Evidence map›Paper›PMID 38637600›Full record

ArticleScientific reports2024

The quasi-xgamma frailty model with survival analysis under heterogeneity problem, validation testing, and risk analysis for emergency care data.

Hamami Loubna, Hafida Goual, Fatimah M Alghamdi, Manahil SidAhmed Mustafa, Getachew Tekle Mekiso, M Masoom Ali, Abdullah H Al-Nefaie, Hassan Alsuhabi, Mohamed Ibrahim, Haitham M Yousof

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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 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

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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

10 authors.

Hamami LoubnaLaboratory of Probabilities and Statistics LaPS, Department of Mathematics, Faculty of Sciences, Badji Mokhtar Annaba University, Annaba, Algeria.
Hafida GoualLaboratory of Probabilities and Statistics LaPS, Department of Mathematics, Faculty of Sciences, Badji Mokhtar Annaba University, Annaba, Algeria.
Fatimah M AlghamdiDepartment of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Manahil SidAhmed MustafaDepartment of Statistics, Faculty of Science, University of Tabuk, Tabuk, Saudi Arabia.
Getachew Tekle MekisoDepartment of Statistics, College of Natural and Computational Science, Wachemo University, Hossana, Ethiopia. getachewtekle@wcu.edu.et.ORCID 0000-0002-6606-2541
M Masoom AliDepartment of Mathematical Sciences, Ball State University, Muncie, IN, USA.
Abdullah H Al-NefaieDepartment of Quantitative Methods, School of Business, King Faisal University, 31982, Al-Ahsa, Saudi Arabia.
Hassan AlsuhabiDepartment of Mathematics, Al-Qunfudah University College, Umm Al-Qura University, Mecca, Saudi Arabia.
Mohamed IbrahimDepartment of Quantitative Methods, School of Business, King Faisal University, 31982, Al-Ahsa, Saudi Arabia.
Haitham M YousofDepartment of Statistics, Mathematics and Insurance, Benha University, Benha, Egypt.

Funding

Princess Nourah bint Abdulrahman University PNURSP2024R735
6 · The paper itself

Abstract

Frailty models are important for survival data because they allow for the possibility of unobserved heterogeneity problem. The problem of heterogeneity can be existed due to a variety of factors, such as genetic predisposition, environmental factors, or lifestyle choices. Frailty models can help to identify these factors and to better understand their impact on survival. In this study, we suggest a novel quasi xgamma frailty (QXg-F) model for the survival analysis. In this work, the test of Rao-Robson and Nikulin is employed to test the validity and suitability of the probabilistic model, we examine the distribution's properties and evaluate its performance in comparison with many relevant cox-frailty models. To show how well the QXg-F model captures heterogeneity and enhances model fit, we use simulation studies and real data applications, including a fresh dataset gathered from an emergency hospital in Algeria. According to our research, the QXg-F model is a viable replacement for the current frailty modeling distributions and has the potential to improve the precision of survival analyses in a number of different sectors, including emergency care. Moreover, testing the ability and the importance of the new QXg-F model in insurance is investigated using simulations via different methods and application to insurance data.

Indexed as

Emergency Medical ServicesFrailtyHumansModels, StatisticalProportional Hazards ModelsRisk AssessmentSurvival AnalysisCensored dataFrailty modelHeterogeneityMaximum likelihoodStatistical testSurvival analysis

Identifiers

PMID38637600
PMCPMC11026502

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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.