ArticleHuman mutation2012
Using PhenX measures to identify opportunities for cross-study analysis.
Article in Human mutation, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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
20 citing papers in PubMed.
- Promoting equity in polygenic risk assessment through global collaboration.Nature genetics · 2024Review
- Advancing microbiome research through standardized data and metadata collection: introducing the Microbiome Research Data Toolkit.Database : the journal of biological databases and curation · 2024Article
- Developing Clinical Phenotype Data Collection Standards for Research in Africa.Global health, epidemiology and genomics · 2023Article
- Correlation Analysis of Variables From the Atherosclerosis Risk in Communities Study.Frontiers in pharmacology · 2022Article
- Cognitive Impairment in Non-critical, Mild-to-Moderate COVID-19 Survivors.Frontiers in psychology · 2022Article
- The Minimal Clinical Important Difference (MCID) in Annual Rate of Change of Timed Function Tests in Boys with DMD.Journal of neuromuscular diseases · 2021Article
- An Assessment of Environmental Health Measures in the Deepwater Horizon Research Consortia.Current opinion in toxicology · 2019Article
- Precision epidemiology for infectious disease control.Nature medicine · 2019Review
- Addressing Methodologic Challenges and Minimizing Threats to Validity in Synthesizing Findings from Individual-Level Data Across Longitudinal Randomized Trials.Prevention science : the official journal of the Society for Prevention Research · 2018Article
- Analyzing Real-World Use of Research Common Data Elements.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Research standardization tools: pregnancy measures in the PhenX Toolkit.American journal of obstetrics and gynecology · 2017Article
- Addictions Neuroclinical Assessment: A Neuroscience-Based Framework for Addictive Disorders.Biological psychiatry · 2016Review
- Common Measures for National Institute of Mental Health Funded Research.Biological psychiatry · 2016Article
- Unravelling the human genome-phenome relationship using phenome-wide association studies.Nature reviews. Genetics · 2016Review
- Measures of exposure impact genetic association studies: an example in vitamin K levels and VKORC1.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2015Article
- Advancing the microbiome research community.Cell · 2014Article
- A human rights approach to an international code of conduct for genomic and clinical data sharing.Human genetics · 2014Article
- Article
- The success of pharmacogenomics in moving genetic association studies from bench to bedside: study design and implementation of precision medicine in the post-GWAS era.Human genetics · 2012Review
- PhenDisco: phenotype discovery system for the database of genotypes and phenotypes.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
The PhenX Toolkit provides researchers with recommended, well-established, low-burden measures suitable for human subject research. The database of Genotypes and Phenotypes (dbGaP) is the data repository for a variety of studies funded by the National Institutes of Health, including genome-wide association studies. The dbGaP requires that investigators provide a data dictionary of study variables as part of the data submission process. Thus, dbGaP is a unique resource that can help investigators identify studies that share the same or similar variables. As a proof of concept, variables from 16 studies deposited in dbGaP were mapped to PhenX measures. Soon, investigators will be able to search dbGaP using PhenX variable identifiers and find comparable and related variables in these 16 studies. To enhance effective data exchange, PhenX measures, protocols, and variables were modeled in Logical Observation Identifiers Names and Codes (LOINC® ). PhenX domains and measures are also represented in the Cancer Data Standards Registry and Repository (caDSR). Associating PhenX measures with existing standards (LOINC® and caDSR) and mapping to dbGaP study variables extends the utility of these measures by revealing new opportunities for cross-study analysis.
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.