idmTPreg: Regression Model for Progressive Illness Death Data

The progressive illness-death model is frequently used in medical applications. For example, the model may be used to describe the disease process in cancer studies. We have developed a new R package called idmTPreg to estimate regression coefficients in datasets that can be described by the progressive illness-death model. The motivation for the development of the package is a recent contribution that enables the estimation of possibly time-varying covariate effects on the transition probabilities for a progressive illness-death data. The main feature of the package is that it befits both non-Markov and Markov progressive illness-death data. The package implements the introduced estimators obtained using a direct binomial regression approach. Also, variance estimates and confidence bands are implemented in the package. This article presents guidelines for the use of the package.

Leyla Azarang , Manuel Oviedo de la Fuente

Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at

CRAN packages used

idmTPreg, mstate, msm, p3state.msm, doParallel, foreach, survival

CRAN Task Views implied by cited packages

Survival, ClinicalTrials, Distributions, Econometrics, HighPerformanceComputing, SocialSciences


Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".


For attribution, please cite this work as

Azarang & Fuente, "The R Journal: idmTPreg: Regression Model for Progressive Illness Death Data", The R Journal, 2019

BibTeX citation

  author = {Azarang, Leyla and Fuente, Manuel Oviedo de la},
  title = {The R Journal: idmTPreg: Regression Model for Progressive Illness Death Data},
  journal = {The R Journal},
  year = {2019},
  note = {},
  doi = {10.32614/RJ-2018-081},
  volume = {10},
  issue = {2},
  issn = {2073-4859},
  pages = {317-325}