Evrenoglou, Theodoros;
Metelli, Silvia;
Thomas, Johannes‐Schneider;
Siafis, Spyridon;
Turner, Rebecca M;
Leucht, Stefan;
Chaimani, Anna;
(2024)
Sharing information across patient subgroups to draw conclusions from sparse treatment networks.
Biometrical Journal
, 66
(3)
, Article 2200316. 10.1002/bimj.202200316.
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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.
| Type: | Article |
|---|---|
| Title: | Sharing information across patient subgroups to draw conclusions from sparse treatment networks |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/bimj.202200316 |
| Publisher version: | http://dx.doi.org/10.1002/bimj.202200316 |
| Language: | English |
| Additional information: | © 2024 The Authors. Biometrical Journal published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | informative priors, mixed treatment comparisons, sharing information, sparse data |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10191283 |
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