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Naive pooling of RCTs and non-randomized studies in network meta-analyses is common and problematicImproving the Quality of Evidence Used in Medical Network Meta-Analyses

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Key Takeaway
Watch for naive pooling of RCTs and non-randomized studies in network meta-analyses; results pending.

This is a protocol for a meta-epidemiological study that will investigate the prevalence and impact of naively pooling randomized controlled trials (RCTs) and non-randomized studies of interventions (NRSIs) in the main analysis of published network meta-analyses (NMAs). The planned study will include NMAs that contain both RCTs and at least one comparative NRSI. The primary outcome is the proportion of NMAs that naively pool design-dissimilar evidence without using quantitative methods to address design-related bias.

Secondary outcomes include trends over time, characteristics associated with naive pooling, reporting completeness, protocol availability, and prespecification of design-handling methods. The study will also examine empirical changes in effect direction, null-value crossing, statistical significance, and treatment ranking when comparing mixed-design results to RCT-restricted or design-aware results.

As this is a protocol, no results are yet available. The authors note that the findings may inform methodological training, journal peer review, and evidence appraisal in guideline development. The study has not been conducted, so no conclusions can be drawn at this time.

Medical experts often use a method called network meta-analysis to compare many treatments at once. This process helps doctors decide which medicine works best for patients. However, these analyses sometimes combine two very different types of studies into one calculation.

One type is a high-quality randomized trial, while the other is a non-randomized study. When researchers mix these together without using special math to correct for the differences, it is called naive pooling. This can make the results less reliable and harder to trust when making medical decisions.

This upcoming project will look at how often this happens in published papers. The team will check if authors are following proper rules and reporting their methods clearly. They also want to see if the final rankings of treatments change when the data is handled correctly.

The goal is to help improve how journals review papers and how experts create medical guidelines. By identifying these issues, they can ensure that doctors have the most accurate information possible when choosing treatments for their patients.

What this means for you:
This planned study aims to improve the accuracy of medical data by checking if different study types are mixed correctly.

Common questions

What is naïve pooling in network meta-analysis?

Naïve pooling is when researchers combine results from randomized controlled trials and non-randomized studies in the same analysis without using statistical methods to account for differences in study design. This can lead to biased or misleading conclusions.

Why is this protocol important?

This protocol outlines a future study that will measure how common naïve pooling is in published network meta-analyses. The findings could help improve how evidence is combined in medical research, leading to more reliable results for treatments.

Are there any results from this study yet?

No, this is only a protocol. The study has not been conducted, so no results are available. The protocol describes what the researchers plan to do in the future.

Study Details

Study typeMeta analysis
EvidenceLevel 1
PublishedJun 2026
View Original Abstract ↓
BackgroundNetwork meta-analysis (NMA) can synthesize direct and indirect evidence across multiple interventions and is increasingly used to inform comparative-effectiveness decisions. When randomized controlled trials (RCTs) and non-randomized studies of interventions (NRSI) are jointly analyzed, naïve pooling of design-dissimilar evidence without design-aware modelling may violate exchangeability and distort treatment effect estimates and rankings.Methods and analysisWe will conduct a meta-epidemiological study of published clinical NMAs identified through systematic searches of Ovid MEDLINE(R) ALL, Embase via Ovid, EBM Reviews-Cochrane Database of Systematic Reviews via Ovid, and Web of Science Core Collection. Eligible reports will be full-text articles published between 1 January 2020 and 31 December 2025 that included both RCTs and at least one comparative NRSI in the review evidence base. The primary outcome will be the prevalence of naïve pooling in the main analysis. We will apply a prespecified four-category decision framework to distinguish unacknowledged naïve pooling, acknowledged but unmitigated naïve pooling, naïve main analysis with post hoc quantitative mitigation, and no naïve pooling in the main analysis. We will estimate prevalence with exact binomial 95% confidence intervals, assess whether naïve pooling has become more or less common over time, explore characteristics associated with naïve pooling or with not using quantitative methods to address design-related bias, summarize reporting completeness, protocol or registration availability, prespecification of design-handling methods, and availability of extractable paired alternative results, and evaluate empirical changes in effect direction, null-value crossing or statistical significance, and treatment ranking in articles reporting paired mixed-design and RCT-restricted or design-aware results.DiscussionThis study will provide contemporary evidence on how published NMAs handle mixed randomized and non-randomized evidence, distinguish lack of risk recognition from failure to implement corrective analyses, and quantify how often naïve pooling may change conclusions. The protocol includes prespecified procedures to describe reporting completeness and to compare articles with and without extractable paired alternative analyses. The findings may inform methodological training, journal peer review, and evidence appraisal in guideline development.Systematic review registrationhttps://doi.org/10.17605/OSF.IO/AM2T6, Unique Identifier: 10.17605/OSF.IO/AM2T6.
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