Good data science practice: moving towards a code of practice for drug development
Data Ethics in Research Seminar Series more information...
Dr. Mark Baillie, Novartis, Switzerland
There is growing interest in data science and the challenges that could be solved through its application. The growing interest is in part due to the promise of "extracting value from data". The pharmaceutical industry is no different in this regard reflected by the advancement and excitement surrounding data science. Data science brings new perspectives, new methods, new skill sets and the wider use of new data modalities. For example, there is a belief that extracting value from data integrated from multiple sources and modalities using advances in statistics, machine learning, informatics and computation can answer fundamental questions. These questions span a variety of themes including: disease understanding (i.e. "precision" medicine, disease endo/pheno-typing, etc.), drug discovery (i.e. new targets and therapies), measurement (i.e. multi-omics, digital biomarkers, software as a medical device, etc.), and drug development (i.e. dose-exposure-response, efficacy, safety, compliance, etc.). By answering these fundamental questions, we can not only increase knowledge and understanding but more importantly inform decision making; accelerating drug and medical device development through data-driven prioritisation, precise measurement, optimised trial design and operational excellence. However, with the promise of data science, there are also a number of obstacles to overcome, especially if data science is to live up to this promise and deliver a positive impact. These obstacles include consensus on a common understanding of the very definition of data science, the relationship between data science and existing fields such as statistics and computing science, what should be involved in the day to day practices of data science, and what is "good" practice. In this talk I will cover some of these issues, with the aim of opening a dialogue on good data science practice in the context of drug development, analogous to guidance such as ICH E8 (International Conference on Harmonisation 2018a) or E9 (International Conference on Harmonisation 2018b).
References: International Conference on Harmonisation (ICH). 2018a. "ICH E8 General Considerations for Clinical Studies." September 17, 2018. https://www.ema.europa.eu/en/ich-e8-general-considerations-clinical-studies. 2018b. "ICH E9 Statistical Principles for Clinical Trials." September 17, 2018. https://www.ema.europa.eu/en/ich-e9-statistical-principles-clinical-trials.
Co-Sponsored by the Center for Statistical Training and Consulting (CSTAT) and MSU Libraries