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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