ArticleCommunications in statistics: Simulation and computation2017
Sparse canonical correlation analysis between an alcohol biomarker and self-reported alcohol consumption.
Article in Communications in statistics: Simulation and computation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- Secondary Analysis of a Randomized Clinical Trial of Naltrexone Among Women Living With HIV: Correlations Between Reductions in Self-Reported Alcohol Use and Changes in Phosphatidylethanol.Alcoholism, clinical and experimental research · 2021Trial
- Phosphatidylethanol in Comparison to Self-Reported Alcohol Consumption Among HIV-Infected Women in a Randomized Controlled Trial of Naltrexone for Reducing Hazardous Drinking.Alcoholism, clinical and experimental research · 2018Trial
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In investigating the correlation between an alcohol biomarker and self-report, we developed a method to estimate the canonical correlation between two high-dimensional random vectors with a small sample size. In reviewing the relevant literature, we found that our method is somewhat similar to an existing method, but that the existing method has been criticized as lacking theoretical grounding in comparison with an alternative approach. We provide theoretical and empirical grounding for our method, and we customize it for our application to produce a novel method, which selects linear combinations that are step functions with a sparse number of steps.
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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.