Evidence map›Paper›PMID 33871887›Full record

ArticleBiometrical journal. Biometrische Zeitschrift2021

Combining biomarkers by maximizing the true positive rate for a fixed false positive rate.

Allison Meisner, Marco Carone, Margaret S Pepe, Kathleen F Kerr

Abstract read
In one paragraph

Article in Biometrical journal. Biometrische Zeitschrift, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
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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.

Allison MeisnerVaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.ORCID 0000-0001-5177-7635
Marco CaroneDepartment of Biostatistics, University of Washington, Seattle, WA, USA.
Margaret S PepePublic Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Kathleen F KerrDepartment of Biostatistics, University of Washington, Seattle, WA, USA.

Funding

Novel Biomarkers in Cardiac Surgery to Detect Acute Kidney InjuryR01HL085757 · NHLBI · YALE UNIVERSITY · PI PARIKH, CHIRAG R · 2007 to 2019
$10.3M
Statistical Methods for Evaluating Markers for Treatment SelectionR01CA152089 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI JANES, HOLLY · 2010 to 2021
$3.8M
Biomarkers of Kidney Injury: Statistical Methods for Risk Model Development and EvaluationF31DK108356 · NIDDK · UNIVERSITY OF WASHINGTON · PI MEISNER, ALLISON · 2015 to 2016
$78k
NCI NIH HHS R01 CA152089NHLBI NIH HHS R01 HL085757NIDDK NIH HHS F31 DK108356
6 · The paper itself

Abstract

Biomarkers abound in many areas of clinical research, and often investigators are interested in combining them for diagnosis, prognosis, or screening. In many applications, the true positive rate (TPR) for a biomarker combination at a prespecified, clinically acceptable false positive rate (FPR) is the most relevant measure of predictive capacity. We propose a distribution-free method for constructing biomarker combinations by maximizing the TPR while constraining the FPR. Theoretical results demonstrate desirable properties of biomarker combinations produced by the new method. In simulations, the biomarker combination provided by our method demonstrated improved operating characteristics in a variety of scenarios when compared with alternative methods for constructing biomarker combinations. Thus, use of our method could lead to the development of better biomarker combinations, increasing the likelihood of clinical adoption.

Indexed as

Mass ScreeningBiomarkersFalse Positive ReactionsProbabilityPrognosisBiomarkersbiomarker combinationsfalse positive ratesensitivityspecificitytrue positive rate

Identifiers

PMID33871887
PMCPMC9845072

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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.