Abstract:
Background: Ischemic heart disease (IHD) is a disorder of cardiac function caused
by insu cient blood
ow to the muscle tissue of the heart. The decreased blood
ow
is in most cases due to coronary arteriosclerosis or to obstruction by a thrombus of the
coronary arteries. Acute myocardial infarction, unstable angina, and angina pectoris
are manifestations of ischemic heart disease.
Objective: The general objective of this study is to model the time to death of patients
with ischemic heart disease using various parametric shared frailty models.
Methods: Di erent parametric frailty models were compared using exponential, weibull,
and log-logistic as baseline hazard functions and the gamma as well as the inverse Gaussian
for the frailty distributions, with the goal of developing an appropriate survival
model that adequately describes the ischemic heart disease dataset. All models were
then compared using the AIC and BIC criteria.
Results: The median time to death of the ischemic heart disease patients was about
ve days, with a maximum death time of thirty days, of which about 35.37% died. The
clustering e ect is signi cant in modeling the time to death of ischemic heart disease.
The log-logistic model with an inverse Gaussian frailty distribution has the minimum
AIC value among the models compared. According to the output of the model (loglogistic
with inverse Gaussian frailty), diabetes mellitus, hypertension, obesity, smoking
status, cholesterol, and other diseases were the main determinant factors of IHD.
Conclusions: Compared to other distributions employed in this study, the log-logistic
with inverse Gaussian frailty model provided a superior description of the ischemic
heart disease dataset. The time to death of ischemic heart disease patients vary between
woredas, indicating that frailty models must be used to take into account this
clustering feature.
