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.

本数据集为发表在《比较疗效研究杂志》上的文章《通过基于多项式的非线性优化方法进行匹配调整的间接比较》的同行评审补充材料。包含针对场景A（窄范围容忍度）的PolyMAIC SAS代码以及针对场景D（宽范围容忍度）的PolyMAIC SAS代码。目标：旨在展示在非线性优化框架内四阶多项式进行匹配调整的间接比较（MAIC）的潜力。材料与方法：通过四阶多项式（polyMAIC）对模拟的个体患者数据进行重新加权，以匹配多个基线特征层面的汇总数据。polyMAIC方法采用了预先指定的匹配容忍度和最大允许权重。评估了匹配性能对汇总层面的目标，并与当前行业标准MAIC方法（Signorovitch）进行了比较。结果：polyMAIC方法在预先指定的容忍度范围内匹配了汇总层面的目标。有效样本量与Signorovitch方法获得的样本量相似，或略高。随着匹配复杂性的增加，polyMAIC的性能提升趋势逐渐增强。结论：PolyMAIC方法相较于行业标准MAIC方法具有更高的灵活性，并展示了其匹配潜力。