Individualized therapies for patients with biomarkers are moving more and more into the focus of research interest when developing new treatments. Hereby, the term individualized (or targeted) therapy denotes a treatment specifically developed for biomarker-positive patients. A network meta-analysis model for a binary endpoint combining the evidence for a targeted therapy from individual patient data with the evidence for a non-targeted therapy from aggregate data is presented and investigated. The biomarker status of the patients is either available at patient-level in individual patient data or at study-level in aggregate data. Both types of biomarker information have to be included. The evidence synthesis model follows a Bayesian approach and applies a meta-regression to the studies with aggregate data. In a simulation study, we address three treatment arms, one of them investigating a targeted therapy. The bias and the root-mean-square error of the treatment effect estimate for the subgroup of biomarker-positive patients based on studies with aggregate data are investigated. Thereby, the meta-regression approach is compared to approaches applying alternative solutions. The regression approach has a surprisingly small bias even in the presence of few studies. By contrast, the root-mean-square error is relatively greater. An illustrative example is provided demonstrating implementation of the presented network meta-analysis model in a clinical setting.

中文翻译：

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网络荟萃分析中靶向和非靶向治疗的综合评价

在开发新疗法时，针对具有生物标志物的患者的个性化疗法越来越成为研究兴趣的焦点。因此，术语个体化（或靶向）治疗表示专门为生物标志物阳性患者开发的治疗。提出并研究了二元终点的网络元分析模型，该模型将来自个体患者数据的靶向治疗证据与来自汇总数据的非靶向治疗证据相结合。患者的生物标志物状态可在个体患者数据中的患者级别或汇总数据中的研究级别获得。两种类型的生物标志物信息都必须包括在内。证据综合模型遵循贝叶斯方法，并将元回归应用于具有汇总数据的研究。在模拟研究中，我们介绍了三个治疗组，其中一个研究了靶向治疗。研究了基于汇总数据研究的生物标志物阳性患者亚组的治疗效果估计的偏差和均方根误差。因此，将元回归方法与应用替代解决方案的方法进行比较。即使在存在少数研究的情况下，回归方法也具有令人惊讶的小偏差。相比之下，均方根误差相对较大。提供了一个说明性示例，演示了所呈现的网络元分析模型在临床环境中的实现。研究了基于汇总数据研究的生物标志物阳性患者亚组的治疗效果估计的偏差和均方根误差。因此，将元回归方法与应用替代解决方案的方法进行比较。即使在存在少数研究的情况下，回归方法也具有令人惊讶的小偏差。相比之下，均方根误差相对较大。提供了一个说明性示例，演示了所呈现的网络元分析模型在临床环境中的实现。研究了基于汇总数据研究的生物标志物阳性患者亚组的治疗效果估计的偏差和均方根误差。因此，将元回归方法与应用替代解决方案的方法进行比较。即使在存在少数研究的情况下，回归方法也具有令人惊讶的小偏差。相比之下，均方根误差相对较大。提供了一个说明性示例，演示了所呈现的网络元分析模型在临床环境中的实现。