Quality of Life Outcomes in Clinical Trials and Health-Care Evaluation : A Practical Guide to Analysis and Interpretation.

Walters, Stephen J.
Hoboken : John Wiley & Sons, Incorporated, 2009.
1 online resource (381 pages)
1st ed.
Statistics in Practice Ser.
Statistics in Practice Ser. ; v.84

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Clinical trials.
Quality of life.
Outcome assessment (Medical care).
Electronic books.
"The book covers a wide range of issues and techniques. ... Subjects are exposed clearly, with the obvious aim of being accessible to clinicians unfamiliar with mathematical formalism, and the methods are nicely illustrated with QoL data examples." (Journal of Biopharmaceutical Statistics, April 2010).
Quality of Life Outcomes in Clinical Trials and Health-Care Evaluation
1 Introduction
1.1 What is quality of life?
1.2 Terminology
1.3 History
1.4 Types of quality of life measures
1.5 Why measure quality of life?
1.6 Further reading
2 Measuring quality of life
2.1 Introduction
2.2 Principles of measurement scales
2.2.1 Scales and items
2.2.2 Constructs and latent variables
2.3 Indicator and causal variables
2.3.1 Indicator variables
2.3.2 Causal variables
2.3.3 Why do we need to worry about the distinction between indicator and causal items?
2.3.4 Single-item versus multi-item scales
2.4 The traditional psychometric model
2.4.1 Psychometrics and QoL scales
2.5 Item response theory
2.5.1 Traditional scales versus IRT
2.6 Clinimetric scales
2.7 Measuring quality of life: Indicator or causal items
2.8 Developing and testing questionnaires
2.8.1 Specify the research question and define the target population
2.8.2 Identify concepts
2.8.3 Create instrument
2.8.4 Assess measurement properties
2.8.5 Modify instrument
2.9 Further reading
3 Choosing a quality of life measure for your study
3.1 Introduction
3.2 How to choose between instruments
3.3 Appropriateness
3.4 Acceptability
3.5 Feasibility
3.6 Validity
3.6.1 Tests for criterion validity
3.6.2 Tests for face and content validity
3.6.3 Tests for construct validity
3.7 Reliability
3.7.1 Repeatability reliability
3.7.2 Graphical methods for assessing reliability between two repeated measurements
3.7.3 Internal reliability or internal consistency reliability
3.8 Responsiveness
3.8.1 Floor and ceiling effects
3.9 Precision
3.10 Interpretability
3.11 Finding quality of life instruments.
4 Design and sample size issues: How many subjects do I need for my study?
4.1 Introduction
4.2 Significance tests, P-values and power
4.3 Sample sizes for comparison of two independent groups
4.3.1 Normally distributed continuous data - comparing two means
4.3.2 Transformations
4.3.3 Comparing two groups with continuous data using non-parametric methods
4.3.4 Dichotomous categorical data - comparing two proportions
4.3.5 Ordered categorical (ordinal) data
4.4 Choice of sample size method with quality of life outcomes
4.5 Paired data
4.5.1 Paired continuous data - comparison of means
4.5.2 Paired binary data - comparison of proportions
4.6 Equivalence/non-inferiority studies
4.6.1 Continuous data - comparing the equivalence of two means
4.6.2 Binary data - comparing the equivalence of two proportions
4.7 Unknown standard deviation and effect size
4.7.1 Tips on obtaining the standard deviation
4.8 Cluster randomized controlled trials
4.9 Non-response
4.10 Unequal groups
4.11 Multiple outcomes/endpoints
4.12 Three or more groups
4.13 What if we are doing a survey, not a clinical trial?
4.13.1 Sample sizes for surveys
4.13.2 Confidence intervals for estimating the mean QoL of a population
4.13.3 Confidence intervals for a proportion
4.14 Sample sizes for reliability and method comparison studies
4.15 Post-hoc sample size calculations
4.16 Conclusion: Usefulness of sample size calculations
4.17 Further reading
5 Reliability and method comparison studies for quality of life measurements
5.1 Introduction
5.2 Intra-class correlation coefficient
5.2.1 Inappropriate method
5.3 Agreement between individual items on a quality of life questionnaire
5.3.1 Binary data: Proportion of agreement
5.3.2 Binary data: Kappa.
5.3.3 Ordered categorical data: Weighted kappa
5.4 Internal consistency and Cronbach's alpha
5.5 Graphical methods for assessing reliability or agreement between two quality of life measures or assessments
5.6 Further reading
5.7 Technical details
5.7.1 Calculation of ICC
5.7.2 Calculation of kappa
5.7.3 Calculation of weighted kappa
5.7.4 Calculation of Cronbach's alpha
6 Summarizing, tabulating and graphically displaying quality of life outcomes
6.1 Introduction
6.2 Graphs
6.2.1 Dot plots
6.2.2 Histograms
6.2.3 Box-and-whisker plot
6.2.4 Scatter plots
6.3 Describing and summarizing quality of life data
6.3.1 Measures of location
6.3.2 Measures of spread
6.4 Presenting quality of life data and results in tables and graphs
6.4.1 Tables for summarizing QoL outcomes
6.4.2 Tables for multiple outcome measures
6.4.3 Tables and graphs for comparing two groups
6.4.4 Profile graphs
7 Cross-sectional analysis of quality of life outcomes
7.1 Introduction
7.2 Hypothesis testing (using P-values)
7.3 Estimation (using con.dence intervals)
7.4 Choosing the statistical method
7.5 Comparison of two independent groups
7.5.1 Independent samples t -test for continuous outcome data
7.5.2 Mann-Whitney U-test
7.6 Comparing more than two groups
7.6.1 One-way analysis of variance
7.6.2 The Kruskal-Wallis test
7.7 Two groups of paired observations
7.7.1 Paired t -test
7.7.2 Wilcoxon test
7.8 The relationship between two continuous variables
7.9 Correlation
7.10 Regression
7.11 Multiple regression
7.12 Regression or correlation?
7.13 Parametric versus non-parametric methods
7.14 Technical details: Checking the assumptions for a linear regression analysis
8 Randomized controlled trials
8.1 Introduction.
8.2 Randomized controlled trials
8.3 Protocols
8.4 Pragmatic and explanatory trials
8.5 Intention-to-treat and per-protocol analyses
8.6 Patient flow diagram
8.7 Comparison of entry characteristics
8.8 Incomplete data
8.9 Main analysis
8.10 Interpretation of changes/differences in quality of life scores
8.11 Superiority and equivalence trials
8.12 Adjusting for other variables
8.13 Three methods of analysis for pre-test/post-test control group designs
8.14 Cross-over trials
8.15 Factorial trials
8.16 Cluster randomized controlled trials
8.17 Further reading
9 Exploring and modelling longitudinal quality of life data
9.1 Introduction
9.2 Summarizing, tabulating and graphically displaying repeated QoL assessments
9.3 Time-by-time analysis
9.4 Response feature analysis - the use of summary measures
9.4.1 Area under the curve
9.4.2 Acupuncture study - analysis of covariance
9.5 Modelling of longitudinal data
9.5.1 Autocorrelation
9.5.2 Repeated measures analysis of variance
9.5.3 Marginal general linear models - generalized estimating equations
9.5.4 Random effects models
9.5.5 Random effects versus marginal modelling
9.5.6 Use of marginal and random effects models to analyse data from a cluster RCT
9.6 Conclusions
10 Advanced methods for analysing quality of life outcomes
10.1 Introduction
10.2 Bootstrap methods
10.3 Bootstrap methods for confidence interval estimation
10.4 Ordinal regression
10.5 Comparing two independent groups: Ordinal quality of life measures (with less than 7 categories)
10.6 Proportional odds or cumulative logit model
10.7 Continuation ratio model
10.8 Stereotype logistic model
10.9 Conclusions and further reading
11 Economic evaluations
11.1 Introduction.
11.2 Economic evaluations
11.3 Utilities and QALYs
11.4 Economic evaluations alongside a controlled trial
11.5 Cost-effectiveness analysis
11.6 Cost-effectiveness ratios
11.7 Cost-utility analysis and cost-utility ratios
11.8 Incremental cost per QALY
11.9 The problem of negative (and positive) incremental cost-effectiveness ratios
11.10 Cost-effectiveness acceptability curves
11.11 Further reading
12 Meta-analysis
12.1 Introduction
12.2.1 Is a meta-analysis appropriate?
12.2.2 Combining the results of different studies
12.2.3 Choosing the appropriate statistical method
12.2 Planning a meta-analysis
12.3 Statistical methods in meta-analysis
12.3.1 The choice of effect measure: What outcome measures am I combining?
12.3.2 Model choice: fixed or random?
12.3.3 Homogeneity
12.3.4 Fixed effects model
12.3.5 Forest plots
12.3.6 Random effects
12.3.7 Funnel plots
12.4 Presentation of results
12.5 Conclusion
12.6 Further reading
13 Practical issues
13.1 Missing data
13.1.1 Why do missing data matter?
13.1.2 Methods for missing items within a form
13.1.3 Methods for missing forms
13.1.4 The regulator's view on statistical considerations for patient-level missing data
13.1.5 Conclusions and further reading on missing QoL data
13.2 Multiplicity, multi-dimensionality and multiple quality of life outcomes
13.2.1 Which multiple comparison procedure to use?
13.3 Guidelines for reporting quality of life studies
Solutions to exercises
Appendix A: Examples of questionnaires
Appendix B: Statistical tables
Description based on publisher supplied metadata and other sources.
Local notes:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2021. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Other format:
Print version: Walters, Stephen J. Quality of Life Outcomes in Clinical Trials and Health-Care Evaluation