Estimation of semi-Markov multi-state models: a comparison of the sojourn times and transition intensities approaches

Azam Asanjarani, Benoit Liquet*, Yoni Nazarathy

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Semi-Markov models are widely used for survival analysis and reliability analysis. In general, there are two competing parameterizations and each entails its own interpretation and inference properties. On the one hand, a semi-Markov process can be defined based on the distribution of sojourn times, often via hazard rates, together with transition probabilities of an embedded Markov chain. On the other hand, intensity transition functions may be used, often referred to as the hazard rates of the semi-Markov process. We summarize and contrast these two parameterizations both from a probabilistic and an inference perspective, and we highlight relationships between the two approaches. In general, the intensity transition based approach allows the likelihood to be split into likelihoods of two-state models having fewer parameters, allowing efficient computation and usage of many survival analysis tools. Nevertheless, in certain cases the sojourn time based approach is natural and has been exploited extensively in applications. In contrasting the two approaches and contemporary relevant R packages used for inference, we use two real datasets highlighting the probabilistic and inference properties of each approach. This analysis is accompanied by an R vignette.

Original languageEnglish
JournalInternational Journal of Biostatistics
DOIs
Publication statusE-pub ahead of print - 6 Jan 2021

Keywords

  • hazard rate
  • intensity transition
  • multi-state model
  • semi-Markov model
  • sojourn time

Fingerprint

Dive into the research topics of 'Estimation of semi-Markov multi-state models: a comparison of the sojourn times and transition intensities approaches'. Together they form a unique fingerprint.

Cite this