Use of Bayesian Methods for Markov Modelling in Cost Effectiveness Analysis: An application to taxane use in advanced breast cancer Nicola Cooper, Keith.

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Presentation transcript:

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 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

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

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

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

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

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

Docetaxel Doxorubicin RESULTS Stable Progressive Respond Death

CE PLANE (MC)

CE PLANE (MCMC)

RESULTS

INB CURVES

NET BENEFIT (cont.)

NET BENEFIT (cont.)

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

ACCEPTABILITY CURVE

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)