Design and Analysis of Quality of Life Studies in Clinical Trials
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Summary
More and more frequently, clinical trials include the evaluation of Health-Related Quality of Life (HRQoL), yet many investigators remain unaware of the unique measurement and analysis issues associated with the assessment of HRQoL. At the end of a study, clinicians and statisticians often face challenging and sometimes insurmountable analytic problems.
Design and Analysis of Quality of Life Studies in Clinical Trials details these issues and presents a range of solutions. Written from the author's extensive experience in the field, it focuses on the very specific features of QoL data: its longitudinal nature, multidimensionality, and the problem of missing data. The author uses three real clinical trials throughout her discussions to illustrate practical implementation of the strategies and analytic methods presented.
As Quality of Life becomes an increasingly important aspect of clinical trials, it becomes essential for clinicians, statisticians, and designers of these studies to understand and meet the challenges this kind of data present. In this book, SAS and S-PLUS programs, checklists, numerous figures, and a clear, concise presentation combine to provide readers with the tools and skills they need to successfully design, conduct, analyze, and report their own studies.
Table of Contents
INTRODUCTION
Health-Related Quality of Life
Measuring Health-Related Quality of Life
Example 1: Adjuvant Breast Cancer Trial
Example 2: Advanced Non-Small-Cell Lung Cancer (NSCLC)
Example 3: Renal Cell Carcinoma Trial
Summary
STUDY DESIGN AND PROTOCOL DEVELOPMENT
Introduction
Background and Rationale
Research Objectives
Selection of Subjects
Longitudinal Designs
Selection of a Quality of Life Measure
Conduct
Summary
MODELS FOR LONGITUDINAL STUDIES
Introduction
Building the Analytic Models
Building Repeated Measures Models
Building Growth Curve Models
Summary
MISSING DATA
Introduction
Patterns of Missing Data
Mechanisms of Missing Data
Summary
ANALYTIC METHODS FOR IGNORABLE MISSING DATA
Introduction
Repeated Univariate Analyses
Multivariate Methods
Baseline Assessment as a Covariate
Change from Baseline
Empirical Bayes Estimates
Summary
SIMPLE IMPUTATION
Introduction
Mean Value Substitution
Explicit Regression Models
Last Value Carried Forward
Underestimation of Variance
Sensitivity Analysis
Summary
MULTIPLE IMPUTATION
Introduction
Overview of Multiple Imputation
Explicit Univariate Regression
Closest Neighbor and Predictive Mean Matching
Approximate Bayesian Bootstrap
Multivariate Procedures for Nonmonotone Missing Data
Combining the M Analyses
Sensitivity Analyses
Imputation vs. Analytic Models
Implications for Design
Summary
PATTERN MIXTURE MODELS
Introduction
Bivariate Data (Two Repeated Measures)
Monotone Dropout
Parametric Models
Additional Reading
Algebraic Details
Summary
RANDOM-EFFECTS MIXTURE, SHARED-PARAMETER, AND SELECTION MODELS
Introduction
Conditional Linear Model
Joint Mixed-Effects and Time to Dropout
Selection Model for Monotone Dropout
Advanced Readings
Summary
SUMMARY MEASURES
Introduction
Choosing a Summary Measure
Constructing Summary Measures
Summary Statistics across Time
Summarizing Across HRQoL Domains or Subscales
Advanced Notes
Summary
MULTIPLE ENDPOINTS
Introduction
Background Concepts and Definitions
Multivariate Statistics
Univariate Statistics
Resampling Techniques
Summary
DESIGN: ANALYSIS PLANS
Introduction
General Analysis Plan
Models for Longitudinal Data
Multiplicity of Endpoints
Sample Size and Power
Reported Results
Summary
APPENDICES
BIBLIOGRAPHY
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