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

Author

Cardoso Pereira, Lucas, Silva, Sóstenes Jerônimo da, Romualdo Fidelis, Cleanderson, de Lima Brito, Alisson, Alves Xavier Júnior, Silvio Fernando, dos Santos Andrade, Lorena Sofia, Casé de Oliveira, Milena Edite, and Almeida de Oliveira, Tiago

Subjects
*BREAST cancer prognosis, *BREAST tumor treatment, *DECISION trees, *MATHEMATICAL statistics, *PARAMETERS (Statistics), *SPECIALTY hospitals, *HORMONE therapy, *TIME, *RETROSPECTIVE studies, *ACQUISITION of data, *REGRESSION analysis, *MOLECULAR pathology, *CANCER patients, *TREATMENT effectiveness, *CANCER treatment, *COMPARATIVE studies, *RISK assessment, *MEDICAL records, *KAPLAN-Meier estimator, *DESCRIPTIVE statistics, *RESEARCH funding, *STATISTICAL models, *BREAST tumors, *PROPORTIONAL hazards models, *LONGITUDINAL method, *HORMONE receptor positive breast cancer, *IMMUNOTHERAPY, MORTALITY risk factors
Abstract

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 Kaplan-Meier 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. [ABSTRACT FROM AUTHOR]

Published
2022
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2. Current Mathematical Models for Analyzing Anti-Malarial Antibody Data with an Eye to Malaria Elimination and Eradication.

Author

Sepúlveda N, Stresman G, White MT, and Drakeley CJ

Subjects
Animals, Brazil, Datasets as Topic, Equatorial Guinea, Humans, Kenya, Malaria immunology, Antibodies, Protozoan therapeutic use, Antimalarials therapeutic use, Immunotherapy, Malaria therapy, Malaria Vaccines immunology, Models, Theoretical, Plasmodium immunology
Abstract

The last decade has witnessed a steady reduction of the malaria burden worldwide. With various countries targeting disease elimination in the near future, the popular parasite infection or entomological inoculation rates are becoming less and less informative of the underlying malaria burden due to a reduced number of infected individuals or mosquitoes at the time of sampling. To overcome such problem, alternative measures based on antibodies against specific malaria antigens have gained recent interest in malaria epidemiology due to the possibility of estimating past disease exposure in absence of infected individuals. This paper aims then to review current mathematical models and corresponding statistical approaches used in antibody data analysis. The application of these models is illustrated with three data sets from Equatorial Guinea, Brazilian Amazonia region, and western Kenyan highlands. A brief discussion is also carried out on the future challenges of using these models in the context of malaria elimination.

Published
2015
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