TITLE:
Mixed-Effects Parametric Proportional Hazard Model with Generalized Log-Logistic Baseline Distribution
AUTHORS:
Maryrose Wausi Peter, Samuel Musili Mwalili, Anthony Kibira Wanjoya, Abdsalam Hassan Muse
KEYWORDS:
Survival Analysis, Generalized Log-Logistic, Parametric, Proportional Hazard, Mixed-Effects, Monte Carlo, Maximum Likelihood Estimation
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.11 No.2,
March
14,
2023
ABSTRACT: Clustered survival data are widely observed in a variety of setting. Most
survival models incorporate clustering and grouping of data accounting for
between-cluster variability that creates correlation in order to prevent
underestimate of the standard errors of the parameter estimators but do not include random effects. In this study, we developed a
mixed-effect parametric proportional hazard (MEPPH) model with a
generalized log-logistic
distribution baseline. The parameters of the
model were estimated by the application of the maximum likelihood estimation
technique with an iterative optimization procedure (quasi-Newton Raphson). The
developed MEPPH model’s performance was evaluated using Monte Carlo simulation. The Leukemia dataset with right-censored data was used to
demonstrate the model’s applicability.
The results revealed that all covariates, except age in PH models, were
significant in all considered distributions.
Age and Townsend score were significant when the GLL distribution was used in
MEPPH, while sex, age and Townsend score were significant in MEPPH model when
other distributions were used. Based on information criteria values, the
Generalized Log-Logistic Mixed-Effects Parametric Proportional Hazard model
(GLL-MEPPH) outperformed other models.