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

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.

Modeling hormesis using multivariate nonlinear regression in plant biology: A comprehensive approach to understanding dose-response relationships.

For over a century, ecotoxicological studies have reported the occurrence of hormesis as a significant phenomenon in many areas of science. In plant biology, hormesis research focuses on measuring morphological, physiological, biochemical, and productivity changes in plants exposed to low doses of herbicides. These studies involve multiple features that are often correlated.

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

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.