[PDF] Bayesian Approaches To Clinical Trials And Health Care Evaluation eBook

Bayesian Approaches To Clinical Trials And Health Care Evaluation Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Bayesian Approaches To Clinical Trials And Health Care Evaluation book. This book definitely worth reading, it is an incredibly well-written.

Bayesian Approaches to Clinical Trials and Health-Care Evaluation

Author : David J. Spiegelhalter
Publisher : John Wiley & Sons
Page : 416 pages
File Size : 40,84 MB
Release : 2004-01-16
Category : Mathematics
ISBN : 9780471499756

GET BOOK

READ ALL ABOUT IT! David Spiegelhalter has recently joined the ranks of Isaac Newton, Charles Darwin and Stephen Hawking by becoming a fellow of the Royal Society. Originating from the Medical Research Council’s biostatistics unit, David has played a leading role in the Bristol heart surgery and Harold Shipman inquiries. Order a copy of this author’s comprehensive text TODAY! The Bayesian approach involves synthesising data and judgement in order to reach conclusions about unknown quantities and make predictions. Bayesian methods have become increasingly popular in recent years, notably in medical research, and although there are a number of books on Bayesian analysis, few cover clinical trials and biostatistical applications in any detail. Bayesian Approaches to Clinical Trials and Health-Care Evaluation provides a valuable overview of this rapidly evolving field, including basic Bayesian ideas, prior distributions, clinical trials, observational studies, evidence synthesis and cost-effectiveness analysis. Covers a broad array of essential topics, building from the basics to more advanced techniques. Illustrated throughout by detailed case studies and worked examples Includes exercises in all chapters Accessible to anyone with a basic knowledge of statistics Authors are at the forefront of research into Bayesian methods in medical research Accompanied by a Web site featuring data sets and worked examples using Excel and WinBUGS - the most widely used Bayesian modelling package Bayesian Approaches to Clinical Trials and Health-Care Evaluation is suitable for students and researchers in medical statistics, statisticians in the pharmaceutical industry, and anyone involved in conducting clinical trials and assessment of health-care technology.

Bayesian Methods in Pharmaceutical Research

Author : Emmanuel Lesaffre
Publisher : CRC Press
Page : 547 pages
File Size : 39,22 MB
Release : 2020-04-15
Category : Medical
ISBN : 1351718673

GET BOOK

Since the early 2000s, there has been increasing interest within the pharmaceutical industry in the application of Bayesian methods at various stages of the research, development, manufacturing, and health economic evaluation of new health care interventions. In 2010, the first Applied Bayesian Biostatistics conference was held, with the primary objective to stimulate the practical implementation of Bayesian statistics, and to promote the added-value for accelerating the discovery and the delivery of new cures to patients. This book is a synthesis of the conferences and debates, providing an overview of Bayesian methods applied to nearly all stages of research and development, from early discovery to portfolio management. It highlights the value associated with sharing a vision with the regulatory authorities, academia, and pharmaceutical industry, with a view to setting up a common strategy for the appropriate use of Bayesian statistics for the benefit of patients. The book covers: Theory, methods, applications, and computing Bayesian biostatistics for clinical innovative designs Adding value with Real World Evidence Opportunities for rare, orphan diseases, and pediatric development Applied Bayesian biostatistics in manufacturing Decision making and Portfolio management Regulatory perspective and public health policies Statisticians and data scientists involved in the research, development, and approval of new cures will be inspired by the possible applications of Bayesian methods covered in the book. The methods, applications, and computational guidance will enable the reader to apply Bayesian methods in their own pharmaceutical research.

Bayesian Adaptive Methods for Clinical Trials

Author : Scott M. Berry
Publisher : CRC Press
Page : 316 pages
File Size : 48,71 MB
Release : 2010-07-19
Category : Mathematics
ISBN : 1439825513

GET BOOK

Already popular in the analysis of medical device trials, adaptive Bayesian designs are increasingly being used in drug development for a wide variety of diseases and conditions, from Alzheimer's disease and multiple sclerosis to obesity, diabetes, hepatitis C, and HIV. Written by leading pioneers of Bayesian clinical trial designs, Bayesian Adapti

Bayesian Methods in Health Economics

Author : Gianluca Baio
Publisher : CRC Press
Page : 246 pages
File Size : 49,53 MB
Release : 2012-11-12
Category : Mathematics
ISBN : 1439895554

GET BOOK

Health economics is concerned with the study of the cost-effectiveness of health care interventions. This book provides an overview of Bayesian methods for the analysis of health economic data. After an introduction to the basic economic concepts and methods of evaluation, it presents Bayesian statistics using accessible mathematics. The next chapters describe the theory and practice of cost-effectiveness analysis from a statistical viewpoint, and Bayesian computation, notably MCMC. The final chapter presents three detailed case studies covering cost-effectiveness analyses using individual data from clinical trials, evidence synthesis and hierarchical models and Markov models. The text uses WinBUGS and JAGS with datasets and code available online.

Small Clinical Trials

Author : Institute of Medicine
Publisher : National Academies Press
Page : 221 pages
File Size : 43,48 MB
Release : 2001-01-01
Category : Medical
ISBN : 0309171148

GET BOOK

Clinical trials are used to elucidate the most appropriate preventive, diagnostic, or treatment options for individuals with a given medical condition. Perhaps the most essential feature of a clinical trial is that it aims to use results based on a limited sample of research participants to see if the intervention is safe and effective or if it is comparable to a comparison treatment. Sample size is a crucial component of any clinical trial. A trial with a small number of research participants is more prone to variability and carries a considerable risk of failing to demonstrate the effectiveness of a given intervention when one really is present. This may occur in phase I (safety and pharmacologic profiles), II (pilot efficacy evaluation), and III (extensive assessment of safety and efficacy) trials. Although phase I and II studies may have smaller sample sizes, they usually have adequate statistical power, which is the committee's definition of a "large" trial. Sometimes a trial with eight participants may have adequate statistical power, statistical power being the probability of rejecting the null hypothesis when the hypothesis is false. Small Clinical Trials assesses the current methodologies and the appropriate situations for the conduct of clinical trials with small sample sizes. This report assesses the published literature on various strategies such as (1) meta-analysis to combine disparate information from several studies including Bayesian techniques as in the confidence profile method and (2) other alternatives such as assessing therapeutic results in a single treated population (e.g., astronauts) by sequentially measuring whether the intervention is falling above or below a preestablished probability outcome range and meeting predesigned specifications as opposed to incremental improvement.

Modern Approaches to Clinical Trials Using SAS: Classical, Adaptive, and Bayesian Methods

Author : Sandeep Menon
Publisher : SAS Institute
Page : 364 pages
File Size : 19,7 MB
Release : 2015-12-09
Category : Computers
ISBN : 1629600849

GET BOOK

This book covers domains of modern clinical trial design: classical, group sequential, adaptive, and Bayesian methods applicable to and used in various phases of pharmaceutical development. Written for biostatisticians, pharmacometricians, clinical developers, and statistical programmers involved in the design, analysis, and interpretation of clinical trials, as well as students in graduate and postgraduate programs in statistics or biostatistics, it covers topics including: dose-response and dose-escalation designs; sequential methods to stop trials early for overwhelming efficacy, safety, or futility; Bayesian designs incorporating historical data; adaptive sample size re-estimation and randomization to allocate subjects to effective treatments; population enrichment designs. Methods are illustrated using clinical trials from diverse therapeutic areas, including dermatology, endocrinology, infectious disease, neurology, oncology and rheumatology. --

Use of Bayesian Techniques in Randomized Clinical Trials

Author : Gillian D. Sanders
Publisher :
Page : 184 pages
File Size : 24,77 MB
Release : 2009
Category :
ISBN :

GET BOOK

The use of Bayesian statistical approaches has gained broader acceptance within the clinical trial community. The impact of these methods on CMS decisional contexts and policy-level decisionmaking however was uncertain. Our analyses explore the main proclaimed advantages of Bayesian statistics (namely, the use of prior information, sample size determination, borrowing strength from different trials, and sequential monitoring of trials) and provide examples of decisionmaking situations where the findings reached using these approaches both agree with and differ from findings reached using frequentist statistical techniques. Our findings confirm that, like classical techniques, Bayesian approaches are affected by the problems of model specification (i.e., the relationship between various factors - patient, provider, intervention, and other contextual features - and the outcome of interest). In addition, Bayesian approaches can be substantially affected by the "Bayesian prior" - the representation of beliefs about the quantity of interest (e.g., relative risk of events when a new device is used vs. a conventional device). Thus, when considering using or interpreting Bayesian analyses, the focus of attention and thoughtful ex ante agreement are the specification of the model and specification of the Bayesian prior. The case study of the use of ICD therapy in the prevention of sudden cardiac death demonstrates the application of these techniques and highlights some of the practical challenges. The use of Bayesian statistical approaches, and incorporation of our findings concerning their strengths and limitations into the CMS decisionmaking process will enable policymakers to harness the power of the available sources of clinical evidence, explore subgroup effects within a trial and across trials in a methodologically rigorous manner, assess the uncertainty in clinical trial findings, and - ideally - improve health outcomes for Medicare beneficiaries.

Advanced Bayesian Methods for Medical Test Accuracy

Author : Lyle D. Broemeling
Publisher : CRC Press
Page : 482 pages
File Size : 42,35 MB
Release : 2016-04-19
Category : Mathematics
ISBN : 1439838798

GET BOOK

Useful in many areas of medicine and biology, Bayesian methods are particularly attractive tools for the design of clinical trials and diagnostic tests, which are based on established information, usually from related previous studies. Advanced Bayesian Methods for Medical Test Accuracy begins with a review of the usual measures such as specificity

Elementary Bayesian Biostatistics

Author : Lemuel A. Moye
Publisher : CRC Press
Page : 400 pages
File Size : 18,46 MB
Release : 2016-04-19
Category : Mathematics
ISBN : 1584887257

GET BOOK

Bayesian analyses have made important inroads in modern clinical research due, in part, to the incorporation of the traditional tools of noninformative priors as well as the modern innovations of adaptive randomization and predictive power. Presenting an introductory perspective to modern Bayesian procedures, Elementary Bayesian Biostatistics explo