ArticleCancer informatics2015
Publication Bias in Methodological Computational Research.
Article in Cancer informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 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
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Wearable Blood Pressure Monitoring Devices: Understanding Heterogeneity in Design and Evaluation.IEEE transactions on bio-medical engineering · 2024Pooled it
- Wide range of applications for machine-learning prediction models in orthopedic surgical outcome: a systematic review.Acta orthopaedica · 2021Pooled it
- Phases of methodological research in biostatistics-Building the evidence base for new methods.Biometrical journal. Biometrische Zeitschrift · 2024Article
- Sustained software development, not number of citations or journal choice, is indicative of accurate bioinformatic software.Genome biology · 2022Article
- On the optimistic performance evaluation of newly introduced bioinformatic methods.Genome biology · 2021Article
- A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets.Briefings in bioinformatics · 2019Review
- Towards evidence-based computational statistics: lessons from clinical research on the role and design of real-data benchmark studies.BMC medical research methodology · 2017Article
- The Relationship of Industry Payments to Prescribing Behavior: A Study of Degarelix and Denosumab.Urology practice · 2017Article
- Strong Inference in Mathematical Modeling: A Method for Robust Science in the Twenty-First Century.Frontiers in microbiology · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The problem of publication bias has long been discussed in research fields such as medicine. There is a consensus that publication bias is a reality and that solutions should be found to reduce it. In methodological computational research, including cancer informatics, publication bias may also be at work. The publication of negative research findings is certainly also a relevant issue, but has attracted very little attention to date. The present paper aims at providing a new formal framework to describe the notion of publication bias in the context of methodological computational research, facilitate and stimulate discussions on this topic, and increase awareness in the scientific community. We report an exemplary pilot study that aims at gaining experiences with the collection and analysis of information on unpublished research efforts with respect to publication bias, and we outline the encountered problems. Based on these experiences, we try to formalize the notion of publication bias.
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.