Regression Analysis of Masked Competing Risks Data under Cumulative Incidence Function Framework

Abstract

In the studies that involve competing risks, somehow, masking issues might arise. That is, the cause of failure for some subjects is only known as a subset of possible causes. In this study, a Bayesian analysis is developed to assess the effect of risks factor on the Cumulative Incidence Function (CIF) by adopting the proportional subdistribution hazard model. Simulation is conducted to evaluate the performance of the proposed model and it shows that the model is feasible for the possible applications.

Bayesian Analysis of Masked Competing Risks Data Based on Proportional Subdistribution Hazards Model

“…Moreover, Figure 1 compares the estimated CIF between Fine & Gray model and the developed model with 26% masked units. Figure 2 shows another comparison where there is a 49% masked units [11]. Obviously, the CIF curves are comparable and show a substantial consistency with…”

“…Earlier, Miyakawa [9] discussed this type of data by considering parametric and non-parametric approaches to reliability estimation. Previously, we developed a Bayesian approach to estimate the effect of explanatory variables motivated by incomplete data with masked causes of failure [10,11]. We discussed the effect of covariates on CIF in the presence of a moderate masking level, and preliminary results were introduced [11].…”

“…Previously, we developed a Bayesian approach to estimate the effect of explanatory variables motivated by incomplete data with masked causes of failure [10,11]. We discussed the effect of covariates on CIF in the presence of a moderate masking level, and preliminary results were introduced [11]. However, this paper considered different levels of masking for the simulated data in addition to an application on a real data set.…”