Making Data Science Work for Clinical Reporting 

Making data science work for clinical reporting

Introduction to Clinical Trials

Why use data science in clinical reporting?

Module Review

Learning more about clinical trials

Module review

Introduction

Motivation

Module Structure

Transparency vs. Reproducibility

Introduction

CDISC Standards

Dictionaries

Coding Standards

Reams of (Virtual) Paper

Industry Developments

Introduction

Standard Operating Procedures (SOPs)

Qualification & Validation

Data Quality Control

Quality Control of Analysis Programs

Reams of (Virtual) Paper

Industry Development

Introduction

Pseudonymization & Anonymization

FSPs & CROs

Unblinding

Reams of (Virtual) Paper

Module Review

More Details on MedDRA

More Details on WHO Drug Dictionary

Module Assessment

Introduction to Module 2

Data Science as a new way of thinking

Introduction to agile

DevOps practices

The Data Science mindset

Getting started

Pilots and doing agile

Scaling up

Module 2 Recap

Links and resources for Module 2

Lesson 2 Quiz

Lesson 3 Quiz

Version control and git flows for reproducible clinical reporting

Lesson 1 Introduction

The whats and whys of version control

What is Git?

Key ideas in Git

Collaboration via Github

Introduction to Lesson 2

Workflows in Git

Git Flow

Selecting workflows for clinical use

Using Git for Agile

Introduction to lesson 3

Using Git in RStudio

Being truly reproducible in R

Well Structured Projects

R Libraries

R Version

Module Review

Further Reading on Git

Module Assessment

Introduction to Module 4

What is an InnerSourcing?

When to OpenSource?

Why should we use R packages for code development?

Different types of R packages

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

CI/CD as a feedback loop for in-development R packages

Anatomy of a CI/CD workflow for an R package

Module Review

Module readings

Module readings

Module readings

Module Assessment

Introduction to risk in your codebase

Why should we consider package quality?

Considering the communities behind Open Source projects

Asessing the implementation of complex statistical methods in a package you use

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

Assessing a package quiz

Conclusion