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:一些病人从治疗中受益,而另一些病人则可能从治疗中受益较少,或根本没有受益。我们以前已经开发了一个两阶段网络元递减预测模型,该模型综合随机试验,并评估治疗效应在病人特征方面的不同差异。在本条中,我们扩展了这一模型,将不同类型来源以不同格式结合起来:来自随机和非随机证据的综合数据(AD)和个人参与者数据(IPD);在第一阶段,开发了预测模型,利用大型组群研究预测结果的基线风险。在第二阶段,我们调整了这一预测模型,以改进我们对随机试验所登记的病人的预测。在第三阶段,我们使用基线风险作为网络元反转基因模型中的效果改变剂,将AD、IDD RCTs合并为混合治疗效应的估计。我们在重新分析比较三种药物以重新复制释放多重硬性硬性病的研究网络中说明了方法。几个病人特征影响了复发的基线风险,这反过来改变了药物的效果。在第三阶段,我们使用这一基线风险作为网络元风险的改变作用。在网络中,我们使用将基准风险作为影响所有非随机性健康预测方法。拟议模型在相关的治疗中包括了各种个人证据。</s>