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，效应修正因子通常被描述为通过个体水平参数化的结果模型中的治疗-协变量交互作用，在个体水平上诱发治疗效果异质性的变量。因此，效应修正是针对条件测量定义的，但在卫生技术评估中，人群层面的决策需要边际效应估计。对于非可折叠测量，即使存在个体水平的治疗效果同质性，纯粹预后变量（即非个体水平治疗反应决定因素）也可能改变边际效应。存在异质性时，对于非直接可折叠的测量，其边际效应无法用边际协变量矩表达，通常取决于条件效应测量修正因子与纯粹预后变量的联合分布。这对证据综合的推荐实践具有启示：未经调整的锚定间接比较在缺乏个体水平治疗效果异质性时，或当研究间边际协变量矩平衡时可能产生偏倚。当涉及纯粹预后变量的研究间协变量联合分布不平衡时，可能需要协变量调整。在缺乏目标人群个体患者数据的情况下，协变量调整方法在消除非直接可折叠测量的偏倚方面存在固有局限性。直接可折叠测量可通过以下两种方式促进研究间边际效应的可迁移性：(1) 在存在个体水平治疗效果同质性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果异质性时，促进基线协变量的选择调整。