Evidence map›Paper›PMID 32921837›Full record

ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2020

Selecting Biomarkers for building optimal treatment selection rules using Kernel Machines.

Sayan Dasgupta, Ying Huang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2020. 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

2 authors.

Sayan DasguptaVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, USA.
Ying HuangVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, USA.

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
Nutrition and Physical Activity Assessment Study (NPAAS)R01CA119171 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Marian L Neuhouser · 2006 to 2026
$14.3M
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 R01 CA119171NCI NIH HHS U01 CA086368NCI NIH HHS U24 CA086368NIGMS NIH HHS R01 GM106177
6 · The paper itself

Abstract

Optimal biomarker combinations for treatment-selection can be derived by minimizing total burden to the population caused by the targeted disease and its treatment. However, when multiple biomarkers are present, including all in the model can be expensive and hurt model performance. To remedy this, we consider feature selection in optimization by minimizing an extended total burden that additionally incorporates biomarker costs. Formulating it as a 0-norm penalized weighted-classification, we develop various procedures for estimating linear and nonlinear combinations. Through simulations and a real data example, we demonstrate the importance of incorporating feature-selection and marker cost when deriving treatment-selection rules.

Indexed as

Biomarker costFeature selectionL0 penalizationTreatment selectionWeighted support vector machines

Identifiers

PMID32921837
PMCPMC7485396

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.