2nd Edition
Design and Analysis of Quality of Life Studies in Clinical Trials
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, 2013I 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, 2011It 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 2011Professor 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, 30The 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