A joint longitudinal-survival framework for dynamic treatment regimen evaluation in sequential multiple assignment randomized trials
Sequential multiple assignment randomized trials (SMARTs) provide a systematic framework for constructing and evaluating dynamic treatment regimens (DTRs). In clinical studies, longitudinal biomarkers are routinely collected to monitor disease progression and define treatment response. However, the integration of longitudinal biomarker data into survival analysis for DTR evaluation within a SMART remains unexplored. We propose a joint longitudinal-survival framework to estimate DTR-specific survival outcomes within a two-stage SMART. A linear mixed model specifies the biomarker trajectory, and a relative risk model links the survival process to the current latent biomarker value. To accommodate the time-varying treatment assignment, treatment effects are parameterized through piecewise cumulative exposure terms with a structural change at the decision point. Joint-model parameters are estimated by maximum likelihood using pseudo-adaptive Gauss-Hermite quadrature for random-effect integration. Under standard causal identification assumptions, DTR-specific marginal survival outcomes are obtained through a plug-in parametric g-formula. We compare the joint-model framework to the inverse probability of treatment weighted Kaplan-Meier estimator and implement the multiple comparisons with the best method to identify the optimal DTR with multiplicity control. Through a simulation study and an application to a SMART for androgen-independent prostate cancer, we demonstrate that the joint-model framework produces unbiased estimates under correct model specification, with substantial efficiency gains and higher accuracy in optimal DTR identification. This framework exploits prognostic information from longitudinal biomarkers, enables valid causal inference for DTR-specific survival outcomes, and provides a generalizable analytic tool for developing evidence-based adaptive treatment strategies.