Evidence map›Paper›PMID 36412978›Full record

ArticleStatistics in medicine2023

Two-sample test with g-modeling and its applications.

Jingyi Zhai, Hui Jiang

Abstract read
In one paragraph

Article in Statistics in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Jingyi ZhaiDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Hui JiangDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0003-2718-9811

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many real data analyses involve two-sample comparisons in location or in distribution. Most existing methods focus on problems where observations are independently and identically distributed in each group. However, in some applications the observed data are not identically distributed but associated with some unobserved parameters which are identically distributed. To address this challenge, we propose a novel two-sample testing procedure as a combination of the

Indexed as

AlgorithmsModels, StatisticalHumansPoisson Distributionbootstrapdifferential expression analysissingle-cell RNA-seqtwo-sample testzero-inflated Poisson

Identifiers

PMID36412978
PMCPMC10099579

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.