In some circumstances, in particular for rare diseases, accelerated approval may be sought for medicines in the absence of data from comparative randomised trials. In this situation the effectiveness of the new medicine needs to be estimated from single arm trials of the new medicine and trials or observational studies of comparator interventions.
Population-adjusted indirect comparisons (a type of standardisation), have been developed to map treatment effects observed in one population into effects that would be observed in another population. Matching-Adjusted Indirect Comparison (MAIC, based on propensity score weighting) and Simulated Treatment Comparison (STC, based on outcome regression) use individual patient data (IPD) from one study to adjust for between-study differences in the distribution of variables that influence outcome. ‘Unanchored’ comparisons are required when considering single arm studies as there is no common comparator across studies. Although these methods are superior to naïve comparisons (e.g. with historical ‘controls’) they require strong assumptions about the presence of all effect modifiers and prognostic variables in the data. Their results need to be treated with some caution as an unknown amount of residual bias may remain in the statistically modelled comparisons.
These methods were not reviewed explicitly by the GetReal project. Further information can be found in NICE DSU Technical Support Document 18 (Phillippo, 2016) and this article published in Value in Health (Signorovitch, 2012).