Evidence map›Paper›PMID 38524797›Full record

ArticleJournal of applied statistics2024

A new flexible regression model with application to recovery probability Covid-19 patients.

F Prataviera, E M Hashimoto, E M M Ortega, G M Cordeiro, V G Cancho, R Vila

Abstract read
In one paragraph

Article in Journal of applied statistics, 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
0.7field-weighted citation impact, top 32% of its field
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, 2 citations in OpenAlex.

  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

6 authors at 4 institutions in 1 country.

F PratavieraDepartment of Exact Sciences, University of S ao Paulo, Piracicaba, Brazil.ORCID https://orcid.org/0000-0001-8190-1086
E M HashimotoAcademic Department of Mathematics, Federal University of Technology - Paraná, Londrina, Brazil.ORCID https://orcid.org/0000-0003-0949-2358
E M M OrtegaDepartment Exact Sciences, University of São Paulo, Piracicaba, Brazil.ORCID https://orcid.org/0000-0003-3999-7402
G M CordeiroDepartment of Statistics, Federal University of Pernambuco, Recife, Brazil.ORCID https://orcid.org/0000-0002-3052-6551
V G CanchoDepartment of Statistics, University of São Paulo, São Carlos, Brazil.
R VilaDepartment of Statistics, University of Brasilia, Brasilia, Brazil.ORCID https://orcid.org/0000-0003-1073-0114
Universidade de São Paulo · BRUniversidade de Brasília · BRUniversidade Federal de Pernambuco · BRUniversidade Tecnológica Federal do Paraná · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study is to propose a generalized odd log-logistic Maxwell mixture model to analyze the effect of gender and age groups on lifetimes and on the recovery probabilities of Chinese individuals with COVID-19. We add new properties of the generalized Maxwell model. The coefficients of the regression and the recovered fraction are estimated by maximum likelihood and Bayesian methods. Further, some simulation studies are done to compare the regressions for different scenarios. Model-checking techniques based on the quantile residuals are addressed. The estimated survival functions for the patients are reported by age range and sex. The simulation study showed that mean squared errors decay toward zero and the average estimates converge to the true parameters when sample size increases. According to the fitted model, there is a significant difference only in the age group on the lifetime of individuals with COVID-19. Women have higher probability of recovering than men and individuals aged

Indexed as

Censored dataCOVID-19Maxwell distributionmixture modelquantile residuals

Identifiers

PMID38524797
PMCPMC10956937
OpenAlexW4313592734

What OpenQuestion holds

Textmetadata
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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.