Evidence map›Paper›PMID 38811667›Full record

ArticleScientific reports2024

The development of an extended Weibull model with applications to medicine, industry and actuarial sciences.

Muhammad Imran, Najwan Alsadat, M H Tahir, Farrukh Jamal, Mohammed Elgarhy, Hijaz Ahmad, Arne Johannssen

Erratum issuedAbstract read
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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. An erratum has been issued. 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

5 · Who and what money

Authors and funding

7 authors.

Muhammad ImranDepartment of Statistics, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan.
Najwan AlsadatDepartment of Quantitative Analysis, College of Business Administration, King Saud University, P.O. Box 71115, 11587, Riyadh, Saudi Arabia.
M H TahirDepartment of Statistics, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan.
Farrukh JamalDepartment of Statistics, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan.
Mohammed ElgarhyDepartment of Basic Sciences, Higher Institute for Administrative Sciences, Belbeis, Al-Sharqia, Egypt.
Hijaz AhmadNear East University, Operational Research Center in Healthcare, TRNC Mersin 10, Nicosia, 99138, Turkey.
Arne JohannssenFaculty of Business Administration, University of Hamburg, 20146, Hamburg, Germany. arne.johannssen@uni-hamburg.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper delves into the theoretical and practical exploration of the complementary Bell Weibull (CBellW) model, which serves as an analogous counterpart to the complementary Poisson Weibull model. The study encompasses a comprehensive examination of various statistical properties of the CBellW model. Real data applications are carried out in three different fields, namely the medical, industrial and actuarial fields, to show the practical versatility of the CBellW model. For the medical data segment, the study utilizes four data sets, including information on daily confirmed COVID-19 cases and cancer data. Additionally, a Group Acceptance Sampling Plan (GASP) is designed by using the median as quality parameter. Furthermore, some actuarial risk measures for the CBellW model are obtained along with a numerical illustration of the Value at Risk and the Expected Shortfall. The research is substantiated by a comprehensive numerical analysis, model comparisons, and graphical illustrations that complement the theoretical foundation.

Indexed as

COVID-19Models, StatisticalHumansIndustryNeoplasmsPoisson DistributionSARS-CoV-2CancerComplementary Bell-G familyCOVID-19Group acceptance sampling planQuality controlRisk measures

Identifiers

PMID38811667
PMCPMC11190291

What OpenQuestion holds

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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.