Matching-adjusted indirect comparison via a polynomial-based non-linear optimisation method

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Reimbursement authorities need the best available evidence when comparing treatments. This evidence is arguably best obtained from randomised controlled trials. However, for many valid scientific and commercial reasons, it is not always possible for sponsors to conduct these trials. A lack of head-to-head data makes the reimbursor’s task considerably more difficult - is the new treatment sufficiently better than the currently standard to warrant health authority funding?

This is where indirect treatment comparisons play an important role. One extremely common, industry-wide technique used is Matching-Adjusted Indirect Comparison (MAIC). Simply stated, MAIC is a statistical approach where individual patient data from a sponsor trial is re-weighted, such that important baseline patient characteristics are aligned with those in the comparator trial. Results from weighted analysis of the clinical outcomes in the sponsor trial can then be more fairly compared with those published from the comparator trial.

Calculating what weight each patient should be assigned is a critical step in MAIC processes. Numerus is extremely proud and excited about our newly developed approach to this weight calculation step. We call our novel method polyMAIC.

The method offers great potential, with increased user flexibility and information retention. Most importantly, it will improve indirect treatment comparisons. Ultimately, it will lead to a greater probability of reimbursement success.

Access the paper here.

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