Evidence map›Paper›PMID 41286579›Full record

ArticleObesity (Silver Spring, Md.)2026

Pair-Feeding Study Designs Can Create Biases and Inflate Type I Error Rates: A Simulation Study.

Wasiuddin Najam, Daniel E Kpormegbey, Deependra K Thapa, Bret Rust, Lilian Golzarri-Arroyo, Lance H Baumgard, Louis Wai-Tong Fan, Joseph G Ibrahim, Edith J Mayorga, David B Allison

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Obesity (Silver Spring, Md.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Wasiuddin NajamDepartment of Epidemiology and Biostatistics, School of Public Health-Bloomington, Indiana University, Bloomington, Indiana, USA.ORCID https://orcid.org/0000-0001-5867-8883
Daniel E KpormegbeyDepartment of Epidemiology and Biostatistics, School of Public Health-Bloomington, Indiana University, Bloomington, Indiana, USA.
Deependra K ThapaDepartment of Epidemiology and Biostatistics, School of Public Health-Bloomington, Indiana University, Bloomington, Indiana, USA.
Bret RustDepartment of Applied Health Science, School of Public Health-Bloomington, Indiana University, Bloomington, Indiana, USA.
Lilian Golzarri-ArroyoBiostatistics Consulting Center, School of Public Health-Bloomington, Indiana University, Bloomington, Indiana, USA.
Lance H BaumgardDepartment of Animal Science, Iowa State University, Ames, Iowa, USA.
Louis Wai-Tong FanSchool of Data Science and Society, University of North Carolina, Chapel Hill, North Carolina, USA.
Joseph G IbrahimDepartment of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA.
Edith J MayorgaDepartment of Animal Science, Iowa State University, Ames, Iowa, USA.
David B AllisonDepartment of Epidemiology and Biostatistics, School of Public Health-Bloomington, Indiana University, Bloomington, Indiana, USA.

Funding

Sex hormones and arthritis in a long lived animal modelP30AG050886 · NIA · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI Jianhua Zhang · 2015 to 2026
$11.3M
Nathan Shock Centers Coordinating CenterU24AG056053 · NIA · AMERICAN FEDERATION FOR AGING RESEARCH · PI STEVEN N. AUSTAD, Stephanie Lederman · 2017 to 2026
$6.1M
Strengthening Causal Inference in Behavioral Obesity ResearchR25HL124208 · NHLBI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI ALLISON, DAVID B, BROWN, ANDREW W · 2014 to 2023
$1.8M
The Mathematical Sciences in Obesity ResearchR25DK099080 · NIDDK · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI DAVID B ALLISON, Andrew W Brown · 2013 to 2026
$1.2M
National Institutes of Health (NIH) P30AG050886National Institutes of Health (NIH) R25DK099080National Institutes of Health (NIH) R25HL124208National Institutes of Health (NIH) U24AG056053NHLBI NIH HHS R25 HL124208NIA NIH HHS P30 AG050886NIA NIH HHS U24 AG056053NIDDK NIH HHS R25 DK099080
6 · The paper itself

Abstract

objectivePair-feeding is a study design element where one group's food intake is provided to another group to assess whether a treatment effect is independent of food intake. Investigators often assume equivalent food intake across experimental conditions and exclude it from the statistical analysis. However, the impact of this practice on type I error (T1Er) rates has not been quantified.

methodsWe conducted a Monte Carlo simulation in which animals were assigned baseline weights and food intakes, then randomized to non-pair-fed or pair-fed groups. Daily food intake for both groups was initially drawn from the baseline food intake distribution. For pair-fed animals, food intake was truncated if it exceeded the previous day's intake of the matched non-pair-fed animal (individual pair-feeding) or the group's average (group pair-feeding). Weight changes were calculated as a function of food intake, and final weight change was analyzed with and without adjusting for mean food intake.

resultsBoth individual and group pair-feeding inflated T1Er rates ranging from 0.12 to 0.71 in unadjusted models. However, adjustment for food intake reduced error rates to around 0.05.

conclusionsUnder some circumstances, pair-feeding designs can inflate T1Er rates. Investigators can mitigate this inflation by adjusting the analyses for food intake.

Indexed as

EatingResearch DesignAnimalsBiasBody WeightComputer SimulationFeeding BehaviorMonte Carlo Methodbiasdata analysispair‐feedingrigorstatistical sciencestudy designtype one error

Identifiers

PMID41286579
PMCPMC12850568

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.