Posts tagged: Open Science
Why I teach a workshop on codelists
Codelists decide which patients end up in a study, but building one is rarely taught as a method. This post explains why I run a three hour codelist workshop for universities and what it covers.
What is the difference between selection bias and sampling bias?
A reference guide to the difference between selection bias and sampling bias in health data research, with worked examples of how each one distorts your results.
What is a confounder, really?
A reference guide to what confounding means in health data research and how it is different from mediators and colliders.
What Do We Mean by Bias in Health Data Research?
A reference guide to what bias means in health data research and the distinct types you meet in electronic health records and other health datasets.
Why Synthetic Data is Good for Open Science
Understanding the benefits of synthetic data for open science and reproducibility.
Applying the Open-Closed Principle to Research Code
Applying the Open/Closed Principle is how you stop accidentally changing yesterday's results.
The Single Responsibility Principle for Scientists Who Write Code
Exploring the Single Responsibility Principle in the context of scientific programming and data analysis.
Accidental Functional Programming in Rust (From an Epidemiologist's Perspective)
Rust quietly pushes you into functional patterns. An epidemiologist explains Result, match, enums, iterators, and when readability beats idioms.
How to Create a Codelist
A practical guide to creating codelists from scratch for health data research.