Abstract
In many clinical areas, there is no single, widely accepted outcome measure. For example, in rheumatoid arthritis three different outcome measures are in widespread use: the Health Assessment Questionnaire (HAQ) designed for measuring the severity of inflammatory joint disorders, the Disease Activity Score (DAS-28) to assess the level of disease activity and the American College of Rheumatology (ACR) response criteria. Similarly, in respiratory disease, we have (among other measures) Forced Expiratory Volume in one second (FEV1), Forced Vital Capacity (FVC) and Peak Expiratory Flow (PEF).
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Notes
- 1.
To install R package mvmeta use R command install.packages("mvmeta").
- 2.
\(\mbox{ Cor}(X,Y ) = \mbox{ Cov}(X,Y )/\sqrt{\mbox{ Var} \,(X)\ \mbox{ Var} \,(Y )},\quad \mbox{ so }\quad \mbox{ Cov}(X,Y ) = \mbox{ Cor}(X,Y )\sqrt{\mbox{ Var} \,(X)\ \mbox{ Var} \,(Y )}\).
- 3.
Assigning row and column names to matrices theta and S.arth using the base R function dimnames is optional, however it makes the printouts easier to follow!
- 4.
To install the ellipse package use R command install.packages("ellipse").
References
Bujkiewicz, S., Thompson, J.R., Sutton, A.J., Cooper, N.J., Harrison, M.J., Symmons, D.P.M., Abrams, K.R.: Multivariate meta-analysis of mixed outcomes: a Bayesian approach. Stat. Med. 32(22), 3926–43 (2013). doi:10.1002/sim.5831
Carpenter, J.R., Kenward, M.G.: Multiple Imputation and Its Application. Wiley, Chichester (2013)
Gasparrini, A., Armstrong, B., Kenward, M.G.: Multivariate meta-analysis for non-linear and other multi-parameter associations. Stat. Med. 31(29), 3821–3839 (2012). doi:10.1002/sim.5471
Jackson, D., Riley, R., White, I.R.: Multivariate meta-analysis: potential and promise. Stat. Med. 30, 2481–2498 (2011)
Jackson, D., White, I.R., Riley, R.D.: Quantifying the impact of between-study heterogeneity in multivariate meta-analyses. Stat. Med. 31(29), 3805–3820 (2012)
Jackson, D., White, I.R., Riley, R.D.: A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression. Biom. J. 55(2), 231–245 (2013). doi: 10.1002/bimj.201200152
Kenward, M.G., Roger, J.H.: Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 53, 983–997 (1997)
Lloyd, S., Bujkiewicz, S., Wailoo, A.J., Sutton, A.J., Scott, D.: The effectiveness of anti-TNF-α therapies when used sequentially in rheumatoid arthritis patients: a systematic review and meta-analysis. Rheumatology (Oxford) 49(12), 2313–21 (2010). doi:10.1093/rheumatology/keq169
Pinto, E.M., Willan, A.R., O’Brien, B.J.: Cost-effectiveness analysis for multinational clinical trials. Stat. Med. 24(13), 1965–82 (2005). doi:10.1002/sim.2078
Riley, R.D., Abrams, K.R., Lambert, P.C., Sutton, A.J., Thompson, J.R.: An evaluation of bivariate random-effects meta-analysis for the joint synthesis of two correlated outcomes. Stat. Med. 26, 78–97 (2013)
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Schwarzer, G., Carpenter, J.R., Rücker, G. (2015). Multivariate Meta-Analysis. In: Meta-Analysis with R. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-319-21416-0_7
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