Quality of Life The Assessment, Analysis and Reporting of Patient-reported Outcomes

by Fayers, Peter M.; Machin, David

• ISBN13: 9781444337952
• ISBN10: 1444337955
• Edition: 3rd
• Format: Hardcover
• Copyright: 2016-01-19
• Publisher: Wiley-Blackwell

Summary

The assessment of patient reported outcomes and health-related quality of life continue to be rapidly evolving areas of research and this new edition reflects the development within the field from an emerging subject to one that is an essential part of the assessment of clinical trials and other clinical studies.

The analysis and interpretation of quality-of-life assessments relies on a variety of psychometric and statistical methods which are explained in this book in a non-technical way. The result is a practical guide that covers a wide range of methods and emphasizes the use of simple techniques that are illustrated with numerous examples, with extensive chapters covering qualitative and quantitative methods and the impact of guidelines. The material in this new third edition reflects current teaching methods and content widened to address continuing developments in item response theory, computer adaptive testing, analyses with missing data, analysis of ordinal data, systematic reviews and meta-analysis.

This book is aimed at everyone involved in quality-of-life research and is applicable to medical and non-medical, statistical and non-statistical readers. It is of particular relevance for clinical and biomedical researchers within both the pharmaceutical industry and clinical practice.

Author Biography

Peter Fayers, Emeritus Professor of Medical Statistics, University of Aberdeen, UK; Norwegian University of Science and Technology (NTNU), Trondheim, Norway.

David Machin, Emeritus Professor of Clinical Trials Research, University of Sheffield, UK and Emeritus Professor of Clinical Statistics, University of Leicester.

Table of Contents

Preface to the third edition xiii

Preface to the second edition xv

Preface to the first edition xvii

List of abbreviations xix

Part 1 Developing and Validating Instruments for Assessing

Quality of Life and Patient-Reported Outcomes

1 Introduction 3

1.1 Patient‐reported outcomes 3

1.2 What is a patient‐reported outcome? 4

1.3 What is quality of life? 4

1.4 Historical development 6

1.5 Why measure quality of life? 9

1.6 Which clinical trials should assess QoL? 17

1.7 How to measure quality of life 18

1.8 Instruments 19

1.9 Computer‐adaptive instruments 32

1.10 Conclusions 32

2 Principles of measurement scales 35

2.1 Introduction 35

2.2 Scales and items 35

2.3 Constructs and latent variables 36

2.4 Single global questions versus multi‐item scales 37

2.5 Single‐item versus multi‐item scales 40

2.6 Effect indicators and causal indicators 42

2.7 Psychometrics, factor analysis and item response theory 48

2.8 Psychometric versus clinimetric scales 52

2.9 Sufficient causes, necessary causes and scoring items 53

2.10 Discriminative, evaluative and predictive instruments 54

2.11 Measuring quality of life: reflective, causal and composite indicators? 55

2.12 Further reading 56

2.13 Conclusions 56

3 Developing a questionnaire 57

3.1 Introduction 57

3.2 General issues 58

3.3 Defining the target population 58

3.4 Phases of development 59

3.5 Phase 1: Generation of issues 61

3.6 Qualitative methods 63

3.7 Sample sizes 66

3.8 Phase 2: Developing items 68

3.9 Multi‐item scales 72

3.10 Wording of questions 73

3.11 Face and content validity of the proposed questionnaire 74

3.12 Phase 3: Pre‐testing the questionnaire 74

3.13 Cognitive interviewing 77

3.14 Translation 80

3.15 Phase 4: Field‐testing 80

3.16 Conclusions 86

3.17 Further reading 87

4 Scores and measurements: validity, reliability, sensitivity 89

4.1 Introduction 89

4.2 Content validity 90

4.3 Criterion validity 94

4.4 Construct validity 96

4.5 Repeated assessments and change over time 104

4.6 Reliability 104

4.7 Sensitivity and responsiveness 117

4.8 Conclusions 124

4.9 Further reading 124

5 Multi‐item scales 125

5.1 Introduction 125

5.2 Significance tests 126

5.3 Correlations 127

5.4 Construct validity 133

5.5 Cronbach’s α and internal consistency 139

5.6 Validation or alteration? 143

5.7 Implications for formative or causal items 144

5.8 Conclusions 147

6 Factor analysis and structural equation modelling 149

6.1 Introduction 149

6.2 Correlation patterns 150

6.3 Path diagrams 152

6.4 Factor analysis 154

6.5 Factor analysis of the HADS questionnaire 154

6.6 Uses of factor analysis 159

6.7 Applying factor analysis: Choices and decisions 161

6.8 Assumptions for factor analysis 167

6.9 Factor analysis in QoL research 171

6.10 Limitations of correlation-based analysis 172

6.11 Formative or causal models 173

6.12 Confirmatory factor analysis and structural equation modelling 176

6.13 Chi-square goodness-of-fit test 178

6.14 Approximate goodness-of-fit indices 180

6.15 Comparative fit of models 181

6.16 Difficulty-factors 182

6.17 Bifactor analysis 183

6.18 Do formative or causal relationships matter? 186

6.19 Conclusions 187

6.20 Further reading, and software 188

7 Item response theory and differential item functioning 189

7.1 Introduction 189

7.2 Item characteristic curves 191

7.3 Logistic models 193

7.4 Polytomous item response theory models 196

7.5 Applying logistic IRT models 197

7.6 Assumptions of IRT models 205

7.7 Fitting item response theory models: Tips 208

7.8 Test design and validation 209

7.9 IRT versus traditional and Guttman scales 209

7.10 Differential item functioning 210

7.11 Sample size for DIF analyses 218

7.12 Quantifying differential item functioning 219

7.13 Exploring differential item functioning: Tips 219

7.14 Conclusions 221

7.15 Further reading, and software 222

8 Item banks, item linking and computer-adaptive tests 223

8.1 Introduction 223

8.2 Item bank 224

8.3 Item evaluation, reduction and calibration 226

8.4 Item linking and test equating 228

8.5 Test information 231

8.6 Computer-adaptive testing 232

8.7 Stopping rules and simulations 235

8.8 Computer-adaptive testing software 236

8.9 CATs for PROs 237

8.10 Computer-assisted tests 238

8.11 Short-form tests 239

8.12 Conclusions 239

8.13 Further reading 240

Part 2 Assessing, Analysing and Reporting Patient-Reported Outcomes and the Quality of Life of Patients

9 Choosing and scoring questionnaires 243

9.1 Introduction 243

9.2 Finding instruments 244

9.3 Generic versus specific 245

9.4 Content and presentation 246

9.5 Choice of instrument 247

9.6 Scoring multi-item scales 250

9.7 Conclusions 256

9.8 Further reading 257

10 Clinical trials 259

10.1 Introduction 259

10.2 Basic design issues 260

10.3 Compliance 262

10.4 Administering a quality‐of‐life assessment 268

10.5 Recommendations for writing protocols 270

10.6 Standard operating procedures 280

10.7 Summary and checklist 281

10.8 Further reading 282

11 Sample sizes 283

11.1 Introduction 283

11.2 Significance tests, p‐values and power 284

11.3 Estimating sample size 284

11.4 Comparing two groups 289

11.5 Comparison with a reference population 298

11.6 Non‐inferiority studies 298

11.7 Choice of sample size method 301

11.8 Non‐Normal distributions 302

11.9 Multiple testing 303

11.10 Specifying the target difference 305

11.11 Sample size estimation is pre‐study 305

11.12 Attrition 306

11.13 Circumspection 306

11.14 Conclusion 306

11.15 Further reading 307

12 Cross‐sectional analysis 309

12.1 Types of data 309

12.2 Comparing two groups 312

12.3 Adjusting for covariates 324

12.4 Changes from baseline 330

12.5 Analysis of variance 331

12.6 Analysis of variance models 336

12.7 Graphical summaries 337

12.8 Endpoints 342

12.9 Conclusions 343

13 Exploring longitudinal data 345

13.1 Area under the curve 345

13.2 Graphical presentations 348

13.3 Tabular presentations 358

13.4 Reporting 360

13.5 Conclusions 365

14 Modelling longitudinal data 367

14.1 Preliminaries 367

14.2 Auto-correlation 368

14.3 Repeated measures 373

14.4 Other situations 388

14.5 Modelling versus area under the curve 389

14.6 Conclusions 390

15 Missing data 393

15.1 Introduction 393

15.2 Why do missing data matter? 396

15.3 Types of missing data 400

15.4 Missing items 403

15.5 Methods for missing items within a form 404

15.6 Missing forms 408

15.7 Methods for missing forms 410

15.8 Simple methods for missing forms 410

15.9 Methods of imputation that incorporate variability 415

15.10 Multiple imputation 421

15.11 Pattern mixture models 422

15.12 Comments 424

15.13 Degrees of freedom 425

15.14 Sensitivity analysis 426

15.15 Conclusions 426

15.16 Further reading 427

16 Practical and reporting issues 429

16.1 Introduction 429

16.2 The reporting of design issues 430

16.3 Data analysis 430

16.4 Elements of good graphics 436

16.5 Some errors 440

16.6 Guidelines for reporting 442

16.7 Further reading 445

17 Death, and quality-adjusted survival 447

17.1 Introduction 447

17.2 Attrition due to death 448

17.3 Preferences and utilities 449

17.4 Multi-attribute utility (MAU) measures 453

17.5 Utility-based instruments 454

17.6 Quality-adjusted life years (QALYs) 456

17.7 Utilities for traditional instruments 457

17.8 Q-TWiST 462

17.9 Sensitivity analysis 467

17.10 Prognosis and variation with time 470

17.11 Alternatives to QALY 472

17.12 Conclusions 473

17.13 Further reading 474

18 Clinical interpretation 475

18.1 Introduction 475

18.2 Statistical significance 476

18.3 Absolute levels and changes over time 477

18.4 Threshold values: percentages 478

18.5 Population norms 479

18.6 Minimal important difference 488

18.7 Anchoring against other measurements 492

18.8 Minimum detectable change 493

18.9 Expert judgement for evidence-based guidelines 494

18.10 Impact of the state of quality of life 495

18.11 Changes in relation to life events 496

18.12 Effect size statistics 498

18.13 Patient variability 505

18.14 Number needed to treat 506

18.15 Conclusions 509

18.16 Further reading 509

19 Biased reporting and response shift 511

19.1 Bias 511

19.2 Recall bias 512

19.3 Selective reporting bias 513

19.4 Other biases affecting PROs 514

19.5 Response shift 516

19.6 Assessing response shift 521

19.7 Impact of response shift 523

19.8 Clinical trials 523

19.9 Non‐randomised studies 525

19.10 Conclusions 526

20 Meta‐analysis 527

20.1 Introduction 527

20.2 Defining objectives 528

20.3 Defining outcomes 528

20.4 Literature searching 528

20.5 Assessing quality 529

20.6 Summarising results 533

20.7 Measures of treatment effect 534

20.8 Combining studies 537

20.9 Forest plot 542

20.10 Heterogeneity 542

20.11 Publication bias and funnel plots 544

20.12 Conclusions 545

20.13 Further reading 546

Appendix 1: Examples of instruments 547

Generic instruments

E1 Sickness Impact Profile (SIP) 549

E2 Nottingham Health Profile (NHP) 551

E3 SF36v2^TM Health Survey Standard Version 552

E4 EuroQoL EQ-5D-5L 555

E5 Patient Generated Index of quality of life (PGI) 557

Disease-specific instruments 559

E6 European Organisation for Research and Treatment of Cancer QLQ-C30 (EORTC QLQ-C30) 559

E7 Elderly cancer patients module (EORTC QLQ-ELD14) 561

E8 Functional Assessment of Cancer Therapy – General (FACT-G) 562

E9 Rotterdam Symptom Checklist (RSCL) 564

E10 Quality of Life in Epilepsy Inventory (QOLIE-89) 566

E11 Paediatric Asthma Quality of Life Questionnaire (PAQLQ) 570

Domain-specific instruments 573

E12 Hospital Anxiety and Depression Scale (HADS) 573

E13 Short-Form McGill Pain Questionnaire (SF-MPQ) 574

E14 Multidimensional Fatigue Inventory (MFI-20) 575

ADL and disability 577

E 15 (Modified) Barthel Index of Disability (MBI) 577

Appendix 2: Statistical tables 579

Table T1: Normal distribution 579

Table T2: Probability points of the Normal distribution 581

Table T3: Student’s t‐distribution 582

Table T4: The χ2 distribution 583

Table T5: The F‐distribution 584

References 585

Index 613