Competing risk multistate censored data modeling by propensity score matching method.

Atanu Bhattacharjee, Gajendra K Vishwakarma, Abhipsa Tripathy, Bhrigu Kumar Rajbongshi

Abstract read

In one paragraph

Article in Scientific reports, 2024. 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.

Censoring, Competing Events, and Multistate Models: Comment on Beyersmann et al. "Hazards Constitute Key Quantities for Analyzing, Interpreting and Understanding Time-to-Event Data".Biometrical journal. Biometrische Zeitschrift · 2026
Article
A Comparison of Methods for Modeling Multistate Cancer Progression Using Screening Data with Censoring after Intervention.Medical decision making : an international journal of the Society for Medical Decision Making · 2026
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.

Atanu BhattacharjeeDivision of Population Health and Genomics, Medical School, University of Dundee, Dundee, UK.

Gajendra K VishwakarmaDepartment of Mathematics and Computing, Indian Institute of Technology, Dhanbad, India. vishwagk@rediffmail.com.

Abhipsa TripathyDepartment of Mathematics and Computing, Indian Institute of Technology, Dhanbad, India.

Bhrigu Kumar RajbongshiDepartment of Mathematics and Computing, Indian Institute of Technology, Dhanbad, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The potential contribution of the paper is the use of the propensity score matching method for updating censored observations within the context of multi-state model featuring two competing risks.The competing risks are modelled using cause-specific Cox proportional hazard model.The simulation findings demonstrate that updating censored observations tends to lead to reduced bias and mean squared error for all estimated parameters in the risk of cause-specific Cox model.The results for a chemoradiotherapy real dataset are consistent with the simulation results.

Indexed as

Propensity ScoreComputer SimulationProportional Hazards ModelsCensoringCompeting riskMultistate modelNon parametric estimationPropensity Score

Identifiers

PMID38388653

DOI10.1038/s41598-024-54149-y

PMCPMC10884420

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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.