Designing and implementing consumer research and clinical studies is expensive, intrusive, and can introduce critical delays in getting products to market. The stringent requirements for clinical trials make them a particularly challenging example of this problem. As they gather and seek to interpret data, businesses want tools to extract maximum insight, for example, to enable them to interactively adapt the design of studies to gain statistically-significant information on key behaviours with fewer, more targeted tests.
In this webinar, we’ll see how machine learning (ML) can enable faster progress and insightful learnings. The session includes a case study from the science team at BAT on applying ML as a pre-screening tool when optimising clinical studies on the pharmacokinetic response of participants using a new product.
