Suchergebnisse

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.

"There are a growing number of researchers and analysts who find the probability-based approaches for assessing risk and uncertainties to be too narrow and limiting. Uncertainty in Risk Assessment provides a broad conceptual framework and describes various alternative approaches of uncertainty representation and characterization therein such as probability-bound analysis, imprecise probability and evidence theory. The authors, whose own research has been at the forefront of developments in the field, include a number of real-life applications which demonstrate the practical use of the various methods in the different realistic circumstances. They provide invaluable practical guidance and clear recommendations on how and when to use the various approaches"--

Like its bestselling predecessor, Multilevel Modeling Using R, Second Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. This thoroughly updated revision gives the reader state-of-the-art tools to launch their own investigations in multilevel modeling and gain insight into their research.

Front Cover; Game Theory and Applications; Copyright Page; Table of Contents; Contributors; Preface; Chapter 1. Refinement of Nash Equilibrium: The Main Ideas; 1. INTRODUCTION; 2. BACKWARD INDUCTION AND FORWARD INDUCTION; 3. INFORMAL APPLICATIONS; 4. THE PROBLEM OF INCONSISTENCY; 5. THE EXISTENCE THEOREM: VERSION I; 6. FORMAL APPLICATIONS; 7. CONSISTENCY OF BELIEFS; 8. ADMISSIBILITY; 9. RESTRICTIONS ON OUT-OF-EQUILIBRIUM BELIEFS; 10. ALTERNATIVE VIEWS OF ""IRRELEVANT ALTERNATIVES""; 11. DEDUCTIONS BEYOND IIA; 12. CONCLUDING REMARKS; ACKNOWLEDGMENTS; APPENDIX A; APPENDIX B: PROOF OF THEOREM 3.

In many branches of science relevant observations are taken sequentially over time. Bayesian Analysis of Time Series discusses how to use models that explain the probabilistic characteristics of these time series and then utilizes the Bayesian approach to make inferences about their parameters. This is done by taking the prior information and via Bayes theorem implementing Bayesian inferences of estimation, testing hypotheses, and prediction. The methods are demonstrated using both R and WinBUGS. The R package is primarily used to generate observations from a given time series model, while the WinBUGS packages allows one to perform a posterior analysis that provides a way to determine the characteristic of the posterior distribution of the unknown parameters. Features Presents a comprehensive introduction to the Bayesian analysis of time series. Gives many examples over a wide variety of fields including biology, agriculture, business, economics, sociology, and astronomy. Contains numerous exercises at the end of each chapter many of which use R and WinBUGS. Can be used in graduate courses in statistics and biostatistics, but is also appropriate for researchers, practitioners and consulting statisticians. About the author Lyle D. Broemeling, Ph.D., is Director of Broemeling and Associates Inc., and is a consulting biostatistician. He has been involved with academic health science centers for about 20 years and has taught and been a consultant at the University of Texas Medical Branch in Galveston, The University of Texas MD Anderson Cancer Center and the University of Texas School of Public Health. His main interest is in developing Bayesian methods for use in medical and biological problems and in authoring textbooks in statistics. His previous books for Chapman & Hall/CRC include Bayesian Biostatistics and Diagnostic Medicine, and Bayesian Methods for Agreement.

This book collects select chapters on modern industrial problems related to uncertainties and vagueness in the expert domain of knowledge. The book further provides the knowledge related to application of various mathematical and statistical tools in these areas. The results presented in the book help the researchers and scientists in handling complicated projects in their domains. Useful to industrialists, academicians, researchers and students alike, the book aims to help managers and technical specialists in designing and implementation of reliability and risk programs as below: Ensure the system safety and risk informed asset management Follow a proper strategy to maintain the mechanical components of the system Schedule the proper actions throughout the product life cycle Understand the structure and cost of a complex system Plan the proper schedule to improve the reliability and life of the system Identify unwanted failures and set up preventive and correction action

Front Cover -- Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Chapter 1: Introduction -- 1.1: Interplay of Psychology and Physics: Historical Overview -- 1.2: Quantum Brain -- 1.3: Quantum-Like Modeling of Cognition and Decision Making -- 1.3.1: From Probabilistic Foundations of Quantum Mechanics to Quantum-Like Modeling -- 1.3.2: Quantum-Like Models Outside Physics -- 1.4: Operational Formalism: Creation and Annihilation Operators -- 1.5: Social Laser as a Fruit of the Quantum Information Revolution