Precision medicine research often searches for treatment‐covariate interactions, which refers to when a treatment effect (eg, measured as a mean difference, odds ratio, hazard ratio) changes across values of a participant‐level covariate (eg, age, gender, biomarker). Single trials do not usually have sufficient power to detect genuine treatment‐covariate interactions, which motivate the sharing of individual participant data (IPD) from multiple trials for meta‐analysis. Here, we provide statistical recommendations for conducting and planning an IPD meta‐analysis of randomized trials to examine treatment‐covariate interactions. For conduct, two‐stage and one‐stage statistical models are described, and we recommend: (i) interactions should be estimated directly, and not by calculating differences in meta‐analysis results for subgroups; (ii) interaction estimates should be based solely on within‐study information; (iii) continuous covariates and outcomes should be analyzed on their continuous scale; (iv) nonlinear relationships should be examined for continuous covariates, using a multivariate meta‐analysis of the trend (eg, using restricted cubic spline functions); and (v) translation of interactions into clinical practice is nontrivial, requiring individualized treatment effect prediction. For planning, we describe first why the decision to initiate an IPD meta‐analysis project should not be based on between‐study heterogeneity in the overall treatment effect; and second, how to calculate the power of a potential IPD meta‐analysis project in advance of IPD collection, conditional on characteristics (eg, number of participants, standard deviation of covariates) of the trials (potentially) promising their IPD. Real IPD meta‐analysis projects are used for illustration throughout.

中文翻译：

---

个体参与者数据荟萃分析，以检查治疗效果和参与者水平协变量之间的相互作用：行为和计划的统计建议。

精准医学研究经常寻找治疗-协变量的相互作用，即治疗效果（例如，测量为平均差、优势比、风险比）在参与者水平协变量（例如，年龄、性别、生物标志物）的值之间发生变化时）。单一试验通常没有足够的能力来检测真正的治疗协变量相互作用，这激发了来自多个试验的个体参与者数据 (IPD) 的共享以进行荟萃分析。在这里，我们为进行和计划随机试验的 IPD 荟萃分析提供统计建议，以检查治疗协变量的相互作用。对于行为，描述了两阶段和一阶段统计模型，我们建议：（i）应直接估计相互作用，而不是通过计算亚组荟萃分析结果的差异；(ii) 交互作用估计应仅基于研究内部信息；(iii) 连续协变量和结果应在其连续尺度上进行分析；(iv) 应使用趋势的多元元分析（例如，使用受限三次样条函数）检查非线性关系的连续协变量；(v) 将相互作用转化为临床实践并非易事，需要个性化的治疗效果预测。对于计划，我们首先描述了为什么启动 IPD 荟萃分析项目的决定不应基于整体治疗效果的研究间异质性；其次，如何在 IPD 收集之前计算潜在 IPD 荟萃分析项目的功效，取决于特征（例如，参与者的数量，协变量的标准偏差）的试验（可能）承诺其 IPD。真实 IPD 荟萃分析项目始终用于说明。