Richard Pugh
Mango Solutions
Objectives: In recent years, the analytic world has become awash with buzz phrases such as “data science” and “big data”, with organisations across a variety of sectors investing heavily to become more “data-driven”. There are close parallels between the worlds of data science and pharmacometrics with opportunities and potential threats to both from this shift to proactive analytics.
Methods: Experience gained within the wider “data science” community provides a unique perspective on the rapid growth and demand for proactive analytics and how this can relate to the aims, constraints and behaviours found in the world of Pharmacometrics. The increasing “data science” demands in terms of core skills, techniques and approaches are evaluated to determine their alignment with the pharmacometrics world.
Results: Pharmacometricians are, by definition, early “data science” adopters in respect of the skills being sought by world-leading organisations. The identification of gaps in the pharmacometricians’ typical skillsets can help identify opportunities for further training. Analysis of how the analytics function is structured within other organisations provides a key to how analytics could be further exploited in the world of PKPD. At first glance, the world of “big data” would seem to have nothing to offering to the world of pharmacometrics, since it is aimed at harnessing data sizes that are not present in life sciences. However, if we ignore the “big data” label, there are opportunities that can be harnessed (such as data streaming and distributed computing). Not only are there opportunities, but many of the required skills to exploit these opportunities already exist in the pharmacometricians’ day-to-day lives (for example, an understanding of cluster computing).
Conclusions: Pharmacometricians are certainly “data scientists” based on their skillsets and remit. However, there are further opportunities in this evolving “data science” field that could be harnessed to great effect.
References:
[1] Justin J. Wilkins, E. Niclas Jonsson. Reproducible Research. PAGE Meeting, Glasgow (2013)
[2] S Locke (2015, November 20). Launching the Data Science Radar [blog]. Retrieved from http://www.mango-solutions.com/wp/2015/11/launching-the-data-science-radar/
[3] R Pugh (2015, June 25). The Single Most Important Skill for a Data Scientist [blog]. Retrieved from http://www.mango-solutions.com/wp/2015/06/the-single-most-important-skill-for-a-data-scientist/
Reference: PAGE 25 () Abstr 5800 [www.page-meeting.org/?abstract=5800]
Poster: Methodology - Other topics