Evidence map›Paper›PMID 23703944›Full record

ArticleStatistics in medicine2013

Regression analysis for multiple-disease group testing data.

Boan Zhang, Christopher R Bilder, Joshua M Tebbs

Abstract read
In one paragraph

Article in Statistics in medicine, 2013. 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

3 authors.

Boan ZhangDepartment of Statistics, University of Nebraska-Lincoln, Lincoln, NE 68583, U.S.A.
Christopher R Bilder
Joshua M Tebbs

Funding

Disease Detection and Prevalence Estimation through Informative Group TestingR01AI067373 · NIAID · UNIVERSITY OF NEBRASKA LINCOLN · PI BILDER, CHRISTOPHER R · 2007 to 2009
$713k
NIAID NIH HHS R01 AI067373
6 · The paper itself

Abstract

Group testing, where individual specimens are composited into groups to test for the presence of a disease (or other binary characteristic), is a procedure commonly used to reduce the costs of screening a large number of individuals. Group testing data are unique in that only group responses may be available, but inferences are needed at the individual level. A further methodological challenge arises when individuals are tested in groups for multiple diseases simultaneously, because unobserved individual disease statuses are likely correlated. In this paper, we propose new regression techniques for multiple-disease group testing data. We develop an expectation-solution based algorithm that provides consistent parameter estimates and natural large-sample inference procedures. We apply our proposed methodology to chlamydia and gonorrhea screening data collected in Nebraska as part of the Infertility Prevention Project and to prenatal infectious disease screening data from Kenya.

Indexed as

Models, StatisticalRegression AnalysisAlgorithmsChlamydia InfectionsCommunicable DiseasesComputer SimulationFemaleGonorrheaHepatitis BHIV InfectionsHumansKenyaMass ScreeningNebraskaSyphiliscorrelated binary dataexpectation-solution algorithmgeneralized estimating equationsInfertility Prevention Projectpooled testingspecimen pooling

Identifiers

PMID23703944
PMCPMC4301740

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.