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Daniel Boisvert TT03 Harnessing the Web to Streamline Statistical Programming Processes

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Page 1: Harnessing the Web to Streamline Statistical Programming ...Harnessing the Web to Streamline Statistical Programming Processes 2 •Share high level overview and key features of our

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Daniel BoisvertTT03

Harnessing the Web to Streamline Statistical Programming Processes

Page 2: Harnessing the Web to Streamline Statistical Programming ...Harnessing the Web to Streamline Statistical Programming Processes 2 •Share high level overview and key features of our

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• Share high level overview and key features of our applications

• Encourage web development for other Biometrics applications

Purpose

Page 3: Harnessing the Web to Streamline Statistical Programming ...Harnessing the Web to Streamline Statistical Programming Processes 2 •Share high level overview and key features of our

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12 Applications2 Validated

200+ Users

2 Developers

Biogen Biometrics Web Applications

Page 4: Harnessing the Web to Streamline Statistical Programming ...Harnessing the Web to Streamline Statistical Programming Processes 2 •Share high level overview and key features of our

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Web Application Architecture

INTRANET

Page 5: Harnessing the Web to Streamline Statistical Programming ...Harnessing the Web to Streamline Statistical Programming Processes 2 •Share high level overview and key features of our

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• Open source, no licensing:o Written in PHP, JavaScript, Python, and MySQL, which are

available for use with no licensing fees and are backed by large user communities.

• Rapid development: o Many existing modules/procedures which have undergone

rigorous QAo Packages to automate common tasks are available and can be

applied with little configuration (e.g.. PDF generation).• Accessibility, Easy Deployment:

o No separate software installation besides a web browsero Global updates are applied on the server side and no

reinstallation is required.

Why Web?

Page 6: Harnessing the Web to Streamline Statistical Programming ...Harnessing the Web to Streamline Statistical Programming Processes 2 •Share high level overview and key features of our

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Mock Shells SDTM ADaM TLF Define.xml

Challenges in the Statistical Programming Process

Decentralized Information

High Project Management Time

Tedious Tasks

Page 7: Harnessing the Web to Streamline Statistical Programming ...Harnessing the Web to Streamline Statistical Programming Processes 2 •Share high level overview and key features of our

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Workflow• Tracks requests through their work cycle

(Programmer to QC to Stats to Med. Writing)

Phoenix• Interface to enter SDTM/ADaM Dataset specifications• Define.xml creation

Venus • Interface to enter Mock TLF Shells

Biogen Web Applications

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2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Requests Transactions

Project Management in Statistical Programming

2016 Data Requests 18,269Transactions 250,12613.7 Transactions per Request

Page 9: Harnessing the Web to Streamline Statistical Programming ...Harnessing the Web to Streamline Statistical Programming Processes 2 •Share high level overview and key features of our

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Workflow

Page 10: Harnessing the Web to Streamline Statistical Programming ...Harnessing the Web to Streamline Statistical Programming Processes 2 •Share high level overview and key features of our

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Workflow - Project Management

Workflow data is analyzable and reportable

Page 11: Harnessing the Web to Streamline Statistical Programming ...Harnessing the Web to Streamline Statistical Programming Processes 2 •Share high level overview and key features of our

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Workflow - Quantity, Quality, Duration Metrics

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2012 2013 2014 2015 2016 2017Variables Revisions

Decentralized Information –Dataset Specifications

2016 Data118,818 Variables155,997 Revisions

4,098 Datasets

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Phoenix –Electronic Dataset Specifications

Page 14: Harnessing the Web to Streamline Statistical Programming ...Harnessing the Web to Streamline Statistical Programming Processes 2 •Share high level overview and key features of our

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Phoenix – Write Data Once Use Multiple Times

Page 15: Harnessing the Web to Streamline Statistical Programming ...Harnessing the Web to Streamline Statistical Programming Processes 2 •Share high level overview and key features of our

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Phoenix – Enforce Standards

Granular editing control

Page 16: Harnessing the Web to Streamline Statistical Programming ...Harnessing the Web to Streamline Statistical Programming Processes 2 •Share high level overview and key features of our

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Tedious Tasks - Mock Table Shell Creation

Page 17: Harnessing the Web to Streamline Statistical Programming ...Harnessing the Web to Streamline Statistical Programming Processes 2 •Share high level overview and key features of our

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Venus – Paper to Data,Targeted Functionality

Page 18: Harnessing the Web to Streamline Statistical Programming ...Harnessing the Web to Streamline Statistical Programming Processes 2 •Share high level overview and key features of our

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Venus – Enforce Table Standards

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Page 20: Harnessing the Web to Streamline Statistical Programming ...Harnessing the Web to Streamline Statistical Programming Processes 2 •Share high level overview and key features of our

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• Predictive Analyticso Predict Request Assignmento Predict Total Work

• Automationo Tie systems together to reduce manual

intervention• Innovation

o Venus to create tables/listings using only PHP, Python

Future

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• High level overview of key functionality• Web development presents a opportunity

to improve statistical programming processes with low overhead

Conclusion

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Questions