Layman’s abstract from Pharmaceutical Statistics on Statistical Considerations in a Delayed-Start Design to Demonstrate Disease Modification Effect in Neurodegenerative Disorders

Every few days, we will be publishing layman’s abstracts of new articles from our prestigious portfolio of journals in statistics. The aim is to highlight the latest research to a broader audience in an accessible format.

The article featured today is from Pharmaceutical Statistics:  Wang, D, Robieson, W, Zhao, J, Wiener, C, Koch, G. Statistical considerations in a delayed‐start design to demonstrate disease modification effect in neurodegenerative disorders. Pharmaceutical Statistics. 2019; 18: 407– 419. The full article is available to read online here.


Alzheimer’s disease (AD) and Parkinson’s disease (PD) are two of the most common neurodegenerative disorders. There has been a paradigm shift in the diagnostic concept for AD. The current evidence suggests that structure and biology changes in AD patients start to occur before clinical symptoms emerge. Due to this change, drug development for AD is shifting toward treating early AD patients using biomarkers for enrichment in clinical trials. A similar paradigm shift is also occurring for PD. 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. A typical 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 for the DM effect 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 previous analytical approach to show DM effect sought to demonstrate the divergence of slopes during Period 1 and the parallelism of slopes during Period 2. However, due to heterogeneity in timing and the magnitude of maximal effect among patients, non-linear response over time could be observed within the two treatment arms in both periods. The authors proposed using a Mixed-effects Model for Repeated Measures (MMRM) with an innovative design matrix to analyze the longitudinal data from the two-period delayed-start design trial. The model does not assume linearity of treatment effect within each arm for slope analysis but does assume linearity of treatment differences between treatment arms in each period. This paper constructs three hypothesis tests and reports power for various settings within the proposed model to evaluate the DM effect.


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