The Evaluation of Surrogate Endpoints Statistics for Biology and Health 2005th Edition by Tomasz Burzykowski, Geert Molenberghs, Marc Buyse ISBN 9780387202778 by Tomasz Burzykowski, Geert Molenberghs, Marc Buyse 0387202773, 9780387202778 instant download after payment.

The Evaluation of Surrogate Endpoints Statistics for Biology and Health 2005th Edition by Tomasz Burzykowski, Geert Molenberghs, Marc Buyse - Ebook PDF Instant Download/Delivery: 9780387202778
Full download The Evaluation of Surrogate Endpoints Statistics for Biology and Health 2005th Edition after payment

Product details:

ISBN 13: 9780387202778
Author: Tomasz Burzykowski, Geert Molenberghs, Marc Buyse

Both humanitarian and commercial considerations have spurred intensive search for methods to reduce the time and cost required to develop new therapies. The identification and use of surrogate endpoints, i.e., measures that can replace or supplement other endpoints in evaluations of experimental treatments or other interventions, is a general strategy that has stimulated both enthusiasm and skepticism. Surrogate endpoints are useful when they can be measured earlier, more conveniently, or more frequently than the "true" endpoints of primary interest. Regulatory agencies around the globe, particularly in the United States, Europe, and Japan, are introducing provisions and policies relating to the use of surrogate endpoints in registration studies. But how can one establish the adequacy of a surrogate? What kind of evidence is needed, and what statistical methods portray that evidence most appropriately?

This book offers a balanced account on this controversial topic. The text presents major developments of the last couple of decades, together with a unified, meta-analytic framework within which surrogates can be evaluated from several angles. Methodological development is coupled with perspectives on various therapeutic areas. Academic views are juxtaposed with standpoints of scientists working in the biopharmaceutical industry as well as of colleagues from the regulatory authorities.

Tomasz Burzykowski is Assistant Professor of Biostatistics at the Limburgs Universitair Centrum in Belgium. Dr. Burzykowski has published methodological work on the analysis of survey data, meta-analyses of clinical trials, and validation of surrogate endpoints. He is a co-author of numerous papers applying statistical methods to clinical data in different disease areas (cancer, cardiovascular diseases, dermatology, orthodontics).

Geert Molenberghs is Professor of Biostatistics at the Limburgs Universitair Centrum in Belgium. Dr. Molenberghs published methodological work on surrogate markers in clinical trials, categorical data, longitudinal data analysis, and on the analysis of non-response in clinical and epidemiological studies. He serves as Joint Editor for Applied Statistics (2001-2004) and is President of the International Biometric Society (2004-2005). He was elected Fellow of the American Statistical Association and received the Guy Medal in Bronze from the Royal Statistical Society.

Marc Buyse founded the International Drug Development Institute in 1991. He is Past President of the International Society for Clinical Biostatistics, Past President of the Quetelet Society, and Past Board Member of the Society for Clinical Trials. He is currently the Executive Director of IDDI (International Drug Development Institute) and Associate Professor of biostatistics at the Limburgs Universitair Centrum, Center for Statistics, Diepenbeek, Belgium. He has published extensively in the fields of biostatistics and oncology. His research interests include meta-analysis, surrogate endpoints, statistical detection of fraud, and the design and statistical analysis of clinical trials.

The Evaluation of Surrogate Endpoints Statistics for Biology and Health 2005th Table of contents:

Part I: Introduction and Fundamentals

• Chapter 1: Introduction to Surrogate Endpoints

  • 1.1 What are Surrogate Endpoints? Definitions and Concepts
  • 1.2 Why Use Surrogate Endpoints? (Efficiency, Cost, Ethics, Feasibility)
  • 1.3 True Endpoints vs. Surrogate Endpoints
  • 1.4 Historical Examples and Lessons Learned (e.g., Cardiac Arrhythmia Suppression Trial - CAST)
  • 1.5 Regulatory Perspectives on Surrogate Endpoints (FDA, EMA guidelines)
  • 1.6 Overview of the Book's Structure

• Chapter 2: Statistical Preliminaries and Notations

  • 2.1 Basic Concepts of Clinical Trial Design
  • 2.2 Causal Inference in Clinical Trials
  • 2.3 Measures of Association and Agreement
  • 2.4 Regression Models (Linear, Logistic, Proportional Hazards)
  • 2.5 Introduction to Hierarchical and Mixed Models

Part II: Conceptual and Statistical Frameworks for Surrogate Endpoint Evaluation

• Chapter 3: The Prentice Criteria and Beyond

  • 3.1 Original Prentice Criteria for Surrogate Endpoints
  • 3.2 Limitations and Criticisms of Prentice Criteria
  • 3.3 Extensions and Refinements of the Prentice Framework
  • 3.4 Surrogate Endpoints as Predictors of Treatment Effect on the True Endpoint

• Chapter 4: Individual-Level and Trial-Level Surrogacy

  • 4.1 Understanding Individual-Level Association
  • 4.2 Understanding Trial-Level Association (Treatment Effect Correlation)
  • 4.3 The Relationship Between Individual and Trial-Level Surrogacy
  • 4.4 Statistical Methods for Assessing Individual-Level Surrogacy

• Chapter 5: Information-Theoretic Approaches to Surrogacy

  • 5.1 Proportion of Treatment Effect Explained (PTE)
  • 5.2 Surrogate R-squared (Rsurr2​) and its Variants
  • 5.3 Information Fraction
  • 5.4 Other Metrics of Information Transfer

• Chapter 6: Causal Inference Frameworks for Surrogacy

  • 6.1 Potential Outcomes Framework
  • 6.2 Principal Stratification and Its Application to Surrogacy
  • 6.3 Mediation Analysis in the Context of Surrogate Endpoints
  • 6.4 Assumptions and Identifiability Issues in Causal Surrogacy Evaluation

Part III: Statistical Methods for Surrogate Endpoint Evaluation

• Chapter 7: Methods for Continuous Surrogate and True Endpoints

  • 7.1 Simple Linear Regression Approaches
  • 7.2 Bivariate Normal Models
  • 7.3 Mixed Models for Repeated Measures Data
  • 7.4 Sensitivity Analysis for Unmeasured Confounding

• Chapter 8: Methods for Binary Surrogate and True Endpoints

  • 8.1 Contingency Table Analysis
  • 8.2 Logistic Regression Models
  • 8.3 Bivariate Probit Models
  • 8.4 Measures of Agreement (Kappa, ROC curves)

• Chapter 9: Methods for Time-to-Event Surrogate and True Endpoints

  • 9.1 Cox Proportional Hazards Models
  • 9.2 Bivariate Survival Models
  • 9.3 Joint Models for Longitudinal Surrogates and Time-to-Event True Endpoints
  • 9.4 Accounting for Competing Risks

• Chapter 10: Meta-Analytic Approaches to Surrogate Endpoint Evaluation

  • 10.1 Combining Evidence Across Multiple Trials
  • 10.2 Meta-Analytic Surrogacy Measures
  • 10.3 Heterogeneity in Surrogate Relationships
  • 10.4 Hierarchical Models for Meta-Analysis of Surrogacy

Part IV: Practical Considerations and Applications

• Chapter 11: Design of Trials Using Surrogate Endpoints

  • 11.1 Sample Size Calculation with Surrogate Endpoints
  • 11.2 Adaptive Designs Involving Surrogate Endpoints
  • 11.3 Enrichment Strategies
  • 11.4 Ethical Considerations in Trial Design

• Chapter 12: Specific Applications and Case Studies

  • 12.1 Cardiovascular Diseases
  • 12.2 Oncology (e.g., Tumor Response, Progression-Free Survival)
  • 12.3 HIV/AIDS (e.g., CD4 Count, Viral Load)
  • 12.4 Chronic Diseases (e.g., Diabetes, Osteoporosis)
  • 12.5 Public Health and Epidemiology

• Chapter 13: Challenges, Limitations, and Future Directions

  • 13.1 Limitations of Current Surrogate Evaluation Methods
  • 13.2 The Problem of Imperfect Surrogates
  • 13.3 Surrogate Endpoints in Personalized Medicine
  • 13.4 Dynamic Surrogacy and Time-Varying Effects
  • 13.5 Emerging Methodologies and Research Needs

People also search for The Evaluation of Surrogate Endpoints Statistics for Biology and Health 2005th:

the evaluation of surrogate endpoints
    
surrogate endpoint definition
    
surrogate endpoint examples
    
what is a surrogate endpoint in a clinical trial
    
surrogate endpoint validation

Tags: Tomasz Burzykowski, Geert Molenberghs, Marc Buyse, Evaluation, Surrogate