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INSIGHTS | INDIRECT TREATMENT COMPARISONS
FIGURE 1: META-ANALYSIS AND NETWORK META-ANALYSIS
A B A A
Direct comparison
Direct evidence
TRIAL SELECTION KEY TO QUALITY
To ensure that all relevant trials are considered for inclusion, ideally the potentially eligible
studies should be identified via a systematic review.
In order to obtain an internally consistent set of estimates, while respecting the randomization
in the evidence, the underlying assumption is that the trials included are sufficiently
homogeneous to be quantitatively combined, via a common comparator. This assumption
requires not only similarity of trial populations (in- and exclusion criteria, patient
characteristics, etc) but also methodological similarity (eg, trial quality, definition of outcomes,
randomization method, etc). An example of consistency is that the treatment effect estimated
by the BC trials would be the same as the treatment effect estimated by the AC and AB trials if
they had included B and C arms.
Trial quality should be sufficient for consideration. It is important to bear in mind that a set of
badly conducted studies does not lead to a good analysis; hence, the importance of defining a
coherent set of inclusion and exclusion criteria at an initial stage – usually when systematically
reviewing the literature.
PAGE 28 IMS HEALTH ECONOMICS & OUTCOMES RESEARCH
B
C C
Indirect comparison Mixed treatment comparison
Meta-analysis Network meta-analysis
D
Indirect evidence
NMAs enable the evaluation of competing treatment options that may not otherwise have been
directly compared. Although the level of evidence they generate may be perceived to be inferior to
that of gold standard RCTs, they nevertheless have important strengths.
B