Multi-state statistical modeling: a tool to build a lung cancer micro-simulation model that includes parameter uncertainty and patient heterogeneity

With the shift towards individualized treatment, cost-effectiveness models need to incorporate patient and tumor characteristics that may be relevant to treatment planning. In this study, we used multi-state statistical modeling to inform a micro-simulation model for cost-effectiveness analysis of individualized radiotherapy in lung cancer. The model tracks clinical events over time and takes patient and tumor features into account.

Four clinical states were included in the model: ‘Alive without progression’, ‘Local Recurrence’, ‘Metastasis’, and ‘Death’. Individual patients were simulated by repeatedly sampling a patient profile, consisting of patient and tumor characteristics. The transitioning of patients between the health states is governed by personalized time dependent hazard rates, which were obtained from multi-state statistical modeling.

We show that model simulations for both the individualized and conventional radiotherapy strategies demonstrated internal and external validity. Therefore, multi-state statistical modeling is a useful technique for obtaining the correlated individualized transition rates that are required for the quantification of a patient-level micro-simulation model. Moreover, the hazard ratios, their 95% confidence intervals and their covariance can be used to quantify the parameter uncertainty of the model in a correlated way. In this fashion, we account for patient heterogeneity, stochastic uncertainty, and parameter uncertainty. The obtained model will be used to evaluate the cost-effectiveness of individualized radiotherapy treatment planning, including the uncertainty of input parameters. We discuss the model-building process, and the strengths and weaknesses of using multi-state statistical modeling in a micro-simulation model for individualized radiotherapy in lung cancer.