Survival time analysis in women with breast cancer using distributional regression models
Abstract: Cancer is a global public health concern due to its high mortality rates. In Brazil, breast cancer is one of the leading causes of disease and death among women in all regions of the country, with higher mortality rates in less developed regions. Hence, this study analyzes variables associ...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Escola Nacional de Saúde Pública, Fundação Oswaldo Cruz
2025-08-01
|
| Series: | Cadernos de Saúde Pública |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-311X2025000801402&tlng=en |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849222122568679424 |
|---|---|
| author | Isabela da Silva Lima Sóstenes Jerônimo da Silva Carla Regina Guimarães Brighenti Luiz Ricardo Nakamura Tiago Almeida de Oliveira Milena Edite Casé de Oliveira Thiago Gentil Ramires |
| author_facet | Isabela da Silva Lima Sóstenes Jerônimo da Silva Carla Regina Guimarães Brighenti Luiz Ricardo Nakamura Tiago Almeida de Oliveira Milena Edite Casé de Oliveira Thiago Gentil Ramires |
| author_sort | Isabela da Silva Lima |
| collection | DOAJ |
| description | Abstract: Cancer is a global public health concern due to its high mortality rates. In Brazil, breast cancer is one of the leading causes of disease and death among women in all regions of the country, with higher mortality rates in less developed regions. Hence, this study analyzes variables associated with survival time in breast cancer patients in Campina Grande, Paraíba State, Brazil. Distributional regression models, also known as generalized additive models for location, scale, and shape (GAMLSS), were used due to their flexibility in explaining complex behaviors of a given response (for example, survival time) based on other variables. Tumor site, age, number of hormone therapy, radiotherapy and chemotherapy sessions, and molecular markers such as estrogen receptor, progesterone receptor, Ki-67 protein, p53, HER2 mutation and molecular subtype were examined. Two different GAMLSS were fitted considering Weibull and log-normal distributions, the former of which is more appropriate per the Akaike information criterion. Using a variable selection procedure specific to GAMLSS, we identified four covariates that directly affect average survival time: number of hormone therapy and chemotherapy sessions, p53 status, and estrogen receptor status. Excepting estrogen receptor status, the other covariates selected to explain average survival time were also used to explicitly explain the variability of these times. |
| format | Article |
| id | doaj-art-3a084fee2f344ac5a1597cae77fc0779 |
| institution | Kabale University |
| issn | 1678-4464 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Escola Nacional de Saúde Pública, Fundação Oswaldo Cruz |
| record_format | Article |
| series | Cadernos de Saúde Pública |
| spelling | doaj-art-3a084fee2f344ac5a1597cae77fc07792025-08-26T07:47:07ZengEscola Nacional de Saúde Pública, Fundação Oswaldo CruzCadernos de Saúde Pública1678-44642025-08-0141810.1590/0102-311xen073324Survival time analysis in women with breast cancer using distributional regression modelsIsabela da Silva Limahttps://orcid.org/0000-0001-9955-4465Sóstenes Jerônimo da Silvahttps://orcid.org/0000-0002-5981-4266Carla Regina Guimarães Brighentihttps://orcid.org/0000-0002-7822-3744Luiz Ricardo Nakamurahttps://orcid.org/0000-0002-7312-2717Tiago Almeida de Oliveirahttps://orcid.org/0000-0003-4147-7721Milena Edite Casé de Oliveirahttps://orcid.org/0000-0003-2266-5890Thiago Gentil Ramireshttps://orcid.org/0000-0002-1972-7045Abstract: Cancer is a global public health concern due to its high mortality rates. In Brazil, breast cancer is one of the leading causes of disease and death among women in all regions of the country, with higher mortality rates in less developed regions. Hence, this study analyzes variables associated with survival time in breast cancer patients in Campina Grande, Paraíba State, Brazil. Distributional regression models, also known as generalized additive models for location, scale, and shape (GAMLSS), were used due to their flexibility in explaining complex behaviors of a given response (for example, survival time) based on other variables. Tumor site, age, number of hormone therapy, radiotherapy and chemotherapy sessions, and molecular markers such as estrogen receptor, progesterone receptor, Ki-67 protein, p53, HER2 mutation and molecular subtype were examined. Two different GAMLSS were fitted considering Weibull and log-normal distributions, the former of which is more appropriate per the Akaike information criterion. Using a variable selection procedure specific to GAMLSS, we identified four covariates that directly affect average survival time: number of hormone therapy and chemotherapy sessions, p53 status, and estrogen receptor status. Excepting estrogen receptor status, the other covariates selected to explain average survival time were also used to explicitly explain the variability of these times.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-311X2025000801402&tlng=enBreast NeoplasmsMortalitySurvival Analysis |
| spellingShingle | Isabela da Silva Lima Sóstenes Jerônimo da Silva Carla Regina Guimarães Brighenti Luiz Ricardo Nakamura Tiago Almeida de Oliveira Milena Edite Casé de Oliveira Thiago Gentil Ramires Survival time analysis in women with breast cancer using distributional regression models Cadernos de Saúde Pública Breast Neoplasms Mortality Survival Analysis |
| title | Survival time analysis in women with breast cancer using distributional regression models |
| title_full | Survival time analysis in women with breast cancer using distributional regression models |
| title_fullStr | Survival time analysis in women with breast cancer using distributional regression models |
| title_full_unstemmed | Survival time analysis in women with breast cancer using distributional regression models |
| title_short | Survival time analysis in women with breast cancer using distributional regression models |
| title_sort | survival time analysis in women with breast cancer using distributional regression models |
| topic | Breast Neoplasms Mortality Survival Analysis |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-311X2025000801402&tlng=en |
| work_keys_str_mv | AT isabeladasilvalima survivaltimeanalysisinwomenwithbreastcancerusingdistributionalregressionmodels AT sostenesjeronimodasilva survivaltimeanalysisinwomenwithbreastcancerusingdistributionalregressionmodels AT carlareginaguimaraesbrighenti survivaltimeanalysisinwomenwithbreastcancerusingdistributionalregressionmodels AT luizricardonakamura survivaltimeanalysisinwomenwithbreastcancerusingdistributionalregressionmodels AT tiagoalmeidadeoliveira survivaltimeanalysisinwomenwithbreastcancerusingdistributionalregressionmodels AT milenaeditecasedeoliveira survivaltimeanalysisinwomenwithbreastcancerusingdistributionalregressionmodels AT thiagogentilramires survivaltimeanalysisinwomenwithbreastcancerusingdistributionalregressionmodels |