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Structural Uncertainty of Markov Models for Advanced Breast Cancer

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  • Quang A. Le

Abstract

Objective. To examine the impact of structural uncertainty of Markov models in modeling cost-effectiveness for the treatment of advanced breast cancer (ABC). Methods. Four common Markov models for ABC were identified and examined. Markov models 1 and 2 have 4 health states (stable-disease, responding-to-therapy, disease-progressing, and death), and Markov models 3 and 4 only have 3 health states (stable-disease, disease-progressing, and death). In models 1 and 3, the possibility of death can occur in any health state, while in models 2 and 4, the chance of dying can only occur in the disease-progressing health state. A simulation was conducted to examine the impact of using different model structures on cost-effectiveness results in the context of a combination therapy of lapatinib and capecitabine for the treatment of HER2-positive ABC. Model averaging with an assumption of equal weights in all 4 models was used to account for structural uncertainty. Results. Markov model 3 yielded the lowest incremental cost-effectiveness ratio (ICER) of $303,909 per quality-adjusted life year (QALY), while Markov model 1 produced the highest ICER ($495,800/QALY). At a willingness-to-pay threshold of $150,000/QALY, the probabilities that the combination therapy is considered to be cost-effective for Markov models 1, 2, 3, and 4 were 14.5%, 14.1%, 21.6%, and 17.0%, respectively. When using model averaging to synthesize different model structures, the resulting ICER was $389,270/QALY. Conclusions. Our study shows that modeling ABC with different Markov model structures yielded a wide range of cost-effectiveness results, suggesting the need to investigate structural uncertainty in health economic evaluation. When applied in the context of HER2-positive ABC treatment, the combination therapy with lapatinib is not cost-effective, regardless of which model was used and whether uncertainties were accounted for.

Suggested Citation

  • Quang A. Le, 2016. "Structural Uncertainty of Markov Models for Advanced Breast Cancer," Medical Decision Making, , vol. 36(5), pages 629-640, July.
  • Handle: RePEc:sae:medema:v:36:y:2016:i:5:p:629-640
    DOI: 10.1177/0272989X15622643
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    References listed on IDEAS

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    1. Mark Strong & Jeremy E. Oakley & Jim Chilcott, 2012. "Managing structural uncertainty in health economic decision models: a discrepancy approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(1), pages 25-45, January.
    2. Peasgood, T & Ward, S & Brazier, J, 2010. "A review and meta-analysis of health state utility values in breast cancer," MPRA Paper 29950, University Library of Munich, Germany.
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    1. Robert Kraig Helmeczi & Can Kavaklioglu & Mucahit Cevik & Davood Pirayesh Neghab, 2023. "A multi-objective constrained partially observable Markov decision process model for breast cancer screening," Operational Research, Springer, vol. 23(2), pages 1-42, June.
    2. Hossein Haji Ali Afzali & Jonathan Karnon & Olga Theou & Justin Beilby & Matteo Cesari & Renuka Visvanathan, 2019. "Structuring a conceptual model for cost-effectiveness analysis of frailty interventions," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-13, September.

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