Semiparametric Modelling of Cancer Mortality Trends in Colombia

TL;DR

This study compares semiparametric and parametric models for analyzing cancer mortality trends in Colombia, finding that semiparametric models provide better fit and insights into age and period effects across different cancers.

Contribution

It introduces and evaluates semiparametric models for cancer mortality, demonstrating their superiority over traditional Poisson models in this context.

Findings

Semiparametric models outperform traditional models based on AIC and correlation.

Cancer mortality rates increase with age across studied types.

Period effects vary: some cancers rise, others remain stable or decline over time.

Abstract

In this paper, we compare semiparametric and parametric model adjustments for cancer mortality in breast and cervical cancer and prostate and lung cancer in men, according to age and period of death. Semiparametric models were adjusted for the number of deaths from the two localizations of greatest mortality by sex: breast and cervix in women; prostate and lungs in men. Adjustments in different semiparametric models were compared; which included making adjustments with different distributions and variable combinations in the parametric and non-parametric part, for localization as well as for scale. Finally, the semiparametric model with best adjustment was selected and compared to traditional model; that is, to the generalized lineal model with Poisson response and logarithmic link. Best results for the four kinds of cancer were obtained for the selected semiparametric model by comparing it to the traditional Poisson model based upon AIC, envelope correlation between estimated logarithm rate and real rate logarithm. In general, we observe that in estimation, rate increases with age; however, with respect to period, breast cancer and stomach cancer in men show a tendency to rise over time; on the other hand, for cervical cancer, it remains virtually constant, but for lung cancer in men, as of 2007, it tends to decrease.