ArticleResearch synthesis methods2018
A framework for identifying treatment-covariate interactions in individual participant data network meta-analysis.
Article in Research synthesis methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 3 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- Pooled it
- Individual participant data meta-analysis of intervention studies with time-to-event outcomes: A review of the methodology and an applied example.Research synthesis methods · 2020Pooled it
- Prostate Radiotherapy for Metastatic Hormone-sensitive Prostate Cancer: A STOPCAP Systematic Review and Meta-analysis.European urology · 2019Pooled it
- Estimating additive interaction in 2-stage individual participant data meta-analysis.American journal of epidemiology · 2025Article
- Challenges of modelling approaches for network meta-analysis of time-to-event outcomes in the presence of non-proportional hazards to aid decision making: Application to a melanoma network.Statistical methods in medical research · 2022Article
- Individual Patient Data Meta-Analysis and Network Meta-Analysis.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Statistical analyses and quality of individual participant data network meta-analyses were suboptimal: a cross-sectional study.BMC medicine · 2020Article
- A framework for identifying treatment-covariate interactions in individual participant data network meta-analysis.Research synthesis methods · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundStratified medicine seeks to identify patients most likely to respond to treatment. Individual participant data (IPD) network meta-analysis (NMA) models have greater power than individual trials to identify treatment-covariate interactions (TCIs). Treatment-covariate interactions contain "within" and "across" trial interactions, where the across-trial interaction is more susceptible to confounding and ecological bias.
methodsWe considered a network of IPD from 37 trials (5922 patients) for cervical cancer (2394 events), where previous research identified disease stage as a potential interaction covariate. We compare 2 models for NMA with TCIs: (1) 2 effects separating within- and across-trial interactions and (2) a single effect combining within- and across-trial interactions. We argue for a visual assessment of consistency of within- and across-trial interactions and consider more detailed aspects of interaction modelling, eg, common vs trial-specific effects of the covariate. This leads us to propose a practical framework for IPD NMA with TCIs.
resultsFollowing our framework, we found no evidence in the cervical cancer network for a treatment-stage interaction on the basis of the within-trial interaction. The NMA provided additional power for an across-trial interaction over and above the pairwise evidence. Following our proposed framework, we found that the within- and across-trial interactions should not be combined.
conclusionAcross-trial interactions are susceptible to confounding and ecological bias. It is important to separate the sources of evidence to check their consistency and identify which sources of evidence are driving the conclusion. Our framework provides practical guidance for researchers, reducing the risk of unduly optimistic interpretation of TCIs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.