Evidence map›Paper›PMID 40530793›Full record

ArticleJournal of clinical laboratory analysis2025

Comparison of Univariate and Multivariate Reference Interval Methods.

Esra Kutsal Mergen, Sevilay Karahan

Abstract readComparative Study
In one paragraph

Article in Journal of clinical laboratory analysis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

2 authors.

Esra Kutsal MergenDepartment of Biostatistics, Hacettepe University Faculty of Medicine, Ankara, Turkey.ORCID https://orcid.org/0000-0003-4984-2615
Sevilay KarahanDepartment of Biostatistics, Hacettepe University Faculty of Medicine, Ankara, Turkey.

Funding

Hacettepe University Scientific Research Projects Coordination Unit (BAP) THD-2021-19616
6 · The paper itself

Abstract

backgroundIn clinical practice, reference intervals play a pivotal role in interpreting laboratory test results. Yet, when several tests are taken into consideration simultaneously, the traditional univariate intervals might not suffice due to the elevated risk of Type 1 errors.

methodsThis study introduces and evaluates two multivariate reference interval techniques: one based on Mahalanobis distance and the other an adaptation of the multivariate confidence interval (MCI). Using Monte Carlo simulations, we focused our assessments on the interplay between "serum ferritin and transferrin saturation" values.

resultsUpon evaluation, it became evident that the multivariate methods significantly reduced false positives. They presented enhanced accuracy over traditional univariate intervals. Notably, the method involving Mahalanobis distance stood out in terms of efficacy. CONTRIBUTIONS: Beyond presenting novel techniques, our research underscores the importance and potential of using multivariate approaches in clinical lab settings. The findings can guide better medical decision making, ensuring optimized allocation of healthcare resources.

Indexed as

Confidence IntervalsReference ValuesFerritinsHumansMonte Carlo MethodMultivariate AnalysisTransferrinFerritinsTransferrinMahalanobis distancemultivariate reference regionreference intervalsimulation studyunivariate reference interval

Identifiers

PMID40530793
PMCPMC12329337

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Registered trials

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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.