A tutorial on estimating dynamic treatment regimes from observational longitudinal data using lavaan

Psychological and behavioral scientists develop interventions toward addressing pressing societal challenges. But such endeavors are complicated by treatments that change over time as individuals' needs and responses evolve. For instance, students initially in a multiyear mentoring program to improve future academic outcomes may not continue with the program after interim school engagement improves. Conventional interventions bound by rigid treatment assignments cannot adapt to such time-dependent heterogeneity, thus undermining the interventions' practical relevance and leading to inefficient implementations. Dynamic treatment regimes (DTRs) are a class of interventions that are more tailored, relevant, and efficient than conventional interventions. DTRs, an established approach in the causal inference and personalized medicine literature, are designed to address the causal query: how can individual treatment assignments in successive time points be adapted, based on time-evolving responses, to optimize the intervention's effectiveness? This tutorial offers an accessible introduction to DTRs using a simple example from the psychology literature. We describe how, using observational data from a single naturally occurring longitudinal study, to estimate the outcomes had different DTRs been counterfactually implemented. To improve accessibility, we implement the estimation procedure in lavaan, a freely available statistical software popular in psychology and social science research. We hope this tutorial guides researchers on framing, interpreting, and testing DTRs in their investigations. (PsycInfo Database Record (c) 2025 APA, all rights reserved).