Muutke küpsiste eelistusi

Meta-Analysis in Stata: An Updated Collection from the Stata Journal, Second Edition 2nd edition [Pehme köide]

Edited by (University of Bristol, UK), Edited by (Stata Press, College Station, TX, USA)
  • Formaat: Paperback / softback, 534 pages, kõrgus x laius: 235x187 mm, kaal: 1008 g
  • Ilmumisaeg: 08-Oct-2015
  • Kirjastus: Stata Press
  • ISBN-10: 1597181471
  • ISBN-13: 9781597181471
Teised raamatud teemal:
Meta-Analysis in Stata: An Updated Collection from the Stata Journal, Second Edition 2nd editionSuurem pilt
  • Formaat: Paperback / softback, 534 pages, kõrgus x laius: 235x187 mm, kaal: 1008 g
  • Ilmumisaeg: 08-Oct-2015
  • Kirjastus: Stata Press
  • ISBN-10: 1597181471
  • ISBN-13: 9781597181471
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781597181471.html
Märksõnad:

Meta-analysis allows researchers to combine the results of several studies into a unified analysis that provides an overall estimate of the effect of interest. This collection of articles from the Stata Journal and Stata Technical Bulletin will be indispensable to researchers who wish to conduct meta-analyses using Stata and learn about the full range of user-written Stata meta-analysis commands. With these articles and the associated Stata software, you gain access to the statistical methods behind the rapid increase in the number of meta-analyses reported in the social and medical literature.

Collectively, the articles provide a detailed description of a range of meta-analytic methods. They show how to conduct and interpret meta-analyses; how to produce highly flexible graphical displays; how to use meta-regression; how to examine bias; how to conduct individual participant data meta-analysis; and how to conduct multivariate meta-analysis. This edition also contains three articles on network metaanalysis, a major recent development in meta-analysis methodology.

Introduction to the second edition vii
Introduction to the first edition xi
Install the software xv
1 Meta-analysis in Stata: metan, metaan, metacum, and metap
1(82)
Metan --- a command for meta-analysis in Stata
3(26)
M. J. Bradburn
J. J. Deeks
D. G. Altman
metan: fixed- and random-effects meta-analysis
29(26)
R. J. Harris
ML J. Bradburn
J. J. Deeks
R. M. Harbord
D. G. Altman
J. A. C. Sterne
metaan: Random-effects meta-analysis
55(13)
E. Kontopantelis
D. Reeves
Cumulative meta-analysis
68(10)
J. A. C. Sterne
Meta-analysis of p-values
78(5)
A. Tobias
2 Meta-regression: metareg
83(38)
Meta-regression in Stata
85(27)
R. M. Harbord
J. P. T. Higgins
Meta-analysis regression
112(9)
S. Sharp
3 Investigating bias in meta-analysis: metafunnel, confunnel, metabias, metatrim, and extfunnel
121(90)
Funnel plots in meta-analysis
124(15)
J. A. C. Sterne
R. M. Harbord
Contour-enhanced funnel plots for meta-analysis
139(14)
T. M. Palmer
J. L. Peters
A. J. Sutton
S. G. Moreno
Updated tests for small-study effects in meta-analyses
153(13)
R. M. Harbord
R. J. Harris
J. A. C. Sterne
Tests for publication bias in meta-analysis
166(11)
T. J. Steichen
Tests for publication bias in meta-analysis
177(3)
T. J. Steichen
M. Egger
J. A. C. Sterne
Nonparametric trim and fill analysis of publication bias in meta-analysis
180(13)
T. J. Steichen
Graphical augmentations to the funnel plot to assess the impact of a new study on an existing meta-analysis
193(18)
M. J. Crowther
D. Langan
A. J. Sutton
4 Multivariate meta-analysis: metandi, mvmeta
211(54)
metandi: Meta-analysis of diagnostic accuracy using hierarchical logistic regression
213(19)
R. M. Harbord
P. Whiting
Multivariate random-effects meta-analysis
232(17)
I. R. White
Multivariate random-effects meta-regression: Updates to mvmeta
249(16)
I. R. White
5 Individual patient data meta-analysis: ipdforest and ipdmetan
265(44)
A short guide and a forest plot command (ipdforest) for one-stage meta-analysis
266(14)
E. Kontopantelis
D. Reeves
Two-stage individual participant data meta-analysis and generalized forest plots
280(29)
D.J. Fisher
6 Network meta-analysis: indirect, network package, network_graphs package
309(92)
Indirect treatment comparison
311(10)
B. Miladinovic
I. Hozo
A. Chaimani
B. Djulbegovic
Network meta-analysis
321(34)
I. R. White
Visualizing assumptions and results in network meta-analysis: The network graphs Package
355(46)
A. Chaimani
G. Salanti
7 Advanced methods: gist, metamiss, sem, gsem, metacum-bounds, metasim, metapow, and metapowplot
401(98)
Generalized least squares for trend estimation of summarized dose-response data
404(18)
N. Orsini
R. Bellocco
S. Greenland
Meta-analysis with missing data
422(13)
I. R. White
J. P. T. Higgins
Fitting fixed- and random-effects meta-analysis models using structural equation modeling with the sem and gsem commands
435(27)
T. M. Palmer
J. A. C. Sterne
Trial sequential boundaries for cumulative meta-analyses
462(14)
B. Miladinovic
I. Hozo
B. Djulbegovic
Simulation-based sample-size calculation
476(23)
M. J. Crowther
S. R. Hinchliffe
A. Donald
A. J. Sutton
Appendix 499(4)
Author Index 503(12)
Command Index 515
Tom M. Palmer is a lecturer in statistics in the Department of Mathematics and Statistics at Lancaster University, UK. He is the author of the confunnel command for contour-enhanced funnel plots. His research focuses on statistical methodology for epidemiological studies, including Mendelian randomization studies. He is also the author of several other Stata commands, including bpbounds, the reffadjust package, and the winbugsfromstata package.

Jonathan A. C. Sterne is professor of medical statistics and epidemiology and of social and community medicine, University of Bristol, UK. His research interests include methods for systematic reviews and meta-analyses, the clinical epidemiology of HIV and AIDS in the era of effective therapy, statistical methods for epidemiology, and the epidemiology of asthma and allergic diseases.