The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Combination of direct and indirect evidence in mixed treatment comparisons
Abstract
Mixed treatment comparison (MTC) meta‐analysis is a generalization of standard pairwise meta‐analysis for A vs B trials, to data structures that include, for example, A vs B, B vs C, and A vs C trials. There are two roles for MTC: one is to strengthen inference concerning the relative efficacy of two treatments, by including both ‘direct’ and ‘indirect’ comparisons. The other is to facilitate simultaneous inference regarding all treatments, in order for example to select the best treatment. In this paper, we present a range of Bayesian hierarchical models using the Markov chain Monte Carlo software WinBUGS. These are multivariate random effects models that allow for variation in true treatment effects across trials. We consider models where the between‐trials variance is homogeneous across treatment comparisons as well as heterogeneous variance models. We also compare models with fixed (unconstrained) baseline study effects with models with random baselines drawn from a common distribution. These models are applied to an illustrative data set and posterior parameter distributions are compared. We discuss model critique and model selection, illustrating the role of Bayesian deviance analysis, and node‐based model criticism. The assumptions underlying the MTC models and their parameterization are also discussed. Copyright © 2004 John Wiley & Sons, Ltd.
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Cites
Network meta‐analysis for indirect treatment comparisons
Lumley, T. (2002) · Statistics in Medicine · doi:10.1002/sim.1201 · Cited by 1,206
Meta‐Analysis Of Controlled Clinical Trials
Whitehead, A. (2002) · doi:10.1002/0470854200 · Cited by 827
Bayesian approaches to random-effects meta-analysis: a comparative study
Smith, T. C., Spiegelhalter, D. J., & Thomas, A. (1995) · Statistics in Medicine · doi:10.1002/sim.4780142408 · Cited by 568 · cited 6×
“Experience has shown that the inverse Gamma priors −2 ∼Gamma(10−3; 10−3) used by many investigators for variance terms in random e ects meta-analysis [2, 8, 13] can on occasion produce di erent results to other formulations, such as ∼Uniform(0; 2), that appear equally ‘uninformative’.”
BackgroundMethodInfluentialA bivariate approach to meta-analysis
Van Houwelingen, H. C., Zwinderman, K. H., & Stijnen, T. (1993) · Statistics in Medicine · doi:10.1002/sim.4780122405 · Cited by 327
Warn, D. E., Thompson, S. G., & Spiegelhalter, D. J. (2002) · Statistics in Medicine · doi:10.1002/sim.1189 · Cited by 277
Cited by
Hutton, B., Salanti, G., Caldwell, D. M., Chaimani, A., Schmid, C. H., Cameron, C., Ioannidis, J. P. A., Straus, S., Thorlund, K., Jansen, J. P., et al. (2015) · Annals of Internal Medicine · doi:10.7326/m14-2385 · Cited by 8,269
Salanti, G., Ades, A. E., & Ioannidis, J. P. A. (2011) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2010.03.016 · Cited by 3,766
Self-citationChecking consistency in mixed treatment comparison meta-analysis
Dias, S., Welton, N. J., Caldwell, D. M., & Ades, A. E. (2010) · Statistics in Medicine · doi:10.1002/sim.3767 · Cited by 2,521
Self-citationLeucht, S., Cipriani, A., Spineli, L., Mavridis, D., Örey, D., Richter, F., Samara, M., Barbui, C., Engel, R. R., Geddes, J. R., et al. (2013) · The Lancet · doi:10.1016/s0140-6736(13)60733-3 · Cited by 2,561
Graphical tools for network meta-analysis in STATA
Chaimani, A., Higgins, J. P. T., Mavridis, D., Spyridonos, P., & Salanti, G. (2013) · PLoS ONE · doi:10.1371/journal.pone.0076654 · Cited by 2,473
Metadata
- Authors
- Lu, G., & Ades, A. E.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2004-09-24
- Volume
- 23
- Issue
- 20
- Pages
- 3105-3124
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2113144077
- Cited by
- 2,166
- References
- 5
- FWCI
- 14.26
- Type
- journal article
- Language
- en
- Open access
- closed
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