BayesTSM - Bayesian Progressive Three State Model with Censoring Due to
Intervention
In screening programs, individuals are usually followed up
and tested (screened) for the development of a disease, such as
cancer. The target disease often develops progressively in
stages; for example healthy (state 1), pre-state disease (state
2), and the disease state (state 3). When the pre-state disease
is found during screening it is intervened upon, for example by
surgical removal of a lesion, so that the progression of the
pre-state disease to disease is interrupted. This is called
censoring due to intervention. Researchers often want to
estimate the time from baseline to the pre-state disease, the
time from the pre-state disease to the disease, and the total
time from baseline to the disease. In addition, researchers
often want to regress these times on baseline covariates. To
these ends, BayesTSM estimates a progressive three-state model
with censoring due to intervention using Bayesian estimation
methods, as described in Klausch et al. (2023)
<doi:10.1214/22-AOAS1669>.
