Introduction
Conventional types of economic evaluation
The variables of cost-effectiveness analysis
Sources of uncertainty in cost-effectiveness analysis
Conventional tools for cost-effectiveness analysis
The incremental cost-effectiveness ratio
The incremental net benefit
Cost-effectiveness acceptability curve
Conventional subgroup analysis
An outline of Bayesian cost-effectiveness analysis
Introduction
Parametric sampling models
The likelihood function
Likelihood sets
The maximum likelihood estimator
Proving consistency and asymptotic normality
Reparametrization to a subparameter
Parametric Bayesian models
Subjective priors
Conjugate priors
Objective priors
The predictive distribution
Bayesian model selection
Intrinsic priors for model selection
The normal linear model
Maximum likelihood estimators
Bayesian estimators
An outline of variable selection
Introduction
Elements of a decision problem
Ordering rewards
Lotteries
The Utility function
Axioms for the existence of the utility function
Criticisms to the utility function
Lotteries that depend on a parameter
The minimax strategy
The Bayesian strategy
Comparison
Optimal decisions in the presence of sampling information
The frequentist procedure
The Bayesian procedure
Introduction
The net benefit of a treatment
Utility functions of the net benefit
The utility function U Optimal treatments
Interpretation of the expected utility
The utility function U Optimal treatments
Interpretation of the expected utility
Penalizing a new treatment
Parametric classes of probabilistic rewards
Frequentist predictive distribution of the net bene
Bayesian predictive distribution of the net benefit
Statistical models for cost and effectiveness
The normal-normal model
The lognormal-normal model
The lognormal-Bernoulli model
The bivariate normal model
The dependent lognormal-Bernoulli model
A case study
The cost-effectiveness acceptability curve for the utility
function U
The case of completely unknown rewards
The case of parametric rewards
The cost-effectiveness acceptability curve for the utility
function U
Comments on cost-effectiveness acceptability curve
Introduction
Clustering
Prior distributions
Posterior distribution of the cluster models
Examples
Bayesian meta-analysis
The Bayesian meta-model
The likelihood of the meta-parameter and the
linking distribution
Properties of the linking distribution
Examples
Contents
The predictive distribution of (c; e) conditional on a partition
The unconditional predictive distribution of (c; e)
The predictive distribution of the net benefit z
The case of independent c and e
Optimal treatments
Examples
Introduction
The data and the Bayesian model
The independent normal-normal model
The normal-normal model
The lognormal-normal model
The probit sampling model
Bayesian variable selection
Notation
Posterior model probability
The hierarchical uniform prior for models
Zellner's gpriors for model parameters
Intrinsic priors for model parameters
Bayes factors for normal linear models
Bayes factors for probit models
Bayesian predictive distribution of the net benefit
The normal-normal case
The case where c and e are independent
The lognormal-normal case
Optimal treatments for subgroups
Examples
Improving subgroup definition
N2 - Cost-effectiveness analysis is becoming an increasingly important tool for decision making in the health systems. Cost-Effectiveness of Medical Treatments formulates the cost-effectiveness analysis as a statistical decision problem, identifies the sources of uncertainty of the problem, and gives an overview of the frequentist and Bayesian statistical approaches for decision making. Basic notions on decision theory such as space of decisions, space of nature, utility function of a decision and optimal decisions, are explained in detail using easy to read mathematics. Features Focuses on cost-effectiveness analysis as a statistical decision problem and applies the well-established optimal statistical decision methodology. Discusses utility functions for cost-effectiveness analysis. Enlarges the class of models typically used in cost-effectiveness analysis with the incorporation of linear models to account for covariates of the patients. This permits the formulation of the group (or subgroup) theory. Provides Bayesian procedures to account for model uncertainty in variable selection for linear models and in clustering for models for heterogeneous data. Model uncertainty in cost-effectiveness analysis has not been considered in the literature. Illustrates examples with real data. In order to facilitate the practical implementation of real datasets, provides the codes in Mathematica for the proposed methodology. The motivation for the book is to make the achievements in cost-effectiveness analysis accessible to health providers, who need to make optimal decisions, to the practitioners and to the students of health sciences. Elaias Moreno is Professor of Statistics and Operational Research at the University of Granada, Spain, Corresponding Member of the Royal Academy of Sciences of Spain, and elect member of ISI. Francisco Josae Vaazquez-Polo is Professor of Mathematics and Bayesian Methods at the University of Las Palmas de Gran Canaria, and Head of the Department of Quantitative Methods. Miguel aAngel Negrain is Senior Lecturer in the Department of Quantitative Methods at the ULPGC. His main research topics are Bayesian methods applied to Health Economics, economic evaluation and cost-effectiveness analysis, meta-analysis and equity in the provision of healthcare services UR - https://www.taylorfrancis.com/books/9781315188850 UR - http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf ER -