Evidence map›Paper›PMID 39802430›Full record

ArticleMethodsX2025

Modeling the hospitalization time of stroke patients at Abdul Wahab Sjahranie Hospital Samarinda using the Weibull Regression Model.

Suyitno, Darnah, Andrea Tri Rian Dani, Nurul Tri Oktavia

Abstract read
In one paragraph

Article in MethodsX, 2025. 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

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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

2 citing papers in PubMed.

  1. Article
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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.

SuyitnoStatistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Mulawarman University, Indonesia.
DarnahStatistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Mulawarman University, Indonesia.
Andrea Tri Rian DaniStatistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Mulawarman University, Indonesia.
Nurul Tri OktaviaStatistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Mulawarman University, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Weibull regression model is a regression model derived from the Weibull distribution, where the Weibull distribution is influenced by covariates. In this study, parameter estimation for the Weibull regression model was conducted using the Maximum Likelihood (ML) estimation. The aim of the study is to develop a Weibull regression model based on the hospitalization time of stroke patients at Abdul Wahab Sjahranie Hospital, Samarinda, during the period of 2021-2022, and to identify the factors affecting it. The event of interest in this study is patient recovery. The results indicate that the ML estimator of the Weibull regression model was obtained numerically using the Newton-Raphson iterative. The factors influencing the Weibull regression model include age, body mass index (BMI), and a history of diabetes mellitus. An increase in patient age and a history of diabetes mellitus are associated with an increase in the probability of the patient not recovering, a decrease in the likelihood of recovery, a lower recovery rate, and a longer recovery time. In contrast, an increase in BMI is associated with a decrease in the probability of the patient not recovering, an increase in the likelihood of recovery, a higher recovery rate, and a shorter recovery time. Some highlights in this article, the proposed method are:•We present The Weibull distribution influenced by covariates is called the Weibull regression model•The potential recovery of stroke disease and the factors that influence it can be analyzed through Weibull regression modeling.•The chance of a patient not recovering is modeled through a Weibull survival regression model, the chance of a patient recovering is modeled through a Weibull cumulative distribution regression model, the patient's recovery rate is modeled through a Weibull hazard regression model, and the average patient hospitalization time is modeled through a Weibull mean regression model.

Indexed as

Newton–Raphson iterativeRiskStroke patientsSurvival dataWeibull Regression ModelWeibull Regression Model and Applications

Identifiers

PMID39802430
PMCPMC11718336

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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.