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Indirect Comparison (MAIC) exist to adjust for these cross-trial differences, they are increasingly being superseded by regression-based marginalization methods. Historically, software implementations for these methods 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). outstandR implements advanced G-computation methods - within both maximum likelihood and Bayesian frameworks, and Multiple Imputation Marginalization (MIM) to address non-collapsibility. 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：摘要：在缺乏头对头临床试验的情况下，间接治疗比较(ITC)是卫生技术评估(HTA)中的关键工具。当需要将具有个体患者数据(IPD)的治疗方案与仅提供汇总水平数据(ALD)的竞争方案进行比较时，若试验人群在效应修饰因子方面存在差异，则会产生常见挑战。尽管已有诸如匹配调整间接比较(MAIC)等方法用于调整这些跨试验差异，但基于回归的边缘化方法正逐渐取代它们。既往这些方法的软件实现往往零散或范围有限。本文介绍R包outstandR，其旨在为人群调整间接比较(PAIC)提供全面统一框架。outstandR实现了先进G计算方法——涵盖最大似然框架与贝叶斯框架，以及针对非可折叠性问题的多重插补边缘化(MIM)。通过简化协变量模拟、模型标准化和对比估计的工作流，outstandR能在复杂决策场景中提供稳健且兼容的证据整合。