 |
| |||||
 |
| >> Home >> Bookstore >> Statistics >> Biostatistics and epidemiology >> Applied Health Economics | |||||||||||||||||||||||
Applied Health Economics
Comments from the Stata technical groupApplied Health Economics, by Andrew Jones, Nigel Rice, Teresa Bago d’Uva, and Silvia Balia, shows how to summarize and analyze health-economic data with Stata. The authors teach topics in health economics by defining and asking real questions of real data with Stata. The book includes all the Stata code used in the analyses, and the authors carefully interpret the output. Applied Health Economics lives up to its name by teaching through application. This book is an excellent choice for anyone interested in empirical health economics. It offers a nice introduction for graduate students and useful discussions and modeling strategies for more advanced researchers. The wealth of Stata examples make the book an outstanding resource to researchers analyzing health-economic data with Stata. Applied Health Economics is nicely organized into parts, which correspond to data types, and chapters within each part, which focus on particular topics in health economics. The coverage is thorough, as the table of contents below makes clear. Table of contents
List of illustrations
Preface
Acknowledgments
Part I: Data description
1. Data and survey design
1.1 The Health and Lifestyle Survey (HALS)
1.2 The British Household Panel Survey (BHPS) 1.3 The European Community Household Survey (ECHP) 1.4 The Canadian National Population Health Survey (NPHS) 1.5 The WHO Multi-Country Survey Study (WHO-MCS) 1.6 Overview
2. Describing the dynamics of health
2.1 Introduction
2.2 Graphical analysis 2.3 Tabulating the data
3. Inequality in health utility and self-assessed health
3.1 Introduction
3.2 Distribution analysis 3.3 Regression analysis of HUI: Ordinary Least Squares (OLS) 3.4 Regression analysis of SAH: ordered probit model 3.5 Combined analysis of HUI and SAH: interval regression 3.6 Overview
Part II: Categorical data
4. Bias in self-reported data
4.1 Introduction
4.2 Vignettes 4.3 Standard ordered probit model 4.4 Using vignettes to control for heterogeneous reporting
5. Health and lifestyles
5.1 Introduction
5.2 HALS data and sample 5.3 Descriptive analysis 5.4 Estimation strategy and results 5.5 Overview
Part III: Survival data
6. Smoking and mortality
6.1 Introduction
6.2 Basic concepts of survival analysis 6.3 The HALS data 6.4 Survival data in HALS 6.5 Descriptive analysis 6.6 Duration models
7. Health and retirement
7.1 Introduction
7.2 Preparing and summarizing the data 7.3 The endogeneity of health 7.4 Empirical approach to duration modelling 7.5 Stock sampling and discrete-time hazard analysis 7.6 Overview
Part IV: Panel data
8. Health and wages
8.1 Introduction
8.2 BHPS sample and variables 8.3 Empirical model and estimation 8.4 Overview
9. Modelling the dynamics of health
9.1 Introduction
9.2 Static models 9.3 Dynamic models
10. Non-response and attrition bias
10.1 Introduction
10.2 Static models 10.3 Estimation
11. Models for health-care use
11.1 Introduction
11.2 The Poisson model 11.3 The Negative Binomial model 11.4 Zero inflated models 11.5 Hurdle models 11.6 Finite mixture/latent class models 11.7 Latent class hurdle model
Bibliography
Index
|
|||||||||||||||||||||||
)
){kind=link}