6 ways lab data can improve clinical trials: the value of real-world data in clinical

In this video interview, Saamir Pasha and Carole Berini, PhD, of Ontada, identify three persistent obstacles to consistent real-world evidence generation in oncology—inconsistent data capture, structural data limitations that modeling alone can't fix, and the need for earned generalizability through deliberate multi-site validation.

How a phased CRO partnership built on ICH E6(R2) and E6(R3) principles supported a multi-indication biotech portfolio.

The second of a two-part perspective examining why clinical trial enrollment and representation outcomes are shaped long before the first patient is screened.

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.