Combination of direct and indirect evidence in mixed treatment comparisons

By Guobing Lu, A. Ades. Statistics in Medicine, 2004

2,117 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7663d40ff7a6c113ea0cc17b016be6a39e79bc5d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Georgia Salanti
2. Ian R. White
3. Julian P. T. Higgins
4. A. Ades
5. A. Chaimani
6. John P. A. Ioannidis
7. Guobing Lu
8. N. Welton

In Statistics and Probability

1. Georgia Salanti
2. Ian R. White
3. A. Chaimani
4. Julian P. T. Higgins
5. John P. A. Ioannidis
6. A. Ades
7. Dan Jackson
8. N. Welton

Citations in the corpus

• GetReal in network meta‐analysis: a review of the methodology (2016, methodology): “Hierarchical NMA models (Lu and Ades, 2004; Salanti et al., 2008a) appear to be implemented most often (Coleman et al., 2012; Nikolakopoulou et al., 2014).”
• Assessing Evidence Inconsistency in Mixed Treatment Comparisons (2006, methodology): “Analyses of inconsistency factors, cross-validation, posterior contributions to deviance, or extreme residuals (Lu and Ades 2004) all offer means of identifying evidence cycles or edges that exhibit high levels of inconsistency.”
• Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies (2012, background): “…analysis of both direct and indirect comparisons among multiple treatments across multiple studies, usually randomized trials (Gleser and Olkin, 1994; Higgins andWhitehead, 1996; Lumley, 2002; Lu and Ades, 2004; Caldwell et al., 2005; Lu and Ades, 2006; Salanti et al., 2008; Ioannidis, 2009).”
• Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. (2011, methodology): “Bayesian models for MTM have been previously described [3].”
• Automated generation of node‐splitting models for assessment of inconsistency in network meta‐analysis (2015, positive): “Network meta-analysis (Caldwell et al ., 2005; Lumley, 2002; Lu and Ades, 2004) is a general framework for the synthesis of evidence from clinical trials comparing any number of treatments.”
• Graphical Tools for Network Meta-Analysis in STATA (2013, background): “Thus, in the presence of multi-arm trials, researchers have often preferred to perform NMA in a Bayesian framework that offers more flexibility [21,22].”
• Network Meta-Analysis Using R: A Review of Currently Available Automated Packages (2014, methodology): “It models the relative effects (e.g., log-odds ratio) fitting a generalized linear model (GLM) under the Bayesian framework by linking to JAGS, OpenBUGS or WinBUGS as first described by Lu and Ades [7], and extended by others [6, 12, 28, 31].”
• Random effects meta‐analysis of event outcome in the framework of the generalized linear mixed model with applications in sparse data (2010, methodology): “Another difference from Lu and Ades [35] is that they fit their models with Bayesian methods using WinBUGS, which is more laborious in practice.”
• Reasons or excuses for avoiding meta-analysis in forest plots (2008, methodology): “The number of possible comparisons is prohibitive. A meta-analysis grouped these regimens into 12 treatment types and then performed a network analysis that evaluated their relative effectiveness. Instead of taking one comparison at a time, the network considered concomitantly all the data from all ”
• Network Meta-Analysis: An Introduction for Clinicians (2016, methodology): “Examples of commonly used models include the multivariate model [18, 19] or hierarchical model [20, 21].”