Technical Tutorials

Educational resources on data science and statistical concepts for medical research using OHDSI's OMOP Common Data Model.

If there's a specific topic you would like covered, please submit a request.

Data Science Workflow

Overview of the modeling framework and data science process for clinical research.

📹 Video 📊 Slides

Exploratory Data Analysis

Comprehensive tutorial on EDA techniques for clinical data with practical examples.

📹 Video 📓 Notebook 📄 R Code 📊 Data

SQL Tutorials

Learn SQL fundamentals and advanced queries for working with clinical databases.

Introduction to SQL
📹 Video
Advanced Queries
📹 Video 📄 Sample SQL

Statistical Concepts

In healthcare and biomedical research, statistical methods are essential for drawing reliable conclusions from data, assessing treatment effectiveness, and making informed clinical decisions. This tutorial series introduces fundamental statistical concepts for conducting research in healthcare.

Hypothesis Testing

Hypothesis testing is a statistical method used to evaluate assumptions about a population based on sample data. It plays a crucial role in clinical trials and medical research, such as assessing whether a new treatment provides greater benefits compared to the current standard of care.

Rejection Region

Real-world examples guide you through step-by-step calculations and statistical reasoning.

📹 Video 📊 Slides 🐍 Python 📄 R
Type I and Type II Errors

Learn about error types, their meanings, and how they affect statistical decision-making.

📹 Video 📊 Slides 🐍 Python 📄 R
Power Function

Understand the relationship with sample size, significance level, effect size, and Type II errors.

📹 Video 📊 Slides 🐍 Python 📄 R
Significance Level

Learn about alpha's role in defining the rejection region and its impact on errors.

📹 Video 📊 Slides
P-values

How P-values help determine statistical significance and their relationship with rejection regions.

📹 Video 📊 Slides 🐍 Python 📄 R
Confidence and Prediction Intervals

Quantify uncertainty around point estimates in medical research.

🐍 Python 📄 R

Model Evaluation

Essential metrics for evaluating predictive models in healthcare analytics.

Bias and Variance

Explore bias-variance tradeoffs in statistical inference and predictive accuracy.

📹 Video 📊 Slides 📄 R 📊 Test Data 📊 Train Data
ROC Curves and AUC

Measure model ability to distinguish between positive and negative cases across classification thresholds.

📹 Video 📊 Slides 📄 R
R-Squared and Adjusted R-Squared

Understand model fit without overfitting or underfitting, including bias-variance tradeoff.

📹 Video 📊 Slides 🐍 Python 📄 R
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