Accounting for group differences in study retention in a randomized trial of specialized treatment for first episode psychosis

Background: Schizophrenia is a chronic disabling disorder for which current treatments are only partially effective. While the evaluation of novel interventions is a high priority, loss to follow-up is a major threat to validity. Methods: Pattern mixture modeling is a statistical technique that incorporates information on patterns of retention that may bias comparisons between randomized treatment groups. This study used pattern mixture mixed model (PMMM) in the analysis of outcomes of a two-year cluster-randomized trial, the Recovery after an Initial Schizophrenia Episode-Early Treatment Program, which compared a coordinated specialty care intervention called NAVIGATE to usual community care (CC). PMM-adjusted outcome differences between NAVIGATE and CC were estimated by the weighted-average of effects across the retention patterns. Results: Compared to the original analysis, PMMM improved model fit and the estimated effectiveness of NAVIGATE as compared to CC. On the Quality of Life Scale NAVIGATE effectiveness increased by 1.50 points (25.4%); on the Positive and Negative Syndrome Scale, by 1.72 points (39.8%), and on the Calgary Depression Scale by 0.49 points (62.1%). PMMM did not improve model fit for employment days, substance use days, or hospital days. Conclusion: Use of PMMM improved model fit and increased the estimated differences between NAVIGATE and CC for major outcomes. Patients with differential retention patterns may have different outcome trajectories. PMMM is a useful tool for addressing potential biases arising from these differences.