Statistical considerations in a delayed-start design to demonstrate disease modification effect in neurodegenerative disorders.

There has been a paradigm shift in diagnostic conceptualization of Alzheimer's disease (AD) based on the current evidence suggesting that structure and biology changes start to occur before clinical symptoms emerge. Consequently, therapeutic drug development is also shifting to treat early AD patients using biomarkers for enrichment in clinical trials. A similar paradigm shift is occurring for Parkinson disease. In the absence of acceptable biomarkers that could be combined with a clinical endpoint to demonstrate a disease modification (DM) effect in neurodegenerative disorders, a delayed-start design can be applied to demonstrate a lasting effect on the disease course. The delayed-start design includes two treatment periods, where in period 1, patients are randomized to receive an active treatment or placebo, and in period 2, placebo patients are switched to the active treatment while patients in the active treatment arm will continue the same treatment. The hypothesis is that patients who start the active treatment later will fail to catch up to the treatment benefit achieved by patients who receive the active treatment in both periods. A usual analytical approach has sought to demonstrate the divergence of slope during period 1 and the parallelism of slopes during period 2 as the DM effect. However, due to heterogeneity in timing and the magnitude of maximal effect among patients, nonlinear response over time could be observed within the two treatment arms in both periods. We propose an approach to evaluate the DM effect with the linearity assumption for treatment differences, but not for each arm separately.