Optimizing the Population Representativeness of Older Adults in Cancer Trials

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 generalizability has long been a concern and widely documented, especially, in cancer research community.

Certain population subgroups, such as older adults, are often underrepresented in cancer studies due to [[overly restrictive (and potentially unjustified) exclusion criteria,]] which are arguably the biggest yet modifiable barriers causing low generalizability.

Regulatory agencies (e.g., FDA), funding agencies (e.g., NCI), and research communities (e.g., ASCO) have called and provided guidance to broaden trial eligibility criteria to promote enrollment practices so that trials can better reflecting the population most likely to use the drug if approved.

Nevertheless, trial sponsors and investigators are reluctant to broaden eligibility criteria due to concerns over potential increases in the risk of serious adverse events (SAEs) and their negative impact on the investigational drug?s safety and effectiveness profile.

As a consequence, in cancer trials, elderlies are often excluded implicitly through excluding clinical characteristics that are more prevalent in the elderly.

There is a gap between the need to broaden trial criteria and ways available to [[identify unjustified, overly restrictive exclusion criteria and then adjust them accordingly 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 cancer 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, (2) the lack of a validation study that assesses GIST?s reliability and validity in cancer trials, and (3) 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 trial?s generalizability and patient outcomes simultaneously.

To remove these barriers, we will systematically analyze existing female breast, lung, and colorectal trials in clinicaltrails.gov to create an ontology-driven, standardized library of CEC, validate GIST among cancer trials, and develop [[statistical models on how adjustments to eligibility criteria, especially those that limit the participation of older adults]], would affect (1) trial generalizability measured by GIST, and (2) outcomes (i.e., SAEs) of the target population, approximated using a large collection of real-world data (RWD) source ? the OneFlorida network, that contains linked EHRs, claims, and cancer registry data for ~15 million Floridians.