Evidence map›Paper›PMID 25124089›Full record

ArticleBiometrics2014

Identifying optimal biomarker combinations for treatment selection via a robust kernel method.

Ying Huang, Youyi Fong

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biometrics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.4field-weighted citation impact, top 20% of its field
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

9 citing papers in PubMed, 30 citations in OpenAlex.

  1. Article
  2. Threshold-based subgroup testing in logistic regression models in two-phase sampling designs.Journal of the Royal Statistical Society. Series C, Applied statistics · 2021
    Article
  3. Article
  4. Selecting Biomarkers for building optimal treatment selection rules using Kernel Machines.Journal of the Royal Statistical Society. Series C, Applied statistics · 2020
    Article
  5. Personalized medicine-concepts, technologies, and applications in inflammatory skin diseases.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2019
    Review
  6. Article
  7. Article
  8. Article
  9. 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

2 authors at 1 institution in 1 country.

Ying HuangFred Hutchinson Cancer Research Center, Public Health Sciences, 1100 Fairview Avenue N., Seattle, Washington 98109-1024, U.S.A.; Department of Biostatistics, University of Washington, Seattle, Washington 98195, U.S.A.
Youyi Fong
University of Washington · US

Funding

The Early Detection Research Network: Data Management and Coordinating CenterU24CA086368 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI RUTH D ETZIONI, Ziding Feng · 2010 to 2026
$75.7M
EDRN--DATA MANAGEMENT AND COORDINATING CENTERU01CA086368 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI FENG, ZIDING · 2000 to 2010
$16.9M
Use of Biomarkers in Diagnosis, Prognosis, Risk Prediction and Early Detection ofP01CA053996 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI PRENTICE, ROSS L · 1991 to 2015
$9.4M
Statistical Methods in HIV Vaccine Efficacy TrialsR37AI054165 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Peter B. Gilbert · 2010 to 2026
$6.7M
Statistical Methods for Selection and Evaluation of BiomarkersR01GM106177 · NIGMS · FRED HUTCHINSON CANCER RESEARCH CENTER · PI HUANG, YING · 2013 to 2018
$1.6M
NCI NIH HHS P01 CA053996NCI NIH HHS U01 CA086368NCI NIH HHS U24 CA086368NIAID NIH HHS 2R37AI05465-10NIAID NIH HHS R37 AI054165NIGMS NIH HHS R01 GM106177NIGMS NIH HHS R01 GM106177-01
6 · The paper itself

Abstract

Treatment-selection markers predict an individual's response to different therapies, thus allowing for the selection of a therapy with the best predicted outcome. A good marker-based treatment-selection rule can significantly impact public health through the reduction of the disease burden in a cost-effective manner. Our goal in this article is to use data from randomized trials to identify optimal linear and nonlinear biomarker combinations for treatment selection that minimize the total burden to the population caused by either the targeted disease or its treatment. We frame this objective into a general problem of minimizing a weighted sum of 0-1 loss and propose a novel penalized minimization method that is based on the difference of convex functions algorithm (DCA). The corresponding estimator of marker combinations has a kernel property that allows flexible modeling of linear and nonlinear marker combinations. We compare the proposed methods with existing methods for optimizing treatment regimens such as the logistic regression model and the weighted support vector machine. Performances of different weight functions are also investigated. The application of the proposed method is illustrated using a real example from an HIV vaccine trial: we search for a combination of Fc receptor genes for recommending vaccination in preventing HIV infection.

Indexed as

Data Interpretation, StatisticalAlgorithmsBiomarkersGenetic Predisposition to DiseaseGenetic TestingHIV InfectionsHumansOutcome Assessment, Health CarePatient SelectionPolymorphism, Single NucleotidePrevalencePrognosisReceptors, FcReproducibility of ResultsRisk FactorsSensitivity and SpecificityBiomarkersReceptors, FcBiomarker combinationKernel methodRandomized trialRobustSupport vector machineTreatment selection

Identifiers

PMID25124089
PMCPMC4277554
OpenAlexW1920839661

What OpenQuestion holds

Textmetadata
LicenceTDM
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.