TITLE:
The Cox Proportional Hazard Regression Model Vis-à-Vis ITN-Factor Impact on Mortality Due to Malaria
AUTHORS:
Anthony Joe Turkson, John Awuah Addor, Francis Ayiah-Mensah
KEYWORDS:
Baseline Hazard, Cox Model, Hazard Function, Hazard Ratio, Survival Function
JOURNAL NAME:
Open Journal of Statistics,
Vol.11 No.6,
December
6,
2021
ABSTRACT: This study has provided a starting point for defining and working with
Cox models in respect of multivariate modeling. In medical researches, there
may be situations, where several risk factors potentially affect patient
prognosis, howbeit, only one or two might predict patient’s predicament. In
seeking to find out which of the risk factors contribute the most to the
survival times of patients, there was the need for researchers to adjust the covariates
to realize their impact on survival times of patients. Aside the multivariate
nature of the covariates, some covariates might be categorical while others
might be quantitative. Again, there might be cases where researchers need a
model that has the capability of extending
survival analysis methods to assessing simultaneously the effect of
several risk factors on survival times. This study unveiled the Cox model as a
robust technique which could accomplish the aforementioned cases. An investigation meant to
evaluate the ITN-factor vis-à-vis its contribution
towards death due to Malaria was exemplified with the Cox model. Data were
taken from hospitals in Ghana. In doing so, we assessed hospital in-patients
who reported cases of malaria (origin state) to time until death or censoring
(destination stage) as a result of predictive factors (exposure to the malaria
parasites) and some socioeconomic variables. We purposefully used Cox models to
quantify the effect of the ITN-factor in the presence of other risk factors to
obtain some measures of effect that could describe the relationship between the exposure variable and time
until death adjusting for other variables. PH assumption holds for all
three covariates. Sex of patient was insignificant to deaths due to malaria. Age
of patient and user status were both significant. The magnitude of the coefficient (0.384) of ITN user
status depicts its high contribution to the variation in the dependent
variable.