Sharing information across patient subgroups to draw conclusions from sparse treatment networks

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Theodoros Evrenoglou, Silvia Metelli, Johannes-Schneider Thomas, Spyridon Siafis, Rebecca M. Turner, Stefan Leucht, Anna Chaimani
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Abstract

Network meta-analysis (NMA) usually provides estimates of the relative effects with the highest possible precision. However, sparse networks with few available studies and limited direct evidence can arise, threatening the robustness and reliability of NMA estimates. In these cases, the limited amount of available information can hamper the formal evaluation of the underlying NMA assumptions of transitivity and consistency. In addition, NMA estimates from sparse networks are expected to be imprecise and possibly biased as they rely on large-sample approximations that are invalid in the absence of sufficient data. We propose a Bayesian framework that allows sharing of information between two networks that pertain to different population subgroups. Specifically, we use the results from a subgroup with a lot of direct evidence (a dense network) to construct informative priors for the relative effects in the target subgroup (a sparse network). This is a two-stage approach where at the first stage, we extrapolate the results of the dense network to those expected from the sparse network. This takes place by using a modified hierarchical NMA model where we add a location parameter that shifts the distribution of the relative effects to make them applicable to the target population. At the second stage, these extrapolated results are used as prior information for the sparse network. We illustrate our approach through a motivating example of psychiatric patients. Our approach results in more precise and robust estimates of the relative effects and can adequately inform clinical practice in presence of sparse networks.

Abstract Image

跨患者亚群共享信息,从稀疏的治疗网络中得出结论
网络荟萃分析(NMA)通常能提供尽可能精确的相对效应估计值。然而,稀疏的网络中可用的研究很少,直接证据有限,这可能会威胁到 NMA 估计值的稳健性和可靠性。在这种情况下,有限的可用信息量可能会妨碍对 NMA 的基本假定--传递性和一致性--进行正式评估。此外,由于稀疏网络的近似值依赖于大样本近似值,而大样本近似值在缺乏足够数据的情况下是无效的,因此预计稀疏网络的 NMA 估计值不精确,并可能存在偏差。我们提出了一种贝叶斯框架,允许在两个涉及不同人群亚群的网络之间共享信息。具体来说,我们利用具有大量直接证据的子群(密集网络)的结果,为目标子群(稀疏网络)的相对效应构建信息先验。这是一种分两个阶段的方法,在第一阶段,我们将稠密网络的结果推断为稀疏网络的预期结果。这是通过使用改进的分层 NMA 模型来实现的,在该模型中,我们添加了一个位置参数,以改变相对效应的分布,使其适用于目标人群。在第二阶段,这些外推结果被用作稀疏网络的先验信息。我们以精神病患者为例说明我们的方法。我们的方法可以得到更精确、更稳健的相对效应估计值,并能在稀疏网络存在的情况下为临床实践提供充分的信息。
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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