State transition modeling of complex monitored health data

Jörn Schulz, Jan Terje Kvaløy, Kjersti Engan, Trygve Eftestøl, Samwel Jatosh, Hussein Kidanto, Hege Ersdal

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


This article considers the analysis of complex monitored health data, where often one or several signals are reflecting the current health status that can be represented by a finite number of states, in addition to a set of covariates. In particular, we consider a novel application of a non-parametric state intensity regression method in order to study time-dependent effects of covariates on the state transition intensities. The method can handle baseline, time varying as well as dynamic covariates. Because of the non-parametric nature, the method can handle different data types and challenges under minimal assumptions. If the signal that is reflecting the current health status is of continuous nature, we propose the application of a weighted median and a hysteresis filter as data pre-processing steps in order to facilitate robust analysis. In intensity regression, covariates can be aggregated by a suitable functional form over a time history window. We propose to study the estimated cumulative regression parameters for different choices of the time history window in order to investigate short- and long-term effects of the given covariates. The proposed framework is discussed and applied to resuscitation data of newborns collected in Tanzania.

Original languageEnglish
Pages (from-to)1915-1935
Number of pages21
JournalJournal of Applied Statistics
Issue number11
Publication statusPublished - 17 Aug 2020
Externally publishedYes


  • Aalen's linear model
  • Aalen-Johansen estimator
  • monitored health data
  • multi-state models
  • state transition intensity


Dive into the research topics of 'State transition modeling of complex monitored health data'. Together they form a unique fingerprint.

Cite this