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Nicola Cooper, Keith Abrams, Alex Sutton, David Turner, Paul Lambert

Use of Bayesian Methods for Markov Modelling in Cost Effectiveness Analysis: An application to taxane use in advanced breast cancer. Nicola Cooper, Keith Abrams, Alex Sutton, David Turner, Paul Lambert Department of Epidemiology & Public Health, University of Leicester, UK. OBJECTIVE.

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Nicola Cooper, Keith Abrams, Alex Sutton, David Turner, Paul Lambert

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  1. Use of Bayesian Methods for Markov Modelling in Cost Effectiveness Analysis:An application to taxane use in advanced breast cancer Nicola Cooper, Keith Abrams, Alex Sutton, David Turner, Paul Lambert Department of Epidemiology & Public Health, University of Leicester, UK

  2. OBJECTIVE • To demonstrate how CE decision analysis may be implemented from a Bayesian perspective, using MCMC simulation methods. • Illustrative example:CE analysis of taxane use for the second-line treatment of advanced breast cancer compared to conventional treatment

  3. OUTLINE • Decision-Analytical Model • Transition Probabilities • Model Evaluation Methods • Model Results • Summary & Conclusions

  4. MODEL • 4 Stage stochastic Markov Model • 4 Health states • Response • Stable • Progressive • Death • Cycle length = 3 weeks (35 cycles) • Maximum of 7 treatment sessions

  5. MODEL cont. Stages 1 & 2 (cycles 1 to 3) Treatment cycles Stage 3 (cycles 4 to 7) Stage 4 (cycles 8 to 35) Post -Treatment cycles

  6. TRANSITION PROBABILITIES 1) Pooled estimates • 2) Distribution 4) Application to model 3) Transformation of distribution to transition probability (i) time variables: (ii) prob. variables:

  7. MODEL EVALUATION • Stochastic Markov Models: • Classical Model - Monte Carlo (MC) simulation model (EXCEL) • Bayesian Model - Markov Chain Monte Carlo (MCMC) simulation model (WinBUGS)

  8. RESULTS Docetaxel Death Progressive Respond Doxorubicin Stable

  9. CE PLANE (MC)

  10. CE PLANE (MCMC)

  11. RESULTS

  12. INB CURVES

  13. NET BENEFIT (cont.)

  14. NET BENEFIT (cont.)

  15. CONCLUSIONS • Advantages of the Bayesian approach compared to equivalent Classical approach • Incorporation of greater parameter uncertainty • Ability to make direct probability statements & thus direct answers to the question of interest • Incorporation of expert opinion either directly or regarding the relative credibility of different data sources

  16. ACCEPTABILITY CURVE

  17. FURTHER WORK • Sensitivity analysis • One / multi-way analysis • Choice of prior distributions • MCMC convergence • Simple versus Complex Markov model • Time dependent variables • Two-way pathways • (e.g. stable to response to stable)

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