Evidence map›Paper›PMID 41110963›Full record

ArticleJournal of epidemiology2026

Evaluation of Time-related Bias With Non-user Control.

Hiroya Morita, Kentaro Matsuura, Nodoka Seya, Masataka Taguri

Abstract read
In one paragraph

Article in Journal of epidemiology, 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

4 authors.

Hiroya MoritaDepartment of Health Data Science, Tokyo Medical University.ORCID 0009-0009-1337-1488
Kentaro MatsuuraChugai Pharmaceutical Co., Ltd.
Nodoka SeyaDepartment of Health Data Science, Tokyo Medical University.
Masataka TaguriDepartment of Health Data Science, Tokyo Medical University.ORCID 0000-0001-8902-0056

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn observational studies estimating the association between treatment and time-to-event outcomes, time-related biases can substantially impact results. Immortal time bias is one of such biases, and two types are known: misclassified immortal time bias and excluded immortal time bias. These biases often arise from incorrect time-zero definition, especially with non-user controls. This study aims to illustrate immortal time bias in non-user controls using formulas, simulations, and real-world data.

methodsFor our simulations, we considered two scenarios: one with no confounding and no treatment effect, and the other with time-dependent confounding. We compare three different settings of time-zero for treatment and control groups. Method 1: Both groups were followed from cohort entry date (CED). Method 2: The treatment group was followed from treatment initiation date (TID), while the non-user group was followed from CED. Method 3: The treatment group was followed from TID, and non-users were matched to treatment patients, followed from the corresponding TID of their matched patient.

resultsOur simulation showed that both method 1 and method 2 can exhibit large biases in the estimated treatment effect due to immortal time bias. The magnitude of the bias is greater for method 1 than for method 2. On the other hand, method 3 showed almost no bias. Even in the presence of time-dependent confounding, method 3 did not introduce bias.

conclusionTo reduce time-related biases, it is crucial for researchers to carefully define an appropriate time-zero, especially when using a non-user control group.

Indexed as

Control GroupsObservational Studies as TopicBiasComputer SimulationConfounding Factors, EpidemiologicHumansTime Factorsexcluded immortal time biasmisclassified immortal time biastime-varying confoundingtime-zero

Identifiers

PMID41110963
PMCPMC12975769

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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.