ArticleWiley interdisciplinary reviews. Computational statistics2014
Statistical Learning Methods for Longitudinal High-dimensional Data.
Article in Wiley interdisciplinary reviews. Computational statistics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Extracting default mode network based on graph neural network for resting state fMRI study.Frontiers in neuroimaging · 2022Article
- Article
- Designing risk prediction models for ambulatory no-shows across different specialties and clinics.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.European heart journal · 2017Review
- A Longitudinal Support Vector Regression for Prediction of ALS Score.IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine · 2015Article
- Statistical analysis for genome-wide association study.Journal of biomedical research · 2015Review
- Regularization method for predicting an ordinal response using longitudinal high-dimensional genomic data.Statistical applications in genetics and molecular biology · 2015Article
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
Abstract
Recent studies have collected high-dimensional data longitudinally. Examples include brain images collected during different scanning sessions and time-course gene expression data. Because of the additional information learned from the temporal changes of the selected features, such longitudinal high-dimensional data, when incorporated with appropriate statistical learning techniques, are able to more accurately predict disease status or responses to a therapeutic treatment. In this article, we review recently proposed statistical learning methods dealing with longitudinal high-dimensional data.
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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.