Stata and ML resources

Some links to Stata and ML resources

Conference Articles / Presentations / Stata Journal

• 2023 – Presentation_UK_Stata_Conf_2023 – A review of machine learning commands in Stata: Giovanni Cerulli
• 2022 – Applying Machine Learning Techniques in Stata to Predict Health Outcomes Using HIV-related Data (youtube.com) – use of LASSO in HIV setting using Stata
• 2021 – Cerulli_StataConf2021 : ML using Stata and Python
• 2019 – An Introduction to Machine Learning [.2cm] with Stata – Achim Ahrens
• 2024 – ddml – ddml: Double/debiased machine learning in Stata (sagepub.com)
• 2023 – pystackd – pystacked: Stacking generalization and machine learning in Stata (sagepub.com)
• 2020 – lassopack – lassopack: Model selection and prediction with regularized regression in Stata (sagepub.com)
• 2016 – Support Vector Machines (sagepub.com) svmachines
• ELASTICREGRESS: Stata module to perform elastic net regression, lasso regression, ridge regression (repec.org)
• LASSOPACK: Stata module for lasso, square-root lasso, elastic net, ridge, adaptive lasso estimation and cross-validation (repec.org)
• PDSLASSO: Stata module for post-selection and post-regularization OLS or IV estimation and inference (repec.org)

Stata Blog Articles from 2020

• The Stata Blog » Stata/Python integration part 1: Setting up Stata to use Python
• The Stata Blog » Stata/Python integration part 2: Three ways to use Python in Stata
• The Stata Blog » Stata/Python integration part 3: How to install Python packages
• The Stata Blog » Stata/Python integration part 4: How to use Python packages
• The Stata Blog » Stata/Python integration part 5: Three-dimensional surface plots of marginal predictions
• The Stata Blog » Stata/Python integration part 6: Working with APIs and JSON data
• The Stata Blog » Stata/Python integration part 7: Machine learning with support vector machines
• The Stata Blog » Stata/Python integration part 8: Using the Stata Function Interface to copy data from Stata to Python
• The Stata Blog » Stata/Python integration part 9: Using the Stata Function Interface to copy data from Python to Stata

Other Resources

• User’s corner: Machine learning| Stata News
• Giovanni Cerulli – Machine Learning in Stata (google.com)
• Towards better clinical prediction models: seven steps for development and an ABCD for validation – PMC (nih.gov)

Seven Steps in developing a prediction model

Source: Towards better clinical prediction models: seven steps for development and an ABCD for validation – PMC (nih.gov)

1. Problem definition and data inspection/ Research Question
   • What is the precise research question
   • How were patients selected
   • What is already known about the predictors? 
   • Define the Predictors
   • Were the predictors reliably and completely measured?
   • Define the outcomes of Interest
2. Coding of predictors
   • Categorical predictors
   • Continuous predictors
3. Model specification
   • Selection of main effects?
   • Assessment of assumptions?
   • Overfitting?
4. Model estimation – Estimate model parameters
   • Shrinkage included ?
5. Model performance:
   • Calibration: Caliberation plot
     • A: alpha – Calibration-in-the-large – Intercept in plot; the agreement between observed endpoints and predictions
     • B: beta – Calibration slope – Regression slope in plot; related to shrinkage of regression coefficients
   • Discrimination: the ability of the model to distinguish a patient with the endpoint from a patient without
     • Concordance C-statistic –
       • ROC curve – For a binary endpoint, c is identical to the area under the receiver operating characteristic (ROC) curve
       • For a time-to-event endpoint, such as survival, the calculation of c may be affected by the amount of incomplete follow-up (censoring)
       • Probability of correct classification for a pair of subjects with and without the endpoint
       • A better discriminating model has more spread between the predictions than a poorly discriminating model
   • Clinical usefulness:
     • D – Decision-curve analysis – Net true-positive classification rate by using a model over a range of thresholds –
     • Net benefit (NB)
6. Model validation
   • Internal validity
   • External validity
   • Techniques used: split smaple, cross-valdiation, etc
7. Model presentation