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Making Data Science Work for Clinical Reporting
  • Level Professional
  • المدة 11 ساعات hours
  • الطبع بواسطة Genentech
  • Offered by

عن

This course is aimed to demonstate how principles and methods from data science can be applied in clinical reporting. By the end of the course, learners will understand what requirements there are in reporting clinical trials, and how they impact on how data science is used. The learner will see how they can work efficiently and effectively while still ensuring that they meet the needed standards.

الوحدات

Module Introduction

    • 1 Videos
Show info about module content

1 Videos

  • Making data science work for clinical reporting

Lesson 1 :Introduction to Clinical Trials

    • 1 Videos
    • 1 Readings
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1 Videos

  • Introduction to Clinical Trials

1 Readings

  • Learning more about clinical trials

Lesson 2: Why Data Science?

    • 1 Videos
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1 Videos

  • Why use data science in clinical reporting?

Module Review

    • 1 Videos
    • 1 Quiz
Show info about module content

1 Videos

  • Module Review

1 Quiz

  • Module review

Introduction

    • 4 Videos
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4 Videos

  • Introduction
  • Motivation
  • Module Structure
  • Transparency vs. Reproducibility

Lesson 1: Data and Results Sharing

    • 6 Videos
    • 2 Readings
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6 Videos

  • Introduction
  • CDISC Standards
  • Dictionaries
  • Coding Standards
  • Reams of (Virtual) Paper
  • Industry Developments

2 Readings

  • More Details on MedDRA
  • More Details on WHO Drug Dictionary

Lesson 2: Quality Assurance

    • 7 Videos
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7 Videos

  • Introduction
  • Standard Operating Procedures (SOPs)
  • Qualification & Validation
  • Data Quality Control
  • Quality Control of Analysis Programs
  • Reams of (Virtual) Paper
  • Industry Development

Lesson 3: Data Access Restrictions

    • 5 Videos
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5 Videos

  • Introduction
  • Pseudonymization & Anonymization
  • FSPs & CROs
  • Unblinding
  • Reams of (Virtual) Paper

Module Review

    • 1 Videos
    • 1 Quiz
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1 Videos

  • Module Review

1 Quiz

  • Module Assessment

Module introduction

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Introduction to Module 2

1 Readings

  • Links and resources for Module 2

Lesson 1: Data Science as a new way of thinking

    • 1 Videos
    • 1 Discussion
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1 Discussions

  • Time to reflect

1 Videos

  • Data Science as a new way of thinking

Lesson 2: Why are agile and DevOps a good fit?

    • 3 Videos
    • 1 Quiz
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3 Videos

  • Introduction to agile
  • DevOps practices
  • The Data Science mindset

1 Quiz

  • Lesson 2 Quiz

Lesson 3: Changing together

    • 3 Videos
    • 1 Quiz
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3 Videos

  • Getting started
  • Pilots and doing agile
  • Scaling up

1 Quiz

  • Lesson 3 Quiz

Module review

    • 1 Videos
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1 Videos

  • Module 2 Recap

Module Introduction

    • 1 Videos
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1 Videos

  • Version control and git flows for reproducible clinical reporting

Lesson 1: Introduction to git and version control

    • 5 Videos
    • 1 Readings
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5 Videos

  • Lesson 1 Introduction
  • The whats and whys of version control
  • What is Git?
  • Key ideas in Git
  • Collaboration via Github

1 Readings

  • Further Reading on Git

Lesson 2: Git Flows

    • 5 Videos
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5 Videos

  • Introduction to Lesson 2
  • Workflows in Git
  • Git Flow
  • Selecting workflows for clinical use
  • Using Git for Agile

Lesson 3: Reproducible Projects in R

    • 6 Videos
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6 Videos

  • Introduction to lesson 3
  • Using Git in RStudio
  • Being truly reproducible in R
  • Well Structured Projects
  • R Libraries
  • R Version

Module Review

    • 1 Videos
    • 1 Quiz
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1 Videos

  • Module Review

1 Quiz

  • Module Assessment

Lesson 1: InnerSource & OpenSource

    • 3 Videos
    • 1 Readings
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3 Videos

  • Introduction to Module 4
  • What is an InnerSourcing?
  • When to OpenSource?

1 Readings

  • Module readings

Lesson 2: Developing our own R packages

    • 2 Videos
    • 1 Readings
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2 Videos

  • Why should we use R packages for code development?
  • Different types of R packages

1 Readings

  • Module readings

Lesson 3: Core principles (and tools) for R package development

    • 8 Videos
    • 1 Readings
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8 Videos

  • Environment for R package development
  • R package structure and content
  • R package documentation
  • Clean code
  • Code smells
  • Development workflow
  • Before release
  • Writing statistical software that can robustly implement complex methods

1 Readings

  • Module readings

Lesson 4 : CI/CD for R packages

    • 2 Videos
    • 1 Lab
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1 Labs

  • Set up CI/CD for an R package on GitHub

2 Videos

  • CI/CD as a feedback loop for in-development R packages
  • Anatomy of a CI/CD workflow for an R package

Module review

    • 1 Videos
    • 1 Quiz
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1 Videos

  • Module Review

1 Quiz

  • Module Assessment

Lesson 1: Why you need to understand the risk in using others code

    • 2 Videos
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2 Videos

  • Introduction to risk in your codebase
  • Why should we consider package quality?

Lesson 2: Building an understanding of risks

    • 2 Videos
    • 1 Quiz
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2 Videos

  • Considering the communities behind Open Source projects
  • Asessing the implementation of complex statistical methods in a package you use

1 Quiz

  • Assessing a package quiz

Communicating your position on an R package

    • 1 Videos
    • 1 PeerReview
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1 Peer Review

  • Advise a new colleague on the health and robustness of a package

1 Videos

  • What tools and approaches can help to assess and understand risk in R packages I use?

Conclusion

    • 1 Videos
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1 Videos

  • Conclusion

Auto Summary

Unlock the power of data science in clinical reporting with this professional-level course on Coursera. Designed for data science and AI enthusiasts, it delves into applying data science principles to clinical trial reporting. Over 660 minutes, learn to meet stringent clinical standards efficiently and effectively. Available through Starter and Professional subscriptions, this course is perfect for professionals looking to enhance their clinical data reporting skills.

Dinakar Kulkarni
Instructors

Dinakar Kulkarni

Kamila Duniec
Instructors

Kamila Duniec

Kamil Wais
Instructors

Kamil Wais

Daniel Sabanes Bove
Instructors

Daniel Sabanes Bove

James Black
Instructors

James Black

Kieran Martin
Instructors

Kieran Martin

Holger Langkabel
Instructors

Holger Langkabel