Design and Analysis of Quality of Life Studies in Clinical Trials  book cover
2nd Edition

Design and Analysis of Quality of Life Studies in Clinical Trials

ISBN 9781420061178
Published January 7, 2010 by Chapman and Hall/CRC
424 Pages 69 B/W Illustrations

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Book Description

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.

Table of Contents

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


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


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


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




Other exploratory analyses

Characterization of Missing Data


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



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


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


Pattern mixture models

Restrictions for growth curve models

Restrictions for repeated measures models

Variance estimation for mixture models

Random Effects Dependent Dropout


Conditional linear model

Varying coefficient models

Joint models with shared parameters

Selection Models


Outcome selection model for monotone dropout

Multiple Endpoints


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


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




Analysis Plans and Reporting Results


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


A Summary appears at the end of each chapter.

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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