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.
Highlights
Assessing the effect of risk factors on the lifetime of targeted subjects is one of the interests when competing risks data under discussion
The researchers noted that the methods based on cause-specific hazard under proportional hazard formulation disallow the analyst direct assessment of the effect of a covariate on the cumulative incidence function
Study conducted by Jeong and Fine (2006) presented a parametric regression analysis of cumulative incidence function that involved the maximum likelihood inferences and were derived to fit the parametric models of cumulative incidence functions for all causes simultaneously
Summary
Assessing the effect of risk factors on the lifetime of targeted subjects is one of the interests when competing risks data under discussion. Study conducted by Jeong and Fine (2006) presented a parametric regression analysis of cumulative incidence function that involved the maximum likelihood inferences and were derived to fit the parametric models of cumulative incidence functions for all causes simultaneously. The coefficient of interest, its variance-covariance matrix, and the baseline cumulative incidence function are updated from multiple posterior estimation derived from the Fine and Gray (1999) sub-distribution hazards regression that provides augmented data. There were exceptions where the masking issues was considered, such as, Do and Kim (2017) regarded the case of missing causes of failure They applied a Klein?Andersen’s pseudo-value approach based on the estimated cumulative incidence function and a regression coefficient is estimated through a multiple imputation.
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