In Bayesian model selection or testingproblems one cannot utilize standard or default
noninformativepriors, since these priors are typically improper and are definedonly up to …

We study Bayesian approaches to causal inference via propensity score regression. Much of
Bayesian methodology relies on parametric and distributional assumptions, with presumed …

Identification can be a major issue in causal modeling contexts, and in contexts where
observational studies have various limitations. Partially identified models can arise, whereby …

In this paper we propose inference methods based on the EM algorithm for estimating the
parameters of a weakly parameterised competing risks model with masked causes of failure …

In this paper, we develop a Bayesian method for joint analysis of longitudinal measurements
and competing risks failure time data. The model allows one to analyze the longitudinal …

The competing risks model is useful in settings in which individuals/units may die/fail for
different reasons. The cause specific hazard rates are taken to be piecewise constant …

We develop and study an innovative method for jointly modeling longitudinal response and
time-to-event data with a covariate subject to a limit of detection. The joint model assumes a …

The problem of competing risks analysis arises often in public health, demography, actuarial
science, industrial reliability applications, and experiments in medical therapeutics. In the …

The deviance information criterion (DIC) is widely used for Bayesian model comparison,
despite the lack of a clear theoretical foundation. DIC is shown to be an approximation to a …

The R package CFC performs cause-specific, competing-risk survival analysis by computing
cumulative incidence functions from unadjusted, cause-specific survival functions. A high …