Optimizing Alzheimer's Disease Clinical Trial Generalizability

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ABSTRACT Clinical trials are often conducted under idealized and rigorously controlled conditions to ensure internal validity, but such conditions, paradoxically, compromise trials external validity (i.e., generalizability to the target population). Low trial generalizability has long been a concern and widely documented across different clinical areas. For instance, participants of Alzheimer's disease (AD) clinical trials are systematically younger than AD patients in the general population. Overly restrictive eligibility criteria are arguably the biggest yet modifiable barriers causing low generalizability. The FDA has launched numerous initiatives, primarily through broadening eligibility criteria, to promote enrollment practices so that trial participants can better reflect the population who would most likely use the treatment if approved. Nevertheless, trial sponsors and investigators are reluctant to broaden eligibility criteria due to concerns over potential increases in risk of serious adverse events (SAEs) and its negative impact on the investigational drug’s safety and effectiveness profile. As a result, many elderly patients are excluded from AD trials either explicitly through an age restriction or implicitly through excluding clinical characteristics more prevalent in the elderly. There is a gap between the need to broaden trial criteria and ways available to fulfill the need in practice. Previous studies, including ours, have validated and used the Generalizability Index of Study Traits (GIST), the best available quantitative, eligibility- driven, a priori generalizability measure, in a number of disease domains. GIST scores can potentially be used to guide adjustments to criteria towards better population representativeness. However, there are key barriers for its adoption in practice, especially in AD trials: (1) the lack of a standardized, computable eligibility criteria (CEC) framework to translate criteria to data queries – a necessary step to define the populations for generalizability assessment, and (2) the need to map the mathematical relationships between eligibility criteria and GIST as well as patient outcomes (i.e. SAE), which answers the critical question how broadened criteria will affect AD trial’s generalizability and patient outcomes simultaneously. To remove these barriers, we propose to systematically analyze existing AD trials in ClinicalTrials.gov to create a standardized library of CEC for AD trials and develop statistical models on how adjustments to eligibility criteria, especially age, would affect (1) trial generalizability measured by GIST, and (2) outcomes (i.e., SAEs) of the target population, approximated using real-world data (RWD) from the OneFlorida network. OneFlorida contains linked electronic health record (EHR), claims, and cancer registries data for ~15 million Floridians. This study will provide the necessary data to support future development of a trial eligibility criteria design tool that can optimize trial generalizability while balancing potential increases in risk of SAEs in the target population.