Overview

More and more frequently, clinical trials include the evaluation of Health-Related Quality of Life (HRQoL), yet many investigators remain unaware of the unique measurement and analysis issues associated with the assessment of HRQoL. At the end of a study, clinicians and statisticians often face challenging and sometimes insurmountable analytic problems. Design and Analysis of Quality of Life Studies in Clinical Trials details these issues and presents a range of solutions. Written from the author's extensive experience in the field, it focuses on the very specific features of QoL data: its longitudinal nature, multidimensionality, and the problem of missing data. The author uses three real clinical trials throughout her discussions to illustrate practical implementation of the strategies and analytic methods presented. As Quality of Life becomes an increasingly important aspect of clinical trials, it becomes essential for clinicians, statisticians, and designers of these studies to understand and meet the challenges this kind of data present. In this book, SAS and S-PLUS programs, checklists, numerous figures, and a clear, concise presentation combine to provide readers with the tools and skills they need to successfully design, conduct, analyze, and report their own studies.

Full Product Details

Author:   Diane L. Fairclough
Publisher:   Taylor & Francis Ltd
Imprint:   Chapman & Hall/CRC
Dimensions:   Width: 15.60cm , Height: 2.40cm , Length: 23.50cm
Weight:   0.612kg
ISBN:  

9781584882633

ISBN 10:   1584882638
Pages:   328
Publication Date:   28 March 2002
Audience:   Professional and scholarly ,  Professional & Vocational
Replaced By:   9781420061178
Format:   Hardback
Publisher's Status:   Out of Print
Availability:   In Print  
Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock.

Table of Contents

INTRODUCTION Health-Related Quality of Life Measuring Health-Related Quality of Life Example 1: Adjuvant Breast Cancer Trial Example 2: Advanced Non-Small-Cell Lung Cancer (NSCLC) Example 3: Renal Cell Carcinoma Trial Summary STUDY DESIGN AND PROTOCOL DEVELOPMENT Introduction Background and Rationale Research Objectives Selection of Subjects Longitudinal Designs Selection of a Quality of Life Measure Conduct Summary MODELS FOR LONGITUDINAL STUDIES Introduction Building the Analytic Models Building Repeated Measures Models Building Growth Curve Models Summary MISSING DATA Introduction Patterns of Missing Data Mechanisms of Missing Data Summary ANALYTIC METHODS FOR IGNORABLE MISSING DATA Introduction Repeated Univariate Analyses Multivariate Methods Baseline Assessment as a Covariate Change from Baseline Empirical Bayes Estimates Summary SIMPLE IMPUTATION Introduction Mean Value Substitution Explicit Regression Models Last Value Carried Forward Underestimation of Variance Sensitivity Analysis Summary MULTIPLE IMPUTATION Introduction Overview of Multiple Imputation Explicit Univariate Regression Closest Neighbor and Predictive Mean Matching Approximate Bayesian Bootstrap Multivariate Procedures for Nonmonotone Missing Data Combining the M Analyses Sensitivity Analyses Imputation vs. Analytic Models Implications for Design Summary PATTERN MIXTURE MODELS Introduction Bivariate Data (Two Repeated Measures) Monotone Dropout Parametric Models Additional Reading Algebraic Details Summary RANDOM-EFFECTS MIXTURE, SHARED-PARAMETER, AND SELECTION MODELS Introduction Conditional Linear Model Joint Mixed-Effects and Time to Dropout Selection Model for Monotone Dropout Advanced Readings Summary SUMMARY MEASURES Introduction Choosing a Summary Measure Constructing Summary Measures Summary Statistics across Time Summarizing Across HRQoL Domains or Subscales Advanced Notes Summary MULTIPLE ENDPOINTS Introduction Background Concepts and Definitions Multivariate Statistics Univariate Statistics Resampling Techniques Summary DESIGN: ANALYSIS PLANS Introduction General Analysis Plan Models for Longitudinal Data Multiplicity of Endpoints Sample Size and Power Reported Results Summary APPENDICES BIBLIOGRAPHY

Reviews

This book not only provides a comprehensive review of the growing literatures and research activities, but also sheds light on new strategic and research directions. Throughout the book chapters, many real examples or simulation studies are used to illustrate the concepts, which make the contents relevant for the reader sand easier to understand. In general, this book provides an excellent source of references for anyone, including statisticians and non-statisticians, who are interested in global drug development and registration. William W.B. Wang, Merck Research Laboratories, Merck & Co., Inc., Beijing, China

<strong><em> This book not only provides a comprehensive review of the growing literatures and research activities, but also sheds light on new strategic and research directions. Throughout the book chapters, many real examples or simulation studies are used to illustrate the concepts, which make the contents relevant for the reader sand easier to understand. In general, this book provides an excellent source of references for anyone, including statisticians and non-statisticians, who are interested in global drug development and registration. </strong> William W.B. Wang, <i> </i>Merck Research Laboratories, Merck & Co., Inc., Beijing, China</p>

This book not only provides a comprehensive review of the growing literatures and research activities, but also sheds light on new strategic and research directions. Throughout the book chapters, many real examples or simulation studies are used to illustrate the concepts, which make the contents relevant for the reader sand easier to understand. ... In general, this book provides an excellent source of references for anyone, including statisticians and non-statisticians, who are interested in global drug development and registration. --William W.B. Wang, Merck Research Laboratories, Merck & Co., Inc., Beijing, China This book not only provides a comprehensive review of the growing literatures and research activities, but also sheds light on new strategic and research directions. Throughout the book chapters, many real examples or simulation studies are used to illustrate the concepts, which make the contents relevant for the reader sand easier to understand. In general, this book provides an excellent source of references for anyone, including statisticians and non-statisticians, who are interested in global drug development and registration. William W.B. Wang, Merck Research Laboratories, Merck & Co., Inc., Beijing, China

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