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Comparison of the flexible parametric survival model and Cox model in estimating Markov transition probabilities using real-world data

Author

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  • Xudong Du
  • Mier Li
  • Ping Zhu
  • Ju Wang
  • Lisha Hou
  • Jijie Li
  • Hongdao Meng
  • Muke Zhou
  • Cairong Zhu

Abstract

Background and objective: Markov 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 methods: The 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. Results: The 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. Conclusions: The flexible parametric survival model represents a good approach for estimating individual transition probabilities for a Markov micro-simulation model.

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  • Xudong Du & Mier Li & Ping Zhu & Ju Wang & Lisha Hou & Jijie Li & Hongdao Meng & Muke Zhou & Cairong Zhu, 2018. "Comparison of the flexible parametric survival model and Cox model in estimating Markov transition probabilities using real-world data," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-13, August.
    • Handle: RePEc:plo:pone00:0200807
    DOI: 10.1371/journal.pone.0200807
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    2. Mark Sculpher & Stephen Palmer, 2020. "After 20 Years of Using Economic Evaluation, Should NICE be Considered a Methods Innovator?," PharmacoEconomics, Springer, vol. 38(3), pages 247-257, March.
    3. Adeniyi Francis Fagbamigbe & Karolina Karlsson & Jan Derks & Max Petzold, 2021. "Performance evaluation of survival regression models in analysing Swedish dental implant complication data with frailty," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-16, January.
    4. Adeniyi Francis Fagbamigbe & Emma Norrman & Christina Bergh & Ulla-Britt Wennerholm & Max Petzold, 2021. "Comparison of the performances of survival analysis regression models for analysis of conception modes and risk of type-1 diabetes among 1985–2015 Swedish birth cohort," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-23, June.

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