Treatment-competing events in dynamic regimes.
A dynamic treatment regime is a sequence of decision rules for assigning treatment based on a patient's current need for treatment. Dynamic regimes are viewed, by many, as a natural way of treating patients with chronic diseases; that is, treating patients with adaptive, complex, longitudinal treatment regimens. In developing dynamic treatment strategies, treatment-competing events may play an important role in the overall treatment strategy, and their effects on subsequent treatment decisions and eventual outcome should be considered. Treatment-competing events may be defined generally as patient-specific, random events which interrupt the ongoing treatment decision process in a dynamic regime. Treatment-competing events censor later treatment decisions that would otherwise be made on a particular dynamic treatment regime had the competing events not occurred. For example, in therapeutic studies of HIV, physicians may assign treatment based on a patient's current level HIV1-RNA; this defines a treatment assignment rule. However, the presence of opportunistic infections or severe adverse events may preclude a strict adherence of the treatment assignment rule. In other contexts, the "censoring"-by-death phenomenon may be viewed as an example of a treatment-competing event for a particular dynamic treatment regime. Treatment-competing events can be built into the dynamic treatment regime framework and counting processes are a natural mechanism to facilitate this development. In this paper, we develop treatment-competing events in a dynamic infusion policy, a random dynamic treatment regime where multiple infusion treatments are initiated simultaneously and given continuously over time subject to the presence/absence of a treatment-competing event. We illustrate how our methodology may be used to suggest an estimator for a particular causal estimand of recent interest. Finally, we exemplify our methods in a recent study of patients undergoing coronary stent implantation.
Johnson
Brent A
BA
Department of Biostatistics, Rollins School of Public Health, Emory University, 1518 Clifton Rd. NE, 3rd fl, Atlanta, GA 30307, USA. bajohn3@emory.edu
D004358:Drug Therapy / Q000379:methods*; D064420:Drug-Related Side Effects and Adverse Reactions; D000077542:Eptifibatide; D006801:Humans; D015233:Models, Statistical; D010455:Peptides / Q000008:administration & dosage / Q000627:therapeutic use; D010975:Platelet Aggregation Inhibitors / Q000008:administration & dosage / Q000627:therapeutic use