Dynamic treatment regimes (DTRs) are sequences of decision rules which use cumulative patient information to decide which treatments to provide during the course of an intervention. Sequential, multiple assignment randomized trials (SMARTs) are randomized trials which can be used to build effective DTRs. A SMART can have multiple, embedded DTRs, and a common primary aim of a SMART is the comparison of the marginal mean outcome under two or more embedded DTRs. In this work we develop a mixed effect modeling and estimation method for comparing embedded DTRs in a SMART with a continuous, longitudinal outcome. The desire for valid, causal estimators when comparing embedded DTRs in a SMART requires marginalizing over time-dependent covariates and prevents direct application of standard mixed models. We compare our method to existing approaches in which a working model is proposed for the marginal correlations among repeated measures in a SMART. We describe how mixed effects models provide an alternative parametrization of flexible correlation structures in longitudinal SMARTs and use simulations to demonstrate potential efficiency gains of mixed effects models in realistic scenarios.