Abstract
Background and objectiveMarkov micro-simulation models are being increasingly used in health economic evaluations. An important feature of the Markov micro-simulation model is its ability to consider transition probabilities of heterogeneous subgroups with different risk profiles. A survival analysis is generally performed to accurately estimate the transition probabilities associated with the risk profiles. This study aimed to apply a flexible parametric survival model (FPSM) to estimate individual transition probabilities.Materials and methodsThe data were obtained from a cohort study investigating ischemic stroke outcomes in Western China. In total, 585 subjects were included in the analysis. To explore the goodness of fit of the FPSM, we compared the estimated hazard ratios and baseline cumulative hazards, both of which are necessary to the calculate individual transition probabilities, and the Markov micro-simulation models constructed using the FPSM and Cox model to determine the validity of the two Markov micro-simulation models and cost-effectiveness results.ResultsThe flexible parametric proportional hazards model produced hazard ratio and baseline cumulative hazard estimates that were similar to those obtained using the Cox proportional hazards model. The simulated cumulative incidence of recurrent ischemic stroke and 5-years cost-effectiveness of Incremental cost-effectiveness Ratios (ICERs) were also similar using the two approaches. A discrepancy in the results was evident between the 5-years cost-effectiveness and the 10-years cost-effectiveness of ICERs, which were approximately 0.9 million (China Yuan) and 0.5 million (China Yuan), respectively.ConclusionsThe flexible parametric survival model represents a good approach for estimating individual transition probabilities for a Markov micro-simulation model.
Highlights
Markov Monte Carlo simulation models are being increasingly used in health economic evaluations[1,2,3,4]
The flexible parametric survival model represents a good approach for estimating individual transition probabilities for a Markov micro-simulation model
To compare the validity of the model constructed based on the flexible parametric survival model (FPSM) and that of the Cox model, we studied how closely the simulated cumulative incidence of recurrent ischemic stroke agreed with the observed cumulative incidence of recurrent ischemic stroke
Summary
Markov Monte Carlo simulation models are being increasingly used in health economic evaluations[1,2,3,4]. The Markov Monte Carlo model can incorporate individual risk factors and an individual’s historical experiences (demographics and clinical history), and the effect of the risk factors is often reflected in the state-transition probabilities[5]. These transition probabilities are important input parameters in models used to inform clinical and policy decisionmaking [6,7,8]. This study aimed to apply a flexible parametric survival model (FPSM) to estimate individual transition probabilities
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