival.
Main Results:
- A validated diagnosis-treatment decision model for advanced non-squamous NSCLC was created using real-world data.
- The model successfully incorporated personalized treatment strategies, including molecular diagnostics and novel therapies.
- The model demonstrated the ability to simulate patient journeys and predict survival outcomes under different treatment paradigms.
Conclusions:
- The developed model provides a robust tool for evaluating the cost-effectiveness of personalized treatments for advanced non-squamous NSCLC in the Netherlands.
- This approach addresses the limitations of RCTs by utilizing real-world data for more representative patient simulations.
- The model facilitates informed decision-making in lung cancer care, optimizing resource allocation for novel therapies.
