经典元分析教材 Introduction to Meta-Analysis

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经典元分析教材  Introduction to Meta-Analysis Introduction to Meta-Analysis.pdf (6.82 MB, 需要: 10 个论坛币)

2015-4-21 08:45:09 上传

Introduction to Meta-Analysis
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关键词：introduction troduction Analysis Analysi alysis 经典

文献来源说清楚嘛

能不能详细点，谁写的，哪个出版社，哪一年的

是2009年的，WILEY出版社，作者是Michael Borenstein
Biostat, Inc, New Jersey, USA.
Larry V. Hedges
Northwestern University, Evanston, USA.
Julian P.T. Higgins
MRC, Cambridge, UK.
Hannah R. Rothstein

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 12
Consistency of effects 12
Summary points 14
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 32
5 EFFECT SIZES BASED ON BINARY DATA (22 TABLES) 33
Introduction 33
Risk ratio 34
Odds ratio 36
Risk difference 37
Choosing an effect size index 38
Summary points 39
6 EFFECT SIZES BASED ON CORRELATIONS 41
Introduction 41
Computing r 41
Other approaches 43
Summary points 43
7 CONVERTING AMONG EFFECT SIZES 45
Introduction 45
Converting from the log odds ratio to d 47
Converting from d to the log odds ratio 47
Converting from rto d 48
Converting from d to r 48
Summary points 49
8 FACTORS THAT AFFECT PRECISION 51
Introduction 51
Factors that affect precision 52
Sample size 52
Study design 53
Summary points 55
9 CONCLUDING REMARKS 57
PART 3: FIXED-EFFECT VERSUS RANDOM-EFFECTS MODELS
10 OVERVIEW 61
Introduction 61
Nomenclature 62
11 FIXED-EFFECT MODEL 63
Introduction 63
The true effect size 63
Impact of sampling error 63
Performing a fixed-effect meta-analysis 65
Summary points 67
12 RANDOM-EFFECTS MODEL 69
Introduction 69
The true effect sizes 69
Impact of sampling error 70
Performing a random-effects meta-analysis 72
Summary points 74
13 FIXED-EFFECT VERSUS RANDOM-EFFECTS MODELS 77
Introduction 77
Definition of a summary effect 77
Estimating the summary effect 78
Extreme effect size in a large study or a small study 79
Confidence interval 80
The null hypothesis 83
Which model should we use? 83
Model should not be based on the test for heterogeneity 84
Concluding remarks 85
Summary points 85
14 WORKED EXAMPLES (PART 1) 87
Introduction 87
Worked example for continuous data (Part 1) 87
Worked example for binary data (Part 1) 92
Worked example for correlational data (Part 1) 97
Summary points 102
PART 4: HETEROGENEITY
15 OVERVIEW 105
Introduction 105
Nomenclature 106
Worked examples 106
16 IDENTIFYING AND QUANTIFYING HETEROGENEITY 107
Introduction 107
Isolating the variation in true effects 107
Computing Q 109
Estimating 2 114
The I 2 statistic 117
Comparing the measures of heterogeneity 119
Confidence intervals for 2 122
Confidence intervals (or uncertainty intervals) for I 2 124
Summary points 125
17 PREDICTION INTERVALS 127
Introduction 127
Prediction intervals in primary studies 127
Prediction intervals in meta-analysis 129
Confidence intervals and prediction intervals 131
Comparing the confidence interval with the prediction interval 132
Summary points 133
18 WORKED EXAMPLES (PART 2) 135
Introduction 135
Worked example for continuous data (Part 2) 135
Worked example for binary data (Part 2) 139
Worked example for correlational data (Part 2) 143
Summary points 147
19 SUBGROUP ANALYSES 149
Introduction 149
Fixed-effect model within subgroups 151
Computational models 161
Random effects with separate estimates of  2 164
Random effects with pooled estimate of  2 171
The proportion of variance explained 179
Mixed-effects model 183
Obtaining an overall effect in the presence of subgroups 184
Summary points 186
20 META-REGRESSION 187
Introduction 187
Fixed-effect model 188
Fixed or random effects for unexplained heterogeneity 193
Random-effects model 196
Summary points 203
21 NOTES ON SUBGROUP ANALYSES AND META-REGRESSION 205
Introduction 205
Computational model 205
Multiple comparisons 208
Software 209
Analyses of subgroups and regression analyses are observational 209
Statistical power for subgroup analyses and meta-regression 210
Summary points 211
PART 5: COMPLEX DATA STRUCTURES
22 OVERVIEW 215
23 INDEPENDENT SUBGROUPS WITHIN A STUDY 217
Introduction 217
Combining across subgroups 218
Comparing subgroups 222
Summary points 223
24 MULTIPLE OUTCOMES OR TIME-POINTS WITHIN A STUDY 225
Introduction 225
Combining across outcomes or time-points 226
Comparing outcomes or time-points within a study 233
Summary points 238
25 MULTIPLE COMPARISONS WITHIN A STUDY 239
Introduction 239
Combining across multiple comparisons within a study 239
Differences between treatments 240
Summary points 241
26 NOTES ON COMPLEX DATA STRUCTURES 243
Introduction 243
Summary effect 243
Differences in effect 244
PART 6: OTHER ISSUES
27 OVERVIEW 249
28 VOTE COUNTING – A NEW NAME FOR AN OLD PROBLEM 251
Introduction 251
Why vote counting is wrong 252
Vote counting is a pervasive problem 253
Summary points 255
29 POWER ANALYSIS FOR META-ANALYSIS 257
Introduction 257
A conceptual approach 257
In context 261
When to use power analysis 262
Planning for precision rather than for power 263
Power analysis in primary studies 263
Power analysis for meta-analysis 267
Power analysis for a test of homogeneity 272
Summary points 275
30 PUBLICATION BIAS 277
Introduction 277
The problem of missing studies 278
Methods for addressing bias 280
Illustrative example 281
The model 281
Getting a sense of the data 281
Is there evidence of any bias? 283
Is the entire effect an artifact of bias? 284
How much of an impact might the bias have? 286
Summary of the findings for the illustrative example 289
Some important caveats 290
Small-study effects 291
Concluding remarks 291
Summary points 291
PART 7: ISSUES RELATED TO EFFECT SIZE
31 OVERVIEW 295
32 EFFECT SIZES RATHER THAN p-VALUES 297
Introduction 297
Relationship between p-values and effect sizes 297
The distinction is important 299
The p-value is often misinterpreted 300
Narrative reviews vs. meta-analyses 301
Summary points 302
33 SIMPSON’S PARADOX 303
Introduction 303
Circumcision and risk of HIV infection 303
An example of the paradox 305
Summary points 308
34 GENERALITY OF THE BASIC INVERSE-VARIANCE METHOD 311
Introduction 311
Other effect sizes 312
Other methods for estimating effect sizes 315
Individual participant data meta-analyses 316
Bayesian approaches 318
Summary points 319
PART 8: FURTHER METHODS
35 OVERVIEW 323
36 META-ANALYSIS METHODS BASED ON DIRECTION AND p-VALUES 325
Introduction 325
Vote counting 325
The sign test 325
Combining p-values 326
Summary points 330
37 FURTHER METHODS FOR DICHOTOMOUS DATA 331
Introduction 331
Mantel-Haenszel method 331
One-step (Peto) formula for odds ratio 336
Summary points 339
38 PSYCHOMETRIC META-ANALYSIS 341
Introduction 341
The attenuating effects of artifacts 342
Meta-analysis methods 344
Example of psychometric meta-analysis 346
Comparison of artifact correction with meta-regression 348
Sources of information about artifact values 349
How heterogeneity is assessed 349
Reporting in psychometric meta-analysis 350
Concluding remarks 351
Summary points 351
PART 9: META-ANALYSIS IN CONTEXT
39 OVERVIEW 355
40 WHEN DOES IT MAKE SENSE TO PERFORM A META-ANALYSIS? 357
Introduction 357
Are the studies similar enough to combine? 358
Can I combine studies with different designs? 359
How many studies are enough to carry out a meta-analysis? 363
Summary points 364
41 REPORTING THE RESULTS OF A META-ANALYSIS 365
Introduction 365
The computational model 366
Forest plots 366
Sensitivity analysis 368
Summary points 369
42 CUMULATIVE META-ANALYSIS 371
Introduction 371
Why perform a cumulative meta-analysis? 373
Summary points 376
43 CRITICISMS OF META-ANALYSIS 377
Introduction 377
One number cannot summarize a research field 378
The file drawer problem invalidates meta-analysis 378
Mixing apples and oranges 379
Garbage in, garbage out 380
Important studies are ignored 381
Meta-analysis can disagree with randomized trials 381
Meta-analyses are performed poorly 384
Is a narrative review better? 385
Concluding remarks 386
Summary points 386
PART 10: RESOURCES AND SOFTWARE
44 SOFTWARE 391
Introduction 391
The software 392
Three examples of meta-analysis software 393
Comprehensive Meta-Analysis (CMA) 2.0 395
RevMan 5.0 398
Stata macros with Stata 10.0 400
Summary points 403
45 BOOKS, WEB SITES AND PROFESSIONAL ORGANIZATIONS 405
Books on systematic review methods 405
Books on meta-analysis 405
Web sites 406
REFERENCES 409
INDEX 415

Thanks so much

statax 发表于 2015-4-22 09:44
PART 1: INTRODUCTION
1 HOW A META-ANALYSIS WORKS 3
Introduction 3

呵呵，非常谢谢！资源共享，希望大家都能学会这个方法

statax 发表于 2015-4-22 09:44
PART 1: INTRODUCTION
1 HOW A META-ANALYSIS WORKS 3
Introduction 3

呵呵，非常谢谢！资源共享，希望大家都能学会这个方法

这本书比cochrane handbook讲的生动形象一些

没有金币的就是难过