Analytics - Pharma's Fosbury Flop

By Shankar Arun, VP (Data & Analytics) & Director, Chiltern

“Fosbury Flop” –this radically new style in High Jump introduced by the American athlete Dick Fosbury during the 1968 Olympics permanently changed the sporting event upside down (literally and figuratively). Until then, the landing surfaces used to be hard but along came sweeping changes during the 1960s in the form of deep-foam mattings which allowed the athletes alternate options of not necessarily landing on their feet. Fosbury responded to this vital change by inventing the “backwards over bar” jump, landing on his shoulders which gave him the ability to jump higher. And the rest was history!

So how is Fosbury connected to Pharma? Fosbury showed the world that one can become a winner if one quickly adapts to changes. The world of technology is constantly evolving with the latest buzzwords being Analytics, Cloud, Mobility and Social Media. Like Fosbury, the Pharma Industry is responding to these technological advancements in a big way. This article will seek to understand how the Pharma landscape is ready to “jump higher” with the advent of Business Analytics and Machine Learning. Areas like Biostatistics have been using analytics for more than 30 years but here we will discuss three other business functions within Clinical Research.

Risk Based Monitoring
Clinical Trials are performed on hundreds of sites, each comprising a sizeable chunk of patient population and this translates into a humongous volume of data. Risk Based Monitoring (a.k.a RBM) is a new paradigm which attempts to analyze data with the ultimate aim of ensuring quality and safety of clinical trials. RBM is built on the fundamental concepts of risk management i.e detection, assessment and mitigation of risks.

Business Analytics can play a crucial role in determining the success of RBM through the usage of descriptive (visualization), predictive and prescriptive algorithms. Visualizations like Histograms, Scatterplots and Boxplots can detect the “outlier” sites which have disproportionately higher number of patient safety issues, delays in data entry/data review, fraudulent data capture, delayed patient enrollment, so on and so forth. 

With the advent of Machine Learning Algorithms, the potential of predictive analytics in this field is huge. There are too many variables or features that affect the performance of a site which gives rise to the possibility of supervised learning techniques like Decision Tree and unsupervised learning tools like clustering analysis (K-means). These machine learning systems can provide the ability to predict risks of a particular site that can slip into potentially serious safety, deviation or fraudulent data issues. And finally, Prescriptive Analytics can provide an optimal deployment algorithm wherein adequate resources get allocated to those sites carrying higher risks.

Safety Signal Detection
Safety Signal is reported information on a possible causal relationship between an adverse event (a harmful side effect) and a drug. Early identification of the hazards associated with drugs is the main goal of signal detection. 

Traditional Detection algorithms use either the frequentist approaches like Proportional Rate Ratio or the Bayesian techniques like Multi Gamma Poisson Shrinkage. However, predictive analytics through data mining techniques can be effectively deployed to estimate the probability at which an adverse event is caused by a drug. There are studies on Signal Detection that use association rule learning and PRR from the user contributed content available in Social Media. 

Patient Recruitment
Studies reveal that more than 30 to 40 percent of research budget goes to patient enrollment and at the same time, 80 percent of the clinical trials get delayed due to recruitment targets not being met. Predictive Algorithms that forecast Patient Enrollment rates can help set the right expectation at the beginning of a trial. 

Geography, Therapeutic Area, Competition, Epidemiology, Phase and Duration of a Trial are some key variables that influence enrollment. Multi-variate regression analysis can be a very useful tool in determining those variables which have the strongest influence on enrollment. In many cases, lack of patient awareness of the trial benefits can stifle recruitment plans and hence promotions targeting potential population with the relevant information can be very effective. Cost-benefit analysis through simulations determines if there is any significant value in promotional spend. 

So the central idea here is the use of analytics to gain enrollment feasibility awareness early in the trial lifecycle so that substantial delays can be averted.

Clinical Trials constitute a major share of the budget and timelines that go into drug development. Like elsewhere, the hunt is always on to reduce cost, improve quality and decrease timelines. As you can see from the above examples, Analytics can provide the edge to Pharma as they shift gears from reactive approaches to prospects that are more proactive in nature. So Pharma is certainly taking a leaf out of Fosbury’s book, isn’t it? 

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