ArticlePLoS biology2021
Leveraging vibration of effects analysis for robust discovery in observational biomedical data science.
Article in PLoS biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis 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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Grilling the data: application of specification curve analysis to red meat and all-cause mortality.Journal of clinical epidemiology · 2024Pooled it
- Diverse high-fat diets drive multi-omic reprogramming that persists after dietary reversal.bioRxiv : the preprint server for biology · 2026Article
- An atlas of exposome-phenome associations in health and disease risk.Nature medicine · 2026Article
- "Shaking the ladder" reveals how analytic choices can influence associations in nutrition epidemiology: beef intake and coronary heart disease as a case study.Critical reviews in food science and nutrition · 2026Review
- Evidence triangulation in health research.European journal of epidemiology · 2025Article
- Uncertainty in the estimated effects of statin initiation on risk of dementia: using a multiverse analysis to assess sources of variability.European journal of epidemiology · 2025Article
- What is the vibration of effects?BMJ evidence-based medicine · 2025Article
- Heterogeneity Analysis of Associations Involving the Large-Scale Online MindCrowd Survey Memory Test.Gerontology · 2025Article
- A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies.Genome biology · 2024Article
- Prioritization of COVID-19 risk factors in July 2020 and February 2021 in the UK.Communications medicine · 2023Article
- Dynamic effects of bilingualism on brain structure map onto general principles of experience-based neuroplasticity.Scientific reports · 2023Article
- Systematically assessing microbiome-disease associations identifies drivers of inconsistency in metagenomic research.PLoS biology · 2022Article
- The first six years of meta-research at PLOS Biology.PLoS biology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Hypothesis generation in observational, biomedical data science often starts with computing an association or identifying the statistical relationship between a dependent and an independent variable. However, the outcome of this process depends fundamentally on modeling strategy, with differing strategies generating what can be called "vibration of effects" (VoE). VoE is defined by variation in associations that often lead to contradictory results. Here, we present a computational tool capable of modeling VoE in biomedical data by fitting millions of different models and comparing their output. We execute a VoE analysis on a series of widely reported associations (e.g., carrot intake associated with eyesight) with an extended additional focus on lifestyle exposures (e.g., physical activity) and components of the Framingham Risk Score for cardiovascular health (e.g., blood pressure). We leveraged our tool for potential confounder identification, investigating what adjusting variables are responsible for conflicting models. We propose modeling VoE as a critical step in navigating discovery in observational data, discerning robust associations, and cataloging adjusting variables that impact model output.
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What Socratic 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 Socratic graph.