Evidence map›Paper›PMID 37197479›Full record

ArticleStatistica Sinica2023

PENALIZED REGRESSION FOR MULTIPLE TYPES OF MANY FEATURES WITH MISSING DATA.

Kin Yau Wong, Donglin Zeng, D Y Lin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistica Sinica, 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

3 authors.

Kin Yau WongThe Hong Kong Polytechnic University.
Donglin ZengThe University of North Carolina at Chapel Hill.
D Y LinThe University of North Carolina at Chapel Hill.

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
Semiparametric Regression Analysis of Censored DataR01GM047845 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LIN, DANYU · 1997 to 2018
$2.9M
Semiparametric Analysis of Big Censored DataR01HL149683 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LIN, DANYU · 2020 to 2023
$1.9M
Statistical Methods in Trans-Omics Chronic Disease ResearchR01HG009974 · NHGRI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LIN, DANYU · 2018 to 2022
$1.5M
NCI NIH HHS P01 CA142538NHGRI NIH HHS R01 HG009974NHLBI NIH HHS R01 HL149683NIGMS NIH HHS R01 GM047845
6 · The paper itself

Abstract

Recent technological advances have made it possible to measure multiple types of many features in biomedical studies. However, some data types or features may not be measured for all study subjects because of cost or other constraints. We use a latent variable model to characterize the relationships across and within data types and to infer missing values from observed data. We develop a penalized-likelihood approach for variable selection and parameter estimation and devise an efficient expectation-maximization algorithm to implement our approach. We establish the asymptotic properties of the proposed estimators when the number of features increases at a polynomial rate of the sample size. Finally, we demonstrate the usefulness of the proposed methods using extensive simulation studies and provide an application to a motivating multi-platform genomics study.

Indexed as

Adaptive lassoFactor modelsIntegrative analysisMulti-modality dataMulti-platform genomics studiesPenalized regression

Identifiers

PMID37197479
PMCPMC10187615

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.