Abstract：
Individual participant data (IPD) meta-analyses of randomised trials are considered a reliable way to assess participant-level treatment effect modifiers but may not make the best use of the available data. Traditionally, effect modifiers are explored one covariate at a time, which gives rise to the possibility that evidence of treatment-covariate interaction may be due to confounding from a different, related covariate. We aimed to evaluate current practice when estimating treatment-covariate interactions in IPD meta-analysis, specifically focusing on involvement of additional covariates in the models. We reviewed 100 IPD meta-analyses of randomised trials, published between 2015 and 2020, that assessed at least one treatment-covariate interaction. We identified four approaches to handling additional covariates: (1) Single interaction model (unadjusted): No additional covariates included (57/100 IPD meta-analyses); (2) Single interaction model (adjusted): Adjustment for the main effect of at least one additional covariate (35/100); (3) Multiple interactions model: Adjustment for at least one two-way interaction between treatment and an additional covariate (3/100); and (4) Three-way interaction model: Three-way interaction formed between treatment, the additional covariate and the potential effect modifier (5/100). IPD is not being utilised to its fullest extent. In an exemplar dataset, we demonstrate how these approaches lead to different conclusions. Researchers should adjust for additional covariates when estimating interactions in IPD meta-analysis providing they adjust their main effects, which is already widely recommended. Further, they should consider whether more complex approaches could provide better information on who might benefit most from treatments, improving patient choice and treatment policy and practice.
摘要：
随机试验的个体参与者数据（IPD）荟萃分析被认为是评估参与者水平治疗效果调节剂的可靠方法，但可能无法充分利用现有数据。传统上,一次探索一个协变量的效果修饰符，这导致了治疗-协变量相互作用的证据可能是由于来自不同的混淆，相关协变量。我们旨在评估IPD荟萃分析中估计治疗-协变量相互作用时的当前实践，特别关注模型中其他协变量的参与。我们回顾了100个随机试验的IPD荟萃分析，发表于2015年至2020年，评估了至少一种治疗-协变量相互作用。我们确定了四种处理其他协变量的方法：（1）单相互作用模型（未调整）：不包括其他协变量（57/100IPD荟萃分析）；（2）单相互作用模型（调整）：调整至少一个附加协变量的主要作用（35/100）；（3）多相互作用模型：调整治疗和附加协变量之间的至少一个双向相互作用（3/100）之间的相互作用（3-4）附加协变量和潜在效应修饰符(5/100)。IPD没有得到最大程度的利用。在示例数据集中，我们演示了这些方法如何导致不同的结论。研究人员在评估IPD荟萃分析中的相互作用时，应调整其他协变量，前提是他们调整其主要效应，这已经被广泛推荐。Further,他们应该考虑更复杂的方法是否可以提供更好的信息，说明谁可能从治疗中受益最大，改善患者选择和治疗政策和实践。
