ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2026
Refined trial sequential analysis for meta-analyses with few studies.
Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Authors and funding
2 authors.
Funding
Abstract
Evidence syntheses are often updated as new trials become available. A cumulative meta-analysis repeats a meta-analysis in chronological order, and trial sequential analysis applies group-sequential principles to cumulative meta-analysis to control the risk of spurious findings due to repeated testing. Despite the growing popularity of trial sequential analysis, existing methods rely heavily on normal approximations derived from interim analyses of randomized controlled trials, where participants are typically more homogeneous than in meta-analyses. In random-effects meta-analyses, the conventional assumption that the synthesized effect estimate follows a normal distribution can perform poorly when the number of studies is small. In such settings, the Hartung-Knapp-Sidik-Jonkman method, which is based on a
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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.