Conducting indirect-treatment-comparison and network-meta-analysis studies: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: part 2.
By David C. Hoaglin, N. Hawkins, Jeroen P. Jansen, D. Scott, R. Itzler, Joseph C. Cappelleri, C. Boersma, D. Thompson, K. Larholt, Mireya Díaz and 1 more. Value in Health, 2011
751 citations recorded by the source corpus.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/3ca9aa6448036c5d232ab3d9c523859536910c38
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistics and Probability
- Johannes B Reitsma
- Karel G. M. Moons
- T. Debray
- O. Efthimiou
- G. van Valkenhoef
- S. Trelle
- A. Shang
- K. Panayidou
Citations in the corpus
- Indirect and mixed‐treatment comparison, network, or multiple‐treatments meta‐analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool (2012, positive): “Several application and method papers have outlined the benefits of a joint analysis (Caldwell et al., 2005; Cooper et al., 2011; Hoaglin et al., 2011; Mills et al., 2011).”
- GetReal in network meta‐analysis: a review of the methodology (2016, methodology): “decision making in general is that in order to assess the magnitude of the relative treatment effects, the analyst must choose a value of the covariate at which to make the comparison (Hoaglin et al., 2011).”