Meta-Analysis in Stata: An Updated Collection from the Stata Journal, Second Edition

Introduction to the second edition
	References
Introduction to the first edition
	References
Install the software
I Meta-analysis in Stata: metan, metaan, metacum, and metap
	References
1 metan—a command for meta-analysis in Stata
	1.1 Background
	1.2 Data structure
	1.3 Analysis of binary data using fixed-effects models
	1.4 Analysis of continuous data using fixed-effects models
	1.5 Test for heterogeneity
	1.6 Analysis of binary or continuous data using random-effects models
	1.7 Tests of overall effect
	1.8 Graphical analyses
	1.9 Syntax for metan
	1.10 Options for metan
	1.11 Saved results from metan (macros)
	1.12 Syntax for funnel
	1.13 Options for funnel
	1.14 Syntax for labbe
	1.15 Options for labbe
	1.16 Example 1: Interventions in smoking cessation
	1.17 Example 2
	1.18 Formulas
	1.19 Individual study responses: binary outcomes
	1.20 Individual study responses: continuous outcomes
	1.21 Mantel–Haenszel methods for combining trials
	1.22 Inverse variance methods for combining trials
	1.23 Peto’s assumption free method for combining trials
	1.24 DerSimonian and Laird random-effects models
	1.25 Confidence intervals
	1.26 Test statistics
	1.27 Acknowledgments
	1.28 References
2 metan: fixed- and random-effects meta-analysis
	2.1 Introduction
	2.2 Example data
	2.3 Syntax
	2.4 Basic use
	2.5 Displaying data columns in graphs
	2.6 by() processing
	2.7 User-defined analyses
	2.8 New analysis options
	2.9 New output
	2.10 More graph options
	2.11 Variables and results produced by metan
	2.12 References
3 metaan: Random-effects meta-analysis
	3.1 Introduction
	3.2 The metaan command
	3.3 Methods
	3.4 Example
	3.5 Discussion
	3.6 Acknowledgments
	3.7 References
4 Cumulative meta-analysis
	4.1 Syntax
	4.2 Options
	4.3 Background
	4.4 Example
	4.5 Note
	4.6 Acknowledgments
	4.7 References
5 Meta-analysis of p-values
	5.1 Fisher’s method
	5.2 Edgington’s methods
	5.3 Syntax
	5.4 Option
	5.5 Example
	5.6 Individual or frequency records
	5.7 Saved results
	5.8 References
II Meta-regression: metareg
	References
6 Meta-regression in Stata
	6.1 Introduction
	6.2 Basis of meta-regression
	6.3 Relation to other Stata commands
	6.4 Background to examples
	6.5 New and enhanced features
	6.6 Syntax, options, and saved results
	6.7 Methods and formulas
	6.8 Acknowledgments
	6.9 References
7 Meta-analysis regression
	7.1 Background
	7.2 Method-of-moments estimator
	7.3 Iterative procedures
	7.4 Syntax
	7.5 Options
	7.6 Example
	7.7 Saved results
	7.8 Acknowledgments
	7.9 References
III Investigating bias in meta-analysis: metafunnel, confunnel, metabias, metatrim, and extfunnel
	References
8 Funnel plots in meta-analysis
	8.1 Introduction
	8.2 Funnel plots
	8.3 Syntax
	8.4 Description
	8.5 Options
	8.6 Examples
	8.7 Acknowledgments
	8.8 References
9 Contour-enhanced funnel plots for meta-analysis
	9.1 Introduction
	9.2 Contour-enhanced funnel plots
	9.3 The confunnel command
	9.4 Use of confunnel
	9.5 Discussion
	9.6 References
10 Updated tests for small-study effects in meta-analyses
	10.1 Introduction
	10.2 Syntax
	10.3 Options
	10.4 Background
	10.5 Example
	10.6 Saved results
	10.7 Discussion
	10.8 Acknowledgment
	10.9 References
11 Tests for publication bias in meta-analysis
	11.1 Syntax
	11.2 Description
	11.3 Options
	11.4 Input variables
	11.5 Explanation
	11.6 Begg’s test
	11.7 Egger’s test
	11.8 Examples
	11.9 Saved results
	11.10 References
12 Tests for publication bias in meta-analysis
	12.1 Modification of the metabias program
	12.2 References
13 Nonparametric trim and fill analysis of publication bias in meta-analysis
	13.1 Syntax
	13.2 Description
	13.3 Options
	13.4 Specifying input variables
	13.5 Explanation
	13.6 Estimators of the number of suppressed studies
	13.7 The iterative trim and fill algorithm
	13.8 Example
	13.9 Remarks
	13.10 Saved results
	13.11 Note
	13.12 References
14 Graphical augmentations to the funnel plot to assess the impact of a new study on an existing meta-analysis
	14.1 Introduction
	14.2 Methodology
	14.3 The extfunnel command
	14.4 Example uses of extfunnel
	14.5 Additional feature
	14.6 Discussion
	14.7 Acknowledgments
	14.8 References
IV Multivariate meta-analysis: metandi, mvmeta
	References
15 metandi: Meta-analysis of diagnostic accuracy using hierarchical logistic regression
	15.1 Introduction
	15.2 Example: Lymphangiography for diagnosis of lymph node metastasis
	15.3 Models for meta-analysis of diagnostic accuracy
	15.4 metandi output
	15.5 metandiplot
	15.6 predict after metandi
	15.7 Syntax and options for commands
	15.8 Methods and formulas
	15.9 Acknowledgments
	15.10 References
16 Multivariate random-effects meta-analysis
	16.1 Introduction
	16.2 Multivariate random-effects meta-analysis with mvmeta
	16.3 Details of mvmeta
	16.4 A utility command to produce data in the correct format: mvmeta_make
	16.5 Example 1: Telomerase data
	16.6 Example 2: Fibrinogen Studies Collaboration data
	16.7 Perfect prediction
	16.8 Discussion
	16.9 Acknowledgments
	16.10 References
17 Multivariate random-effects meta-regression: Updates to mvmeta
	17.1 Introduction
	17.2 mvmeta: Multivariate random-effects meta-regression
	17.3 Details
	17.4 Example
	17.5 Difficulties and limitations
	17.6 Acknowledgments
	17.7 References
V Individual patient data meta-analysis: ipdforest and ipdmetan
	References
18 A short guide and a forest plot command (ipdforest) for one-stage meta-analysis
	18.1 Introduction
	18.2 Individual patient data meta-analysis
	18.3 The ipdforest command
	18.4 Discussion
	18.5 Acknowledgments
	18.6 References
19 Two-stage individual participant data meta-analysis and generalized forest plots
	19.1 Introduction
	19.2 Two-stage IPD meta-analysis
	19.3 The ipdmetan command
	19.4 Example
	19.5 Discussion
	19.6 Acknowledgments
	19.7 References
VI Network meta-analysis: indirect, network package, network_graphs package
	References
20 Indirect treatment comparison
	20.1 Introduction
	20.2 Adjusted indirect treatment comparison
	20.3 Example: Zoledronate versus Pamidronate in multiple myeloma
	20.4 Conclusion
	20.5 References
21 Network meta-analysis
	21.1 Introduction
	21.2 Model for network meta-analysis
	21.3 The network commands
	21.4 Examples
	21.5 Discussion
	21.6 Acknowledgments
	21.7 References
22 Visualizing assumptions and results in network meta-analysis: The network graphs package
	22.1 Introduction
	22.2 Example datasets
	22.3 The network graphs package
	22.4 Discussion
	22.5 Acknowledgments
	22.6 References
VII Advanced methods: glst, metamiss, sem, gsem, metacumbounds, metasim, metapow, and metapowplot
	References
23 Generalized least squares for trend estimation of summarized dose–response data
	23.1 Introduction
	23.2 Method
	23.3 The glst command
	23.4 Examples
	23.5 Empirical comparison of the WLS and GLS estimates
	23.6 Conclusion
	23.7 References
24 Meta-analysis with missing data
	24.1 Introduction
	24.2 metamiss command
	24.3 Examples
	24.4 Details
	24.5 Discussion
	24.6 References
25 Fitting fixed- and random-effects meta-analysis models using structural equation modeling with the sem and gsem commands
	25.1 Introduction
	25.2 Univariate outcome meta-analysis models
	25.3 Univariate outcome meta-regression models
	25.4 Multivariate outcome meta-analysis with zero within-study covariances
	25.5 Multivariate outcome meta-analysis with nonzero within-study covariances
	25.6 Conclusion
	25.7 Acknowledgments
	25.8 References
26 Trial sequential boundaries for cumulative meta-analyses
	26.1 Introduction
	26.2 Methods
	26.3 R statistical software
	26.4 The metacumbounds command
	26.5 Examples
	26.6 Discussion
	26.7 Acknowledgment
	26.8 References
27 Simulation-based sample-size calculation for designing new clinical trials and diagnostic test accuracy studies to update an existing meta-analysis
	27.1 Introduction
	27.2 Methods
	27.3 The metasim command
	27.4 The metapow command
	27.5 The metapowplot command
	27.6 Other uses
	27.7 Discussion
	27.8 Acknowledgments
	27.9 References
Appendix
	27.10 References