Impact of potential modifications to Alzheimer’s disease clinical trials in response to disruption by COVID-19: a simulation study

Abstract

Background The COVID-19 pandemic disrupted Alzheimer disease randomized clinical trials (RCTs), forcing investigators to make changes in the conduct of such trials while endeavoring to maintain their validity. Changing ongoing RCTs carries risks for biases and threats to validity. To understand the impact of exigent modifications due to COVID-19, we examined several scenarios in symptomatic and disease modification trials that could be made. Methods We identified both symptomatic and disease modification Alzheimer disease RCTs as exemplars of those that would be affected by the pandemic and considered the types of changes that sponsors could make to each. We modeled three scenarios for each of the types of trials using existing datasets, adjusting enrollment, follow-ups, and dropouts to examine the potential effects COVID-19-related changes. Simulations were performed that accounted for completion and dropout patterns using linear mixed effects models, modeling time as continuous and categorical. The statistical power of the scenarios was determined. Results Truncating both symptomatic and disease modification trials led to underpowered trials. By contrast, adapting the trials by extending the treatment period, temporarily stopping treatment, delaying outcomes assessments, and performing remote assessment allowed for increased statistical power nearly to the level originally planned. Discussion These analyses support the idea that disrupted trials under common scenarios are better continued and extended even in the face of dropouts, treatment disruptions, missing outcomes, and other exigencies and that adaptations can be made that maintain the trials’ validity. We suggest some adaptive methods to do this noting that some changes become under-powered to detect the original effect sizes and expected outcomes. These analyses provide insight to better plan trials that are resilient to unexpected changes to the medical, social, and political milieu.

Alzheimer's disease drug development pipeline: 2022

“…Many AD trial outcomes were successfully administered using telemedicine without compromise of data reliability 33 . Statistical modeling and trial simulation of AD trials indicated that stopping trials usually resulted in substantial loss of power and prolonging or enlarging trials were strategies to preserve trial integrity 34 . Despite the many challenges posed by the SARS‐CoV‐2 pandemic, the AD trial ecosystem proved to be resilient with few trials stopped during the pandemic and a substantial number of new trials initiated.…”

Development of a quantitative semi‐mechanistic model of Alzheimer's disease based on the amyloid/tau/neurodegeneration framework (the Q‐ATN model)

“…Moreover, the current model only describes mean behavior without accounting for inter‐subject variability and does not represent the mechanisms of the spatial spread of amyloid and tau 36,39,41 . Real‐world factors, such as missed dosing, unequal distributions of fast and slow progressors, and the potential effects of the COVID pandemic are also not accounted for in the present model 52 . To this end, a population‐based version of the Q‐ATN model incorporating actual dosing regimens, PK and PD modeling of gantenerumab, and inter‐subject variability in model parameters is currently in development.…”