posted on 2019-10-18, 11:36authored bySuzanne C. Freeman, David Fisher, Ian R. White, Anne Auperin, James R. Carpenter
One of the biggest challenges for network meta-analysis (NMA) is inconsistency,
which occurs when the direct and indirect evidence conflict. Inconsistency causes
problems for the estimation and interpretation of treatment effects and treatment contrasts. Krahn and colleagues proposed the net heat approach as a graphical tool for
identifying and locating inconsistency within a network of randomised controlled
trials (RCTs). For networks with a treatment loop, the net heat plot displays statistics
calculated by temporarily removing each design one at a time, in turn, and assessing
the contribution of each remaining design to the inconsistency. The net heat plot takes
the form of a matrix which is displayed graphically with colouring indicating the
degree of inconsistency in the network. Applied to a network of individual participant
data assessing overall survival in 7531 patients with lung cancer we were surprised
to find no evidence of important inconsistency from the net heat approach; this contradicted other approaches for assessing inconsistency such as the Bucher approach,
Cochran’s Q statistic, node-splitting and the inconsistency parameter approach which
all suggested evidence of inconsistency within the network at the 5% level. Further
theoretical work shows that the calculations underlying the net heat plot constitute
an arbitrary weighting of the direct and indirect evidence which may be misleading.
We illustrate this further using a simulation study and a NMA of ten treatments for
diabetes. We conclude that the net heat plot does not reliably signal inconsistency or
identify designs that cause inconsistency.
Funding
UK Medical Research Council. Grant Number: Core funding for the MRC Clinical Trials Unit at UCL and grant funding for the MRC London Hub for Trials Methodology Research (MC UU 12023/21)
Ligue Nationale Contre le Cancer. Grant Number: Funding for Gustave Roussy Meta-Analysis Platform
History
Citation
Statistics in Medicine, Volume 38, Issue 29, 20 December 2019, Pages 5547-5564
Author affiliation
/Organisation/COLLEGE OF LIFE SCIENCES/School of Medicine/Department of Health Sciences