Gianluca Baio – författare

R for Health Technology Assessment discusses the use of proper statistical software, specifically R, to perform the whole pipeline of analytic modelling in health technology assessment (HTA). It has been designed with the objective of establishing the use of R as the standard tool for HTA amongst academics, industry practitioners and regulators. It covers a lot of ground, starting with the necessary background in HTA, R and statistical inference, followed by various modelling tools, ranging from missing data, survival analysis and decision trees, through to multistate models and discrete event simulation. The methods are all illustrated with many detailed worked examples and case studies using real data, and there are detailed descriptions of the code and processes.Key Features:Introductory chapters on the various topics of the book, including HTA, R and statistical inferenceA wide range of common analytical tools used in HTA, from modelling for individual-level data, missing data, survival analysis, decision-modelling and network meta-analysisMore advanced and increasingly popular tools, such as those for population adjustment, discrete event simulation and the use of web applications as front-end for the overall statistical modellingMany detailed worked examples and case studies using real data to illustrate the methodologyFully integrated R code gives detailed guidance on implementation of the techniquesSupplemented by a website with additional resources, including annotated code and dataThis text is primarily aimed at modellers working in the field of HTA, regulators and reviewers of reimbursement dossiers and cost-effectiveness analyses. It also complements a wide range of undergraduate and graduate programmes in HTA, health and public health economics, as well as academic researchers in the field of statistical modelling for HTA.

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 computingBayesian biostatistics for clinical innovative designsAdding value with Real World EvidenceOpportunities for rare, orphan diseases, and pediatric developmentApplied Bayesian biostatistics in manufacturingDecision 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.

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.

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.

R for Health Technology Assessment discusses the use of proper statistical software, specifically R, to perform the whole pipeline of analytic modelling in health technology assessment (HTA). It has been designed with the objective of establishing the use of R as the standard tool for HTA amongst academics, industry practitioners and regulators. It covers a lot of ground, starting with the necessary background in HTA, R and statistical inference, followed by various modelling tools, ranging from missing data, survival analysis and decision trees, through to multistate models and discrete event simulation. The methods are all illustrated with many detailed worked examples and case studies using real data, and there are detailed descriptions of the code and processes.

Key Features:

Introductory chapters on the various topics of the book, including HTA, R and statistical inference A wide range of common analytical tools used in HTA, from modelling for individual-level data, missing data, survival analysis, decision-modelling and network meta-analysis More advanced and increasingly popular tools, such as those for population adjustment, discrete event simulation and the use of web applications as front-end for the overall statistical modelling Many detailed worked examples and case studies using real data to illustrate the methodology Fully integrated R code gives detailed guidance on implementation of the techniques Supplemented by a website with additional resources, including annotated code and data

This text is primarily aimed at modellers working in the field of HTA, regulators and reviewers of reimbursement dossiers and cost-effectiveness analyses. It also complements a wide range of undergraduate and graduate programmes in HTA, health and public health economics, as well as academic researchers in the field of statistical modelling for HTA.

R for Health Technology Assessment discusses the use of proper statistical software, specifically R, to perform the whole pipeline of analytic modelling in health technology assessment (HTA). It has been designed with the objective of establishing the use of R as the standard tool for HTA amongst academics, industry practitioners and regulators. It covers a lot of ground, starting with the necessary background in HTA, R and statistical inference, followed by various modelling tools, ranging from missing data, survival analysis and decision trees, through to multistate models and discrete event simulation. The methods are all illustrated with many detailed worked examples and case studies using real data, and there are detailed descriptions of the code and processes.

Key Features:

Introductory chapters on the various topics of the book, including HTA, R and statistical inference A wide range of common analytical tools used in HTA, from modelling for individual-level data, missing data, survival analysis, decision-modelling and network meta-analysis More advanced and increasingly popular tools, such as those for population adjustment, discrete event simulation and the use of web applications as front-end for the overall statistical modelling Many detailed worked examples and case studies using real data to illustrate the methodology Fully integrated R code gives detailed guidance on implementation of the techniques Supplemented by a website with additional resources, including annotated code and data

This text is primarily aimed at modellers working in the field of HTA, regulators and reviewers of reimbursement dossiers and cost-effectiveness analyses. It also complements a wide range of undergraduate and graduate programmes in HTA, health and public health economics, as well as academic researchers in the field of statistical modelling for HTA.

Bayesian models in Health Technology Assessment aims at presenting a thorough and yet accessible description of the philosophy underlying the Bayesian approach to statistical inference, as specifically applied to the process of health technology assessment (HTA). The book is grounded in practical examples, mostly taken from the HTA context and covering a wide range of real problems, typically encountered by modellers in their day-to-day work. All the chapters present methodological details, as well as carefully curated R and JAGS code, which can be used as a template to unlock the potential of Bayesian modelling, specifically in HTA. Covers introductory chapters on Bayesian modelling and computation, as well as the basics of the statistical modelling for HTA. Discusses a range of modelling topics, including the analysis of individual and aggregated-level data, as well as survival analysis and evidence synthesis. Presents further, more advanced modelling tools (such as for missing data, population adjustment methods and Value of Information), which should be increasingly familiar for practitioners working in HTA and beyond. The text is primarily for modellers and practitioners working in the HTA context, regulators and reviewers of reimbursement dossiers and cost-effectiveness analysis. More generally, it aims at drawing the attention of researchers whose background is firmly statistical onto the interesting and high impactful area of HTA. It complements a wide range of undergraduate and graduate programmes in health technology assessment, health and public health economics, as well as academic researchers in the field of statistical modelling for health technology assessment.

Bayesian models in Health Technology Assessment aims at presenting a thorough and yet accessible description of the philosophy underlying the Bayesian approach to statistical inference, as specifically applied to the process of health technology assessment (HTA). The book is grounded in practical examples, mostly taken from the HTA context and covering a wide range of real problems, typically encountered by modellers in their day-to-day work. All the chapters present methodological details, as well as carefully curated R and JAGS code, which can be used as a template to unlock the potential of Bayesian modelling, specifically in HTA. Covers introductory chapters on Bayesian modelling and computation, as well as the basics of the statistical modelling for HTA. Discusses a range of modelling topics, including the analysis of individual and aggregated-level data, as well as survival analysis and evidence synthesis. Presents further, more advanced modelling tools (such as for missing data, population adjustment methods and Value of Information), which should be increasingly familiar for practitioners working in HTA and beyond. The text is primarily for modellers and practitioners working in the HTA context, regulators and reviewers of reimbursement dossiers and cost-effectiveness analysis. More generally, it aims at drawing the attention of researchers whose background is firmly statistical onto the interesting and high impactful area of HTA. It complements a wide range of undergraduate and graduate programmes in health technology assessment, health and public health economics, as well as academic researchers in the field of statistical modelling for health technology assessment.

Bayesian models in Health Technology Assessment aims at presenting a thorough and yet accessible description of the philosophy underlying the Bayesian approach to statistical inference, as specifically applied to the process of health technology assessment (HTA). The book is grounded in practical examples, mostly taken from the HTA context and covering a wide range of real problems, typically encountered by modellers in their day-to-day work. All the chapters present methodological details, as well as carefully curated R and JAGS code, which can be used as a template to unlock the potential of Bayesian modelling, specifically in HTA. Covers introductory chapters on Bayesian modelling and computation, as well as the basics of the statistical modelling for HTA. Discusses a range of modelling topics, including the analysis of individual and aggregated-level data, as well as survival analysis and evidence synthesis. Presents further, more advanced modelling tools (such as for missing data, population adjustment methods and Value of Information), which should be increasingly familiar for practitioners working in HTA and beyond. The text is primarily for modellers and practitioners working in the HTA context, regulators and reviewers of reimbursement dossiers and cost-effectiveness analysis. More generally, it aims at drawing the attention of researchers whose background is firmly statistical onto the interesting and high impactful area of HTA. It complements a wide range of undergraduate and graduate programmes in health technology assessment, health and public health economics, as well as academic researchers in the field of statistical modelling for health technology assessment.

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.

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.

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.

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.

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.

The book provides a description of the process of health economic evaluation and modelling for cost-effectiveness analysis, particularly from the perspective of a Bayesian statistical approach. Some relevant theory and introductory concepts are presented using practical examples and two running case studies. The book also describes in detail how to perform health economic evaluations using the R package BCEA (Bayesian Cost-Effectiveness Analysis). BCEA can be used to post-process the results of a Bayesian cost-effectiveness model and perform advanced analyses producing standardized and highly customizable outputs. It presents all the features of the package, including its many functions and their practical application, as well as its user-friendly web interface. The book is a valuable resource for statisticians and practitioners working in the field of health economics wanting to simplify and standardize their workflow, for example in the preparation of dossiers in support of marketing authorization, or academic and scientific publications. This new edition includes a more streamlined description of examples and programming, focusing on the newest release of the BCEA package and recent work on Value of Information.

The book provides a description of the process of health economic evaluation and modelling for cost-effectiveness analysis, particularly from the perspective of a Bayesian statistical approach. Some relevant theory and introductory concepts are presented using practical examples and two running case studies. The book also describes in detail how to perform health economic evaluations using the R package BCEA (Bayesian Cost-Effectiveness Analysis). BCEA can be used to post-process the results of a Bayesian cost-effectiveness model and perform advanced analyses producing standardized and highly customizable outputs. It presents all the features of the package, including its many functions and their practical application, as well as its user-friendly web interface. The book is a valuable resource for statisticians and practitioners working in the field of health economics wanting to simplify and standardize their workflow, for example in the preparation of dossiers in support of marketing authorization, or academic and scientific publications. This new edition includes a more streamlined description of examples and programming, focusing on the newest release of the BCEA package and recent work on Value of Information.