Logistic Regression is a statistical method that we use to fit a regression model when the response variable is binary. To assess how well a logistic regression model fits a dataset, we can look at the following two metrics:

**Sensitivity:**the probability that the model predicts a positive outcome for an observation when indeed the outcome is positive.**Specificity:**the probability that the model predicts a negative outcome for an observation when indeed the outcome is negative.

One easy way to visualize these two metrics is by creating a **ROC curve**, which is a plot that displays the sensitivity and specificity of a logistic regression model.

This tutorial explains how to create and interpret a ROC curve in Stata.

**Example: ROC Curve in Stata**

For this example we will use a dataset called *lbw*, which contains the folllowing variables for 189 mothers:

**low**– whether or not the baby had a low birthweight. 1 = yes, 0 = no.**age**– age of the mother.**smoke**– whether or not the mother smoked during pregnancy. 1 = yes, 0 = no.

We will fit a logistic regression model to the data using age and smoking as explanatory variables and low birthweight as the response variable. Then we will create a ROC curve to analyze how well the model fits the data.

**Step 1: Load and view the data.**

Load the data using the following command:

use http://www.stata-press.com/data/r13/lbw

Gain a quick understanding of the dataset using the following command:

summarize

There are 11 different variables in the dataset, but the only three that we care about are low, age, and smoke.

**Step 2: Fit the logistic regression model.**

Use the following command to fit the logistic regression model:

logit low age smoke

**Step 3: Create the ROC curve.**

We can create the ROC curve for the model using the following command:

lroc

**Step 4: Interpret the ROC curve.**

When we fit a logistic regression model, it can be used to calculate the probability that a given observation has a positive outcome, based on the values of the predictor variables.

To determine if an observation should be classified as positive, we can choose a cut-point such that observations with a fitted probability above the cut-point are classified as positive and any observations with a fitted probability below the cut-point are classified as negative.

For example, suppose we choose the cut-point to be 0.5. This means that any observation with a fitted probability greater than 0.5 will be predicted to have a positive outcome, while any observation with a fitted probability less than or equal to 0.5 will be predicted to have a negative outcome.

The ROC curve shows us the values of sensitivity vs. 1-specificity as the value of the cut-off point moves from 0 to 1. A model with high sensitivity and high specificity will have a ROC curve that hugs the top left corner of the plot. A model with low sensitivity and low specificity will have a curve that is close to the 45-degree diagonal line.

The **AUC** **(area under curve)** gives us an idea of how well the model is able to distinguish between positive and negative outcomes. The AUC can range from 0 to 1. The higher the AUC, the better the model is at correctly classifying outcomes. In our example, we can see that the AUC is **0.6111**.

We can use AUC to compare the performance of two or more models. The model with the higher AUC is the one that performs best.

**Additional Resources**

How to Perform Logistic Regression in Stata

How to Interpret the ROC Curve and AUC of a Logistic Regression Model