ArticleStatistics in medicine2019
Identifying gene-environment interactions incorporating prior information.
Article in Statistics in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Joint modeling of mixed outcomes using a rank-based sparse neural network.Journal of biomedical informatics · 2025Article
- High-Dimensional Gene-Environment Interaction Analysis.Annual review of statistics and its application · 2025Article
- Information-incorporated sparse hierarchical cancer heterogeneity analysis.Statistics in medicine · 2024Article
- Article
- Prior information-assisted integrative analysis of multiple datasets.Bioinformatics (Oxford, England) · 2023Article
- Information-incorporated sparse convex clustering for disease subtyping.Bioinformatics (Oxford, England) · 2023Article
- Article
- Network-based cancer heterogeneity analysis incorporating multi-view of prior information.Bioinformatics (Oxford, England) · 2022Article
- Vertical integration methods for gene expression data analysis.Briefings in bioinformatics · 2021Review
- Identification of gene-environment interactions with marginal penalization.Genetic epidemiology · 2020Article
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
Abstract
For many complex diseases, gene-environment (G-E) interactions have independent contributions beyond the main G and E effects. Despite extensive effort, it still remains challenging to identify G-E interactions. With the long accumulation of experiments and data, for many biomedical problems of common interest, there are existing studies that can be relevant and informative for the identification of G-E interactions and/or main effects. In this study, our goal is to identify G-E interactions (as well as their corresponding main G effects) under a joint statistical modeling framework. Significantly advancing from the existing studies, a quasi-likelihood-based approach is developed to incorporate information mined from the existing literature. A penalization approach is adopted for identification and selection and respects the "main effects, interactions" hierarchical structure. Simulation shows that, when the existing information is of high quality, significant improvement can be observed. On the other hand, when the existing information is less informative, the proposed method still performs reasonably (and hence demonstrates a certain degree of "robustness"). The analysis of The Cancer Genome Atlas (TCGA) data on cutaneous melanoma and glioblastoma multiforme demonstrates the practical applicability of the proposed approach and also leads to sensible findings.
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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.