What’s Ailing Adjudication in Clinical Trials?

The clinical trial industry is transforming. The vision for this transformation is simple: embrace quality and automation across the spectrum – especially in the endpoint and adverse event adjudication process – and move closer to clinical trials finally stripped of human errors and other roadblocks that cause unnecessary delays.

Why? It will provide for better, faster results. More information for sponsors. More innovation for CROs. And most importantly, expedited outcomes for patients in need. This transformation is just beginning, and requires that we challenge ourselves, our teams, partners and clients – the entire clinical trial industry – to work smarter and eliminate preventable delays.

Clinical trials are complex, multi-year logistics exercises, resembling roaring rapids more than Olympic synchronized swimming events in a placid pool. An average Phase III oncology trial may include a sponsor team talking to a CRO team that is talking to dozens (or even hundreds) of investigator sites spread across a few dozen countries who, in turn, must recruit patients and ensure that they follow the strict protocol guidelines necessary to carry out the experiment in question. Add to this a good dose of language and cultural differences, and you wonder how a drug, biologic or device is ever approved by regulatory agencies.

In fact, most are not.

The reasons for clinical trials failing to produce expected and/or desired outcomes often lies within the scientific hypothesis. Sometimes, however, you must wonder whether the process of data collection and evaluation plays a role in failure.

Drug or device trials are fundamentally science experiments. We all have some familiarity with how that works from those exciting high school or college labs where we measured Newtonian physics principles. Unlike those experiments, however, we don’t know the outcome in a clinical trial, and the data is never crisp and predictable.

In clinical trials, for the most part, the signal-to-noise ratio is exceedingly small. If the data collection or the analysis processes add more noise to the system, the signal may become undetectable. When that happens, we lose: therapies that might have helped patients in need may never be approved because the data was not handled appropriately.

Is it hard to bring quality control and automation into clinical trials and endpoint adjudication process? At times, but it’s getting easier. Will your CRO, lab or investigator site be a proactive part of this industry transformation? If you choose to pursue this opportunity, we believe everyone wins.

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