Details

Quality of Life


Quality of Life

The Assessment, Analysis and Reporting of Patient-reported Outcomes
3. Aufl.

von: Peter M. Fayers, David Machin

79,99 €

Verlag: Wiley-Blackwell
Format: PDF
Veröffentl.: 23.11.2015
ISBN/EAN: 9781118759011
Sprache: englisch
Anzahl Seiten: 648

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Beschreibungen

<p>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.<br /><br />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.<br /><br />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.</p>
<p>Preface to the third edition xiii</p> <p>Preface to the second edition xv</p> <p>Preface to the first edition xvii</p> <p>List of abbreviations xix</p> <p><b>Part 1 Developing and Validating Instruments for Assessing</b></p> <p>Quality of Life and Patient-Reported Outcomes</p> <p><b>1 Introduction 3</b></p> <p>1.1 Patient‐reported outcomes 3</p> <p>1.2 What is a patient‐reported outcome? 4</p> <p>1.3 What is quality of life? 4</p> <p>1.4 Historical development 6</p> <p>1.5 Why measure quality of life? 9</p> <p>1.6 Which clinical trials should assess QoL? 17</p> <p>1.7 How to measure quality of life 18</p> <p>1.8 Instruments 19</p> <p>1.9 Computer‐adaptive instruments 32</p> <p>1.10 Conclusions 32</p> <p><b>2 Principles of measurement scales 35</b></p> <p>2.1 Introduction 35</p> <p>2.2 Scales and items 35</p> <p>2.3 Constructs and latent variables 36</p> <p>2.4 Single global questions versus multi‐item scales 37</p> <p>2.5 Single‐item versus multi‐item scales 40</p> <p>2.6 Effect indicators and causal indicators 42</p> <p>2.7 Psychometrics, factor analysis and item response theory 48</p> <p>2.8 Psychometric versus clinimetric scales 52</p> <p>2.9 Sufficient causes, necessary causes and scoring items 53</p> <p>2.10 Discriminative, evaluative and predictive instruments 54</p> <p>2.11 Measuring quality of life: reflective, causal and composite indicators? 55</p> <p>2.12 Further reading 56</p> <p>2.13 Conclusions 56</p> <p><b>3 Developing a questionnaire 57</b></p> <p>3.1 Introduction 57</p> <p>3.2 General issues 58</p> <p>3.3 Defining the target population 58</p> <p>3.4 Phases of development 59</p> <p>3.5 Phase 1: Generation of issues 61</p> <p>3.6 Qualitative methods 63</p> <p>3.7 Sample sizes 66</p> <p>3.8 Phase 2: Developing items 68</p> <p>3.9 Multi‐item scales 72</p> <p>3.10 Wording of questions 73</p> <p>3.11 Face and content validity of the proposed questionnaire 74</p> <p>3.12 Phase 3: Pre‐testing the questionnaire 74</p> <p>3.13 Cognitive interviewing 77</p> <p>3.14 Translation 80</p> <p>3.15 Phase 4: Field‐testing 80</p> <p>3.16 Conclusions 86</p> <p>3.17 Further reading 87</p> <p><b>4 Scores and measurements: validity, reliability, sensitivity 89</b></p> <p>4.1 Introduction 89</p> <p>4.2 Content validity 90</p> <p>4.3 Criterion validity 94</p> <p>4.4 Construct validity 96</p> <p>4.5 Repeated assessments and change over time 104</p> <p>4.6 Reliability 104</p> <p>4.7 Sensitivity and responsiveness 117</p> <p>4.8 Conclusions 124</p> <p>4.9 Further reading 124</p> <p><b>5 Multi‐item scales 125</b></p> <p>5.1 Introduction 125</p> <p>5.2 Significance tests 126</p> <p>5.3 Correlations 127</p> <p>5.4 Construct validity 133</p> <p>5.5 Cronbach’s <i>α</i> and internal consistency 139</p> <p>5.6 Validation or alteration? 143</p> <p>5.7 Implications for formative or causal items 144</p> <p>5.8 Conclusions 147</p> <p><b>6 Factor analysis and structural equation modelling 149</b></p> <p>6.1 Introduction 149</p> <p>6.2 Correlation patterns 150</p> <p>6.3 Path diagrams 152</p> <p>6.4 Factor analysis 154</p> <p>6.5 Factor analysis of the HADS questionnaire 154</p> <p>6.6 Uses of factor analysis 159</p> <p>6.7 Applying factor analysis: Choices and decisions 161</p> <p>6.8 Assumptions for factor analysis 167</p> <p>6.9 Factor analysis in QoL research 171</p> <p>6.10 Limitations of correlation-based analysis 172</p> <p>6.11 Formative or causal models 173</p> <p>6.12 Confirmatory factor analysis and structural equation modelling 176</p> <p>6.13 Chi-square goodness-of-fit test 178</p> <p>6.14 Approximate goodness-of-fit indices 180</p> <p>6.15 Comparative fit of models 181</p> <p>6.16 Difficulty-factors 182</p> <p>6.17 Bifactor analysis 183</p> <p>6.18 Do formative or causal relationships matter? 186</p> <p>6.19 Conclusions 187</p> <p>6.20 Further reading, and software 188</p> <p><b>7 Item response theory and differential item functioning 189</b></p> <p>7.1 Introduction 189</p> <p>7.2 Item characteristic curves 191</p> <p>7.3 Logistic models 193</p> <p>7.4 Polytomous item response theory models 196</p> <p>7.5 Applying logistic IRT models 197</p> <p>7.6 Assumptions of IRT models 205</p> <p>7.7 Fitting item response theory models: Tips 208</p> <p>7.8 Test design and validation 209</p> <p>7.9 IRT versus traditional and Guttman scales 209</p> <p>7.10 Differential item functioning 210</p> <p>7.11 Sample size for DIF analyses 218</p> <p>7.12 Quantifying differential item functioning 219</p> <p>7.13 Exploring differential item functioning: Tips 219</p> <p>7.14 Conclusions 221</p> <p>7.15 Further reading, and software 222</p> <p><b>8 Item banks, item linking and computer-adaptive tests 223</b></p> <p>8.1 Introduction 223</p> <p>8.2 Item bank 224</p> <p>8.3 Item evaluation, reduction and calibration 226</p> <p>8.4 Item linking and test equating 228</p> <p>8.5 Test information 231</p> <p>8.6 Computer-adaptive testing 232</p> <p>8.7 Stopping rules and simulations 235</p> <p>8.8 Computer-adaptive testing software 236</p> <p>8.9 CATs for PROs 237</p> <p>8.10 Computer-assisted tests 238</p> <p>8.11 Short-form tests 239</p> <p>8.12 Conclusions 239</p> <p>8.13 Further reading 240</p> <p><b>Part 2 Assessing, Analysing and Reporting Patient-Reported Outcomes and the Quality of Life of Patients</b></p> <p><b>9 Choosing and scoring questionnaires 243</b></p> <p>9.1 Introduction 243</p> <p>9.2 Finding instruments 244</p> <p>9.3 Generic versus specific 245</p> <p>9.4 Content and presentation 246</p> <p>9.5 Choice of instrument 247</p> <p>9.6 Scoring multi-item scales 250</p> <p>9.7 Conclusions 256</p> <p>9.8 Further reading 257</p> <p><b>10 Clinical trials 259</b></p> <p>10.1 Introduction 259</p> <p>10.2 Basic design issues 260</p> <p>10.3 Compliance 262</p> <p>10.4 Administering a quality‐of‐life assessment 268</p> <p>10.5 Recommendations for writing protocols 270</p> <p>10.6 Standard operating procedures 280</p> <p>10.7 Summary and checklist 281</p> <p>10.8 Further reading 282</p> <p><b>11 Sample sizes 283</b></p> <p>11.1 Introduction 283</p> <p>11.2 Significance tests, <i>p</i>‐values and power 284</p> <p>11.3 Estimating sample size 284</p> <p>11.4 Comparing two groups 289</p> <p>11.5 Comparison with a reference population 298</p> <p>11.6 Non‐inferiority studies 298</p> <p>11.7 Choice of sample size method 301</p> <p>11.8 Non‐Normal distributions 302</p> <p>11.9 Multiple testing 303</p> <p>11.10 Specifying the target difference 305</p> <p>11.11 Sample size estimation is pre‐study 305</p> <p>11.12 Attrition 306</p> <p>11.13 Circumspection 306</p> <p>11.14 Conclusion 306</p> <p>11.15 Further reading 307</p> <p><b>12 Cross‐sectional analysis 309</b></p> <p>12.1 Types of data 309</p> <p>12.2 Comparing two groups 312</p> <p>12.3 Adjusting for covariates 324</p> <p>12.4 Changes from baseline 330</p> <p>12.5 Analysis of variance 331</p> <p>12.6 Analysis of variance models 336</p> <p>12.7 Graphical summaries 337</p> <p>12.8 Endpoints 342</p> <p>12.9 Conclusions 343</p> <p><b>13 Exploring longitudinal data 345</b></p> <p>13.1 Area under the curve 345</p> <p>13.2 Graphical presentations 348</p> <p>13.3 Tabular presentations 358</p> <p>13.4 Reporting 360</p> <p>13.5 Conclusions 365</p> <p><b>14 Modelling longitudinal data 367</b></p> <p>14.1 Preliminaries 367</p> <p>14.2 Auto-correlation 368</p> <p>14.3 Repeated measures 373</p> <p>14.4 Other situations 388</p> <p>14.5 Modelling versus area under the curve 389</p> <p>14.6 Conclusions 390</p> <p><b>15 Missing data 393</b></p> <p>15.1 Introduction 393</p> <p>15.2 Why do missing data matter? 396</p> <p>15.3 Types of missing data 400</p> <p>15.4 Missing items 403</p> <p>15.5 Methods for missing items within a form 404</p> <p>15.6 Missing forms 408</p> <p>15.7 Methods for missing forms 410</p> <p>15.8 Simple methods for missing forms 410</p> <p>15.9 Methods of imputation that incorporate variability 415</p> <p>15.10 Multiple imputation 421</p> <p>15.11 Pattern mixture models 422</p> <p>15.12 Comments 424</p> <p>15.13 Degrees of freedom 425</p> <p>15.14 Sensitivity analysis 426</p> <p>15.15 Conclusions 426</p> <p>15.16 Further reading 427</p> <p><b>16 Practical and reporting issues 429</b></p> <p>16.1 Introduction 429</p> <p>16.2 The reporting of design issues 430</p> <p>16.3 Data analysis 430</p> <p>16.4 Elements of good graphics 436</p> <p>16.5 Some errors 440</p> <p>16.6 Guidelines for reporting 442</p> <p>16.7 Further reading 445</p> <p><b>17 Death, and quality-adjusted survival 447</b></p> <p>17.1 Introduction 447</p> <p>17.2 Attrition due to death 448</p> <p>17.3 Preferences and utilities 449</p> <p>17.4 Multi-attribute utility (MAU) measures 453</p> <p>17.5 Utility-based instruments 454</p> <p>17.6 Quality-adjusted life years (QALYs) 456</p> <p>17.7 Utilities for traditional instruments 457</p> <p>17.8 Q-<i>TWiST</i> 462</p> <p>17.9 Sensitivity analysis 467</p> <p>17.10 Prognosis and variation with time 470</p> <p>17.11 Alternatives to <i>QALY</i> 472</p> <p>17.12 Conclusions 473</p> <p>17.13 Further reading 474</p> <p><b>18 Clinical interpretation 475</b></p> <p>18.1 Introduction 475</p> <p>18.2 Statistical significance 476</p> <p>18.3 Absolute levels and changes over time 477</p> <p>18.4 Threshold values: percentages 478</p> <p>18.5 Population norms 479</p> <p>18.6 Minimal important difference 488</p> <p>18.7 Anchoring against other measurements 492</p> <p>18.8 Minimum detectable change 493</p> <p>18.9 Expert judgement for evidence-based guidelines 494</p> <p>18.10 Impact of the state of quality of life 495</p> <p>18.11 Changes in relation to life events 496</p> <p>18.12 Effect size statistics 498</p> <p>18.13 Patient variability 505</p> <p>18.14 Number needed to treat 506</p> <p>18.15 Conclusions 509</p> <p>18.16 Further reading 509</p> <p><b>19 Biased reporting and response shift 511</b></p> <p>19.1 Bias 511</p> <p>19.2 Recall bias 512</p> <p>19.3 Selective reporting bias 513</p> <p>19.4 Other biases affecting PROs 514</p> <p>19.5 Response shift 516</p> <p>19.6 Assessing response shift 521</p> <p>19.7 Impact of response shift 523</p> <p>19.8 Clinical trials 523</p> <p>19.9 Non‐randomised studies 525</p> <p>19.10 Conclusions 526</p> <p><b>20 Meta‐analysis 527</b></p> <p>20.1 Introduction 527</p> <p>20.2 Defining objectives 528</p> <p>20.3 Defining outcomes 528</p> <p>20.4 Literature searching 528</p> <p>20.5 Assessing quality 529</p> <p>20.6 Summarising results 533</p> <p>20.7 Measures of treatment effect 534</p> <p>20.8 Combining studies 537</p> <p>20.9 Forest plot 542</p> <p>20.10 Heterogeneity 542</p> <p>20.11 Publication bias and funnel plots 544</p> <p>20.12 Conclusions 545</p> <p>20.13 Further reading 546</p> <p><b>Appendix 1: Examples of instruments 547</b></p> <p><b>Generic instruments</b></p> <p>E1 Sickness Impact Profile (SIP) 549</p> <p>E2 Nottingham Health Profile (NHP) 551</p> <p>E3 SF36v2<sup>TM</sup> Health Survey Standard Version 552</p> <p>E4 EuroQoL EQ-5D-5L 555</p> <p>E5 Patient Generated Index of quality of life (PGI) 557</p> <p><b>Disease-specific instruments 559</b></p> <p>E6 European Organisation for Research and Treatment of Cancer QLQ-C30 (EORTC QLQ-C30) 559</p> <p>E7 Elderly cancer patients module (EORTC QLQ-ELD14) 561</p> <p>E8 Functional Assessment of Cancer Therapy – General (FACT-G) 562</p> <p>E9 Rotterdam Symptom Checklist (RSCL) 564</p> <p>E10 Quality of Life in Epilepsy Inventory (QOLIE-89) 566</p> <p>E11 Paediatric Asthma Quality of Life Questionnaire (PAQLQ) 570</p> <p><b>Domain-specific instruments 573</b></p> <p>E12 Hospital Anxiety and Depression Scale (HADS) 573</p> <p>E13 Short-Form McGill Pain Questionnaire (SF-MPQ) 574</p> <p>E14 Multidimensional Fatigue Inventory (MFI-20) 575</p> <p><b>ADL and disability 577</b></p> <p>E 15 (Modified) Barthel Index of Disability (MBI) 577</p> <p><b>Appendix 2: Statistical tables 579</b></p> <p>Table T1: Normal distribution 579</p> <p>Table T2: Probability points of the Normal distribution 581</p> <p>Table T3: Student’s<i> t</i>‐distribution 582</p> <p>Table T4: The <i>χ2</i> distribution 583</p> <p>Table T5: The <i>F</i>‐distribution 584</p> <p>References 585</p> <p>Index 613</p>
<p><strong>Peter Fayers</strong>, Emeritus Professor of Medical Statistics, University of Aberdeen, UK; Norwegian University of Science and Technology (NTNU), Trondheim, Norway. <p><strong>David Machin</strong>, Emeritus Professor of Clinical Trials Research, University of Sheffield, UK and Emeritus Professor of Clinical Statistics, University of Leicester.
<p>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.</p> <p>The analysis and interpretation of quality of life 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 using numerous examples, with extensive chapters covering detailed qualitative methods, the impact of guidelines, the analysis of ordinal data and bootstrap methods. The material in this third edition reflects current teaching methods and content widened to address continuing developments in item response theory, computer adaptive testing, and analyses with missing data, Bayesian methods, systematic reviews and meta-analysis.</p> <p>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.</p>

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