Rave Centralized Statistical Analytics

Rave Centralized Statistical Analytics

Put your clinical trial data under the microscope.

Rave CSA goes beyond data visualization and aggregation by using machine learning for advanced anomaly detection, so you can identify known and unknown risks faster and more accurately. Get immediate insight into clinical trial performance and data quality. Then, work from a single, comprehensive overview that enables cross-functional collaboration during the clinical trial process.

Use machine learning to improve quality of your clinical trial data.

Mitigate data quality issues

There’s no argument— manual data reviews lead to poor data quality. You won’t identify issues missed in the SDV process, and- of course, you won’t catch issues you’re not looking for in the first place. There’s no way to analyze patterns and trends across millions of data points or suggest actionable corrective measures. Plus, manual data reviews are incredibly time-consuming.

Avoid Regulatory Challenges

Don’t let one bad apple spoil your whole study. Now you can identify sites exhibiting consistent quality issues across studies, flag inconsistencies within a single patient and across all patients, spot differences in adverse event reporting, note general data irregularities, and, most importantly, call out any misconduct relating to the fabrication of clinical trial data—any one of which could derail your entire clinical trial.

Implement Corrective Measures

Apparently, “an ounce of prevention is worth a pound of cure” holds true for medical studies, as well. Better, quantifiable data makes it far easier to justify any required amendment, adjustment, or change to the protocol early on in the trial. And you don’t need to be a biostatistician make to interpret the findings—, the intuitive dashboard makes course correction much simpler to understand and implement.

Leverage the industry’s most advanced profile designs to provide a consolidated view of patient-specific clinical data for targeted SDV/SDR and supports FDA requirements for patient profile submissions.