National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK.
Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.
Res Synth Methods. 2024 Nov;15(6):1001-1016. doi: 10.1002/jrsm.1750. Epub 2024 Sep 16.
Individual participant data (IPD) meta-analysis projects obtain, harmonise, and synthesise original data from multiple studies. Many IPD meta-analyses of randomised trials are initiated to identify treatment effect modifiers at the individual level, thus requiring statistical modelling of interactions between treatment effect and participant-level covariates. Using a two-stage approach, the interaction is estimated in each trial separately and combined in a meta-analysis. In practice, two complications often arise with continuous outcomes: examining non-linear relationships for continuous covariates and dealing with multiple time-points. We propose a two-stage multivariate IPD meta-analysis approach that summarises non-linear treatment-covariate interaction functions at multiple time-points for continuous outcomes. A set-up phase is required to identify a small set of time-points; relevant knot positions for a spline function, at identical locations in each trial; and a common reference group for each covariate. Crucially, the multivariate approach can include participants or trials with missing outcomes at some time-points. In the first stage, restricted cubic spline functions are fitted and their interaction with each discrete time-point is estimated in each trial separately. In the second stage, the parameter estimates defining these multiple interaction functions are jointly synthesised in a multivariate random-effects meta-analysis model accounting for within-trial and across-trial correlation. These meta-analysis estimates define the summary non-linear interactions at each time-point, which can be displayed graphically alongside confidence intervals. The approach is illustrated using an IPD meta-analysis examining effect modifiers for exercise interventions in osteoarthritis, which shows evidence of non-linear relationships and small gains in precision by analysing all time-points jointly.
个体参与者数据（IPD）荟萃分析项目从多项研究中获取、协调和综合原始数据。许多随机试验的 IPD 荟萃分析旨在确定个体水平的治疗效果调节剂，因此需要对治疗效果与参与者水平协变量之间的相互作用进行统计建模。使用两阶段方法，在每个试验中分别估计相互作用，并在荟萃分析中进行组合。在实践中，对于连续结果通常会出现两个并发症：检查连续协变量的非线性关系和处理多个时间点。我们提出了一种两阶段多变量 IPD 荟萃分析方法，用于汇总连续结果的多个时间点的非线性治疗-协变量相互作用函数。需要一个设置阶段来识别一小部分时间点；在每个试验中相同位置的样条函数的相关结位置；以及每个协变量的共同参考组。至关重要的是，多变量方法可以包括在某些时间点缺失结果的参与者或试验。在第一阶段，拟合受限立方样条函数，并分别在每个试验中单独估计它们与每个离散时间点的相互作用。在第二阶段，在考虑试验内和试验间相关性的多变量随机效应荟萃分析模型中联合综合这些定义多个相互作用函数的参数估计值。这些荟萃分析估计值定义了每个时间点的总结非线性相互作用，可以与置信区间一起以图形方式显示。该方法通过对所有时间点进行联合分析，使用一项检查骨关节炎运动干预效果调节剂的 IPD 荟萃分析进行说明，该分析显示了非线性关系和通过联合分析获得的精度提高的证据。
