Evidence map›Paper›PMID 30880877›Full record

ArticleComputational statistics2018

A fast imputation algorithm in quantile regression.

Hao Cheng, Ying Wei

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational statistics, 2018. 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

2 authors.

Hao ChengNational Academy of Innovation Strategy, China Association for Science and Technology, Beijing, China.ORCID 0000-0003-1143-9601
Ying WeiDepartment of Biostatistics, Columbia University, New York, USA.

Funding

Develop quantile analysis tools for sequencing and eQTL studiesR01HG008980 · NHGRI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI WEI, YING · 2016 to 2019
$1.6M
Quantile analysis on human complex traits in GWASand Sequencing studies.R03HG007443 · NHGRI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI WEI, YING · 2013 to 2014
$160k
NHGRI NIH HHS R01 HG008980NHGRI NIH HHS R03 HG007443
6 · The paper itself

Abstract

In many applications, some covariates could be missing for various reasons. Regression quantiles could be either biased or under-powered when ignoring the missing data. Multiple imputation and EM-based augment approach have been proposed to fully utilize the data with missing covariates for quantile regression. Both methods however are computationally expensive. We propose a fast imputation algorithm (FI) to handle the missing covariates in quantile regression, which is an extension of the fractional imputation in likelihood based regressions. FI and modified imputation algorithms (FIIPW and MIIPW) are compared to existing MI and IPW approaches in the simulation studies, and applied to part of of the National Collaborative Perinatal Project study.

Indexed as

Imputation methodsInverse probability weightingMissing dataQuantile regression

Identifiers

PMID30880877
PMCPMC6417839

What OpenQuestion holds

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LicenceTDM
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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.