Marginal Mean Models for Dynamic Regimes

TLDR

A dynamic treatment regime specifies how treatment intensity should be adjusted over time based on an individual's changing severity, combining planned rule‑based selection with unplanned staff judgment. The authors aim to estimate the mean response to a dynamic treatment regime from observational longitudinal data with unplanned treatment selection. They propose a method that assumes sequential randomization to enable this estimation.

Abstract

A dynamic treatment regime is a list of rules for how the level of treatment will be tailored through time to an individual's changing severity. In general, individuals who receive the highest level of treatment are the individuals with the greatest severity and need for treatment. Thus, there is planned selection of the treatment dose. In addition to the planned selection mandated by the treatment rules, staff judgment results in unplanned selection of the treatment level. Given observational longitudinal data or data in which there is unplanned selection of the treatment level, the methodology proposed here allows the estimation of a mean response to a dynamic treatment regime under the assumption of sequential randomization.