Evidence map›Paper›PMID 23839122›Full record

ArticleStatistical methods in medical research2016

Point success rate for patient therapeutic response prediction by continuous biomarker scores.

Zhenjun Ma, Youngchul Kim, Feifang Hu, Jae K Lee

Abstract read
In one paragraph

Article in Statistical methods in medical research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Zhenjun MaDepartment of Statistics, University of Virginia, Charlottesville, VA, USA.
Youngchul KimDepartment of Public Health Sciences, University of Virginia, Charlottesville, VA, USA.
Feifang HuDepartment of Statistics, University of Virginia, Charlottesville, VA, USA.
Jae K LeeDepartment of Public Health Sciences, University of Virginia, Charlottesville, VA, USA jaeklee@virginia.edu.

Funding

TRANSLATIONAL RESEARCHP30CA076292 · NCI · UNIVERSITY OF SOUTH FLORIDA · PI KENNETH Lynn WRIGHT · 1998 to 2026
$93.5M
Women's Oncology Program - WONP30CA044579 · NCI · UNIVERSITY OF VIRGINIA CHARLOTTESVILLE · PI Thomas P. Loughran · 1987 to 2026
$72.1M
Genome Pathway Modeling Diabetic AtherosclerosisR01HL081690 · NHLBI · UNIVERSITY OF VIRGINIA · PI LEE, JAE K. · 2006 to 2010
$1.6M
NCI NIH HHS P30 CA044579NCI NIH HHS P30 CA076292NHLBI NIH HHS R01 HL081690
6 · The paper itself

Abstract

Various predictive diagnostic tests are highly demanded to guide optimal treatments for individual patients, as individual patients with the same disease such as cancer frequently exhibit dramatically different therapeutic responses to multiple available treatment options. A large number of clinical trials have thus been performed to test the predictive ability and utility of various therapeutic biomarker tests. However, in these trial designs the conventional optimization criteria such as positive predictive value or negative predictive value cannot reflect each patient's true chance of success associated with continuous predictive biomarker scores. We have developed a novel statistical concept, point success rate (PSR), to overcome deficiencies in these conventional methods for optimizing biomarker-based clinical trials. We demonstrate statistical superiority as well as clinical improvement by a PSR-based treatment selection both with simulated and breast cancer patient data.

Indexed as

Antineoplastic AgentsBiomarkers, TumorBreast NeoplasmsFemaleHumansPrognosisTreatment OutcomeAntineoplastic AgentsBiomarkers, TumorbiomarkerCOXENpersonalized chemotherapypoint success rate

Identifiers

PMID23839122
PMCPMC7771551

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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.