Indirect treatment comparisons (ITCs) are essential in Health Technology Assessment (HTA) when head-to-head clinical trials are absent. A common challenge arises when attempting to compare a treatment with available individual patient data (IPD) against a competitor with only reported aggregate-level data (ALD), particularly when trial populations differ in effect modifiers. While methods such as Matching-Adjusted Indirect Comparison (MAIC) and Simulated Treatment Comparison (STC) exist to adjust for these cross-trial differences, software implementations have often been fragmented or limited in scope. This article introduces outstandR, an R package designed to provide a comprehensive and unified framework for population-adjusted indirect comparison (PAIC). Beyond standard weighting and regression approaches, outstandR implements advanced G-computation methods within both maximum likelihood and Bayesian frameworks, and Multiple Imputation Marginalization (MIM) to address non-collapsibility and missing data. By streamlining the workflow of covariate simulation, model standardization, and contrast estimation, outstandR enables robust and compatible evidence synthesis in complex decision-making scenarios.

翻译：间接治疗比较（ITC）在缺乏头对头临床试验的健康技术评估（HTA）中至关重要。当试图将拥有可用个体患者数据（IPD）的治疗与仅报告汇总水平数据（ALD）的竞争者进行比较时，尤其是在试验人群的效应修饰因素存在差异的情况下，常会出现挑战。虽然存在如匹配调整间接比较（MAIC）和模拟治疗比较（STC）等方法来调整这些跨试验差异，但软件实现往往分散或功能有限。本文介绍了outstandR，这是一个R语言包，旨在为基于人群调整的间接比较（PAIC）提供一个全面统一的框架。除了标准的加权和回归方法外，outstandR还在最大似然和贝叶斯框架内实现了高级的G-计算方法，以及多重插补边缘化（MIM）以处理非可折叠性和缺失数据。通过简化协变量模拟、模型标准化和对比估计的工作流程，outstandR能够在复杂的决策场景中实现稳健且可比的证据综合。