Evidence map›Paper›PMID 29203948›Full record

ArticleJournal of multivariate analysis2017

Optimal detection of weak positive latent dependence between two sequences of multiple tests.

Sihai Dave Zhao, T Tony Cai, Hongzhe Li

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of multivariate analysis, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.4field-weighted citation impact, top 35% of its field
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

0 citing papers in PubMed, 7 citations in OpenAlex.

No citing paper in PubMed yet.

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

3 authors at 2 institutions in 1 country.

Sihai Dave ZhaoDepartment of Statistics, University of Illinois at Urbana-Champaign, IL, United States.
T Tony CaiDepartment of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA, United States.
Hongzhe LiDepartment of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
University of Pennsylvania · USUniversity of Illinois Urbana-Champaign · US

Funding

Methods for genomic data with graphical structuresR01CA127334 · NCI · UNIVERSITY OF PENNSYLVANIA · PI LEE, HONGZHE · 2007 to 2016
$2.6M
Statistical Methods for Next-Generation Sequence DataR01GM097505 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI LEE, HONGZHE · 2012 to 2015
$1.2M
NCI NIH HHS R01 CA127334NIGMS NIH HHS R01 GM097505
6 · The paper itself

Abstract

It is frequently of interest to jointly analyze two paired sequences of multiple tests. This paper studies the problem of detecting whether there are more pairs of tests that are significant in both sequences than would be expected by chance. The asymptotic detection boundary is derived in terms of parameters such as the sparsity of non-null cases in each sequence, the effect sizes of the signals, and the magnitude of the dependence between the two sequences. A new test for detecting weak dependence is also proposed, shown to be asymptotically adaptively optimal, studied in simulations, and applied to study genetic pleiotropy in 10 pediatric autoimmune diseases.

Indexed as

Detection boundaryHigher criticismIndependence testingOptimal adaptivitySparsity

Identifiers

PMID29203948
PMCPMC5711487
OpenAlexW2962769341

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.