标题： 评估协变量分布和阳性违反对基于权重的间接比较的影响：一项模拟研究 显示英文标题

标题： Assessing the Impact of Covariate Distribution and Positivity Violation on Weighting-Based Indirect Comparisons: a Simulation Study

摘要： 人口调整的间接比较（PAICs）用于在直接比较不可行且仅有一个试验有个体患者数据（IPD）时估计治疗效果。 在PAIC方法中，匹配调整的间接比较（MAIC）是最广泛使用的方法。 然而，关于MAIC在诸如有限协变量重叠或显著非正态协变量分布等挑战性条件下的表现知之甚少。 我们进行了一项蒙特卡洛模拟研究，比较了三种估计器：(i) 首阶矩匹配的MAIC（MAIC-1），(ii) 首阶和二阶矩匹配的MAIC（MAIC-2），以及(iii) 利用完整IPD的基准方法——倾向评分加权（PSW）。 我们检查了从理想条件到存在可识别性违反和非正态（包括双峰）协变量分布的情况。 我们评估了锚定和未锚定估计器，并检查了调整模型误指的影响。 我们还将这些估计器应用于来自AKIKI和AKIKI-2试验的真实世界数据，比较危重患者中的肾脏替代治疗策略。 MAIC-1表现出稳健的性能，在存在中度可识别性违反和非正态协变量的情况下仍保持无偏，而MAIC-2和PSW对可识别性违反更为敏感。 当关键混杂因素被遗漏时，所有方法都显示出显著偏差，强调了正确模型规范的重要性。 在真实世界数据中，发现了一致趋势，MAIC-1在可识别性违反情况下显示更窄的置信区间。 我们的研究结果支持谨慎使用未锚定的MAIC，并突出了MAIC-1在中度假设违反情况下的适应能力。 然而，该方法的有限灵活性强调了在真实世界环境中需要谨慎使用。 显示英文摘要

摘要： Population-Adjusted Indirect Comparisons (PAICs) are used to estimate treatment effects when direct comparisons are infeasible and individual patient data (IPD) are only available for one trial. Among PAIC methods, Matching-Adjusted Indirect Comparison (MAIC) is the most widely used. However, little is known about how MAIC performs under challenging conditions such as limited covariate overlap or markedly non-normal covariate distributions. We conducted a Monte Carlo simulation study comparing three estimators: (i) MAIC matching first moment (MAIC-1), (ii) MAIC matching first and second moments (MAIC-2), and (iii) a benchmark method leveraging full IPD -- Propensity Score Weighting (PSW). We examined eight scenarios ranging from ideal conditions to situations with positivity violations and non-normal (including bimodal) covariate distributions. We assessed both anchored and unanchored estimators and examined the impact of adjustment model misspecification. We also applied these estimators to real-world data from the AKIKI and AKIKI-2 trials, comparing renal replacement therapy strategies in critically ill patients. MAIC-1 demonstrated robust performance, remaining unbiased in the presence of moderate positivity violations and non-normal covariates, while MAIC-2 and PSW appeared more sensitive to positivity violations. All methods showed substantial bias when key confounders were omitted, emphasizing the importance of correct model specification. In real-world data, a consistent trend was found with MAIC-1 showing narrower confidence intervals with positivity violation. Our findings support the cautious use of unanchored MAICs and highlight MAIC-1's resilience across moderate violations of assumptions. However, the method's limited flexibility underscores the need for careful use in real-world settings.