Evidence map›Paper›PMID 37459178›Full record

ArticleBiometrics2023

Simultaneous selection and inference for varying coefficients with zero regions: a soft-thresholding approach.

Yuan Yang, Ziyang Pan, Jian Kang, Chad Brummett, Yi Li

Abstract read
In one paragraph

Article in Biometrics, 2023. 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

5 authors.

Yuan YangParexel, Waltham, Massachusetts, USA.
Ziyang PanDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0003-1600-5597
Jian KangDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0002-5643-2668
Chad BrummettDepartment of Anesthesiology, University of Michigan, Ann Arbor, Michigan, USA.
Yi LiDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0003-1720-2760

Funding

Statistical ICA Methods for Analysis & Integration of Multi-dimensional Data Diversity SupplementR01MH105561 · NIMH · EMORY UNIVERSITY · PI GUO, YING, KANG, JIAN · 2014 to 2024
$4.3M
New Statistical Methods for Modelling Cancer OutcomesR01CA249096 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Yi Li · 2021 to 2026
$2.5M
Scalable Bayesian methods for big imaging data analysisR01DA048993 · NIDA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI JOHNSON, TIMOTHY D, KANG, JIAN · 2020 to 2024
$1.6M
Bayesian Network Biomarker Selection in Metabolomics DataR01GM124061 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KANG, JIAN · 2017 to 2020
$1.4M
Detecting racial disparities in cancer survival by integrating multiple high-dimensional observational studiesR01CA269398 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GUHA, SUBHARUP, LI, YI · 2022 to 2025
$1.3M
NCI NIH HHS R01 CA249096NCI NIH HHS R01CA249096NCI NIH HHS R01 CA269398NIDA NIH HHS R01DA048993NIGMS NIH HHS R01GM124061NIMH NIH HHS R01MH105561
6 · The paper itself

Abstract

Varying coefficient models have been used to explore dynamic effects in many scientific areas, such as in medicine, finance, and epidemiology. As most existing models ignore the existence of zero regions, we propose a new soft-thresholded varying coefficient model, where the coefficient functions are piecewise smooth with zero regions. Our new modeling approach enables us to perform variable selection, detect the zero regions of selected variables, obtain point estimates of the varying coefficients with zero regions, and construct a new type of sparse confidence intervals that accommodate zero regions. We prove the asymptotic properties of the estimator, based on which we draw statistical inference. Our simulation study reveals that the proposed sparse confidence intervals achieve the desired coverage probability. We apply the proposed method to analyze a large-scale preoperative opioid study.

Indexed as

Computer SimulationProbabilitySample Sizenon-parametric regressionopioid usesparse confidence intervalsvarying coefficient with zero regions

Identifiers

PMID37459178
PMCPMC10792111

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.