With the shift toward individualized treatment, cost-effectiveness models need to incorporate patient and tumor characteristics that may be relevant to treatment planning. In this study, we used multistate statistical modeling to inform a microsimulation 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 multistate statistical modeling (MSSM). The model simulations for both the individualized and conventional radiotherapy strategies demonstrated internal and external validity. Therefore, MSSM is a useful technique for obtaining the correlated individualized transition rates that are required for the quantification of a microsimulation model. Moreover, we have used the hazard ratios, their 95% confidence intervals, and their covariance to quantify the parameter uncertainty of the model in a correlated way. 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 MSSM in a microsimulation model for individualized radiotherapy in lung cancer.

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doi.org/10.1177/0272989X15574500, hdl.handle.net/1765/83214
Medical Decision Making: an international journal
Institute for Medical Technology Assessment (iMTA)

Bongers, M.L, de Ruysscher, D.K.M, Oberije, C, Lambin, P, Uyl-de Groot, C.A, & Coupé, V.M.H. (2016). Multistate statistical modeling: A tool to build a lung cancer microsimulation model that includes parameter uncertainty and patient heterogeneity. Medical Decision Making: an international journal, 36(1), 86–100. doi:10.1177/0272989X15574500