Population-adjusted indirect comparisons (PAICs) are widely used to synthesize evidence when randomized controlled trials enroll different patient populations and head-to-head comparisons are unavailable. Although PAICs adjust for observed population differences across trials, adjustment alone does not ensure transportability of estimated effects to decision-relevant populations for health technology assessment (HTA). We examine and formalize transportability in PAICs from an estimand-based perspective. We distinguish conditional and marginal treatment effect estimands and show how transportability depends on effect modification, collapsibility, and alignment between the scale of effect modification and the effect measure. Using illustrative examples, we demonstrate that even when effect modifiers are shared across treatments, marginal effects are generally population-dependent for commonly used non-collapsible measures, including hazard ratios and odds ratios. Conversely, collapsible and conditional effects defined on the linear predictor scale exhibit more favorable transportability properties. We further show that pairwise PAIC approaches typically identify effects defined in the comparator population and that applying these estimates to other populations entails an additional, often implicit, transport step requiring further assumptions. This has direct implications for HTA, where PAIC-derived effects are routinely applied within cost-effectiveness and decision models defined for different target populations. Our results clarify when applying PAIC-derived treatment effects to desired target populations is justified, when doing so requires additional assumptions, and when results should instead be interpreted as population-specific rather than decision-relevant, supporting more transparent and principled use of indirect evidence in HTA and related decision-making contexts.

翻译：人群调整间接比较（PAICs）在随机对照试验纳入不同患者群体且缺乏头对头比较时，被广泛用于证据合成。尽管PAICs针对试验间观察到的人群差异进行了调整，但仅凭调整并不能确保估计效应可移植至卫生技术评估（HTA）决策相关人群。我们从基于估计量的视角审视并形式化PAICs中的可移植性问题。我们区分了条件与边际处理效应估计量，并阐明可移植性如何取决于效应修正、可折叠性以及效应修正尺度与效应度量之间的对齐关系。通过示例分析，我们证明即使效应修正因子在治疗间共享，对于常用的非可折叠度量（包括风险比和比值比），边际效应通常也依赖于特定人群。相反，在线性预测尺度上定义的可折叠条件效应则展现出更优的可移植特性。我们进一步指出，成对PAIC方法通常识别的是在对照人群中定义的效应，将这些估计值应用于其他人群时，涉及一个额外的、通常隐含的可移植步骤，且需要进一步假设。这对HTA具有直接意义，因为PAIC衍生的效应常被应用于针对不同目标人群定义的成本效益与决策模型中。我们的研究结果明确了以下情况：何时将PAIC衍生的处理效应应用于期望目标人群是合理的，何时这样做需要额外假设，以及何时结果应被解释为人群特异性而非决策相关性，从而支持在HTA及相关决策背景下更透明、更有原则地使用间接证据。