Some patients benefit from a treatment while others may do so less or do not benefit at all. We have previously developed a two-stage network meta-regression prediction model that synthesized randomized trials and evaluates how treatment effects vary across patient characteristics. In this article, we extended this model to combine different sources of types in different formats: aggregate data (AD) and individual participant data (IPD) from randomized and non-randomized evidence. In the first stage, a prognostic model is developed to predict the baseline risk of the outcome using a large cohort study. In the second stage, we recalibrated this prognostic model to improve our predictions for patients enrolled in randomized trials. In the third stage, we used the baseline risk as effect modifier in a network meta-regression model combining AD, IPD RCTs to estimate heterogeneous treatment effects. We illustrated the approach in the re-analysis of a network of studies comparing three drugs for relapsing-remitting multiple sclerosis. Several patient characteristics influence the baseline risk of relapse, which in turn modifies the effect of the drugs. The proposed model makes personalized predictions for health outcomes under several treatment options and encompasses all relevant randomized and non-randomized evidence.

翻译：部分患者能从治疗中获益，而另一些患者获益较少或完全无获益。我们此前开发了一个两阶段网络荟萃回归预测模型，该模型整合随机试验并评估治疗效果如何随患者特征变化。在本文中，我们将此模型扩展，以结合不同格式和来源的数据：来自随机和非随机证据的汇总数据（AD）及个体参与者数据（IPD）。第一阶段，利用大样本队列研究建立预后模型以预测结局的基线风险；第二阶段，重新校准该预后模型以改善对随机试验入组患者的预测；第三阶段，将基线风险作为效应修饰因子纳入结合AD与IPD随机对照试验的网络荟萃回归模型，用于估计异质性治疗效果。我们通过重新分析一项比较三种药物治疗复发-缓解型多发性硬化症的研究网络来展示该方法。多项患者特征影响复发的基线风险，进而改变药物的疗效。该模型能在多种治疗方案下对健康结局进行个性化预测，并涵盖所有相关随机与非随机证据。