Subpopulation Treatment Effect Pattern Plot (STEPP) Methods with R and Stata

Venturini, Sergio; Bonetti, Marco; Lazar, Ann A.; Cole, Bernard F.; Wang, Xin Victoria; Gelber, Richard D.; Yip, Wai-Ki

doi:10.6339/22-JDS1060

Journal of Data Science

Subpopulation Treatment Effect Pattern Plot (STEPP) Methods with R and Stata

Volume 21, Issue 1 (2023), pp. 106–126

Sergio Venturini Marco Bonetti Ann A. Lazar All authors (7)

https://doi.org/10.6339/22-JDS1060

Pub. online: 9 August 2022 Type: Computing In Data Science

Open Access

Received
5 May 2022

Accepted
3 July 2022

Published
9 August 2022

Abstract

We introduce the stepp packages for R and Stata that implement the subpopulation treatment effect pattern plot (STEPP) method. STEPP is a nonparametric graphical tool aimed at examining possible heterogeneous treatment effects in subpopulations defined on a continuous covariate or composite score. More pecifically, STEPP considers overlapping subpopulations defined with respect to a continuous covariate (or risk index) and it estimates a treatment effect for each subpopulation. It also produces confidence regions and tests for treatment effect heterogeneity among the subpopulations. The original method has been extended in different directions such as different survival contexts, outcome types, or more efficient procedures for identifying the overlapping subpopulations. In this paper, we also introduce a novel method to determine the number of subjects within the subpopulations by minimizing the variability of the sizes of the subpopulations generated by a specific parameter combination. We illustrate the packages using both synthetic data and publicly available data sets. The most intensive computations in R are implemented in Fortran, while the Stata version exploits the powerful Mata language.

Supplementary material

Supplementary Material

The R and Stata scripts containing the code related to the examples discussed in the paper and a second application that involves a binary outcome are available in the supplementary material on the journal website.

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2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.

Open access article under the CC BY license.

Keywords

clinical trial interaction subgroup analysis subpopulation treatment-covariate interaction

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