Design and Analysis of Quality of Life Studies in Clinical Trials

Design Principles and Analysis Techniques for HRQoL Clinical TrialsSAS, 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.

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.

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.