Evidence map›Paper›PMID 39507210›Full record

ArticleJournal of applied statistics2024

Reparametrized generalized gamma partially linear regression with application to breast cancer data.

Cleanderson R Fidelis, Edwin M M Ortega, Fábio Prataviera, Roberto Vila, Gauss M Cordeiro

Abstract read
PubMed Full text at PMC Publisher
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. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Cleanderson R FidelisESALQ, Universidade de São Paulo, Piracicaba, Brazil.
Edwin M M OrtegaESALQ, Universidade de São Paulo, Piracicaba, Brazil.ORCID https://orcid.org/0000-0003-3999-7402
Fábio PratavieraESALQ, Universidade de São Paulo, Piracicaba, Brazil.
Roberto VilaDepartamento de Estatística, Universidade de Brasília, Brasília, Brazil.
Gauss M CordeiroDepartamento de Estatística, Universidade Federal de Pernambuco, Recife, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We construct a new partially linear regression based on a reparametrized generalized gamma distribution with two systematic components that can be easily interpreted. Its parameters are estimated by penalized maximum likelihood. For different parameter settings, sample sizes, and censoring percentages, some simulations are performed to examine the accuracy of the maximum likelihood estimators, and the empirical distribution of the residuals compared with the standard normal distribution. The methodology is applied to breast cancer data in the city of João Pessoa in the state of Paraíba in Brazil.

Indexed as

Breast cancer datagamma generalized distributionpartially linear regressionspenalized maximum likelihoodstochastic representation

Identifiers

PMID39507210
PMCPMC11536640

What OpenQuestion holds

Textmetadata
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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.