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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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ter Veer, E., van Oijen, M. G. H., & van Laarhoven, H. W. M. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2019.00822 · Cited by 83
“Limited Interpretation of Indirect Evidence Although an indirect effect is calculated using direct comparisons in RCTs, it is still observational evidence (6, 8, 65).”
BackgroundMethodInfluentialWeng, Q., Goh, S.-L., Wu, J., Persson, M. S. M., Wei, J., Sarmanova, A., Li, X., Hall, M., Doherty, M., Jiang, T., et al. (2023) · British Journal of Sports Medicine · doi:10.1136/bjsports-2022-105898 · Cited by 84
'Third wave' cognitive and behavioural therapies versus treatment as usual for depression
Churchill, R., Moore, T. H., Furukawa, T. A., Caldwell, D. M., Davies, P., Jones, H., Shinohara, K., Imai, H., Lewis, G., & Hunot, V. (2013) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd008705.pub2 · Cited by 82
Thorlund, K., Druyts, E., Wu, P., Balijepalli, C., Keohane, D., & Mills, E. (2015) · Journal of the American Geriatrics Society · doi:10.1111/jgs.13395 · Cited by 79
Li, K.-P., Yang, X.-S., & Wu, T. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.810242 · Cited by 79
Vandvik, P. O., Brignardello-Petersen, R., & Guyatt, G. H. (2016) · BMC Medicine · doi:10.1186/s12916-016-0596-4 · Cited by 69
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
