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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.

Abstract Introduction Use of cognitive composites as primary outcome measures is increasingly common in clinical trials of preclinical and prodromal Alzheimer's disease (AD). Composite outcomes can decrease intra‐individual variability, resulting in improved sensitivity to detect longitudinal change and increased statistical power. We developed a novel composite outcome, the ADAS‐Cog‐Exec, for use in the EXERT trial—a Phase 3 randomized, controlled, 12‐month exercise intervention in mild cognitive impairment (MCI). Methods Three combinations of cognitive measures selected from the Alzheimer's Disease Assessment Scale‐Cognitive Subscale version 13 (ADAS‐Cog13), tests of executive function, and the Clinical Dementia Rating (CDR) were created based on previously documented sensitivity to longitudinal change in MCI and to the effects of exercise. Optimally weighted composites of each combination were modeled using data from the ADNI‐1 MCI cohort. Ten‐fold cross‐validation was performed to obtain a bias‐corrected mean to standard deviation ratio (MSDR). The cognitive composites were assessed for their sensitivity to detect 12‐month change in MCI. Results The MSDR of 12‐month change for each of the composite outcomes tested exceeded that of the ADAS‐Cog13 total score. The composite with the highest MSDR (MSDR = 0.48) and associated statistical power included scores on ADAS‐Cog13 Word Recall, Delayed Word Recall, Orientation, and Number Cancellation subtests; Trail‐Making Tests A & B, Digit Symbol Substitution and Category Fluency; and cognitive components of the CDR (Memory, Orientation, Judgement & Problem Solving). Discussion An optimally weighted cognitive composite measure was identified and validated for use in EXERT. This composite contained selected subtests from the ADAS‐Cog13, additional measures of executive function, and box scores for cognitive components of the CDR. Because this composite score demonstrated high sensitivity to longitudinal change in MCI it will be used as the primary outcome measure for the EXERT trial.