Sparse concordance-assisted learning for optimal treatment decision.

Shuhan Liang, Wenbin Lu, Rui Song, Lan Wang

Abstract read

In one paragraph

Article in Journal of machine learning research : JMLR, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it

0cells of the map it votes in

2citing 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

2 citing papers in PubMed.

Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score.Journal of machine learning research : JMLR · 2022
Article
Selecting Biomarkers for building optimal treatment selection rules using Kernel Machines.Journal of the Royal Statistical Society. Series C, Applied statistics · 2020
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

4 authors.

Shuhan LiangDepartment of Statistics, North Carolina State University, Raleigh, NC 27695, USA.

Wenbin LuDepartment of Statistics, North Carolina State University, Raleigh, NC 27695, USA.

Rui SongDepartment of Statistics, North Carolina State University, Raleigh, NC 27695, USA.

Lan WangSchool of Statistics, University of Minnesota, Minneapolis, MN 55455, USA.

Funding

Statistical Methods for Cancer Clinical TrialsP01CA142538 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI DAVIDIAN, MARIE, KOSOROK, MICHAEL R · 2010 to 2019

$21.3M

NCI NIH HHS P01 CA142538

6 · The paper itself

Abstract

To find optimal decision rule, Fan et al. (2016) proposed an innovative concordance-assisted learning algorithm which is based on maximum rank correlation estimator. It makes better use of the available information through pairwise comparison. However the objective function is discontinuous and computationally hard to optimize. In this paper, we consider a convex surrogate loss function to solve this problem. In addition, our algorithm ensures sparsity of decision rule and renders easy interpretation. We derive the

Indexed as

concordance-assisted learningL1 normoptimal treatment regimesupport vector machinevariable selection

Identifiers

PMID30416396

PMCPMC6226264

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.