Using Real-World Data to Rationalize Clinical Trials Eligibility Criteria Design: A Case Study of Alzheimer's Disease
Abstract:
Low trial generalizability is a concern. The Food and Drug Administration had guidance on broadening trial eligibility criteria to enroll underrepresented populations. However, investigators are hesitant to do so because of concerns over patient safety. There is a lack of methods to rationalize criteria design. In this study, we used data from a large research network to assess how adjustments of eligibility criteria can jointly affect generalizability and patient safety (i.e the number of serious adverse events [SAEs]). We first built a model to predict the number of SAEs. Then, leveraging an a priori generalizability assessment algorithm, we assessed the changes in the number of predicted SAEs and the generalizability score, simulating the process of dropping exclusion criteria and increasing the upper limit of continuous eligibility criteria. We argued that broadening of eligibility criteria should balance between potential increases of SAEs and generalizability using donepezil trials as a case study.
Insights
Broadening clinical trial eligibility criteria can improve generalizability but raises patient safety concerns. This study developed a method to balance these factors, using donepezil trials as a case study.
Area of Science:
- Clinical trial methodology
- Pharmacovigilance
- Health equity
Background:
- Low generalizability in clinical trials limits the applicability of findings to diverse patient populations.
- Regulatory guidance encourages broader eligibility criteria, but investigator concerns regarding patient safety (serious adverse events [SAEs]) persist.
- A lack of systematic methods hinders the rational design of eligibility criteria.
Purpose of the Study:
- To develop and assess a method for evaluating the joint impact of adjusting eligibility criteria on clinical trial generalizability and patient safety.
- To simulate the effects of modifying exclusion criteria and continuous variable limits on SAEs and generalizability scores.
- To provide a framework for balancing generalizability gains against potential SAE increases in trial design.
Main Methods:
- Utilized data from a large research network to predict the number of SAEs.
- Employed an a priori generalizability assessment algorithm to quantify changes in generalizability.
- Simulated modifications to eligibility criteria, including dropping exclusion criteria and adjusting upper limits for continuous variables.
Main Results:
- Demonstrated a method to predict SAEs based on eligibility criteria adjustments.
- Quantified the trade-off between improved generalizability scores and predicted SAEs.
- The case study on donepezil trials illustrated the practical application of the developed approach.
Conclusions:
- Broadening clinical trial eligibility criteria requires a careful balance between enhancing generalizability and managing potential increases in patient safety risks (SAEs).
- The proposed methodology offers a rational approach to designing eligibility criteria that optimizes both generalizability and safety.
- This framework can aid investigators and regulators in making informed decisions about trial design to improve the representativeness of study populations.
More Related Videos
Related Concept Videos
01:16Clinical Trials
There are four phases in a clinical trial. A phase one...
01:22Alzheimer's Disease: Treatment
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
01:11Clinical Trials: Overview
01:15Bioavailability Study Design: Healthy Subjects Versus Patients
01:26Alzheimer's Disease: Overview
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...