title: Extending Treatment Networks in Health Technology Assessment: How Far Should We Go?
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.
authors: School of Social and Community Medicine, University of Bristol, Bristol, UK. Electronic address: d.m.caldwell@bristol.ac.uk.
authors: Deborah M
authors: DM
authors: Caldwell
authors: School of Social and Community Medicine, University of Bristol, Bristol, UK.
authors: Sofia
authors: S
authors: Dias
authors: School of Social and Community Medicine, University of Bristol, Bristol, UK.
authors: Nicky J
authors: NJ
authors: Welton
mesh_terms: D057186:Comparative Effectiveness Research / Q000379:methods* / Q000706:statistics & numerical data; D003362:Cost-Benefit Analysis; D003627:Data Interpretation, Statistical; D019317:Evidence-Based Medicine / Q000379:methods* / Q000706:statistics & numerical data; D017048:Health Care Costs / Q000706:statistics & numerical data; D006801:Humans; D015201:Meta-Analysis as Topic; D018803:Models, Economic; D015233:Models, Statistical; D012196:Review Literature as Topic; D013673:Technology Assessment, Biomedical / Q000379:methods* / Q000706:statistics & numerical data
keywords: comparative effectiveness; health technology assessment; literature searching; mixed treatment comparisons; network meta-analysis; systematic review