Cox model and decision trees: an application to breast cancer data

• Published in: Revista Panamericana de Salud Pública
• Main Authors: Lucas Cardoso Pereira, Sóstenes Jerônimo da Silva, Cleanderson Romualdo Fidelis, Alisson de Lima Brito, Silvio Fernando Alves Xavier Júnior, Lorena Sofia dos Santos Andrade, Milena Edite Casé de Oliveira, Tiago Almeida de Oliveira
• Format: Article in Journal/Newspaper
• Language: English; Spanish; Portuguese
• Published: Pan American Health Organization 2022
• Subjects: survival analysis; breast neoplasms; mortality; brazil; Medicine; R; Arctic medicine. Tropical medicine; RC955-962; Public aspects of medicine; RA1-1270; Arctic
• Online Access: https://doi.org/10.26633/RPSP.2022.17; https://doaj.org/article/35e3580214ba482a85aa3338a8fccc04

Objective. To evaluate, using semiparametric methodologies of survival analysis, the relationship between covariates and time to death of patients with breast cancer, as well as the determination discriminatory power in the conditional inference tree of patients who had cancer. Methods. A retrospective cohort study was conducted using data collected from medical records of women who had breast cancer and underwent treatment between 2005 and 2015 at the Hospital da Fundação de Assistencial da Paraíba in Campina Grande, State of Paraiba, Brazil. Survival curves were estimated using the KaplanMeier method, Cox regression, and conditional decision tree. Results. Women with triple-negative molecular subtypes had a shorter survival time compared to women with positive hormone receptors. The addition of hormone therapy reduced the risk of a patient dying by 5.5%, and the risk of a HER2-positive patient dying was 34.5% lower compared to those who were negative for this gene. Patients undergoing hormone therapy had a median survival time of 4 753 days. Conclusions. This paper shows a favorable scenario for the use of immunotherapy for patients with HER2 overexpression. Further studies could assess the effectiveness of immunotherapy in patients with other conditions, to favor the prognosis and better quality of life for the patient.