Evidence map›Paper›PMID 33895961›Full record

ArticleLifetime data analysis2021

Regression analysis of current status data with latent variables.

Chunjie Wang, Bo Zhao, Linlin Luo, Xinyuan Song

Abstract read
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In one paragraph

Article in Lifetime data analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Chunjie WangThe School of Mathematics and Statistics, Changchun University of Technology, Changchun, 130012, China. wangchunjie@ccut.edu.cn.ORCID 0000-0001-7808-4900
Bo ZhaoThe School of Mathematics and Statistics, Changchun University of Technology, Changchun, 130012, China.
Linlin LuoThe School of Mathematics and Statistics, Changchun University of Technology, Changchun, 130012, China.
Xinyuan SongDepartment of Statistics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current status data occur in many fields including demographical, epidemiological, financial, medical, and sociological studies. We consider the regression analysis of current status data with latent variables. The proposed model consists of a factor analytic model for characterizing latent variables through their multiple surrogates and an additive hazard model for examining potential covariate effects on the hazards of interest in the presence of current status data. We develop a borrow-strength estimation procedure that incorporates the expectation-maximization algorithm and correlated estimating equations. The consistency and asymptotic normality of the proposed estimators are established. A simulation study is conducted to evaluate the finite sample performance of the proposed method. A real-life study on the chronic kidney disease of type 2 diabetic patients is presented.

Indexed as

AlgorithmsModels, StatisticalComputer SimulationHumansProportional Hazards ModelsRegression AnalysisAdditive hazard modelCorrected estimating equationsCurrent status dataFactor analysisLatent variables

Identifiers

PMID33895961

What OpenQuestion holds

Textmetadata
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.