Hypothesis
Adjusted indirect comparisons, increasingly used to compare treatments never tested against each other, give reliable confidence intervals and hypothesis tests.
What failed
In simulations, indirect confidence intervals suffered under-coverage and indirect hypothesis tests had low power whenever between-study heterogeneity was moderate to large; the risk of overestimation was large when one of the two direct comparisons rested on a single trial.
Broken assumption
Indirect comparisons support confidence intervals and significance tests as dependable as direct ones.
Why
The authors trace the fragility to between-study heterogeneity and to unbalanced comparisons, where one side rests on little evidence.
From the published paper: Mills et al. PLoS ONE, 2011. ↗CC BY