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MILC: A Microsimulation Model of the Natural History of Lung Cancer

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Abstract

The Microsimulation Lung Cancer (MILC) model was developed to simulate individual trajectories and predict outcomes of lung cancer for populations. The model describes the natural history of lung cancer from a disease-free state to death. Predictions of individual trajectories depend on a set of covariates including age, sex, and smoking behaviors. The module presented here is designed as part of a comprehensive decision-making toolkit for evaluating lung cancer prevention, screening and treatment policies. The MILC package implements the model in the open-source statistical software R. This paper introduces the main components, simulation algorithm, and specifics of the MILC model, validates it by reproducing observed lung cancer incidence trends in the US population, and uses it to make plausible predictions for 50-year-old men and women with a range of smoking histories.

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Mathematical health policy models, including microsimulation models (MSMs), are widely used to simulate complex processes and predict outcomes consistent with available data. Calibration is a method to estimate parameter values such that model predictions are similar to observed outcomes of interest. Bayesian calibration methods are popular among the available calibration techniques, given their strong theoretical basis and flexibility to incorporate prior beliefs and draw values from the posterior distribution of model parameters and hence the ability to characterize and evaluate parameter uncertainty in the model outcomes. Approximate Bayesian computation (ABC) is an approach to calibrate complex models in which the likelihood is intractable, focusing on measuring the difference between the simulated model predictions and outcomes of interest in observed data. Although ABC methods are increasingly being used, there is limited practical guidance in the medical decision-making literature on approaches to implement ABC to calibrate MSMs. In this tutorial, we describe the Bayesian calibration framework, introduce the ABC approach, and provide step-by-step guidance for implementing an ABC algorithm to calibrate MSMs, using 2 case examples based on a microsimulation model for dementia. We also provide the R code for applying these methods.
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Invasive breast cancer is commonly staged as local, regional or distant disease. We present a stochastic model of the natural history of invasive breast cancer that quantifies (1) the relative rate that the disease transitions from the local, regional to distant stages, (2) the tumour volume at the stage transitions and (3) the impact of symptom-prompted detection on the tumour size and stage of invasive breast cancer in a population not screened by mammography. By symptom-prompted detection, we refer to tumour detection that results when symptoms appear that prompt the patient to seek clinical care. The model assumes exponential tumour growth and volume-dependent hazard functions for the times to symptomatic detection and stage transitions. Maximum likelihood parameter estimates are obtained based on SEER data on the tumour size and stage of invasive breast cancer from patients who were symptomatically detected in the absence of screening mammography. Our results indicate that the rate of symptom-prompted detection is similar to the rate of transition from the local to regional stage and an order of magnitude larger than the rate of transition from the regional to distant stage. We demonstrate that, in the even absence of screening mammography, symptom-prompted detection has a large effect on reducing the occurrence of distant staged disease at initial diagnosis.
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As newer therapies for lung cancer are being explored it becomes more important to understand the natural history of lung cancer. A systematic review of the data shows that untreated lung cancer is almost uniformly rapidly fatal, even if it is stage I. Analysis of data regarding tumor volume doubling times shows that conventionally detected lung cancers have short mean doubling times, and only a small proportion with very long doubling times. Lung cancers found during the course of a CT screening program have markedly longer mean doubling times and a substantially greater proportion with very long doubling times (>400 days). Models of tumor growth, however, are not understood well enough to use the observed doubling time to predict length of survival without treatment.
Multi-state statistical modelling to quantify an individual-based micro simulation model for radiotherapy treatment in lung cancer patients
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Chrysanthopoulou, S. A. (2017). Comparative Analysis of Calibration Methods for Microsimulation Models. (Manuscript in preparation)
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Chrysanthopoulou, S. A. (2018). Assessing the Predictive Accuracy of Microsimulation Models. (Manuscript in preparation)
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Toward a new comprehensive international health and health care policy decision support tool
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Growth kinetics of tumours : cell population kinetics in relation to the growth and treatment of cancer
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International agency for research on cancer. Tobacco smoking
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