Cost–effectiveness analysis is the simultaneous Statistical comparison of two or more groups with respect to costs and effectiveness. A prime example of this is the comparison of health–care interventions, where there is a growing expectation from policymakers that evidence supporting the cost–effectiveness of new interventions be provided along with customary data on efficacy and safety. Statistical Analysis of Cost–effectiveness Data provides an overview of the statistical methods used in such analysis and gives an illustrated summary of the key developments in statistical issues related to cost–effectiveness comparisons, over the last decade.
- Provides an up–to–date overview of statistical methods used in the analysis of cost–effectiveness data.
- Discusses all the major issues in the field, including:
- Parameter estimation for both censored and uncensored data;
- Making inference using cost–effectiveness ratios;
- Incremental net benefit plots and cost–effectiveness ratios;
- Incremental net benefit plots and cost–effectiveness acceptability curves;
- Covariate adjustment and sub–group analyses;
- Multinational trials;
- Sample size determinations using both classical and Bayesian approaches.
- Illustrated throughout by worked examples from the authors′ own experiences.
Statistical Analysis of Cost–effectiveness Data is ideal for biostatisticians and health economists both in academia and industry. There is also much of use for graduate students of biostatistics, public health and economics, as well as individuals working in government regulator agencies.
STATISTICS IN PRACTICE
A series of practical books outlining the use of statistical techniques in a wide range of applications areas:
- HUMAN AND BIOLOGICAL SCIENCES
- EARTH AND ENVIRONMENTAL SCIENCES
- INDUSTRY, COMMERCE AND FINANCE
1.2 Cost–effectiveness data and the parameters of interest.
1.3 The cost–effectiveness plane, the ICER and INB.
2. Parameter Estimation for Non–censored Data.
3. Parameter Estimation for Censored Data.
3.2 Mean Cost.
4. Cost–effectiveness Analysis.
4.2 Incremental cost–effectiveness ratio.
4.3 Incremental net benefit.
4.4 The cost–effectiveness acceptability curve.
4.5 Using bootstrap methods.
4.6 A Bayesian incremental net benefit approach.
4.7 Kinked thresholds.
5. Cost–effectiveness Analysis: Examples.
5.2 The CADET–Hp trial.
5.3 Symptomatic hormone–resistant prostate cancer.
5.4 The Canadian implantable defibrillator study (CIDS).
5.5 The EVALUATE trial.
5.6 Bayesian approach applied to the UK PDS study.
6. Power and Sample Size Determination.
6.2 Approaches based on the cost–effectiveness plane.
6.3 The classical approach based on net benefit.
6.4 Bayesian take on the classical approach.
6.5 The value of information approach.
7. Covariate Adjustment and Sub–group Analysis.
7.2 Non–censored data.
7.3 Censored data.
8. Multicenter and Multinational Trials.
8.2 Background to multinational cost–effectiveness.
8.3 Fixed effect approaches.
8.4 Random effects approaches.
9. Modeling Cost–effectiveness.
9.2 A general framework for modeling cost–effectiveness results.
9.3 Case study: an economic appraisal of the goal study.
"Overall this is a useful book for this new discipline that helps considerably the reader to enter this topic and learn how to handle this type of problems." (Zentralblatt MATH 2008)