Evidence map›Paper›PMID 38076519›Full record

ArticleArXiv2023

Testing for a difference in means of a single feature after clustering.

Yiqun T Chen, Lucy L Gao

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2023. 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
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Yiqun T ChenDepartment of Biomedical Data Science, Stanford University.
Lucy L GaoDepartment of Statistics, University of British Columbia, November 29, 2023.

Funding

A Modeling Framework for Multi-View Data, with Applications to the Pioneer 100 Study and Protein Interaction NetworksR01GM123993 · NIGMS · UNIVERSITY OF WASHINGTON · PI BIEN, JACOB, WITTEN, DANIELA · 2017 to 2020
$1.3M
Models and Methods for Calcium Imaging Data with Application to the Allen Brain ObservatoryR01EB026908 · NIBIB · UNIVERSITY OF WASHINGTON · PI BUICE, MICHAEL, WITTEN, DANIELA · 2018 to 2020
$1.1M
CRCNS: Theory and Experiments to Elucidate Neural Coding in the Reward CircuitR01DA047869 · NIDA · UNIVERSITY OF WASHINGTON · PI WITTEN, DANIELA, WITTEN, ILANA · 2018 to 2021
$1.0M
NIBIB NIH HHS R01 EB026908NIDA NIH HHS R01 DA047869NIGMS NIH HHS R01 GM123993
6 · The paper itself

Abstract

For many applications, it is critical to interpret and validate groups of observations obtained via clustering. A common validation approach involves testing differences in feature means between observations in two estimated clusters. In this setting, classical hypothesis tests lead to an inflated Type I error rate. To overcome this problem, we propose a new test for the difference in means in a single feature between a pair of clusters obtained using hierarchical or

Indexed as

Hypothesis testingPost-selection inferenceType I ErrorUnsupervised learning

Identifiers

PMID38076519
PMCPMC10705581

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.