ArticleJournal of global health2026
African network for statistical excellence in health research.
Article in Journal of global health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite increasing research output across Africa, weaknesses in statistical methodology continue to compromise the quality, reproducibility, and practical value of health research. Although substantial investments have been made in research capacity strengthening, limitations in statistical training and access to biostatistical expertise remain widespread. Existing initiatives, including regional postgraduate programmes and institution-specific training schemes, have contributed to advances in biostatistical capacity but remain fragmented, geographically limited, and insufficiently integrated into routine research practice. In this viewpoint, we argue that strengthening statistical capacity in Africa now requires a coordinated, continent-wide training network embedded within existing academic and research systems, rather than continued reliance on isolated programmes. The proposed model emphasises scalable training infrastructure, integration of statistical expertise throughout the research process, and structured collaboration between institutions facing similar methodological challenges. Central components include accessible training resources, mentorship, incorporation of statistics into existing curricula, and practical support linked to ongoing research activities. Emphasis is placed on standardising core competencies in statistical practice, including appropriate method selection, assessment of assumptions, transparent reporting, and accurate interpretation of findings. By embedding statistical thinking from study design through publication, the proposed network aims to improve methodological consistency, strengthen research credibility, and enhance the global impact and usability of health research conducted across Africa.
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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.