Supplementary materials: Matching-adjusted indirect comparison via a polynomial-based non-linear optimization method

These are peer-reviewed supplementary materials for the article 'Matching-adjusted indirect comparison via a polynomial-based non-linear optimization method' published in the Journal of Comparative Effectiveness Research.
PolyMAIC SAS code for Scenario A (narrow tolerances)PolyMAIC SAS code for Scenario D (wide tolerances)Aim: To demonstrate the potential of fourth-order polynomials within a non-linear optimization framework formatching-adjusted indirect comparison (MAIC). Materials &methods: Simulated individual patient data were reweighted via fourth-order polynomials (polyMAIC) to match aggregate-level data across multiple baseline characteristics. The polyMAIC approach employed pre-specified matching tolerances and maximum allowable weights. Matching performance against aggregate-level targets was assessed, and also compared against the current industry-standard MAIC approach (Signorovitch). Results: The polyMAIC method matched aggregate-level targets within pre-specified tolerances. Effective sample sizes were either similar to or somewhat higher than those obtained from the Signorovitch method. Performance gains from polyMAIC tended to increase as matching complexity increased. Conclusion: PolyMAIC incorporates greater flexibility than the industry-standard MAIC approach and demonstrates matching potential.