Case-control analysis is a study design used in observational research to investigate factors that may contribute to a particular outcome by comparing cases (individuals with the outcome or disease) and controls (individuals without the outcome). This design is commonly used in epidemiology, genomics, and biomedical research to explore associations between risk factors (e.g., genetic variants, environmental exposures) and outcomes (e.g., disease states, adverse effects).

Here’s an overview of the key concepts, setup, and steps in case-control analysis.

💢Code snippets

Case-control analysis with MATLAB

Case-control analysis with R

Case-control analysis with Python

Case-control analysis with Julia

Case-control analysis with Haskell

Key Concepts in Case-Control Analysis

  1. Cases and Controls:
  2. Matching:
  3. Odds Ratio (OR):
  4. Retrospective Design:
  5. Cost-Effectiveness and Efficiency:

Steps in Case-Control Analysis

1. Define the Outcome and Select Cases and Controls

2. Matching (Optional)