2nd Edition

Design and Analysis of Quality of Life Studies in Clinical Trials

By Diane L. Fairclough Copyright 2010
    424 Pages 69 B/W Illustrations
    by Chapman & Hall

    Design Principles and Analysis Techniques for HRQoL Clinical Trials
    SAS, R, and SPSS examples realistically show how to implement methods

    Focusing on longitudinal studies, Design and Analysis of Quality of Life Studies in Clinical Trials, Second Edition addresses design and analysis aspects in enough detail so that readers can apply statistical methods, such as mixed effect models, to their own studies. The author illustrates the implementation of the methods using the statistical software packages SAS, SPSS, and R.

    New to the Second Edition

    • Data sets available for download online, allowing readers to replicate the analyses presented in the text
    • New chapter on testing models that involve moderation and mediation
    • Revised discussions of multiple comparisons procedures that focus on the integration of health-related quality of life (HRQoL) outcomes with other study outcomes using gatekeeper strategies
    • Recent methodological developments for the analysis of trials with missing data
    • New chapter on quality adjusted life-years (QALYs) and QTWiST specific to clinical trials
    • Additional examples of the implementation of basic models and other selected applications in R and SPSS

    This edition continues to provide practical information for researchers directly involved in the design and analysis of HRQoL studies as well as for those who evaluate the design and interpret the results of HRQoL research. By following the examples in the book, readers will be able to apply the steps to their own trials.

    Introduction and Examples

    Health-related quality of life (HRQoL)

    Measuring health-related quality of life

    Study 1: Adjuvant breast cancer trial

    Study 2: Migraine prevention trial

    Study 3: Advanced lung cancer trial

    Study 4: Renal cell carcinoma trial

    Study 5: Chemoradiation (CXRT) trial

    Study 6: Osteoarthritis trial

    Study Design and Protocol Development

    Introduction

    Background and rationale

    Research objectives and goals

    Selection of subjects

    Longitudinal designs

    Selection of measurement instrument(s)

    Conduct of HRQoL assessments

    Scoring instruments

    Models for Longitudinal Studies I

    Introduction

    Building models for longitudinal studies

    Building repeated measures models: The mean structure

    Building repeated measures models: The covariance structure

    Estimation and hypothesis testing

    Models for Longitudinal Studies II

    Introduction

    Building growth curve models: The mean (fixed effects) structure

    Building growth curve models: The covariance structure

    Model reduction

    Hypothesis testing and estimation

    An alternative growth-curve model

    Moderation and Mediation

    Introduction

    Moderation

    Mediation

    Other exploratory analyses

    Characterization of Missing Data

    Introduction

    Patterns and causes of missing data

    Mechanisms of missing data

    Missing completely at random (MCAR)

    Missing at random (MAR)

    Missing not at random (MNAR)

    Example for trial with variation in timing of assessments

    Example with different patterns across treatment arms

    Analysis of Studies with Missing Data

    Introduction

    MCAR

    Ignorable missing data

    Non-ignorable missing data

    Simple Imputation

    Introduction to imputation

    Missing items in a multi-item questionnaire

    Regression-based methods

    Other simple imputation methods

    Imputing missing covariates

    Underestimation of variance

    Final comments

    Multiple Imputation

    Introduction

    Overview of multiple imputation

    Explicit univariate regression

    Closest neighbor and predictive mean matching

    Approximate Bayesian bootstrap (ABB)

    Multivariate procedures for non-monotone missing data

    Analysis of the M data sets

    Miscellaneous issues

    Pattern Mixture and Other Mixture Models

    Introduction

    Pattern mixture models

    Restrictions for growth curve models

    Restrictions for repeated measures models

    Variance estimation for mixture models

    Random Effects Dependent Dropout

    Introduction

    Conditional linear model

    Varying coefficient models

    Joint models with shared parameters

    Selection Models

    Introduction

    Outcome selection model for monotone dropout

    Multiple Endpoints

    Introduction

    General strategies for multiple endpoints

    Background concepts and definitions

    Single step procedures

    Sequentially rejective methods

    Closed testing and gatekeeper procedures

    Composite Endpoints and Summary Measures

    Introduction

    Choosing a composite or summary measure

    Summarizing across HRQoL domains or subscales

    Summary measure across time

    Composite endpoints across time

    Quality Adjusted Life-Years (QALYs) and Q-TWiST

    Introduction

    QALYs

    Q-TWiST

    Analysis Plans and Reporting Results

    Introduction

    General analysis plan

    Sample size and power

    Reporting results

    Appendix C: Cubic Smoothing Splines

    Appendix P: PAWS/SPSS Notes

    Appendix R: R Notes

    Appendix S: SAS Notes

    References

    A Summary appears at the end of each chapter.

    Biography

    Diane L. Fairclough is a professor in the Department of Biostatistics and Informatics in the Colorado School of Public Health and director of the Biostatistics Core of the Colorado Health Outcomes Program at the University of Colorado in Denver. She is also President of the International Society for Quality of Life Research. Dr. Fairclough’s prior appointments include St. Jude Children’s Research Hospital, Harvard School of Public Health, and AMC Cancer Research Center.

    The book is written for a wide range of researchers interested in HRQoL research, including clinicians, epidemiologists, psychologists and statisticians. … the author did her best to make the material accessible to a larger audience through the chapter structure, the datasets, the software code and programs available from the author’s website. She should be commended for her efforts and improvements since the first edition. Every researcher involved in the design and analysis of HRQoL studies will benefit from having this book on their shelf.
    —Stephane Heritier, Australian & New Zealand Journal of Statistics, 2013

    I found that the use of well-placed comment statements and titles, as well as additional coding [on the author’s website], enhanced my understanding considerably.
    —Cynthia A. Rodenberg, Journal of Biopharmaceutical Statistics, 21, 2011

    It is a well-organized and nicely written book, which should be quite useful for researchers involved in HRQoL studies. … it may serve as a textbook for a graduate-level course in applied statistics focused on clinical epidemiology and health services research. … Another bonus for students and instructors refer to the example programs in SAS, SPSS and R provided in the book, in addition to full data sets available for download online, which was not offered with the first edition.
    Biometrics, 67, September 2011

    Professor Fairclough has succeeded in writing a book which can be used by trial statisticians for the valid analysis of quality of life data. It is a remarkable combination of theory and practical advice. … The second edition … [includes] examples in R and SPSS as well as SAS, and gives links to download all the data and much of the code in the book. … excellent book. All in all, this is a useful resource for statisticians working in the areas of quality of life, clinical trials, and/or missing data.
    ISCB News, No. 51, June 2011

    … this book offers unique perspectives and insights that reflect decades of hands-on experience with HRQoL trials and that will certainly benefit researchers in this area. … Written clearly and concisely, the book is a pleasure to read. The technical level is reasonable for statistical practitioners and medical researchers with a good understanding of basic statistical concepts and methods. I would definitely recommend the book to researchers in HRQoL studies, and I think it is worth reading by anyone interested in clinical trials, because many of the issues discussed extend far beyond HRQoL studies.
    Statistics in Medicine, 2011, 30

    The book sits well in the Interdisciplinary Statistics Series, containing much insightful discussion of the issues and not too much mathematics. It is carefully written and well organized and likely to become a standard reference in the area, taking its place on many a bookshelf, both personal and library-based.
    International Statistical Review (2010), 78, 3