Making Data Science work for clinical reporting-Module Introduction
Making data science work for clinical reporting
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Making Data Science work for clinical reporting-Lesson 1 :Introduction to Clinical Trials
Introduction to Clinical Trials
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Learning more about clinical trials
Making Data Science work for clinical reporting-Lesson 2: Why Data Science?
Why use data science in clinical reporting?
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Making Data Science work for clinical reporting-Module Review
Module Review
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The burden of being faultless and transparent-Introduction
Introduction
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Motivation
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Module Structure
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Transparency vs. Reproducibility
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The burden of being faultless and transparent-Lesson 1: Data and Results Sharing
Introduction
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CDISC Standards
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Dictionaries
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More Details on MedDRA
More Details on WHO Drug Dictionary
Coding Standards
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Reams of (Virtual) Paper
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Industry Developments
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The burden of being faultless and transparent-Lesson 2: Quality Assurance
Introduction
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Standard Operating Procedures (SOPs)
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Qualification & Validation
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Data Quality Control
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Quality Control of Analysis Programs
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Reams of (Virtual) Paper
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Industry Development
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The burden of being faultless and transparent-Lesson 3: Data Access Restrictions
Introduction
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Pseudonymization & Anonymization
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FSPs & CROs
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Unblinding
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Reams of (Virtual) Paper
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The burden of being faultless and transparent-Module Review
Module Review
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Bringing DevOps practices and agile mindset to clinical reporting-Module introduction
Introduction to Module 2
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Links and resources for Module 2
Bringing DevOps practices and agile mindset to clinical reporting-Lesson 1: Data Science as a new way of thinking
Data Science as a new way of thinking
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Bringing DevOps practices and agile mindset to clinical reporting-Lesson 2: Why are agile and DevOps a good fit?
Introduction to agile
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DevOps practices
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The Data Science mindset
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Bringing DevOps practices and agile mindset to clinical reporting-Lesson 3: Changing together
Getting started
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Pilots and doing agile
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Scaling up
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Bringing DevOps practices and agile mindset to clinical reporting-Module review
Module 2 Recap
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Version control and git flows for reproducible clinical reporting-Module Introduction
Version control and git flows for reproducible clinical reporting
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Version control and git flows for reproducible clinical reporting-Lesson 1: Introduction to git and version control
Lesson 1 Introduction
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The whats and whys of version control
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What is Git?
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Key ideas in Git
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Collaboration via Github
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Further Reading on Git
Version control and git flows for reproducible clinical reporting-Lesson 2: Git Flows
Introduction to Lesson 2
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Workflows in Git
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Git Flow
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Selecting workflows for clinical use
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Using Git for Agile
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Version control and git flows for reproducible clinical reporting-Lesson 3: Reproducible Projects in R
Introduction to lesson 3
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Using Git in RStudio
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Being truly reproducible in R
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Well Structured Projects
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R Libraries
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R Version
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Version control and git flows for reproducible clinical reporting-Module Review
Module Review
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Making code reusable and robust in clinical reporting — a call for InnerSourcing-Lesson 1: InnerSource & OpenSource
Introduction to Module 4
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What is an InnerSourcing?
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When to OpenSource?
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Module readings
Making code reusable and robust in clinical reporting — a call for InnerSourcing-Lesson 2: Developing our own R packages
Why should we use R packages for code development?
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Different types of R packages
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Module readings
Making code reusable and robust in clinical reporting — a call for InnerSourcing-Lesson 3: Core principles (and tools) for R package development
Environment for R package development
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R package structure and content
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R package documentation
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Clean code
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Code smells
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Development workflow
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Before release
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Writing statistical software that can robustly implement complex methods
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Module readings
Making code reusable and robust in clinical reporting — a call for InnerSourcing-Lesson 4 : CI/CD for R packages
CI/CD as a feedback loop for in-development R packages
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Anatomy of a CI/CD workflow for an R package
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Making code reusable and robust in clinical reporting — a call for InnerSourcing-Module review
Module Review
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Assessing and managing risk-Lesson 1: Why you need to understand the risk in using others code
Introduction to risk in your codebase
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Why should we consider package quality?
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Assessing and managing risk-Lesson 2: Building an understanding of risks
Considering the communities behind Open Source projects
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Asessing the implementation of complex statistical methods in a package you use
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Assessing and managing risk-Communicating your position on an R package
What tools and approaches can help to assess and understand risk in R packages I use?
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Conclusion-Conclusion
Conclusion
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