Evidence map›Paper›PMID 33867602›Full record

ArticleJournal of the American Statistical Association2020

Simultaneous Covariance Inference for Multimodal Integrative Analysis.

Yin Xia, Lexin Li, Samuel N Lockhart, William J Jagust

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Statistical Association, 2020. 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. Statistical Inferences for Complex Dependence of Multimodal Imaging Data.Journal of the American Statistical Association · 2024
    Article
  2. Generalized Liquid Association Analysis for Multimodal Data Integration.Journal of the American Statistical Association · 2023
    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

4 authors.

Yin XiaDepartment of Statistics, School of Management, Fudan University, Shanghai, China.
Lexin LiDepartment of Biostatistics and Epidemiology, Helen Wills Neuroscience Institute, University of California at Berkeley, Berkeley, CA.
Samuel N LockhartDepartment of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC.
William J JagustLawrence Berkeley National Laboratory and School of Public Health, HelenWills Neuroscience Institute, University of California at Berkeley, Berkeley, CA.

Funding

Wake Forest Alzheimer's Disease Core CenterP30AG049638 · NIA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI RAPP, STEPHEN R · 2016 to 2020
$12.2M
Neural and Biochemical Mechanisms of Cognitive AgingR01AG034570 · NIA · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI William J. Jagust · 2009 to 2026
$12.0M
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 P30 AG049638NIA NIH HHS R01 AG034570NIA NIH HHS R01 AG061303
6 · The paper itself

Abstract

Multimodal integrative analysis fuses different types of data collected on the same set of experimental subjects. It is becoming a norm in many branches of scientific research, such as multi-omics and multimodal neuroimaging analysis. In this article, we address the problem of simultaneous covariance inference of associations between multiple modalities, which is of a vital interest in multimodal integrative analysis. Recognizing that there are few readily available solutions in the literature for this type of problem, we develop a new simultaneous testing procedure. It provides an explicit quantification of statistical significance, a much improved detection power, as well as a rigid false discovery control. Our proposal makes novel and useful contributions from both the scientific perspective and the statistical methodological perspective. We demonstrate the efficacy of the new method through both simulations and a multimodal positron emission tomography study of associations between two hallmark pathological proteins of Alzheimer's disease.

Indexed as

Extreme value distributionFalse discovery controlMinimax rate optimalityMultimodal integrative analysisMultiple testingPositron emission tomography

Identifiers

PMID33867602
PMCPMC8048125

What OpenQuestion holds

Textmetadata
LicenceTDM
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.