Contents note continued: 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 --
Contents note continued: 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 --
Contents note continued: 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
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 --
