Case-control analysis is a common statistical method used to compare two groups — typically, a "case" group (with the outcome of interest) and a "control" group (without the outcome). This analysis is frequently used in epidemiology to study risk factors associated with a disease. In MATLAB, case-control analysis usually involves odds ratio calculations, hypothesis testing, and potentially logistic regression.

Here's a general approach to conducting a basic case-control analysis in MATLAB:

1. Set up your data

Assume you have two groups (cases and controls) and a binary exposure variable (e.g., "exposed" vs. "not exposed").

Here’s an example dataset:

• cases: Number of cases (with the condition).
• controls: Number of controls (without the condition).
• exposed_cases: Number of cases exposed to the risk factor.
• not_exposed_cases: Number of cases not exposed.
• exposed_controls: Number of controls exposed.
• not_exposed_controls: Number of controls not exposed.

Example:

 % Example data
 exposed_cases = 50;
 not_exposed_cases = 30;
 exposed_controls = 20;
 not_exposed_controls = 100;

2. Calculate Odds Ratio

The odds ratio (OR) is a measure of the association between exposure and outcome. The formula for OR is:

$$ \text{OR} = \frac{(exposed_cases \times not_exposed_controls)}{(not_exposed_cases \times exposed_controls)} $$

In MATLAB:

 % Calculating the odds ratio
 odds_ratio = (exposed_cases * not_exposed_controls) / (not_exposed_cases * exposed_controls);
 disp(['Odds Ratio: ', num2str(odds_ratio)]);

3. Calculate Confidence Intervals for the Odds Ratio

To estimate a confidence interval for the odds ratio, use the following formula for a 95% confidence interval: