How to do Logistic Regression in Stata? A Comprehensive Guide

Logistic regression in Stata is a statistical technique used to model the relationship between a binary outcome variable and one or more predictor variables. It is widely used in various fields such as healthcare, social sciences, and marketing to predict the likelihood of an event occurring. In this blog post, I will provide an introduction to logistic regression in Stata, covering the key concepts and providing an example of how to run a logistic regression model in Stata. By the end of this post, readers should have a solid understanding of the basics of logistic regression and how to use Stata for this technique.

Definition of logistic regression

Logistic regression is a statistical method used to model the relationship between a binary outcome variable and one or more predictor variables. It is a type of regression analysis that is used when the dependent variable is dichotomous or binary, meaning it has only two possible outcomes. In logistic regression, the dependent variable is usually coded as 0 or 1, with 0 representing the absence of an event or outcome and 1 representing the presence of an event or outcome.

The logistic regression model calculates the probability of the occurrence of an event, given the values of the predictor variables. The probability is modeled using the logit function, which transforms the probability into a value between negative infinity and positive infinity. The logit function is then used to estimate the odds ratio, which is the ratio of the probability of an event occurring to the probability of an event not occurring. The odds ratio is a useful measure of association between the predictor variables and the outcome variable in logistic regression.

Logistic regression is a powerful tool that can be used for a wide range of applications, such as predicting the likelihood of a customer purchasing a product, the probability of a patient developing a certain disease, or the chances of a person voting in a political election. In Stata, logistic regression can be easily implemented using built-in commands and functions, making it a popular choice for researchers.

Importance of logistic regression

Logistic regression is a critical tool in many fields, including healthcare, social sciences, marketing, and finance. One of the primary reasons for its importance is that it can handle binary outcomes, which are common in many areas of research and business. Logistic regression is also useful for modelling the relationship between predictor variables and the probability of an outcome occurring. By understanding the relationship between these variables, organizations can make informed decisions and take appropriate action.

In healthcare, logistic regression is used to predict the likelihood of a patient developing a disease, given certain risk factors such as age, gender, and family history. In marketing, logistic regression can be used to predict the likelihood of a customer purchasing a product, given variables such as their demographic characteristics and purchasing history. In social sciences, logistic regression is used to model the relationship between variables such as income, education level, and political beliefs. These are just a few examples of the wide range of applications of logistic regression.

Overview of how logistic regression works in Stata

Stata is a popular software used by researchers and data analysts for statistical analysis, including logistic regression. In Stata, logistic regression can be easily implemented using built-in commands and functions.

To run a logistic regression model in Stata, the user needs to specify the dependent and independent variables, and use the “logit” command to estimate the coefficients of the model.

The “logit” command in Stata estimates the coefficients of the logistic regression model using the maximum likelihood method. Stata also provides several built-in functions and tools to evaluate the model’s fit and accuracy, such as the “predict” command, which can be used to calculate predicted probabilities for each observation in the dataset.

In Stata, logistic regression can also be used to model more complex relationships between the predictor variables and the outcome variable, such as interactions and nonlinear effects. The user can use Stata’s built-in functions to create interaction terms and polynomial terms to include in the model.

Key Concepts of Logistic Regression

Logistic regression is a powerful statistical method that involves several key concepts that must be understood to build and interpret models effectively. These key concepts include:

Dependent and independent variables

One of the most important concepts in logistic regression is the distinction between dependent and independent variables. The dependent variable is the outcome variable that the model aims to predict, while the independent variables are the predictors used to make predictions.

In logistic regression, the dependent variable is always binary, meaning it has only two possible values, typically coded as 0 or 1. For example, the dependent variable in a logistic regression model could be whether a patient has a particular disease (1) or not (0), or whether a customer purchases a product (1) or not (0).

The independent variables, on the other hand, can be continuous, categorical, or binary. Continuous variables are numeric variables that can take any value within a certain range, such as age or income. Categorical variables are variables that take on discrete values, such as gender or race. Binary variables are categorical variables that have only two possible values, such as yes/no or true/false.

In logistic regression, the independent variables are used to predict the probability of the dependent variable. By analyzing the relationship between the independent variables and the dependent variable, the logistic regression model can make predictions about the probability of the dependent variable.

Selecting the right independent variables is critical to the accuracy and reliability of the logistic regression model. It is important to select variables that are strongly associated with the dependent variable, as well as variables that are not highly correlated with each other. Additionally, including too many or too few variables can lead to overfitting or underfitting the model.

Binary outcome variable

In logistic regression, the dependent variable is always binary, meaning it has only two possible values. This is different from linear regression, where the dependent variable is continuous and can take on any value within a certain range. The binary outcome variable in logistic regression is typically coded as 0 or 1, with 0 representing the absence of an event or outcome and 1 representing the presence of an event or outcome.

Binary outcome variables are commonly used in many fields, such as healthcare, social sciences, and marketing, to predict the occurrence or non-occurrence of an event. For example, logistic regression can be used to predict the likelihood of a patient developing a certain disease or the probability of a customer purchasing a product.

Working with binary outcome variables presents some unique challenges. One challenge is the imbalanced nature of the data, where the number of observations in one category is much larger or smaller than the other. Another challenge is collinearity, where the independent variables are highly correlated with each other, which can lead to multicollinearity issues.

To address these challenges, there are several techniques and tools available in logistic regression, such as regularization methods and model evaluation measures, such as the AUC (area under the curve) and the ROC (receiver operating characteristic) curve.

Logit function

The logit function is a mathematical function used in logistic regression to model the relationship between the predictor variables and the outcome variable. It is the inverse of the logistic function, which is used to transform the linear regression output to probabilities.

The logit function transforms the probability of an event occurring into a value between negative infinity and positive infinity. The logit function is defined as the natural logarithm of the odds of an event occurring, where the odds are the ratio of the probability of an event occurring to the probability of an event not occurring.

The formula for the logit function is:

logit(p) = log(p / (1 - p))

where p is the probability of an event occurring.

The logit function can be used to estimate the coefficients of the logistic regression model, which represent the change in the log odds of the outcome variable associated with a one-unit increase in the predictor variable. The coefficients can then be exponentiated to calculate the odds ratios, which represent the change in the odds of the outcome variable associated with a one-unit increase in the predictor variable.

The logit function is a critical component of logistic regression modelling. It is used to estimate the coefficients of the model and to transform the probability of an event occurring into a value that can be modelled using linear regression techniques.

Odds ratio

The odds ratio is a measure of association between the predictor variables and the outcome variable in logistic regression. It represents the ratio of the odds of an event occurring in one group compared to the odds of the same event occurring in another group. The odds ratio can be used to determine the strength and direction of the relationship between the predictor variables and the outcome variable.

Interpretation of coefficients

Interpreting the coefficients of the logistic regression model is critical for understanding the relationship between the predictor variables and the outcome variable. The coefficients represent the change in the log odds of the outcome variable associated with a one-unit increase in the predictor variable. To interpret the coefficients, they must be exponentiated to obtain the odds ratio.

Example of Running Logistic Regression in Stata

To better understand the practical aspects of logistic regression modelling in Stata, it is essential to see how it works in action. In this section, I will provide an example of how to run a logistic regression model in Stata. I will cover data preparation and variable selection, building the model, interpreting the output, and model evaluation and diagnostics. By following these steps, researchers can gain hands-on experience with logistic regression modelling in Stata and learn how to apply these techniques to their own data.

Data preparation and variable selection

A critical first step in building a logistic regression model is to prepare the data and select the appropriate variables. This involves importing the data into Stata, checking and cleaning the data, and selecting the variables that are most relevant to the outcome variable.

Importing the data into Stata involves ensuring that the data is in the correct format and that Stata recognizes the variables as they are intended. This is essential to prevent errors in the analysis and ensure that the model accurately reflects the data.

Checking and cleaning the data involves identifying and addressing any missing values, outliers, or other issues that may affect the analysis. This may involve imputing missing data, transforming variables, or removing observations with extreme values.

Selecting the variables for the model is critical to building an accurate and reliable logistic regression model. The independent variables should be chosen based on their theoretical relevance to the outcome variable and their statistical significance in predicting the outcome. Additionally, multicollinearity issues should be avoided by selecting independent variables that are not highly correlated with each other.

Building a logistic regression model

Once the data has been prepared and the variables have been selected, the next step is to build the logistic regression model in Stata. This involves running the logistic regression command, choosing the appropriate model specification, and assessing the fit of the model.

The logistic regression command in Stata is “logit“, and it is used to estimate the coefficients of the model and to predict the probability of the outcome variable. The command requires specifying the dependent variable and the independent variables, and it can also include additional options for controlling the model specification.

Choosing the appropriate model specification is critical to building an accurate and reliable logistic regression model. This may involve including interaction terms, polynomial terms, or other transformations of the variables. The model specification should be based on theoretical considerations and statistical significance tests.

Assessing the fit of the model involves evaluating the goodness-of-fit measures, such as the deviance and the likelihood ratio test. These measures indicate how well the model fits the data and whether it is a better fit than a null model with no independent variables.

Here’s an example of running a logistic regression model in Stata using the “auto” dataset:

sysuse auto
logistic foreign length mpg

In this example, “foreign” is the dependent variable, and “Iength” and “mpg” are the independent variables. The output will provide the coefficients and odds ratios for the independent variables, the p-values associated with the coefficients, and the goodness-of-fit measures.

Interpreting the output

Once the logistic regression model has been built, the next step is to interpret the output. This involves understanding the coefficients and odds ratios, assessing the significance of the coefficients, and interpreting the goodness-of-fit measures.

The coefficients in the logistic regression model represent the change in the log odds of the outcome variable associated with a one-unit increase in the predictor variable. These coefficients can be exponentiated to calculate the odds ratios, which represent the change in the odds of the outcome variable associated with a one-unit increase in the predictor variable.

Odds ratios greater than 1 indicate a positive association between the predictor variable and the outcome variable, while odds ratios less than 1 indicate a negative association.

In above output, the coefficients for the independent variables indicate the direction and magnitude of their effects on the odds of a car being foreign. The odds ratio for “length” is 0.90, which means that for each unit increase in length, the odds of a car being foreign decrease by a factor of 0.90 (or 10%). The odds ratio for “mpg” is 0.91, but it is not statistically significant (p-value > 0.05). The intercept term (“_cons”) has an odds ratio of 7.48e+08 (or 748 million), which represents the odds of a car being foreign when both “length” and “mpg” are zero.

Assessing the significance of the coefficients involves checking the p-values associated with each coefficient. If the p-value is less than the significance level, typically set at 0.05, then the coefficient is considered statistically significant and provides evidence of an association between the predictor variable and the outcome variable.

The model’s goodness of fit is evaluated using a pseudo R-squared value, which estimates the proportion of variance explained by the independent variables in the model. In this case, the pseudo R-squared value is 0.33, which suggests that the model accounts for a moderate amount of variance in the dependent variable.

Model evaluation and diagnostics

Once the logistic regression model has been built and the output has been interpreted, the next step is to evaluate the performance of the model and to diagnose any potential issues that may affect its validity.

Model evaluation involves assessing the performance of the model in predicting the outcome variable. This may involve splitting the data into training and testing sets and using performance measures, such as accuracy, sensitivity, specificity, and ROC curves, to evaluate the model’s predictive ability.

Checking the assumptions of the model involves assessing whether the model assumptions are met. These assumptions include linearity, independence, and normality of residuals. Violations of these assumptions can lead to biased estimates and unreliable predictions.

Evaluating the robustness of the model involves assessing how sensitive the model is to changes in the data or model specification. This may involve performing sensitivity analyses or robustness checks to assess the stability and reliability of the model.

Tips and Best Practices

To build accurate and reliable logistic regression models in Stata, there are several tips and best practices that researchers and data analysts should follow. In this section, I will provide some practical advice on good practices for data preparation, choosing appropriate independent variables, checking assumptions and model fit, and interpreting results effectively. By following these tips and best practices, researchers and data analysts can ensure that their logistic regression models are accurate, reliable, and valid for prediction and decision-making.

Good practices for data preparation

Data preparation is a critical step in building accurate and reliable logistic regression models. Good practices for data preparation include:

1. Checking for missing data: Missing data can affect the accuracy and reliability of logistic regression models. It is important to check for missing data and to handle it appropriately, either by imputing missing data or by removing observations with missing data.
2. Cleaning the data: Outliers, extreme values, and errors in the data can also affect the accuracy and reliability of logistic regression models. It is important to clean the data by identifying and addressing any issues that may affect the analysis.
3. Checking for multicollinearity: Multicollinearity, or high correlation between independent variables, can affect the accuracy and reliability of logistic regression models. It is important to check for multicollinearity and to remove or combine variables that are highly correlated.
4. Creating new variables: Creating new variables can improve the accuracy and reliability of logistic regression models. This may involve creating interaction terms, polynomial terms, or other transformations of the variables.
5. Creating dummy variables: Categorical variables should be converted into dummy variables to be included in logistic regression models. This involves creating a binary variable for each category and including them as independent variables in the model.

Choosing appropriate independent variables

Choosing appropriate independent variables is critical to building accurate and reliable logistic regression models. Good practices for choosing independent variables include:

1. Theoretical relevance: Independent variables should be chosen based on their theoretical relevance to the outcome variable. Variables that are not theoretically relevant should not be included in the model.
2. Statistical significance: Independent variables should be chosen based on their statistical significance in predicting the outcome variable. Variables that are not statistically significant should not be included in the model.
3. Multicollinearity: Independent variables should not be highly correlated with each other. Multicollinearity can affect the accuracy and reliability of logistic regression models.
4. Interaction terms: Interaction terms can improve the accuracy and reliability of logistic regression models. Interaction terms are created by multiplying two or more independent variables together.
5. Polynomial terms: Polynomial terms can also improve the accuracy and reliability of logistic regression models. Polynomial terms are created by including a variable raised to a power, such as x^2 or x^3.

Checking assumptions and model fit

Checking assumptions and model fit is an important step in building accurate and reliable logistic regression models. Good practices for checking assumptions and model fit include:

1. Linearity: Logistic regression assumes that the relationship between the independent variables and the outcome variable is linear. This assumption can be checked by plotting the independent variable against the log odds of the outcome variable.
2. Independence: Logistic regression assumes that the observations are independent of each other. This assumption can be checked by examining the residuals of the model.
3. Normality of residuals: Logistic regression assumes that the residuals are normally distributed. This assumption can be checked by examining the distribution of the residuals.
4. Goodness-of-fit measures: Goodness-of-fit measures, such as the deviance and the likelihood ratio test, can be used to assess the fit of the model. A lower deviance or a higher likelihood ratio test statistic indicates a better fit of the model to the data.

Interpreting results effectively

Interpreting the results of a logistic regression model is critical to understanding the relationship between the predictor variables and the outcome variable. Good practices for interpreting results effectively include:

1. Coefficients and odds ratios: Coefficients in the logistic regression model represent the change in the log odds of the outcome variable associated with a one-unit increase in the predictor variable. Odds ratios can be calculated by exponentiating the coefficients and represent the change in the odds of the outcome variable associated with a one-unit increase in the predictor variable.
2. Significance of coefficients: The significance of coefficients can be assessed by checking the p-values associated with each coefficient. A p-value less than the significance level, typically set at 0.05, indicates that the coefficient is statistically significant and provides evidence of an association betIen the predictor variable and the outcome variable.
3. Confounding variables: Confounding variables can affect the relationship betIen the predictor variables and the outcome variable. Confounding variables should be controlled for in the model to obtain an accurate estimate of the relationship betIen the predictor variables and the outcome variable.
4. Effect size: The effect size of the predictor variables can be calculated by dividing the odds ratio by the standard deviation of the predictor variable. This provides a standardized estimate of the effect of the predictor variable on the outcome variable.

Conclusion

In conclusion, logistic regression is a powerful statistical method for analyzing binary outcome data. In this blog post, I provided an overview of logistic regression and how it can be implemented in Stata. I covered the key concepts of logistic regression, including dependent and independent variables, binary outcome variables, the logit function, odds ratios, and interpretation of coefficients. I also provided an example of running a logistic regression model in Stata, including data preparation, variable selection, model building, and model evaluation and diagnostics. Additionally, I provided tips and best practices for data preparation, choosing appropriate independent variables, checking assumptions and model fit, and interpreting results effectively.