Evidence map›Paper›PMID 42813061›Full record

ArticleJournal of applied statistics2026

A data-driven single substitution to overcome bias from conventional single substitution for LOD missingness in covariates in least squares regression.

Ruofei Du, Xinmin Chu, Jing Jin, Li Luo, Ji-Hyun Lee, Laurie G Hudson, Debra MacKenzie, Johnnye Lewis

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of applied statistics, 2026. 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

8 authors.

Ruofei DuDepartment of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
Xinmin ChuDepartment of Mathematics, University of Massachusetts Boston, Boston, MA, USA.
Jing JinDepartment of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
Li LuoDepartment of Internal Medicine, University of New Mexico, Albuquerque, NM, USA.
Ji-Hyun LeeDepartment of Biostatistics, Division of Quantitative Sciences, University of Florida Health Cancer Center, University of Florida, Gainesville, FL, USA.
Laurie G HudsonDepartment of Pharmaceutical Sciences, College of Pharmacy, University of New Mexico, Albuquerque, NM, USA.
Debra MacKenzieCommunity Environmental Health Program, Department of Pharmaceutical Sciences, College of Pharmacy, University of New Mexico, Albuquerque, NM, USA.
Johnnye LewisCommunity Environmental Health Program, Department of Pharmaceutical Sciences, College of Pharmacy, University of New Mexico, Albuquerque, NM, USA.

Funding

UNM Metals Exposure and Toxicity Assessment on tribal Lands in the Southwest (METALS) Superfund Research ProgramP42ES025589 · NIEHS · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI HUDSON, LAURIE G · 2017 to 2025
$16.4M
Understanding Risk Gradients from Environment on Native American Child Health Trajectories: Toxicants, Immunomodulation, Metabolic syndromes, & Metals ExposureUG3OD023344 · OD · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI LEWIS, JOHNNYE L, MACKENZIE, DEBRA · 2016 to 2024
$14.1M
Role of open dumping and open burning of solid waste in the generation of microplastics and products of incomplete combustion on tribal landsP50MD015706 · NIMHD · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI MACKENZIE, DEBRA · 2020 to 2024
$6.8M
Pilot Project CoreP30ES032755 · NIEHS · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI FENG, CHANGJIAN (JIM) · 2022 to 2025
$5.2M
Arsenic co-carcinogenesis with UVR: nitrosation and oxidation of target proteinsR01ES021100 · NIEHS · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI HUDSON, LAURIE G, LIU, KE JIAN · 2012 to 2016
$2.4M
NIEHS NIH HHS P30 ES032755NIEHS NIH HHS P42 ES025589NIEHS NIH HHS R01 ES021100NIH HHS UG3 OD023344NIMHD NIH HHS P50 MD015706
6 · The paper itself

Abstract

The Limit of Detection (LOD) is the threshold below which a measurement is considered too low to be reliably quantified. Even though advanced statistical methods exist for dealing with the values below the LOD, simple approaches are still frequently used in real-world settings. Common methods include complete-case analysis, which must use a reduced sample size, and single substitution using fixed constants (e.g.

Indexed as

data driven missing substitutionleast squares estimateLimit of detectionLODmissing imputationmissingness in covariates

Identifiers

PMID42813061
PMCPMC13621957

What Socratic holds

Textmetadata
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 Socratic graph.