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cff-version: 1.2.0
message: 'To cite package "BayesTSM" in publications use:'
type: software
license: MIT
title: 'BayesTSM: Bayesian Progressive Three State Model with Censoring Due to Intervention'
version: 1.0.1
doi: 10.32614/CRAN.package.BayesTSM
abstract: 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) <https://doi.org/10.1214/22-AOAS1669>.
authors:
- family-names: Klausch
  given-names: Thomas
  email: t.klausch@amsterdamumc.nl
repository: https://thomasklausch2.r-universe.dev
repository-code: https://github.com/thomasklausch2/bayestsm
commit: a829278034f54e35f4ff6b9f6c34d48ba9652e6f
url: https://github.com/thomasklausch2/bayestsm
date-released: '2026-06-08'
contact:
- family-names: Klausch
  given-names: Thomas
  email: t.klausch@amsterdamumc.nl