Posts tagged: Reproducibility
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 a confounder, really?
A reference guide to what confounding means in health data research and how it is different from mediators and colliders.
Bias in Synthetic Data
An exploration of bias in synthetic data and its implications for health research.
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.
The Builder Pattern for Complex Records
Learn how to use the builder pattern in Rust to construct complex records in a flexible and readable way. This helps manage optional fields and maintain code clarity in data pipelines, and can help your data science and engineering teams build more reliable and maintainable systems.
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.
Representativeness in Synthetic Data: What It Means and How to Measure It
Understanding the concept of representativeness in synthetic data and the methods used to measure it.