GUIDANCE FOR PRACTITIONERS ON THE CHOICES OF SOFTWARE IMPLEMENTATION FOR FRAILTY MODELS: SIMULATIONS AND AN APPLICATION IN DETERMINING THE BIRTH INTERVAL DYNAMICS

Abstract

In clustered survival analysis applications, researchers frequently fit frailty models using
parametric and nonparametric approaches to obtain the estimates for the parameters associated
with the survival model covariates and heterogeneity (frailty). Availability of the offthe-
shelve implementations and freely available R software packages makes it convenient
for the practitioners to fit these complicated models easily. Even though there has been a
couple of studies assessing the stability of the older packages (e.g., survival, coxme)
under a variety of scenarios, some of the newer implementations (e.g., frailtySurv,
JM and parfm) have not gone through similar rigorous assessment. It is worth evaluating
these new software implementations, and comparing them with the older packages. In
the current work, via simulations, we will examine the estimates from all of these popularly
used software implementations under a variety of scenarios when the corresponding
assumptions related to the baseline hazard and frailty distributions are misspecified. Additionally,
true heterogeneity parameter, censoring patterns and number of clusters were
varied in the simulations to assess respective impacts on the estimates. From these simulations,
we observed that when there is a large number of clusters and mild censoring,
Cox PH frailty models fitted using a newer semiparametric estimation technique (from the
frailtySurv package) produced regression and heterogeneity parameter estimates that
were associated with unusually large bias and variability. On the other hand, when the true
heterogeneity parameter is substantially large, the Cox PH frailty models fitted using the
coxme package were often producing highly variable estimates of the heterogeneity parameter.
The simulation findings then guided our choice of appropriate frailty model in the
context of determining the birth interval dynamics in Bangladesh.