There are two primary approaches to statistical analysis of NMA: Frequentist and Bayesian.
- Frequentist analyses utilize population probability distributions to assess the probability of the observed data.
- Bayesian analyses (which is the more commonly used analysis for NMA) define the probability distribution based on the observed data and external information about the parameters. For example, if there is a known logarithmic growth shape for caloric intake as a predictor on malnutrition as a response variable, then the Bayesian approach would utilize this predetermined relationship to inform the model.
Local or global approaches test the model for inconsistency.
The local approach determines the presence of inconsistency in a specific head-to-head comparison in the network (i.e. if two trials of the same intervention have different outcomes, then the test for local approach consistency is violated). The global approach evaluates inconsistency across the entire network (i.e. when comparing indirect trials and there are different outcomes, then the test for global approach consistency is violated).
NMA requires at least 3 comparators. The availability of multiple trials for each comparator is advantageous.
- Studies included in NMA assume Congruence.
- Congruence requires inclusion of similar studies to ensure their effect measures are combinable, which includes study and patient characteristics.
- NMA assumes Homogeneity of trial results for the same head to head pair comparisons.
- NMA assumes Transitivity, or equal distribution of effect modifiers, to ensure direct and indirect estimates are consistent.
Example: A valid indirect estimate can be calculated between intervention A and intervention B, if a direct estimate of intervention A versus placebo A and a direct estimate of intervention B versus placebo A were obtained among participants with similar baseline comorbidities, treatment doses, and of similar study quality.