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Introduction to Meta–Analysis 2e de M Borenstein

Introduction to Meta–Analysis 2e

Autor M Borenstein
en Limba Engleză Hardback – 12 mai 2021
The new edition of the market-leading textbook, covering the latest developments in the rapidly growing field of meta-analysis
This book provides a clear and thorough introduction to meta-analysis, the process of synthesizing data from a series of separate studies. The first edition of this text was widely acclaimed for the clarity of the presentation, and quickly established itself as the definitive text in this field. The fully updated second edition includes new and expanded content on avoiding common mistakes in meta-analysis, understanding heterogeneity in effects, publication bias, reporting the Knapp-Hartung Sidik-Jonkman adjustment, and more. Several brand-new chapters provide a systematic "how to" approach to performing and reporting a meta-analysis from start to finish.
Written by four of the world's foremost authorities on all aspects of meta-analysis, the new edition of Introduction to Meta-Analysis:
  • Outlines the role of meta-analysis in the research process
  • Shows how to compute effects sizes and treatment effects
  • Explains the fixed-effect and random-effects models for synthesizing data
  • Demonstrates how to assess and interpret variation in effect size across studies
  • Explains how to avoid common mistakes in meta-analysis
  • Discusses controversies in meta-analysis
  • Includes access to a companion website containing videos, spreadsheets, data files, free software for prediction intervals, and step-by-step instructions for performing analyses using Comprehensive Meta-Analysis (CMA) (TM)
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Specificații

ISBN-13: 9781119558354
ISBN-10: 1119558352
Pagini: 544
Dimensiuni: 185 x 265 x 35 mm
Greutate: 1.15 kg
Ediția:2nd Edition
Editura: Wiley
Locul publicării:Chichester, United Kingdom

Notă biografică

Michael Borenstein is the Director of Biostat, a leading developer of statistical software. He is the primary developer of Comprehensive Meta-Analysis (CMA), the world's most widely used program for meta-analysis. He is the recipient of numerous grants from the NIH to develop methods, software, and educational materials for meta-analysis. He has lectured widely on meta-analysis, including at the NIH, CDC, and FDA. Larry V. Hedges is Board of Trustees Professor of Statistics and Education and Social Policy, Professor of Psychology, Professor of Medical Social Sciences, and IPR Fellow, Northwestern University, USA. He is a national leader in the fields of educational statistics and evaluation and is an elected member of many leading associations. Julian P.T. Higgins is Professor of Evidence Synthesis at the University of Bristol, UK, and a National Institute for Health Research (NIHR) Senior Investigator. He has had numerous core roles in the Cochrane Collaboration, including editing its methodological Handbook since 2003. His many contributions to meta-analysis include the foundation of network meta-analysis, methods for describing and explaining heterogeneity and a general framework for individual participant data meta-analysis. He is a Highly Cited Researcher with over a quarter of a million citations to his work and has been a recipient of the Ingram Olkin Award for distinguished lifetime achievement in research synthesis methodology. Hannah R. Rothstein is Professor of Management at Baruch College and the Graduate Center of the City University of New York. She is a Fellow of the American Psychological Association and a past President of the Society for Research Synthesis Methodology. She is former Editor-in-Chief of Research Synthesis Methods and serves on the editorial boards of Psychological Bulletin, Psychological Methods, and Organizational Research Methods. Professor Rothstein is a co-developer of the Comprehensive Meta-Analysis software and has published numerous systematic reviews and meta-analyses.

Cuprins

List of Tables xv List of Figures xix Acknowledgements xxv Preface xxvii Preface to the Second Edition xxxv Website xxxvii Part 1: Introduction 1 How a Meta-Analysis Works 3 Introduction 3 Individual studies 3 The summary effect 5 Heterogeneity of effect sizes 6 Summary points 7 2 Why Perform a Meta-Analysis 9 Introduction 9 The streptokinase meta-analysis 10 Statistical significance 11 Clinical importance of the effect 11 Consistency of effects 12 Summary points 13 Part 2: Effect Size and Precision 3 Overview 17 Treatment effects and effect sizes 17 Parameters and estimates 18 Outline of effect size computations 19 4 Effect Sizes Based On Means 21 Introduction 21 Raw (unstandardized) mean difference D 21 Standardized mean difference, d and g 25 Response ratios 30 Summary points 31 5 Effect Sizes Based On Binary Data (2 × 2 Tables) 33 Introduction 33 Risk ratio 33 Odds ratio 35 Risk difference 37 Choosing an effect size index 38 Summary points 38 6 Effect Sizes Based On Correlations 39 Introduction 39 Computing r 39 Other approaches 40 Summary points 41 7 Converting Among Effect Sizes 43 Introduction 43 Converting from the log odds ratio to d 44 Converting from d to the log odds ratio 45 Converting from r to d 45 Converting from d to r 46 Summary points 47 8 Factors That Affect Precision 49 Introduction 49 Factors that affect precision 50 Sample size 50 Study design 51 Summary points 53 9 Concluding Remarks 55 Part 3: Fixed-Effect Versus Random-Effects Models 10 Overview 59 Introduction 59 Nomenclature 60 11 Fixed-Effect Model 61 Introduction 61 The true effect size 61 Impact of sampling error 61 Performing a fixed-effect meta-analysis 63 Summary points 64 12 Random-Effects Model 65 Introduction 65 The true effect sizes 65 Impact of sampling error 66 Performing a random-effects meta-analysis 68 Summary points 70 13 Fixed-Effect Versus Random-Effects Models 71 Introduction 71 Definition of a summary effect 71 Estimating the summary effect 72 Extreme effect size in a large study or a small study 73 Confidence interval 73 The null hypothesis 76 Which model should we use? 76 Model should not be based on the test for heterogeneity 78 Concluding remarks 79 Summary points 79 14 Worked Examples (Part 1) 81 Introduction 81 Worked example for continuous data (Part 1) 81 Worked example for binary data (Part 1) 85 Worked example for correlational data (Part 1) 90 Summary points 94 Part 4: Heterogeneity 15 Overview 97 Introduction 97 Nomenclature 98 Worked examples 98 16 Identifying and Quantifying Heterogeneity 99 Introduction 99 Isolating the variation in true effects 99 Computing Q 101 Estimating tau² 106 The I² statistic 109 Comparing the measures of heterogeneity 111 Confidence intervals for tau² 114 Confidence intervals (or uncertainty intervals) for I² 115 Summary points 116 17 Prediction Intervals 119 Introduction 119 Prediction intervals in primary studies 119 Prediction intervals in meta-analysis 121 Confidence intervals and prediction intervals 123 Comparing the confidence interval with the prediction interval 123 Summary points 125 18 Worked Examples (Part 2) 127 Introduction 127 Worked example for continuous data (Part 2) 127 Worked example for binary data (Part 2) 131 Worked example for correlational data (Part 2) 134 Summary points 138 19 An Intuitive Look At Heterogeneity 139 Introduction 139 Motivating example 140 The Q-value and the p-value do not tell us howmuch the effect size varies 141 The confidence interval does not tell us how much the effect size varies 142 The I² statistic does not tell us how much the effect size varies 142 What I² tells us 142 The I² index vs. the prediction interval 145 The prediction interval 145 Prediction interval is clear, concise, and relevant 147 Computing the prediction interval 147 How to use I² 149 How to explain heterogeneity 149 How much does the effect size vary across studies? 150 Caveats 150 Conclusion 150 Further reading 151 Summary points 151 The meaning of I² in Figure 19.2 151 20 Classifying Heterogeneity As Low, Moderate, Or High 155 Introduction 155 Interest should generally focus on an index of absolute heterogeneity 155 The classifications lead themselves to mistakes of interpretation 158 Classifications focus attention in the wrong direction 158 Summary points 158 Part 5: Explaining Heterogeneity 21 Subgroup Analyses 161 Introduction 161 Fixed-effect model within subgroups 163 Computational models 172 Random effects with separate estimates of tau² 174 Random effects with pooled estimate of tau² 181 The proportion of variance explained 189 Mixed-effects model 192 Obtaining an overall effect in the presence of subgroups 193 Summary points 195 22 Meta-Regression 197 Introduction 197 Fixed-effect model 198 Fixed or random effects for unexplained heterogeneity 203 Random-effects model 206 Summary points 212 23 Notes On Subgroup Analyses and Meta-Regression 213 Introduction 213 Computational model 213 Multiple comparisons 216 Software 216 Analyses of subgroups and regression analyses are observational 217 Statistical power for subgroup analyses and meta-regression 218 Summary points 219 Part 6: Putting It All In Context 24 Looking At the Whole Picture 223 Introduction 223 Methylphenidate for adults with ADHD 226 Impact of GLP-1 mimetics on blood pressure 228 Augmenting clozapine with a second antipsychotic 228 Conclusions 231 Caveats 231 Summary points 232 25 Limitations of the Random-Effects Model 233 Introduction 233 Assumptions of the random-effects model 234 A textbook case 234 When studies are pulled from the literature 235 A useful fiction 237 Transparency 238 A narrowly defined universe 238 Two important caveats 239 In context 239 Extreme cases 240 Summary points 241 26 Knapp-Hartung Adjustment 243 Introduction 243 Adjustment is rarely employed in simple analyses 243 Adjusting the standard error 244 The Knapp-Hartung adjustment for other effect size indices 246 t distribution vs. Z distribution 247 Limitations of the Knapp-Hartung adjustment 248 Summary points 249 Part 7: Complex Data Structures 27 Overview 253 28 Independent Subgroups Within a Study 255 Introduction 255 Combining across subgroups 255 Comparing subgroups 260 Summary points 260 29 Multiple Outcomes or Time-Points Within A Study 263 Introduction 263 Combining across outcomes or time-points 264 Comparing outcomes or time-points within a study 270 Summary points 275 30 Multiple Comparisons Within a Study 277 Introduction 277 Combining across multiple comparisons within a study 277 Differences between treatments 278 Summary points 279 31 Notes On Complex Data Structures 281 Introduction 281 Summary effect 281 Differences in effect 282 Part 8: Other Issues 32 Overview 287 33 Vote Counting - A New Name For An Old Problem 289 Introduction 289 Why vote counting is wrong 290 Vote counting is a pervasive problem 291 Summary points 293 34 Power Analysis For Meta-Analysis 295 Introduction 295 A conceptual approach 295 In context 299 When to use power analysis 300 Planning for precision rather than for power 301 Power analysis in primary studies 301 Power analysis for meta-analysis 304 Power analysis for a test of homogeneity 309 Summary points 312 35 Publication Bias 313 Introduction 313 The problem of missing studies 314 Methods for addressing bias 316 Illustrative example 317 The model 317 Getting a sense of the data 318 Is there evidence of any bias? 320 How much of an impact might the bias have? 320 Summary of the findings for the illustrative example 324 Conflating bias with the small-study effect 325 Using logic to disentangle bias from small-study effects 326 These methods do not give us the 'correct' effect size 327 Some important caveats 327 Procedures do not apply to studies of prevalence 328 The model for publication bias is simplistic 328 Concluding remarks 329 Putting it all together 330 Summary points 330 Part 9: Issues Related To Effect Size 36 Overview 335 37 Effect Sizes Rather Than P-Values 337 Introduction 337 Relationship between p-values and effect sizes 337 The distinction is important 339 The p-value is often misinterpreted 340 Narrative reviews vs. meta-analyses 341 Summary points 342 38 Simpson's Paradox 343 Introduction 343 Circumcision and risk of HIV infection 343 An example of the paradox 345 Summary points 348 39 Generality of the Basic Inverse-Variance Method 349 Introduction 349 Other effect sizes 350 Other methods for estimating effect sizes 353 Individual participant data meta-analyses 354 Bayesian approaches 355 Summary points 357 Part 10: Further Methods 40 Overview 361 41 Meta-Analysis Methods Based On Direction and P-Values 363 Introduction 363 Vote counting 363 The sign test 363 Combining p-values 364 Summary points 368 42 Further Methods For Dichotomous Data 369 Introduction 369 Mantel-Haenszel method 369 One-step (Peto) formula for odds ratio 373 Summary points 376 43 Psychometric Meta-Analysis 377 Introduction 377 The attenuating effects of artifacts 378 Meta-analysis methods 380 Example of psychometric meta-analysis 381 Comparison of artifact correction with meta-regression 384 Sources of information about artifact values 384 How heterogeneity is assessed 385 Reporting in psychometric meta-analysis 386 Concluding remarks 386 Summary points 387 Part 11: Meta-Analysis In Context 44 Overview 391 45 When Does It Make Sense To Perform a Meta-Analysis? 393 Introduction 393 Are the studies similar enough to combine? 394 Can I combine studies with different designs? 395 How many studies are enough to carry out a meta-analysis? 399 Summary points 400 46 Reporting The Results of a Meta-Analysis 401 Introduction 401 The computational model 402 Forest plots 402 Sensitivity analysis 404 Summary points 405 47 Cumulative Meta-Analysis 407 Introduction 407 Why perform a cumulative meta-analysis? 409 Summary points 412 48 Criticisms of Meta-Analysis 413 Introduction 413 One number cannot summarize a research field 414 The file drawer problem invalidates meta-analysis 414 Mixing apples and oranges 415 Garbage in, garbage out 416 Important studies are ignored 417 Meta-analysis can disagree with randomized trials 417 Meta-analyses are performed poorly 420 Is a narrative review better? 420 Concluding remarks 422 Summary points 422 49 Comprehensive Meta-Analysis Software 425 Introduction 425 Features in CMA 426 Teaching elements 427 Documentation 427 Availability 427 Acknowledgments 427 Motivating example 428 Data entry 428 Basic analysis 429 What is the average effect size? 430 How much does the effect size vary? 430 Plot showing distribution of effects 431 High-resolution plot 432 Subgroup analysis 433 Meta-regression 435 Publication bias 438 Explaining results 439 50 How To Explain the Results of An Analysis 443 Introduction 443 The overview 444 The mean effect size 444 Variation in effect size 444 Notations 444 Impact of resistance exercise on pain 445 Correlation between letter knowledge and word recognition 450 Statins for prevention of cardiovascular events 455 Bupropion for smoking cessation 460 Mortality following mitral-valve procedures in elderly patients 465 Part 12: Resources 51 Software For Meta-Analysis 471 Comprehensive meta-analysis 471 Metafor 471 Stata 472 Revman 472 52 Web Sites, Societies, Journals, and Books 473 Web sites 473 Professional societies 476 Journals 476 Special issues dedicated to meta-analysis 477 Books on systematic review methods and meta-analysis 477 References 479 Index 491