Evidence map›Paper›PMID 31762476›Full record

ArticleBiometrika2018

Integrative linear discriminant analysis with guaranteed error rate improvement.

Quefeng Li, Lexin Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biometrika, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Quefeng LiDepartment of Biostatistics, University of North Carolina at Chapel Hill, 3105D McGavran-Greenberg Hall, Chapel Hill, North Carolina 27599, U.S.A.
Lexin LiDivision of Biostatistics, University of California at Berkeley, 50 University Hall 7360, Berkeley, California 94720, U.S.A.

Funding

Semiparametric Regression Analysis of Censored DataR01GM047845 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LIN, DANYU · 1997 to 2018
$2.9M
New Statistical Methods for Multicenter Multimodal Longitudinal Neuroimaging AnalysisR01AG061303 · NIA · UNIVERSITY OF CALIFORNIA BERKELEY · PI LI, LEXIN · 2019 to 2022
$1.5M
NIA NIH HHS R01 AG061303NIGMS NIH HHS R01 GM047845
6 · The paper itself

Abstract

Multiple types of data measured on a common set of subjects arise in many areas. Numerous empirical studies have found that integrative analysis of such data can result in better statistical performance in terms of prediction and feature selection. However, the advantages of integrative analysis have mostly been demonstrated empirically. In the context of two-class classification, we propose an integrative linear discriminant analysis method and establish a theoretical guarantee that it achieves a smaller classification error than running linear discriminant analysis on each data type individually. We address the issues of outliers and missing values, frequently encountered in integrative analysis, and illustrate our method through simulations and a neuroimaging study of Alzheimer's disease.

Indexed as

Bayes errorHigh-dimensional classificationIntegrative analysisLinear discriminant analysisMulti-type dataRegularization

Identifiers

PMID31762476
PMCPMC6874859

What OpenQuestion holds

Textmetadata
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Read underepoch 390

Registered trials

None linked

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.