Change-point detection for infinite horizon dynamic treatment regimes

Yair Goldberg, Moshe Pollak, Alexis Mitelpunkt, Mila Orlovsky, Ahuva Weiss-Meilik, Malka Gorfine

Research output: Contribution to journalArticlepeer-review

Abstract

A dynamic treatment regime is a set of decision rules for how to treat a patient at multiple time points. At each time point, a treatment decision is made depending on the patient's medical history up to that point. We consider the infinite-horizon setting in which the number of decision points is very large. Specifically, we consider long trajectories of patients' measurements recorded over time. At each time point, the decision whether to intervene or not is conditional on whether or not there was a change in the patient's trajectory. We present change-point detection tools and show how to use them in defining dynamic treatment regimes. The performance of these regimes is assessed using an extensive simulation study. We demonstrate the utility of the proposed change-point detection approach using two case studies: detection of sepsis in preterm infants in the intensive care unit and detection of a change in glucose levels of a diabetic patient.

Original languageEnglish
Pages (from-to)1590-1604
Number of pages15
JournalStatistical Methods in Medical Research
Volume26
Issue number4
DOIs
StatePublished - 1 Aug 2017

Bibliographical note

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Y. Goldberg was funded in part by NSF grant DMS-1407732. The work of M. Pollak was supported by the Marcy Bogen Chair of Statistics at the Hebrew University of Jerusalem and Israel Science Foundation Grant number 1450/13.

Publisher Copyright:
© The Author(s) 2017.

Keywords

  • dynamic treatment regime
  • infinite horizon
  • Late-onset neonatal sepsis
  • personalized medicine
  • sequential change-point detection

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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