Evidence map›Paper›PMID 34937955›Full record

ArticleStat2020

Outcome weighted

Mingyang Liu, Xiaotong Shen, Wei Pan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Stat, 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

3 authors.

Mingyang LiuSchool of Statistics, University of Minnesota, MN, Minneapolis.
Xiaotong ShenSchool of Statistics, University of Minnesota, MN, Minneapolis.
Wei PanDivision of Biostatistics, University of Minnesota, MN, Minneapolis.

Funding

Powerful Inference and Prediction for Genetic AssociationR01HL105397 · NHLBI · UNIVERSITY OF MINNESOTA · PI PAN, WEI, SHEN, XIAOTONG TOM · 2011 to 2018
$2.9M
New Machine Learning Tools for Biomedical DataR01GM081535 · NIGMS · UNIVERSITY OF MINNESOTA · PI PAN, WEI, SHEN, XIAOTONG TOM · 2007 to 2014
$2.2M
Statistical Methods for Genomic and Proteomic DataR01HL065462 · NHLBI · UNIVERSITY OF MINNESOTA TWIN CITIES · PI PAN, WEI · 2002 to 2012
$1.5M
NHLBI NIH HHS R01 HL065462NHLBI NIH HHS R01 HL105397NIGMS NIH HHS R01 GM081535
6 · The paper itself

Abstract

An individualized treatment rule is often employed to maximize a certain patient-specific clinical outcome based on his/her clinical or genomic characteristics as well as heterogeneous response to treatments. Although developing such a rule is conceptually important to personalized medicine, existing methods such as the partial least squares Qian and Murphy (2011) suffers from the difficulty of indirect maximization of a patient's clinical outcome, while the outcome weighted learning Y. Zhao, Zeng, Rush, and Kosorok (2012) is not robust against any perturbation of the outcome. In this article, we propose a weighted

Indexed as

Lung cancer treatmentsPersonalized medicineRegularizationVariable selection

Identifiers

PMID34937955
PMCPMC8691757

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.