Content: Machine generated contents note: 1.Introduction to health economic evaluation --
1.1.Introduction --
1.2.Health economic evaluation --
1.2.1.Clinical trials versus decision-analytical models --
1.3.Cost components --
1.3.1.Perspective and what costs include --
1.3.2.Sources and types of cost data --
1.4.Outcomes --
1.4.1.Condition specific outcomes --
1.4.2.Generic outcomes --
1.4.3.Valuing outcomes --
1.5.Discounting --
1.6.Types of economic evaluations --
1.6.1.Cost-minimisation analysis --
1.6.2.Cost-benefit analysis --
1.6.3.Cost-effectiveness analysis --
1.6.4.Cost-utility analysis --
1.7.Comparing health interventions --
1.7.1.The cost-effectiveness plane --
2.Introduction to Bayesian inference --
2.1.Introduction --
2.2.Subjective probability and Bayes theorem --
2.2.1.Probability as a measure of uncertainty against a standard --
2.2.2.Fundamental rules of probability --
2.2.3.Coherence --
2.2.4.Bayes theorem --
2.3.Bayesian (parametric) modelling --
2.3.1.Exchangeability and predictive inference --
2.3.2.Inference on the posterior distribution --
2.4.Choosing prior distributions and Bayesian computation --
2.4.1.Vague priors --
2.4.2.Conjugate priors --
2.4.3.Monte Carlo estimation --
2.4.4.Nonconjugate priors --
2.4.5.Markov Chain Monte Carlo methods --
2.4.6.MCMC convergence --
2.4.7.MCMC autocorrelation --
3.Statistical cost-effectiveness analysis --
3.1.Introduction --
3.2.Decision theory and expected utility --
3.2.1.Problem --
3.2.2.Decision criterion: Maximisation of the expected utility --
3.3.Decision-making in health economics --
3.3.1.Statistical framework --
3.3.2.Decision process --
3.3.3.Choosing a utility function: The net benefit --
3.3.4.Uncertainty in the decision process --
3.4.Probabilistic sensitivity analysis to parameter uncertainty --
3.5.Reporting the results of probabilistic sensitivity analysis --
3.5.1.Cost-effectiveness acceptability curves --
3.5.2.The value of information --
3.5.3.The value of partial information --
3.6.Probabilistic sensitivity analysis to structural uncertainty --
3.7.Advanced issues in cost-effectiveness analysis --
3.7.1.Including a risk aversion parameter in the net benefit --
3.7.2.Expected value of information for mixed strategies --
4.Bayesian analysis in practice --
4.1.Introduction --
4.2.Software configuration --
4.3.An example of analysis in JAGS/BUGS --
4.3.1.Model specification --
4.3.2.Pre-processing in R --
4.3.3.Launching JAGS from R --
4.3.4.Checking convergence and post-processing in R --
4.4.Logical nodes --
4.5.For loops and node transformations --
4.5.1.Blocking to improve convergence --
4.6.Predictive distributions --
4.6.1.Predictive distributions as missing values --
4.7.Modelling the cost-effectiveness of a new chemotherapy drug in R/JAGS --
4.7.1.Programming the analysis of the EVPPI --
4.7.2.Programming probabilistic sensitivity analysis to structural uncertainty --
5.Health economic evaluation in practice --
5.1.Introduction --
5.2.Cost-effectiveness analysis alongside clinical trials --
5.2.1.Example: RCT of acupuncture for chronic headache in primary care --
5.2.2.Model description --
5.2.3.JAGS implementation --
5.2.4.Cost-effectiveness analysis --
5.2.5.Alternative specifications of the model --
5.3.Evidence synthesis and hierarchical models --
5.3.1.Example: Neuraminidase inhibitors to reduce influenza in healthy adults --
5.3.2.Model description --
5.3.3.JAGS implementation --
5.3.4.Cost-effectiveness analysis --
5.4.Markov models --
5.4.1.Example: Markov model for the treatment of asthma --
5.4.2.Model description --
5.4.3.JAGS implementation --
5.4.4.Cost-effectiveness analysis --
5.4.5.Adding memory to Markov models --
5.4.6.Indirect estimation of the transition probabilities.