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Author: Qian Guan
Publisher:
ISBN:
Category :
Languages : en
Pages : 99
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Book Description
Author: Qian Guan
Publisher:
ISBN:
Category :
Languages : en
Pages : 99
Get Book Here
Book Description
Author: Saptarshi Chatterjee
Publisher:
ISBN: 9780438392007
Category : Biometry
Languages : en
Pages : 119
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Book Description
In chronic diseases such as cancer, physicians make multiple treatment decisions over the course of a patient's disease depending on his/her biological characteristics and accrued information. Essentially, the treatment rule at each decision point is a function which takes the patients' biomarker information, treatment and outcome history available up to that point as an input and returns the treatment choice as an output. In the single treatment setting, the optimal treatment decision can be obtained by a regression model on the mean outcome conditional on treatment and covariates, where the optimal treatment is the one that corresponds to the most desirable mean outcome. However, due to its overdependence on the outcome regression model, this method is heavily prone to model misspecification. Also, given data from an observational study, the usual regression method does not control for the confounding bias induced by the covariates affecting both treatment and outcomes. Causal inference provides a general framework to estimate the treatment causal effect by comparing the potential outcomes under each treatment group. However, for an individual patient, only one potential outcome is observed limiting the direct comparison of potential outcomes at the patient level. A handful number of methods have been proposed in the recent precision medicine literature where they employ semi-parametric estimation methods such as inverse probability weighting (IPWE) to predict the optimal treatment by maximizing a certain predefined value function. However, the likelihood based methods have received little attention in this area, partly due to making model assumptions. To fill this gap, in this dissertation, we develop two fully Bayesian semiparametric likelihood based methods to predict the optimal treatment for a new patient based on the treatment and covariate information from an observed group of patients. In the first approach (BayesG) we extend the idea of parametric g-formula to include a semiparametric mean function within a marginal structural model framework. In the second approach (PSBayes), we connect the treatment assignment mechanism to a missing data framework and build on the Penalized Spline of Propensity Prediction (PSPP) method in the missing data literature to develop a methodology to predict and compare the potential outcomes of the new patient. The posterior predictive potential outcome distribution is then analyzed to predict the optimal treatment. The performance of the proposed methodologies is illustrated in five different simulation studies covering a wide range of scenarios. Overall, the true specifications of inverse probability methods display comparable performance whereas the misspecified models perform poorly. In the additive mean function scenarios, PSBayes outperform all other methods in having higher accuracy in predicting true optimal treatments, whereas the inverse probability based methods show better performance in nonlinear mean function cases. In the presence of non-effect modifiers, the BayesG approach performs better than other methods. We conclude the dissertation by discussing the extension of our proposed methods to a dynamic treatment setting.
Author: Elias Moreno
Publisher: CRC Press
ISBN: 1351744372
Category : Mathematics
Languages : en
Pages : 284
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Book Description
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. Elías 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 José Vázquez-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 Ángel Negrín 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.
Author: Joseph B. Kadane
Publisher: John Wiley & Sons
ISBN: 1118150597
Category : Medical
Languages : en
Pages : 344
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Book Description
How to conduct clinical trials in an ethical and scientificallyresponsible manner This book presents a methodology for clinical trials that producesimproved health outcomes for patients while obtaining sound andunambiguous scientific data. It centers around a real-world testcase--involving a treatment for hypertension after open heartsurgery--and explains how to use Bayesian methods to accommodateboth ethical and scientific imperatives. The book grew out of the direct involvement in the project by adiverse group of experts in medicine, statistics, philosophy, andthe law. Not only do they contribute essays on the scientific,technological, legal, and ethical aspects of clinical trials, butthey also critique and debate each other's opinions, creating aninteresting, personalized text. Bayesian Methods and Ethics in a Clinical Trial Design * Answers commonly raised questions about Bayesian methods * Describes the advantages and disadvantages of this methodcompared with other methods * Applies current ethical theory to a particular class of designfor clinical trials * Discusses issues of informed consent and how to serve a patient'sbest interest while still obtaining uncontaminated scientific data * Shows how to use Bayesian probabilistic methods to createcomputer models from elicited prior opinions of medical experts onthe best treatment for a type of patient * Contains several chapters on the process, results, andcomputational aspects of the test case in question * Explores American law and the legal ramifications of using humansubjects For statisticians and biostatisticians, and for anyone involvedwith medicine and public health, this book provides both apractical guide and a unique perspective on the connection betweentechnological developments, human factors, and some of the largerethical issues of our times.
Author: Scott M. Berry
Publisher: CRC Press
ISBN: 1439825513
Category : Mathematics
Languages : en
Pages : 316
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Book Description
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
Author: Emmanuel Lesaffre
Publisher: CRC Press
ISBN: 1351718673
Category : Medical
Languages : en
Pages : 547
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Book Description
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.
Author: Mani Lakshminarayanan
Publisher: CRC Press
ISBN: 1351584162
Category : Business & Economics
Languages : en
Pages : 453
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Book Description
The cost for bringing new medicine from discovery to market has nearly doubled in the last decade and has now reached $2.6 billion. There is an urgent need to make drug development less time-consuming and less costly. Innovative trial designs/ analyses such as the Bayesian approach are essential to meet this need. This book will be the first to provide comprehensive coverage of Bayesian applications across the span of drug development, from discovery, to clinical trial, to manufacturing with practical examples. This book will have a wide appeal to statisticians, scientists, and physicians working in drug development who are motivated to accelerate and streamline the drug development process, as well as students who aspire to work in this field. The advantages of this book are: Provides motivating, worked, practical case examples with easy to grasp models, technical details, and computational codes to run the analyses Balances practical examples with best practices on trial simulation and reporting, as well as regulatory perspectives Chapters written by authors who are individual contributors in their respective topics Dr. Mani Lakshminarayanan is a researcher and statistical consultant with more than 30 years of experience in the pharmaceutical industry. He has published over 50 articles, technical reports, and book chapters besides serving as a referee for several journals. He has a PhD in Statistics from Southern Methodist University, Dallas, Texas and is a Fellow of the American Statistical Association. Dr. Fanni Natanegara has over 15 years of pharmaceutical experience and is currently Principal Research Scientist and Group Leader for the Early Phase Neuroscience Statistics team at Eli Lilly and Company. She played a key role in the Advanced Analytics team to provide Bayesian education and statistical consultation at Eli Lilly. Dr. Natanegara is the chair of the cross industry-regulatory-academic DIA BSWG to ensure that Bayesian methods are appropriately utilized for design and analysis throughout the drug-development process.
Author: David J. Spiegelhalter
Publisher: John Wiley & Sons
ISBN: 0470092599
Category : Mathematics
Languages : en
Pages : 406
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Book Description
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.
Author: Ying Yuan
Publisher: CRC Press
ISBN: 1315354225
Category : Mathematics
Languages : en
Pages : 238
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Book Description
Reliably optimizing a new treatment in humans is a critical first step in clinical evaluation since choosing a suboptimal dose or schedule may lead to failure in later trials. At the same time, if promising preclinical results do not translate into a real treatment advance, it is important to determine this quickly and terminate the clinical evaluation process to avoid wasting resources. Bayesian Designs for Phase I–II Clinical Trials describes how phase I–II designs can serve as a bridge or protective barrier between preclinical studies and large confirmatory clinical trials. It illustrates many of the severe drawbacks with conventional methods used for early-phase clinical trials and presents numerous Bayesian designs for human clinical trials of new experimental treatment regimes. Written by research leaders from the University of Texas MD Anderson Cancer Center, this book shows how Bayesian designs for early-phase clinical trials can explore, refine, and optimize new experimental treatments. It emphasizes the importance of basing decisions on both efficacy and toxicity.
Author: Bradley P. Carlin
Publisher: CRC Press
ISBN: 9781584886983
Category : Mathematics
Languages : en
Pages : 552
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Book Description
Broadening its scope to nonstatisticians, Bayesian Methods for Data Analysis, Third Edition provides an accessible introduction to the foundations and applications of Bayesian analysis. Along with a complete reorganization of the material, this edition concentrates more on hierarchical Bayesian modeling as implemented via Markov chain Monte Carlo (MCMC) methods and related data analytic techniques. New to the Third Edition New data examples, corresponding R and WinBUGS code, and homework problems Explicit descriptions and illustrations of hierarchical modeling—now commonplace in Bayesian data analysis A new chapter on Bayesian design that emphasizes Bayesian clinical trials A completely revised and expanded section on ranking and histogram estimation A new case study on infectious disease modeling and the 1918 flu epidemic A solutions manual for qualifying instructors that contains solutions, computer code, and associated output for every homework problem—available both electronically and in print Ideal for Anyone Performing Statistical Analyses Focusing on applications from biostatistics, epidemiology, and medicine, this text builds on the popularity of its predecessors by making it suitable for even more practitioners and students.
