Patient-oriented Randomized Pragmatic Feasibility Trial with RTMS in Depression and Anxiety

This trial compares intermittent theta-burst stimulation (iTBS) to low frequency repetitive transcranial magnetic stimulation (LFR) in regards to depression and anxiety outcomes in 100 patients with treatment resistant depression (TRD).

The overarching goal of this trial is to evaluate whether a definite adaptive pragmatic trial would be feasible and establish clear go/no-go criteria as to whether proceeding to such definite trial is feasible. Specific aims include 1) testing the feasibility of recruiting a sample of TRD patients with less strict inclusion and exclusion criteria; 2) comparing different depression and anxiety scales (both clinician-rated and self-rated) and seek input from patients regarding their preferences; 3) seek input from patients with regards to the use of digital phenotyping as a tool to investigate biomarkers as well as engaging in the design of a potential implementation of such biomarker in a future definite trial. Aim 1. To evaluate the feasibility of a future definite adaptive pragmatic RCT comparing left vs right DLPFC repetitive transcranial magnetic stimulation (rTMS) in TRD. Hypothesis 1a: Enrollment will be 70% of the planned target over the 1-year recruitment period. Hypothesis 1b: Retention rate of randomized participants will be ≥70% at the end of the intervention in both groups. Aim 2. To evaluate patients' preferences regarding information about treatment options when there is no response to allocated treatment. Hypothesis 2: Patients will prefer to modify treatment when there is no response. Aim 3. To assess the feasibility of digital phenotyping as an tool to investigate biomarkers in TRD. Hypothesis 3a: Survey uptake and participation in the study regarding the use of digital phenotyping will be 80% of randomized participants. Hypothesis 3b: Of those survey responders, 75% will indicate they would consent to digital phenotyping in a future definite RCT. Aim 4. To develop a Bayesian statistical model that continuously updates personalized treatment effect estimates as the trial progresses, and identify the circumstances under which use of the model in a full-scale trial could inform treatment choice as the trial progresses. Hypothesis 4: The modeling results will identify at least one subgroup for whom early stopping of the definitive trial in that subgroup may be warranted.