In this article, we demonstrate how to fit fixed- and random-effects meta-analysis, meta-regression, and multivariate outcome meta-analysis models under the structural equation modeling framework using the sem and gsem commands. While all of these models can be fit using existing user-written commands, formulating the models in the structural equation modeling framework provides deeper insight into how they work. Further, the heterogeneity test for meta-regression and multivariate meta-analysis is readily available from this output.
BokerS., NealeM., MaesH., WildeM., SpiegelM., BrickT., SpiesJ., EstabrookR., KennyS., BatesT., MehtaP., and FoxJ.2011. OpenMx: An open source extended structural equation modeling framework. Psychometrika76: 306–317.
2.
BorensteinM., HedgesL. V., HigginsJ. P. T., and RothsteinH. R.2009. Introduction to Meta-Analysis.Chichester, UK: Wiley.
3.
BradburnM. J., DeeksJ. J., and AltmanD. G.1998. sbe24: metan—an alternative meta-analysis command. Stata Technical Bulletin44: 4–15. Reprinted in Stata Technical Bulletin Reprints, vol. 8, pp. 86–100. College Station, TX: Stata Press.
4.
CheungM. W.-L.2008. A model for integrating fixed-, random-, and mixed-effects meta-analyses into structural equation modeling. Pyschological Methods13: 182–202.
5.
CheungM. W.-L.2010. Fixed-effects meta-analyses as multiple-group structural equation models. Structural Equation Modeling: A Multidisciplinary Journal17: 481–509.
CheungM. W.-L.2013b. Implementing restricted maximum likelihood estimation in structural equation models. Structural Equation Modeling: A Multidisciplinary Journal20: 157–167.
8.
CheungM. W.-L.2013c. Multivariate meta-analysis as structural equation models. Structural Equation Modeling: A Multidisciplinary Journal20: 429–454.
9.
CheungM. W.-L.2015a. Advanced topics in SEM-based meta-analysis. In Meta-Analysis: A Structural Equation Modeling Approach, chap. 8. Chichester, UK: Wiley.
10.
CheungM. W.-L.2015b. Conducting meta-analysis with Mplus. In Meta-Analysis: A Structural Equation Modeling Approach, chap. 9. Chichester, UK: Wiley.
11.
DerSimonianR., and LairdN.1986. Meta-analysis in clinical trials. Controlled Clinical Trials7: 177–188.
12.
DwamenaB.2007. midas: Stata module for meta-analytical integration of diagnostic test accuracy studies. Statistical Software Components S456880, Department of Economics, Boston College. https://ideas.repec.org/c/boc/bocode/s456880.html.
13.
Fibrinogen Studies Collaboration.2004. Collaborative meta-analysis of prospective studies of plasma fibrinogen and cardiovascular disease. European Journal of Cardiovascular Prevention and Rehabilitation11: 9–17.
14.
Fibrinogen Studies Collaboration.2005. Plasma fibrinogen level and the risk of major cardiovascular diseases and nonvascular mortality: An individual participant meta-analysis. Journal of the American Medical Association294: 1799–1809.
15.
GlasA. S., RoosD., DeutekomM., ZwindermanA. H., BossuytP. M., and KurthK. H.2003. Tumor markers in the diagnosis of primary bladder cancer: A systematic review. Journal of Urology169: 1975–1982.
16.
HarbordR. M., and HigginsJ. P. T.2008. Meta-regression in Stata. Stata Journal8: 493–519.
17.
HarbordR. M., and WhitingP.2009. metandi: Meta-analysis of diagnostic accuracy using hierarchical logistic regression. Stata Journal9: 211–229.
18.
HarrisR. J., BradburnM. J., DeeksJ. J., HarbordR. M., AltmanD. G., and SterneJ. A. C.2008. metan: Fixed- and random-effects meta-analysis. Stata Journal8: 3–28.
19.
HarvilleD. A.1977. Maximum likelihood approaches to variance component estimation and to related problems. Journal of the American Statistical Association72: 320–338.
20.
HigginsJ. P. T., JacksonD., BarrettJ. K., LuG., AdesA. E., and WhiteI. R.2012. Consistency and inconsistency in network meta-analysis: Concepts and models for multi-arm studies. Research Synthesis Methods3: 98–110.
21.
HigginsJ. P. T., and ThompsonS. G.2002. Quantifying heterogeneity in a meta-analysis. Statistics in Medicine21: 1539–1558.
22.
HigginsJ. P. T., and ThompsonS. G.2004. Controlling the risk of spurious findings from meta-regression. Statistics in Medicine23: 1663–1682.
23.
HigginsJ. P. T., ThompsonS. G., DeeksJ. J., and AltmanD. G.2003. Measuring inconsistency in meta-analyses. British Medical Journal327: 557–560.
24.
KalaianH. A., and RaudenbushS. W.1996. A multivariate mixed linear model for meta-analysis. Psychological Methods1: 227–235.
25.
MehtaP. D., and NealeM. C.2005. People are variables too: Multilevel structural equations modeling. Psychological Methods10: 259–284.
26.
PattersonH. D., and ThompsonR.1971. Recovery of inter-block information when block sizes are unequal. Biometrika58: 545–554.
27.
PrevostA. T., MasonD., GriffinS., KinmonthA.-L., SuttonS., and SpiegelhalterD.2007. Allowing for correlations between correlations in random-effects meta-analysis of correlation matrices. Psychological Methods12: 434–450.
28.
R Core Team. 2014. R: A Language and Environment for Statistical Computing.R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org.
29.
RileyR. D., AbramsK. R., SuttonA. J., LambertP. C., and ThompsonJ. R.2007. Bivariate random-effects meta-analysis and the estimation of between-study correlation. BMC Medical Research Methodology7: 3.
30.
SharpS.1998. sbe23: Meta-analysis regression. Stata Technical Bulletin42: 16–22. Reprinted in Stata Technical Bulletin Reprints, vol. 7, pp. 148–155. College Station, TX: Stata Press.
31.
StataCorp. 2013. Stata 13 Multilevel Mixed-Effects Reference Manual.College Station, TX: Stata Press.
32.
SterneJ. A. C., ed. 2009. Meta-Analysis: An Updated Collection from the Stata Journal.College Station, TX: Stata Press.
33.
SuttonA. J., AbramsK. R., JonesD. R., SheldonT. A., and SongF.2000. Methods for Meta-Analysis in Medical Research.Chichester, UK: Wiley.
34.
ThompsonS. G., and SharpS. J.1999. Explaining heterogeneity in meta-analysis: A comparison of methods. Statistics in Medicine18: 2693–2708.
35.
TurnerR. M., OmarR. Z., YangM., GoldsteinH., and ThompsonS. G.2000. A multilevel model framework for meta-analysis of clinical trials with binary outcomes. Statistics in Medicine19: 3417–3432.
36.
ViechtbauerW.2005. Bias and efficiency of meta-analytic variance estimators in the random-effects model. Journal of Educational and Behavioral Statistics30: 261–293.
37.
WhiteI. R.2009. Multivariate random-effects meta-analysis. Stata Journal9: 40–56.
38.
WhiteI. R.2011. Multivariate random-effects meta-regression: Updates to mvmeta. Stata Journal11: 255–270.
39.
WhiteI. R., BarrettJ. K., JacksonD., and HigginsJ. P. T.2012. Consistency and inconsistency in network meta-analysis: Model estimation using multivariate meta-regression. Research Synthesis Methods3: 111–125.