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  Escoger Formato: Formato Estandar | Tarjeta Catalográfica | Cita | Nombre de Etiquetas | Etiquetas MARC
Número de registro  
000478625
ISBN  
Autor  
Título  
Pie de Imprenta  
Boca Raton, Fl : CRC Press, 2013.
Descr. Física  
225 p.
Serie  
Contenido  
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.
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