Decentralized Trial Technology Insights

1. Real-World Data Won't Replace Trials. Here's Its Real Job 10/6/2026

   A biostatistician on where real-world data changes outcomes, and where FDA still won’t accept it as a trial comparator.

2. Why The Biostatistician's Seat At The Table Keeps Vanishing 9/29/2026

   A Pfizer and Cytel veteran on why statisticians belong at trial design, and why fewer sponsors keep them in-house.

3. Two Planning Mistakes That Quietly Sink Clinical Programs 9/22/2026

   A veteran biostatistician on the planning document and the dose-selection shortcut that quietly decide whether trials succeed.

4. A Biostatistician's Caution Around Digital Twins In Trials 9/15/2026

   A veteran statistician explains why she’s unconvinced digital twins can replace randomization in clinical trials, at least for now.

5. Clinical Research Isn't Even A Recognized Job. That's A Problem. 9/10/2026

   The U.S. doesn’t have an official job code for clinical research professionals. David Vulcano explains why that gap is holding back the workforce.

6. Duplicate Tech, Not Technology, Is Burning Out Trial Sites 9/3/2026

   Clinical trial sites aren’t overwhelmed by technology – they’re overwhelmed by entering the same data into disconnected systems again and again.

7. The Real Reason Clinical Trial Systems Don't Talk To Each Other 8/27/2026

   Clinical trial technology isn’t the barrier to interoperability; the economics are. Two industry leaders explain why nothing forces systems to connect.

8. The Vendor Questions Most Clinical Tech Buyers Forget To Ask 8/24/2026

   A practical checklist for evaluating decentralized trial technology vendors, built from an RF engineer’s view of where data quality actually breaks.

9. AI In Clinical Trials: Stop Hyping It, Start Proving It 8/20/2026

   David Vulcano wants clinical research to report AI failures with the same rigor it applies to adverse events. Here’s why that call-to-action matters.

10. What AI Can't Fix In Decentralized Clinical Trial Data 8/17/2026

    An RF and semiconductor engineer explains why AI can’t compensate for hardware and connectivity errors, and what questions to ask instead.