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

<p dir="ltr"><b>These are peer-reviewed supplementary materials for the article '</b><b>Matching-adjusted indirect comparison via </b><b>a polynomial-based non-linear </b><b>optimization method</b><b>' published in the</b><b> </b><b><i>Journal of Comparative Effectiveness Research</i></b><b>.</b></p><ul><li><b>PolyMAIC SAS code for Scenario A (narrow tolerances)</b></li><li><b>PolyMAIC SAS code for Scenario D (wide tolerances)</b></li></ul><p dir="ltr"><b>Aim: </b>To demonstrate the potential of fourth-order polynomials within a non-linear optimization framework formatching-adjusted indirect comparison (MAIC). <b>Materials &methods:</b> 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). <b>Results: </b>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. <b>Conclusion: </b>PolyMAIC incorporates greater flexibility than the industry-standard MAIC approach and demonstrates matching potential.</p>