Network meta-analysis combines aggregate data (AgD) from multiple randomised controlled trials, assuming that any effect modifiers are balanced across populations. Individual patient data (IPD) meta-regression is the "gold standard" method to relax this assumption, however IPD are frequently only available in a subset of studies. Multilevel network meta-regression (ML-NMR) extends IPD meta-regression to incorporate AgD studies whilst avoiding aggregation bias, but currently requires the aggregate-level likelihood to have a known closed form. Notably, this prevents application to time-to-event outcomes. We extend ML-NMR to individual-level likelihoods of any form, by integrating the individual-level likelihood function over the AgD covariate distributions to obtain the respective marginal likelihood contributions. We illustrate with two examples of time-to-event outcomes, showing the performance of ML-NMR in a simulated comparison with little loss of precision from a full IPD analysis, and demonstrating flexible modelling of baseline hazards using cubic M-splines with synthetic data on newly diagnosed multiple myeloma. ML-NMR is a general method for synthesising individual and aggregate level data in networks of all sizes. Extension to general likelihoods, including for survival outcomes, greatly increases the applicability of the method. R and Stan code is provided, and the methods are implemented in the multinma R package.

翻译：网络meta分析整合多个随机对照试验的汇总数据，假设各人群疗效修饰因子均衡。个体患者数据meta回归是放宽该假设的"金标准"方法，但个体数据通常仅部分研究可获得。多水平网络meta回归在避免聚合偏倚的同时扩展了个体数据meta回归以纳入汇总数据研究，但当前要求汇总水平似然函数具有已知闭式解。这限制了该方法在时间-事件结局中的应用。本研究通过将个体水平似然函数对汇总数据协变量分布积分，获得相应的边际似然贡献，从而将多水平网络meta回归扩展至任意形式的个体水平似然函数。我们以两个时间-事件结局案例验证方法性能：通过模拟比较显示完整个体数据分析精度损失极小，并利用新诊断多发性骨髓瘤的合成数据展示基于三次M样条对基线风险函数的灵活建模。多水平网络meta回归可整合各类规模网络中的个体与汇总数据，扩展至包含生存结局的一般似然函数显著提升了方法适用性。本文提供R和Stan代码，相关方法已在multinma R包中实现。