An effective way to control risks during a clinical trial is to anticipate events induced by human factor before the study even begins. Dr Johann Proeve's third chapter on Adaptive Monitoring talks about how human factor is complementary to numeric data.
Our online KRI (Key Risk Indicator) survey indicated that finding the right calculation method seems the most challenging aspect of the application of KRIs. In addition our survey showed that the vast majority (> 95%) makes use of KRIs. Do these results surprise you?
Adaptive Monitoring is not a “status quo”, it is a dynamic response to clinical research that drives monitoring scope and activities to the evolving areas of greatest need which have the most potential to positively impact. Each clinical study requires its own tailored monitoring approach ensuring risks are minimized.
Quality is a business decision. Everyone instinctively knows that poor quality doesn't come cheap, however until now no attempt was made to calculate the real difference between good and poor quality in clinical trials. Most of the issues that impact cost and expenses are lurking just below the tip of the iceberg.
It is widely broadcasted that pharma companies will have to accelerate adoption of adaptive clinical trial designs to reduce study timelines and costs while increasing success rates. Risk-based Monitoring “Real RBM” integrates the Adaptive Monitoring (AM) process, which addresses all aspects of Quality Risk Management.
There is a rapidly growing amount of information to process in clinical research. You could argue whether it adds value viewing and verifying the reliability of each single data point. Dirty data and frauds have always existed but can eventually be marginalised by responsive people and processes.
Believe it or not, it was Guinness’ biochemist Gosset who developed the first Six Sigma statistical test in the early 1900s. The Guinness brewery was far ahead of its time by hiring statisticians, chemists and other scientists to improve the quality of its beer.
“There are always a million reasons not to do something” A great inspiring quote from Jan on an episode of The Office when Pam was making excuses not to go to Art school. A very recognisable habit, isn’t it? Talking ourselves out of [...]
The RBM software EarlyBird® obtains a powerful and flexible system for ad-hoc reporting of risk-relevant data. It can be used to explore clinical data in more detail, prepare centralized monitoring reports, and build up a risk overview across studies. An example of such a [...]
In our previous "predictive analytics in RBM" article, we started a discussion about algorithms of machine learning (ML), predictive analytics, and artificial intelligence. We also covered that a risk software needs to calculate forecasts of Key Risk Indicators (KRIs) proactively and alerts when they [...]