The 5th Workshop on Case Studies in Bayesian Statistics was held at the Carnegie Mellon University campus on September 24-25, 1999. As in the past, the workshop featured both invited and contributed case studies. The former were presented and discussed in detail while the latter were presented in poster format. This volume contains the three invited case studies with the accompanying discussion as well as ten contributed pa pers selected by a refereeing process. The majority of case studies in the volume come from biomedical research. However, the reader will also find studies in education...
The 5th Workshop on Case Studies in Bayesian Statistics was held at the Carnegie Mellon University campus on September 24-25, 1999. As in the past, th...
The 6th Workshop on Case Studies in Bayesian Statistics was held at the Carnegie Mellon University in October, 2001. This volume contains the invited case studies with the accompanying discussion as well as contributed papers selected by a refereeing process.
The 6th Workshop on Case Studies in Bayesian Statistics was held at the Carnegie Mellon University in October, 2001. This volume contains the invited ...
Constantine Gatsonis James S. Hodges Robert E. Kaas
Presents applications of Bayesian statistical analysis, each of which emphasizes the scientific context of the problems it attempts to solve. This volume focuses on biomedical applications.
Presents applications of Bayesian statistical analysis, each of which emphasizes the scientific context of the problems it attempts to solve. This vol...
Constantine Gatsonis James S. Hodges Robert E. Kass
The past few years have witnessed dramatic advances in computational methods for Bayesian inference. As a result, Bayesian approaches to solving a wide variety of problems in data analysis and decision-making have become feasible, and there is currently a growth spurt in the application of Bayesian methods. The purpose of this volume is to present several detailed examples of applications of Bayesian thinking, with an emphasis on the scientific or technological context of the problem being solved. The papers collected here were presented and discussed at a Workshop held at Carnegie-Mellon...
The past few years have witnessed dramatic advances in computational methods for Bayesian inference. As a result, Bayesian approaches to solving a wid...
Comparative effectiveness research (CER) is the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care (IOM 2009). CER is conducted to develop evidence that will aid patients, clinicians, purchasers, and health policy makers in making informed decisions at both the individual and population levels. CER encompasses a very broad range of types of studies experimental, observational, prospective, retrospective, and research synthesis.
This...
Comparative effectiveness research (CER) is the generation and synthesis of evidence that compares the benefits and harms of alternative methods to...
