In evidence synthesis, effect modifiers are typically described as variables that induce treatment effect heterogeneity at the individual level, through treatment-covariate interactions in an outcome model parametrized at such level. As such, effect modification is defined with respect to a conditional measure, but marginal effect estimates are required for population-level decisions in health technology assessment. For non-collapsible measures, purely prognostic variables that are not determinants of treatment response at the individual level may modify marginal effects, even where there is individual-level treatment effect homogeneity. With heterogeneity, marginal effects for measures that are not directly collapsible cannot be expressed in terms of marginal covariate moments, and generally depend on the joint distribution of conditional effect measure modifiers and purely prognostic variables. There are implications for recommended practices in evidence synthesis. Unadjusted anchored indirect comparisons can be biased in the absence of individual-level treatment effect heterogeneity, or when marginal covariate moments are balanced across studies. Covariate adjustment may be necessary to account for cross-study imbalances in joint covariate distributions involving purely prognostic variables. In the absence of individual patient data for the target, covariate adjustment approaches are inherently limited in their ability to remove bias for measures that are not directly collapsible. Directly collapsible measures would facilitate the transportability of marginal effects between studies by: (1) reducing dependence on model-based covariate adjustment where there is individual-level treatment effect homogeneity or marginal covariate moments are balanced; and (2) facilitating the selection of baseline covariates for adjustment where there is individual-level treatment effect heterogeneity.

翻译：在证据综合中，效应修饰因子通常被描述为通过个体水平结局模型中治疗-协变量交互作用，在个体水平上引发治疗效应异质性的变量。因此，效应修饰是针对条件性度量定义的，但卫生技术评估中群体层面的决策需要边际效应估计。对于非可折叠度量，那些在个体水平上并非治疗反应决定因素的纯预后变量，即使在存在个体水平治疗效应同质性的情况下，也可能修饰边际效应。在存在异质性时，非直接可折叠度量的边际效应无法用边际协变量矩表示，且通常依赖于条件性效应度量修饰因子与纯预后变量的联合分布。这对证据综合中的推荐实践具有重要影响：未经调整的锚定间接比较可能在缺乏个体水平治疗效应异质性时产生偏倚，或在研究间边际协变量矩平衡时仍存在偏倚。为处理涉及纯预后变量的联合协变量分布在研究间的不平衡，协变量调整可能是必要的。在缺乏目标人群个体患者数据的情况下，对于非直接可折叠度量，协变量调整方法在消除偏倚的能力上存在固有局限。直接可折叠度量将通过以下方式促进研究间边际效应的可迁移性：(1) 在个体水平治疗效应同质或边际协变量矩平衡时，减少对基于模型的协变量调整的依赖；(2) 在存在个体水平治疗效应异质性时，促进基线协变量调整的选择。