The second of a two-part perspective examining why clinical trial enrollment and representation outcomes are shaped long before the first patient is screened.
News|Articles|February 3, 2025
Data-Driven Insights can Optimize Clinical Trial Site Selection
Author(s)PurpleLab
Clinical trials are crucial for drug development but often face delays due to poor site selection, which can lead to protocol violations, poor data quality, and increased costs. Ninety percent of clinical trials fail to meet their timelines, and issues like inadequate staffing and patient recruitment are significant challenges. Traditional site selection methods are inefficient, and the COVID-19 pandemic has further complicated the process. Data-driven insights can optimize site selection by evaluating current data on patient populations, investigators, and site capabilities.
Related to this article
In this video interview, Saamir Pasha and Carole Berini, PhD, of Ontada, describe what operationalizing regulatory-grade quality frameworks requires—and why fit-for-purpose evidence generation is a transparency and documentation discipline, not just a data cleanliness exercise.
AI can turn complex protocols into first-pass budget inputs, letting sponsors start budgeting sooner while budget builders shift from data entry to review.
Trialbee CEO Matt Walz explores the current impact of AI in delivering practical gains in clinical development and reflects on its future potential, where enrollment predictability may be the biggest prize of all.
In this video interview, Carole Berini, PhD, of Ontada, explains how validated proxies for variables like ECOG performance status can recover not just sample size but the representativeness of hard-to-reach patient populations in real-world oncology studies.
A biomarker test now decides who enters a precision medicine trial, yet results often go unused and assays are not ready when screening opens.
In today's ACT Brief, we examine proportionate analysis frequency in RBQM, how to build trust into real-world evidence foundations for cancer trials, and how rare disease developers monetize regulatory success.
Real-time RBQM requires proportionate analysis frequency matched to data arrival and issue criticality, not continuous reanalysis, because signal churn from over-frequent runs creates alert fatigue and operational noise that obscures meaningful deterioration.
Trending on Applied Clinical Trials Online
1
AI and Patient Recruitment: Can Predictive Promise Drive True Transformation?
2
Start Study Budgets Sooner With AI-Enabled Protocol Translation
3
The Test Is the Ticket: Building Smarter Pathways to Clinical Trial Participation
4
ACT Brief: RBQM Analysis Frequency, Building Trust in Oncology Data, and Rare Pediatric PRV Monetization
5