Stata package for subpopulation treatment effect pattern plots (STEPP). The package is a porting to Stata of the stepp R package.

To install stepp directly from GitHub you need to use the github Stata command. You can install the latest version of the github command by executing the following code in your Stata session:

net install github, from("https://haghish.github.io/github/")

Then, you can install stepp simply using the following code in Stata:

github install sergioventurini/stepp

Alternatively, you can install the package manually downloading the files from this GitHub repository and placing it in your Stata PERSONAL directory (if you don't know what this is, run the adopath command).

The examples.do file contains many examples taken from the literature as well as some simulated data examples. All the example data sets are downloaded and put in place together with the rest of the package.

Note: if you installed the package prior to 9 May 2019, you need to manually remove the previous installation. You can do it by deleting all the files used by stepp from your Stata PERSONAL directory.

• 30/11/2023: the calculations of the GLM case have been updated
• 10/01/2021: the package now includes the possibility to run single group analyses
• 05/10/2020: the package now includes a procedure called balance_patients for determining the optimal number of subpopulations
• 01/08/2020: the package now allows to generate subpopulations using the event-based approach described in Lazar et al. (2016)

Sergio Venturini, Department of Economic and Social Sciences, Università Cattolica del Sacro Cuore, Cremona, Italy

E-mail: sergio.venturini@unicatt.it

Marco Bonetti, Department of Social and Political Sciences, Università Bocconi, Milan, Italy

E-mail: marco.bonetti@unibocconi.it

Richard Gelber, Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA

E-mail: gelber@jimmy.harvard.edu

Special thanks to:

• Kristian Romano, for testing the package and checking that it works as expected
• Wai-ki Yip, who developed the original R package, for the suggestions and insights on the STEPP procedure

In case you find any bug, please send us an e-mail or open an issue on GitHub.

To cite the stepp package use the following:

Venturini, S., Bonetti, M., Lazar, A. A., Cole, B. F., Wang, X.-V., Gelber, R. D., Yip, W.-K. 2023. Subpopulation treatment effect pattern plot (STEPP) methods with R and Stata. Journal of Data Science, 21(1):106-126.

GitHub repository: https://github.com/sergioventurini/stepp

• Bonetti, M. and Gelber, R .D. 2004. Patterns of treatment effects in subsets of patients in clinical trials. Biostatistics, 5(3):465-481.

• Bonetti, M., Zahrieh, D., Cole, B. F. and Gelber, R .D. 2009. A small sample study of the STEPP approach to assessing treatment-covariate interactions in survival data. Statistics in Medicine, 28(8):1255-68.

• Lazar, A. A., Cole, B. F., Bonetti, M. and Gelber, R .D. 2010. Evaluation of treatment-effect heterogeneity using biomarkers measured on a continuous scale: subpopulation treatment effect pattern plot. Journal of Clinical Oncology, 28(29):4539-4544.

• Lazar, A. A., Bonetti, M., Cole, B. F., Yip, W.-K. and Gelber, R .D. 2016. Identifying treatment effect heterogeneity in clinical trials using subpopulations of events: STEPP. Clinical Trials, 13(2):169–179.

• Venturini, S., Bonetti, M., Lazar, A. A., Cole, B. F., Wang, X.-V., Gelber, R. D., Yip, W.-K. 2023. Subpopulation treatment effect pattern plot (STEPP) methods with R and Stata. Journal of Data Science, 21(1):106-126.

• Yip, W.-K., Bonetti, M., Cole, B. F., Barcella, W., Wang, X. V., Lazar, A. A. and Gelber, R .D. 2016. Subpopulation Treatment Effect Pattern Plot (STEPP) analysis for continuous, binary, and count outcomes. Clinical Trials, 13(4):382–390.

This software is distributed under the GPL-3 license (see LICENSE file).