Comprehensive Recap of Core Stata Commands for Clinical Regression Analysis
- Mayta
- Jun 7
- 3 min read
Introduction
This recap outlines the set of Stata commands employed in clinical regression workflows, particularly for anthropometric and birth outcome data. The focus is on the functional purpose and analytical role of each command. This serves as a standalone reference for clinical researchers and students consolidating command fluency for reproducible statistical modeling.
🔹 1. Data Loading and Script Execution
Command | Purpose |
use "file.dta" | Loads a .dta file into memory, replacing any existing dataset. |
do "script.do" | Executes a saved .do file containing a sequence of commands. |
🔹 2. Dataset Inspection and Missing Data Diagnostics
Command | Purpose |
describe | Lists variables, formats, types, labels, and dataset size. |
summarize | Reports means, SDs, min, max values. |
summarize var, detail | Adds percentiles, skewness, and kurtosis. |
mdesc | Displays count and % of missing values per variable (if installed). |
🔹 3. Data Cleaning and Filtering
Command | Purpose |
drop varname | Deletes variable(s). |
drop if condition | Removes rows matching a specified condition. |
recode | Converts continuous or multi-category vars into new categories. |
🔹 4. Descriptive & Summary Commands
Command | Purpose |
tab var | Frequency tables for categorical variables. |
tabstat var, by(group) stat(n mean sd ...) | Summarizes variables across group levels. |
tab var, sum(var2) | Summary of var2 within categories of var. |
histogram var, by(group) | Distribution comparison by groups. |
edit varlist | Opens the Data Editor for specified vars. |
🔹 5. Visualization Tools
Command | Purpose |
twoway (scatter Y X) | Visualize raw relationships. |
twoway (lowess Y X) | Non-parametric trend (smoothing). |
twoway (lfit Y X) | Overlays a linear fit. |
twoway (qfit Y X) | Adds a quadratic curve for trend detection. |
twoway (...) (...) | Combine multiple layers for comprehensive view. |
graph hbox varname | Visualize data spread and outliers. |
🔹 6. Correlation Assessment
Command | Purpose |
corr varlist | Computes pairwise Pearson correlation coefficients. |
🔹 7. Linear Regression (OLS)
Command | Purpose |
regress Y X | Fits a basic OLS linear model. |
regress Y c.X##c.X | Adds squared term for X to detect curvature. |
regress Y c.X##c.X i.Z | Models continuous and categorical variables together. |
🔹 8. Generalized Linear Modeling (GLM)
Command | Purpose |
glm Y X | Fits GLM with Gaussian errors and identity link (default). |
glm Y i.X | Treats predictor as a categorical (dummy-coded) variable. |
glm Y c.X##c.X | Includes both linear and squared terms (polynomial expansion). |
glm Y i.X1 i.X2 c.X3##c.X3 | Combine multiple categorical and nonlinear continuous predictors. |
estimate store modelname | Saves the current model for later comparison. |
lrtest model1 model2 | Performs likelihood-ratio test between two nested models. |
🔹 9. Polynomial and Interaction Terms
Syntax | Purpose |
gen x_sq = x^2 | Manually create squared term for use in regression. |
gen x_cub = x^3 | Adds cubic term to capture deeper curvature. |
c.x##c.x | Short-form to include x and x² together in regression models. |
🔹 10. Categorical Variable Handling
Syntax | Purpose |
i.var | Encodes var as categorical; generates dummy variables. |
ib#. | Changes the reference category in i.var. |
ttest Y, by(group) | Compare group means of Y between two categories. |
🔹 11. Predictive Tools
Command | Purpose |
predict predvar | Generates predicted values from the last regression model. |
margins, at(x=(...)) | Computes predicted outcomes at specific values of predictors. |
marginsplot | Graphs predicted margins over the range of a variable. |
🔹 12. Model Performance Metrics
Metric | Output Context | Interpretation |
R-squared, Adj R² | regress output | Proportion of variance explained. |
Root MSE | regress output | Average prediction error (residual SD). |
AIC, BIC | glm output | Penalized measures of model complexity. |
Deviance, Pearson | glm output | Error summaries used for model comparisons. |
✅ Usage Logic Recap
Load & Inspect: Begin with use, describe, and summarize.
Explore: Use plots, tabstat, and ttest to understand data patterns.
Model Simply: Start with regress or glm for core variables.
Test Linearity: Use lowess, qfit, and c.X##c.X.
Refine Model: Add confounders and interactions (i., ##).
Compare Models: Use estimate store, lrtest, and AIC/BIC.
Visualize Predictions: Apply margins, marginsplot.
Report: Emphasize coefficients, confidence intervals, and fit indices.
🧾 Conclusion
This guide compiles a full suite of Stata commands used in building, testing, and interpreting clinical regression models. Each command contributes to a critical part of the statistical workflow — from initial data validation to final model comparison and visualization. Proficiency with this command set is essential for transparent and efficient clinical research.
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