Treatment-competing events in dynamic regimes |
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Authors: | Brent A Johnson |
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Institution: | (1) Department of Biostatistics Rollins School of Public Health, Emory University, 1518 Clifton Rd. NE, 3rd fl, Atlanta, GA 30307, USA |
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Abstract: | 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. |
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Keywords: | Adaptive treatment strategies “ Censoring” -by-death Counting processes Infusion trial Observational data |
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