Evidence map›Paper›PMID 26521228›Full record

ArticleBMC medical research methodology2015

Flexible combination of multiple diagnostic biomarkers to improve diagnostic accuracy.

Tu Xu, Yixin Fang, Alan Rong, Junhui Wang

Abstract read
In one paragraph

Article in BMC medical research methodology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

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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.

Tu XuGilead Sciences Inc., 333 Lakeside Dr, Foster City, 94404, CA, USA. Tu.Xu@gilead.com.
Yixin FangDivision of Biostatistics, Department of Population Health, New York University, New York, USA. Yixin.Fang@nyumc.org.
Alan RongAstellas Pharma Inc., Northbrook, USA. Alan.Rong@astellas.com.
Junhui WangDepartment of Mathematics, City University of Hong Kong, Kowloon Tong, Hong Kong. j.h.wang@cityu.edu.hk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn medical research, it is common to collect information of multiple continuous biomarkers to improve the accuracy of diagnostic tests. Combining the measurements of these biomarkers into one single score is a popular practice to integrate the collected information, where the accuracy of the resultant diagnostic test is usually improved. To measure the accuracy of a diagnostic test, the Youden index has been widely used in literature. Various parametric and nonparametric methods have been proposed to linearly combine biomarkers so that the corresponding Youden index can be optimized. Yet there seems to be little justification of enforcing such a linear combination.

methodsThis paper proposes a flexible approach that allows both linear and nonlinear combinations of biomarkers. The proposed approach formulates the problem in a large margin classification framework, where the combination function is embedded in a flexible reproducing kernel Hilbert space.

resultsAdvantages of the proposed approach are demonstrated in a variety of simulated experiments as well as a real application to a liver disorder study.

conclusionLinear combination of multiple diagnostic biomarkers are widely used without proper justification. Additional research on flexible framework allowing both linear and nonlinear combinations is in need.

Indexed as

AlgorithmsBiomarkersComputer SimulationHumansROC CurveBiomarkers

Identifiers

PMID26521228
PMCPMC4628350

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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.