Change-point detection for infinite horizon dynamic treatment regimes. (August 2017)

• Record Type: Journal Article
• Authors: Goldberg, Yair; Pollak, Moshe; Mitelpunkt, Alexis; Orlovsky, Mila; Weiss-Meilik, Ahuva; Gorfine, Malka
• Other Names: Davidian Marie guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 26:Number 4(2017)
• Page Start: 1590
• Page End: 1604
• Publication Date: 2017-08
• Subjects: Late-onset neonatal sepsis -- sequential change-point detection -- personalized medicine -- dynamic treatment regime -- infinite horizon; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280217708655
• Languages: English
• ISSNs: 0962-2802

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.