A Bayesian Split Population Survival Model for Duration Data With Misclassified Failure Events. (25th March 2019) - Bagozzi, Benjamin E.; Joo, Minnie M.; Kim, Bomin; Mukherjee, Bumba
Abstract : We develop a new Bayesian split population survival model for the analysis of survival data with misclassified event failures. Within political science survival data, right-censored survival cases are often erroneously misclassified as failure cases due to measurement error. Treating these cases as failure events within survival analyses will underestimate the duration of some events. This will bias coefficient estimates, especially in situations where such misclassification is associated with covariates of interest. Our split population survival estimator addresses this challenge by using a system of two equations to explicitly model the misclassification of failure events alongside a parametric survival process of interest. After deriving this model, we use Bayesian estimation via slice sampling to evaluate its performance with simulated data, and in several political science applications. We find that our proposed "misclassified failure" survival model allows researchers to accurately account for misclassified failure events within the contexts of civil war duration and democratic survival.