Statistical modelling for cancer mortality
DOI:
https://doi.org/10.30707/LiB6.2Ghosh2Keywords:
Colon and Rectum cancer, Kidney and Renal Pelvis cancer, log-linear models, residual deviance, log-likelihood, Cox-proportional hazards modelAbstract
The usefulness of log-linear models for contingency table analysis is evident from its current general popularity among statisticians. We have extracted U.S. vital rates and survival data in cancer mortality from the Surveillance between the year of 1975–2015. This paper has two distinct fields: (i) Survival and (ii) Contingency table analysis in a single analytical framework based on log-linear model. In this paper, the effects of gender and different types of cancer on death rate have been demonstrated. Testing and estimation are also applicable here. The purpose of the underlying Cox model is to evaluate simultaneously the effect of several factors on survival, i.e. it can examine how specified factors influence the rate of happening of a particular event (e.g. death) during this time interval. The purpose of this work is not to develop new methodologies, but rather to present new uses and interpretations. Simulation is based on R-software.
