Extending Treatment Networks in Health Technology Assessment: How Far Should We Go?

Research output: Contribution to journalArticle (Academic Journal)peer-review

15 Citations (Scopus)
351 Downloads (Pure)

Abstract

Background

Network meta-analysis may require substantially more resources than does a standard systematic review. One frequently asked question is “how far should I extend the network and which treatments should I include?”

Objective

To explore the increase in precision from including additional evidence.

Methods

We assessed the benefit of extending treatment networks in terms of precision of effect estimates and examined how this depends on network structure and relative strength of additional evidence. We introduced a “star”-shaped network. Network complexity is increased by adding more evidence connecting treatments under five evidence scenarios. We also examined the impact of heterogeneity and absence of evidence facilitating a “first-order” indirect comparison.

Results

In all scenarios, extending the network increased the precision of the A versus B treatment effect. Under a fixed-effect model, the increase in precision was modest when the existing direct A versus B evidence was already strong and was substantial when the direct evidence was weak. Under a random-effects model, the gain in precision was lower when heterogeneity was high. When evidence is available for all “first-order” indirect comparisons, including second-order evidence has limited benefit for the precision of the A versus B estimate. This is interpreted as a “ceiling effect.”

Conclusions

Including additional evidence increases the precision of a “focal” treatment comparison of interest. Once the comparison of interest is connected to all others via “first-order” indirect evidence, there is no additional benefit in including higher order comparisons. This conclusion is generalizable to any number of treatment comparisons, which would then all be considered “focal.” The increase in precision is modest when direct evidence is already strong, or there is a high degree of heterogeneity.
Original languageEnglish
Pages (from-to)673-681
Number of pages9
JournalValue in Health
Volume18
Issue number5
Early online date11 Jun 2015
DOIs
Publication statusPublished - Jul 2015

Structured keywords

  • ConDuCT-II

Keywords

  • comparative effectiveness
  • health technology assessment
  • literature searching
  • mixed treatment comparisons
  • network meta-analysis
  • systematic review

Fingerprint Dive into the research topics of 'Extending Treatment Networks in Health Technology Assessment: How Far Should We Go?'. Together they form a unique fingerprint.

Cite this