Evidence map›Paper›PMID 40092669›Full record

ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2025

Inferring bivariate associations with continuous data from studies using respondent-driven sampling.

Samantha Malatesta, Karen R Jacobson, Tara Carney, Eric D Kolaczyk, Krista J Gile, Laura F White

Abstract read
In one paragraph

Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2025. 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

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

6 authors.

Samantha MalatestaDepartment of Biostatistics, Boston University School of Public Health, Boston, MA, USA.ORCID https://orcid.org/0000-0003-0849-2348
Karen R JacobsonSection of Infectious Diseases, Boston University School of Medicine, Boston, MA, USA.
Tara CarneyAlcohol, Tobacco and Other Drug Research Unit, South African Medical Research Council, Cape Town, South Africa.
Eric D KolaczykDepartment of Mathematics and Statistics, McGill University, Montreal, Quebec, Canada.
Krista J GileDepartment of Mathematics and Statistics, University of Massachusetts, Amherst, MA, USA.
Laura F WhiteDepartment of Biostatistics, Boston University School of Public Health, Boston, MA, USA.

Funding

Translational ScienceP30AI042853 · NIAID · MIRIAM HOSPITAL · PI LARRY K BROWN · 1998 to 2026
$51.7M
Transmission Of Tuberculosis Among illicit drug use Linkages (TOTAL)R01AI147316 · NIAID · BOSTON MEDICAL CENTER · PI JACOBSON, KAREN R · 2019 to 2023
$3.5M
Maximizing Investigators' Research Award (R35)R35GM141821 · NIGMS · BOSTON UNIVERSITY MEDICAL CAMPUS · PI WHITE, LAURA FORSBERG · 2021 to 2025
$1.8M
NIAID NIH HHS P30 AI042853NIAID NIH HHS R01 AI147316NIGMS NIH HHS R35 GM141821
6 · The paper itself

Abstract

Respondent-driven sampling (RDS) is a link-tracing sampling design that was developed to sample from hidden populations. Although associations between variables are of great interest in epidemiological research, there has been little statistical work on inference on relationships between variables collected through RDS. The link-tracing design, combined with homophily, the tendency for people to connect to others with whom they share characteristics, induces similarity between linked individuals. This dependence inflates the Type 1 error of conventional statistical methods (e.g.

Indexed as

bivariate associationcontinuous datahomophilynetworkrandomization testrespondent-driven sampling

Identifiers

PMID40092669
PMCPMC11905883

What OpenQuestion holds

Textmetadata
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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.