Quantitative modeling for clinical trials

It’s a fundamental of science that today’s failures are stepping stones to tomorrow’s successes. But that tends to get lost when clinical trial failures hit the headlines. Stock prices take a hit, investors demand answers and patients’ hopes diminish. Fortunately, drug developers have new tools to reduce the likelihood of clinical trial failures, thanks to the emerging field of data science. Pretty much every industry is embracing data science — especially analytics and predictive modeling. It’s helping aeronautics experts anticipate when an aircraft needs servicing or parts need to be replaced, and enables financial companies to maximize profits by identifying subtle correlations between oceans of transaction data. Data science also is driving the evolution of quantitative clinical development, which is helping clinical trial supervisors anticipate where things can go wrong…


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