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Editorial
. 2024 Jul 1;109(7):2032-2034.
doi: 10.3324/haematol.2023.284534.

Indirect treatment comparisons: how to MAIC it right?

Emmanuel Bachy  1
Affiliations
Editorial

Indirect treatment comparisons: how to MAIC it right?

Emmanuel Bachy. Haematologica. .
No abstract available

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Figures

Figure 1.
Figure 1.
Principle and level of clinical evidence of matching-adjusted indirect comparison. (A) Level of evidence of various treatment comparisons from unadjusted and unmatched comparison (poorest level of evidence) to randomized treatment allocation (gold standard). The list of various statistical approaches to make patient population as comparable as possible is not exhaustive. (B) Matching-adjusted indirect comparison (MAIC) allows for the comparison between aggregated patient data (e.g., based on patient characteristics from a trial publication) and individual patient data (from another trial or a real-life cohort or any other source of individual data). Basically, by removing or pondering patient characteristics to closely match final aggregated data (e.g., patient median age depicted here), the final group of patients from the cohort with available individual patient data (IPD) is rendered as similar as possible to the cohort for which only aggregated data are available (e.g., here, similar median age). This is performed for all variables that are considered as critical confounders for treatment comparison. Finally, outcome is compared in the 2 matched populations; here, the prognosis of the IPD is depicted as better after matching (blue line), while, by definition, the survival of the aggregated data population is left unchanged after matching (red line). Depicted data and survivals are for illustration only and are not based on true or relevant values or weights. PS: propensity score; yr: year.
See this image and copyright information in PMC

References

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