Simultaneous comparison of multiple treatments: combining direct and indirect evidence
By Deborah M. Caldwell, A. E. Ades, Julian P T Higgins. British medical journal, 2005
1,585 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Public Health, Environmental and Occupational Health, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/b14ab5c30633ff6b163cffa27939d9b8300a15e9
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
- Georgia Salanti
- Ian R. White
- Julian P. T. Higgins
- Holger J. Schünemann
- G. Guyatt
- Jeroen P. Jansen
- A. Ades
- N. Welton
In Public Health, Environmental and Occupational Health
- Georgia Salanti
- Holger J. Schünemann
- G. Guyatt
- Andrew D. Oxman
- N. Welton
- S. Dias
- A. Ades
- Johannes B Reitsma
In Statistics and Probability
- Georgia Salanti
- Ian R. White
- Julian P. T. Higgins
- A. Chaimani
- A. Ades
- Dan Jackson
- N. Welton
- G. van Valkenhoef
Citations in the corpus
- Social Anxiety Disorder (2008, methodology): “NMA techniques strengthen inference concerning the relative effect of two treatments by including both direct and indirect comparisons between treatments, and, at the same time, allow simultaneous inference on all treatments examined in the pairwise trial comparisons while respecting randomisation (”
- Checking consistency in mixed treatment comparison meta‐analysis (2010, background): “MTC analyses of different subsets of this network have been previously studied [2, 16].”
- Evidence Synthesis for Decision Making 4 (2013, background): “Where inconsistency has been evident, it illustrates the danger introduced by heterogeneity and in particular by the practice of trying to combine evidence on disparate treatment doses or treatment combinations within meta-analyses, often termed lumping.(1,29) The place for an enquiry into consisten”
- Interpreting indirect treatment comparisons and network meta-analysis for health-care decision making: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: part 1. (2011, methodology): “Interpreting Indirect Treatment Comparisons and Network Meta-Analysis for”
- Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression (2012, methodology): “If the consistency model is supported by the data, then it is useful to report the probability that each treatment is the best treatment (Caldwell et al., 2005).”
- Is network meta-analysis as valid as standard pairwise meta-analysis? It all depends on the distribution of effect modifiers (2013, background): “Such a network allows for indirect comparisons of interventions not studied in a head-to-head fashion [2].”
- Conceptual and Technical Challenges in Network Meta-analysis (2013, background): “Several applications and methodological articles have outlined the benefits of network analysis (1, 8, 9), which has become increasingly popular (Appendix Figure 1, available at www.”
- Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies (2012, methodology): “…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).”
- What is “quality of evidence” and why is it important to clinicians? (2008, background): “Increasingly, recommendations must simultaneously tackle multiple interventions. For example, possible approaches to thrombolysis in myocardial infarction include streptokinase, alteplase, reteplase, and tenecteplase. Attempts to deal with multiple interventions inevitably involve indirect compariso”
- GetReal in network meta‐analysis: a review of the methodology (2016, methodology): “As a consequence, indirect treatment comparisons may be more subject to biased treatment effect estimates, owing, for example, to confounding (e.g., when randomized AB and AC studies are systematically different than BC; Caldwell et al., 2005) and selection bias (e.”