Introduction to meta-analysis [printed text] / Michael Borenstein, Author ; Larry V. Hedges ; Julian PT Higgins, Author ; Hannah R. Rothstein, Author . - Chichester : Wiley, 2009 . - xxviii, 421 p. : ill. ISBN : 978-0-470-05724-7 : € 42,00 Languages : English ( eng)
| Descriptors: |
Classification
WA 950 Theory or methods of medical statistics
Indexation
Handbooks ; Meta-Analysis as Topic ; Statistics
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| Abstract: |
This book provides a clear and thorough introduction to meta-analysis, the process of synthesizing data from a series of separate studies. Meta-analysis has become a critically important tool in fields as diverse as medicine, pharmacology, epidemiology, education, psychology, business, and ecology. 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
* Clarifies concepts using text and figures, followed by formulas and examples
* Explains how to avoid common mistakes in meta-analysis
* Discusses controversies in meta-analysis
* Features a web site with additional material and exercises |
| Contents note: |
List of Figures -- List of Tables -- Acknowledgements -- Preface -- PART 1: INTRODUCTION -- 1 HOW A META-ANALYSIS WORKS -- Introduction -- Individual studies -- The summary effect -- Heterogeneity of effect sizes -- Summary points -- 2 WHY PERFORM A META-ANALYSIS -- Introduction -- The SKIV meta-analysis -- Statistical significance -- Clinical importance of the effect -- Consistency of effects -- Summary points -- PART 2: EFFECT SIZE AND PRECISION -- 3 OVERVIEW -- Treatment effects and effect sizes -- Parameters and estimates -- Outline -- 4 EFFECT SIZES BASED ON MEANS -- Introduction -- Raw (unstandardized) mean difference D -- Standardized mean difference, D and G -- Response ratios -- Summary points -- 5 EFFECT SIZES BASED ON BINARY DATA (2×2 TABLES) -- Introduction -- Risk ratio -- Odds ratio -- Risk difference -- Choosing an effect size index -- Summary points -- 6 EFFECT SIZES BASED ON CORRELATIONS -- Introduction -- Computing R -- Other approaches -- Summary points -- 7 CONVERTING AMONG EFFECT SIZES -- Introduction -- Converting from the log odds ratio to D -- Converting from D to the log odds ratio -- Converting from R to D -- Converting from D to R -- Summary points -- 8 FACTORS THAT AFFECT PRECISION -- Introduction -- Factors that affect precision -- Sample size -- Study design -- Summary points -- 9 CONCLUDING REMARKS -- Further reading -- PART 3: FIXED-EFFECT VERSUS RANDOM-EFFECTS MODELS -- 10 OVERVIEW -- Introduction -- Nomenclature -- 11 FIXED-EFFECT MODEL -- Introduction -- The true effect size -- Impact of sampling error -- Performing a fixed-effect meta-analysis -- Summary points -- 12 RANDOM-EFFECTS MODEL -- Introduction -- The true effect sizes -- Impact of sampling error -- Performing a random-effects meta-analysis -- Summary points -- 13 FIXED EFFECT VERSUS RANDOM-EFFECTS MODELS -- Introduction -- Definition of a summary effect -- Estimating the summary effect -- Extreme effect size in large study -- Confidence interval -- The null hypothesis -- Which model should we use? -- Model should not be based on the test for heterogeneity -- Concluding remarks -- Summary points -- 14 WORKED EXAMPLES (PART 1) -- Introduction -- Worked example for continuous data (Part 1) -- Worked example for binary data (Part 1) -- Worked example for correlational data (Part 1) -- Summary points -- PART 4: HETEROGENEITY -- 15 OVERVIEW -- Introduction -- 16 IDENTIFYING AND QUANTIFYING HETEROGENEITY -- Introduction -- Isolating the variation in true effects -- Computing Q -- Estimating tau-squared -- The I 2 statistic -- Comparing the measures of heterogeneity -- Confidence intervals for T 2 -- Confidence intervals (or uncertainty intervals) for I 2 -- Summary points -- 17 PREDICTION INTERVALS -- Introduction -- Prediction intervals in primary studies -- Prediction intervals in meta-analysis -- Confidence intervals and prediction intervals -- Comparing the confidence interval with the prediction interval -- Summary points -- 18 WORKED EXAMPLES (PART 2) -- Introduction -- Worked example for continuous data (Part 2) -- Worked example for binary data (Part 2) -- Worked example for correlational data (Part 2) -- Summary points -- 19 SUBGROUP ANALYSES -- Introduction -- Fixed-effect model within subgroups -- Computational models -- Random effects with separate estimates of T 2 -- Random effects with pooled estimate of T 2 -- The proportion of variance explained -- Mixed-effect model -- Obtaining an overall effect in the presence of subgroups -- Summary points -- 20 META-REGRESSION -- Introduction -- Fixed-effect model -- Fixed or random effects for unexplained heterogeneity -- Random-effects model -- Statistical power for regression -- Summary points -- 21 NOTES ON SUBGROUP ANALYSES AND META-REGRESSION -- Introduction -- Computational model -- Multiple comparisons -- Software -- Analysis of subgroups and regression are observational -- Statistical power for subgroup analyses and meta-regression -- Summary points -- PART 5: COMPLEX DATA STRUCTURES -- 22 OVERVIEW -- 23 INDEPENDENT SUBGROUPS WITHIN A STUDY -- Introduction -- Combining across subgroups -- Comparing subgroups -- Summary points -- 24 MULTIPLE OUTCOMES OR TIME POINTS WITHIN A STUDY -- Introduction -- Combining across outcomes or time-points -- Comparing outcomes or time-points within a study -- Summary points -- 25 MULTIPLE COMPARISONS WITHIN A STUDY -- Introduction -- Combining across multiple comparisons within a study -- Differences between treatments -- Summary points -- 26 NOTES ON COMPLEX DATA STRUCTURES -- Introduction -- Combined effect -- Differences in effect -- PART 6: OTHER ISSUES -- 27 OVERVIEW -- 28 VOTE COUNTING – A NEW NAME FOR AN OLD PROBLEM -- Introduction -- Why vote counting is wrong -- Vote-counting is a pervasive problem -- Summary points -- 29 POWER ANALYSIS FOR META-ANALYSIS -- Introduction -- A conceptual approach -- In context -- When to use power analysis -- Planning for precision rather than for power -- Power analysis in primary studies -- Power analysis for meta-analysis -- Power analysis for a test of homogeneity -- Summary points -- 30 PUBLICATION BIAS -- Introduction -- The problem of missing studies -- Methods for addressing bias -- Illustrative example -- The model -- Getting a sense of the data -- Is the entire effect an artifact of bias -- How much of an impact might the bias have? -- Summary of the findings for the illustrative example -- Small study effects -- Concluding remarks -- Summary points -- PART 7: ISSUES RELATED TO EFFECT SIZE -- 31 OVERVIEW -- 32 EFFECT SIZES RATHER THAN P -VALUES -- Introduction -- Relationship between p-values and effect sizes -- The distinction is important -- The p-value is often misinterpreted -- Narrative reviews vs. meta-analyses -- Summary points -- 33 SIMPSON’S PARADOX -- Introduction -- Circumcision and risk of HIV infection -- An example of the paradox -- Summary points -- 34 GENERALITY OF THE BASIC INVERSE-VARIANCE METHOD -- Introduction -- Other effect sizes -- Other methods for estimating effect sizes -- Individual participant data meta-analyses -- Bayesian approaches -- Summary points -- PART 8: FURTHER METHODS -- 35 OVERVIEW -- 36 META-ANALYSIS METHODS BASED ON DIRECTION AND P -VALUES -- Introduction -- Vote counting -- The sign test -- Combining p-values -- Summary points -- 37 FURTHER METHODS FOR DICHOTOMOUS DATA -- Introduction -- Mantel-Haenszel method -- One-step (Peto) formula for odds ratio -- Summary points -- 38 PSYCHOMETRIC META-ANALYSIS -- Introduction -- The attenuating effects of artifacts -- Meta-analysis methods -- Example of psychometric meta-analysis -- Comparison of artifact correction with meta-regression -- Sources of information about artifact values -- How heterogeneity is assessed -- Reporting in psychometric meta-analysis -- Concluding remarks -- Summary points -- PART 9: META-ANALYSIS IN CONTEXT -- 39 OVERVIEW -- 40 WHEN DOES IT MAKE SENSE TO PERFORM A META-ANALYSIS? -- Introduction -- Are the studies similar enough to combine? -- Can I combine studies with different designs? -- How many studies are enough to carry out a meta-analysis? -- Summary points -- 41 REPORTING THE RESULTS OF A META-ANALYSIS -- Introduction -- The computational model -- Forest plots -- Sensitivity analysis -- Summary points -- 42 CUMULATIVE META-ANALYSIS -- Introduction -- Why perform a cumulative meta-analysis? -- Summary points -- 43 CRITICISMS OF META-ANALYSIS -- Introduction -- One number cannot summarize a research field -- The file drawer problem invalidates meta-analysis -- Mixing apples and oranges -- Garbage in, garbage out -- Important studies are ignored -- Meta-analysis can disagree with randomized trials -- Meta-analyses are performed poorly -- Is a narrative review better? -- Concluding remarks -- Summary points -- PART 10: RESOURCES AND SOFTWARE -- 44 SOFTWARE -- Introduction -- Three examples of meta-analysis software -- The software -- Comprehensive meta-analysis (CMA) 2.0 -- Revman 5.0 -- StataTM macros with Stata 10.0 -- Summary points -- 45 BOOKS, WEB SITES AND PROFESSIONAL ORGANIZATIONS -- Books on systematic review methods -- Books on meta-analysis -- Web sites -- INDEX |
| Record link: |
https://kce.docressources.info/index.php?lvl=notice_display&id=2222 |
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Introduction to meta-analysis / Chichester : Wiley (2009)
