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CATEGORIES:Special Events
DTSTART:20210527T160000Z
DTEND:20210527T170000Z
SUMMARY:Good data science practice: moving towards a code of practice for drug development
DESCRIPTION:
 Data Ethics in Research Seminar Series\n
 \n
 Dr. 
 Mark Baillie, Novartis, Switzerland\n
 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).\n
 \n
 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.\n
 \n
 Co-Sponsored 
 by the Center 
 for Statistical Training and Consulting (CSTAT) 
 and MSU Libraries\n\n
 Price: free\n
 Sponsor: Administration\n
 Sponsor's Homepage: http://www.lib.msu.edu\n
 Contact name: Holly Flynn\n
 Contact phone: 517-884-0901\n
 Contact email: flynnhol@msu.edu\n
 for more info visit the web at:\n 
 https://bookings.lib.msu.edu/event/7823971\n
LOCATION:Online
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