Alzheimer's disease (AD) is the only top 10 causes of death that can't be prevented, cured, or even slowed. Delayed start design has been proposed to assess whether a putative treatment has a disease modifying effect that alters the underlying disease progression. In a delayed start design, following a standard randomized, double-blind, placebo-controlled phase, patients treated with placebo switch to the active treatment while patients treated with active treatment continue during the delayed start period. Data from the delayed start period can be used to assess if the observed treatment effect at the end of the placebo-controlled phase, if significant, is consistent with a disease modifying effect. In particular, if the delayed start (DS) patients do not catch up with early start (ES) patients, the treatment effect may be considered consistent with a disease modifying effect. In this presentation, we will described the statistical methodology to evaluate whether DS patients catch up with ES patients, which is the most important element of a delayed start design. We will also share examples of the implementations of the method in multiple real world scenarios.