论文标题
基于模型的估计和证据合成的可运输能力
Transportability of model-based estimands in evidence synthesis
论文作者
论文摘要
在证据综合中，通常将效应修饰符描述为变量，通过在这种水平参数的结果模型中，通过治疗模型在单个水平上诱导治疗效应异质性。因此，根据有条件的措施定义了效应修改，但是卫生技术评估中人群级别的决策需要边际效应估计。对于不可碰撞的措施，即使在有个体水平的治疗效应均匀性的情况下，在个人水平上不是治疗反应的纯粹预后变量也可能会改变边际影响。对于异质性，无法直接折叠的措施的边际效应不能以边际协变量矩表达，并且通常取决于条件效应的关节分布测量修饰符和纯粹的预后变量。在证据综合方面，推荐实践有影响。在没有个体水平的治疗效果异质性的情况下，或者在跨研究之间平衡边际协变量时，未经调整的锚定间接比较可能会偏见。对于涉及纯粹的预后变量的联合协变量分布中的跨研究不平衡，可能是必要的协变量调整。在缺乏目标患者数据的情况下，协变量调整方法固有地限制了其消除不直接折叠措施的偏见的能力。直接可折叠的措施将促进研究之间的边际影响的运输能力：（1）降低对基于模型的协变量调整的依赖，而存在个人水平的治疗效果均匀性或边际协方差矩平衡； （2）促进基线协变量的选择，以进行调整，在有个体水平的治疗效应异质性。
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.
