Fairclough D.L. Design and Analysis of Quality of Life Studies in Clinical Trials

Chapman & Hall – 2010, 424 pages
ISBN: 1420061178Design 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.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 DevelopmentBackground 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 IBuilding 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 IIBuilding 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 MediationModeration
Mediation
Other exploratory analyses
Characterization of Missing DataPatterns 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 DataMCAR
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 ImputationOverview 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 ModelsPattern mixture models
Restrictions for growth curve models
Restrictions for repeated measures models
Variance estimation for mixture models
Random Effects Dependent DropoutConditional linear model
Varying coefficient models
Joint models with shared parameters
Selection ModelsOutcome selection model for monotone dropout
Multiple EndpointsGeneral strategies for multiple endpoints
Background concepts and definitions
Single step procedures
Sequentially rejective methods
Closed testing and gatekeeper procedures
Composite Endpoints and Summary MeasuresChoosing 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-TWiSTQALYs
Q-TWiST
Analysis Plans and Reporting ResultsGeneral analysis plan
Sample size and power
Reporting resultsAppendix C: Cubic Smoothing Splines
Appendix P: PAWS/SPSS Notes
Appendix R: R Notes
Appendix S: SAS Notes