Evidence map›Paper›PMID 35706470›Full record

ArticleJournal of applied statistics2021

Bayesian analysis of the Box-Cox transformation model based on left-truncated and right-censored data.

Chunjie Wang, Jingjing Jiang, Linlin Luo, Shuying Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of applied statistics, 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. Editorial to special issue Frontiers of Data Analysis.Journal of applied statistics · 2021
    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 WangSchool of Mathematics and Statistics, Changchun University of Technology, Changchun, People's Republic of China.
Jingjing JiangSchool of Mathematics and Statistics, Changchun University of Technology, Changchun, People's Republic of China.
Linlin LuoSchool of Mathematics and Statistics, Changchun University of Technology, Changchun, People's Republic of China.
Shuying WangSchool of Mathematics and Statistics, Changchun University of Technology, Changchun, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we discuss the inference problem about the Box-Cox transformation model when one faces left-truncated and right-censored data, which often occur in studies, for example, involving the cross-sectional sampling scheme. It is well-known that the Box-Cox transformation model includes many commonly used models as special cases such as the proportional hazards model and the additive hazards model. For inference, a Bayesian estimation approach is proposed and in the method, the piecewise function is used to approximate the baseline

Indexed as

62N0162N02additive hazards modelBayesianLeft-truncated and right-censored dataMCMC samplingproportional hazards model

Identifiers

PMID35706470
PMCPMC9041913

What OpenQuestion holds

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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.