Evidence map›Paper›PMID 30799878›Full record

ArticleBiometrika2019

Counting process-based dimension reduction methods for censored outcomes.

Qiang Sun, Ruoqing Zhu, Tao Wang, Donglin Zeng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biometrika, 2019. 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.

  1. Article
  2. 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.

Qiang SunDepartment of Statistical Sciences, University of Toronto, 100 St George Street, Toronto, Ontario, Canada.
Ruoqing ZhuDepartment of Statistics, University of Illinois at Urbana-Champaign, 725 South Wright Street, Champaign, Illinois, USA.
Tao WangDepartment of Bioinformatics and Biostatistics, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, China.
Donglin ZengDepartment of Biostatistics, University of North Carolina at Chapel Hill, 3101 McGavran-Greenberg Hall, Chapel Hill, North Carolina, USA.

Funding

Statistical Methods for Integrating Mixed-type Biomarkers and Phenotypes in Neurodegenerative Disease ModelingR01NS073671 · NINDS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI WANG, YUANJIA · 2011 to 2025
$3.9M
Statistical and Machine Learning Methods to Improve Dynamic Treatment Regimens Estimation Using Real World Data.R01GM124104 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Yuanjia Wang, Donglin Zeng · 2018 to 2026
$3.1M
Semiparametric Regression Analysis of Censored DataR01GM047845 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LIN, DANYU · 1997 to 2018
$2.9M
NIGMS NIH HHS R01 GM047845NIGMS NIH HHS R01 GM124104NINDS NIH HHS R01 NS073671
6 · The paper itself

Abstract

We propose counting process-based dimension reduction methods for right-censored survival data. Semiparametric estimating equations are constructed to estimate the dimension reduction subspace for the failure time model. Our methods address two limitations of existing approaches. First, using the counting process formulation, they do not require estimation of the censoring distribution to compensate for the bias in estimating the dimension reduction subspace. Second, the nonparametric estimation involved adapts to the structural dimension, so our methods circumvent the curse of dimensionality. Asymptotic normality is established for the estimators. We propose a computationally efficient approach that requires only a singular value decomposition to estimate the dimension reduction subspace. Numerical studies suggest that our new approaches exhibit significantly improved performance. The methods are implemented in the [Formula: see text] package [Formula: see text].

Indexed as

Estimating equationSemiparametric inferenceSliced inverse regressionSufficient dimension reductionSurvival analysis

Identifiers

PMID30799878
PMCPMC6373420

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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.