Evidence map›Paper›PMID 42388011›Full record

ArticleBiometrical journal. Biometrische Zeitschrift2026

When to Adjust for Multiple Testing: A Unifying Guiding Principle.

Sabine Hoffmann, Simon Lemster, Gary Collins, Alexander Hapfelmeier, Georg Heinze, Andreas Mayr, Matthias Schmid, Juliane C Wilcke, Anne-Laure Boulesteix

Abstract read
In one paragraph

Article in Biometrical journal. Biometrische Zeitschrift, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

9 authors.

Sabine HoffmannDepartment of Statistics, LMU Munich, Munich, Germany.ORCID https://orcid.org/0000-0001-6197-8801
Simon LemsterInstitute for Medical Information Processing, Biometry, and Epidemiology, LMU Medizin, LMU Munich, Munich, Germany.
Gary CollinsDepartment of Applied Health Sciences, School of Health Sciences, College of Medicine and Health, University of Birmingham, Birmingham, UK.
Alexander HapfelmeierInstitute of AI and Informatics in Medicine, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.
Georg HeinzeInstitute of Clinical Biometrics, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.
Andreas MayrInstitute for Medical Biometry and Statistics, University of Marburg, Marburg, Germany.ORCID https://orcid.org/0000-0001-7106-9732
Matthias SchmidInstitute for Medical Biometry, Informatics, and Epidemiology, University Hospital Bonn, Bonn, Germany.ORCID https://orcid.org/0000-0002-0788-0317
Juliane C WilckeInstitute for Medical Information Processing, Biometry, and Epidemiology, LMU Medizin, LMU Munich, Munich, Germany.
Anne-Laure BoulesteixInstitute for Medical Information Processing, Biometry, and Epidemiology, LMU Medizin, LMU Munich, Munich, Germany.ORCID https://orcid.org/0000-0002-2729-0947

Funding

German Research Foundation BO3139/7-2
6 · The paper itself

Abstract

Most original articles published in the medical literature report the results of multiple statistical tests. In a few simple cases, there is agreement on whether to adjust for the number of performed tests. For many cases encountered in practice, however, this is less clear, and the recommendations in the literature are contradictory, along different dimensions, or otherwise confusing. This lack of clear guidance may impair the conduct and interpretation of analyses, and encourage questionable research practices, ultimately jeopardizing the credibility of medical research. In this article, we refine, illustrate, and discuss a unifying guiding principle to assist both statisticians and applied researchers in deciding whether to adjust for multiple testing and, if so, over which set of tests. The principle is that multiple testing should be adjusted for if and only if authors, when reporting and interpreting their findings, put more emphasis on results of one or several of the tests because of their small p-value(s). We relate this principle to previously proposed rules and show how it can guide and clarify the choice of adjustment strategies in three complex multiple testing settings.

Indexed as

BiometryData Interpretation, StatisticalHumansfamily‐wise error rategood practiceguidancehypothesis testingmultiplicityreportingType I error

Identifiers

PMID42388011
PMCPMC13324398

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.